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Journal articles on the topic 'Adaptive Particle Filter'
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1
Papavasiliou, Anastasia. "A uniformly convergent adaptive particle filter." Journal of Applied Probability 42, no. 4 (2005): 1053–68. http://dx.doi.org/10.1239/jap/1134587816.
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Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.
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Papavasiliou, Anastasia. "A uniformly convergent adaptive particle filter." Journal of Applied Probability 42, no. 04 (2005): 1053–68. http://dx.doi.org/10.1017/s0021900200001108.
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Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.
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Guo, Yuyang, Xiangbo Xu, and Miaoxin Ji. "A Zero-Velocity Update Method for Adaptive Particle Filtering." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 2 (2020): 427–33. http://dx.doi.org/10.1051/jnwpu/20203820427.
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Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.
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Xue, Li, Shesheng Gao, and Yongmin Zhong. "Robust Adaptive Unscented Particle Filter." International Journal of Intelligent Mechatronics and Robotics 3, no. 2 (2013): 55–66. http://dx.doi.org/10.4018/ijimr.2013040104.
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This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of robust adaptive filtering to the unscented particle filter. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of robust adaptive filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, leading to the improved filtering accuracy.
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Pei, Fujun, Mei Wu, and Simin Zhang. "Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/239531.
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The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness.
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Xue, Li, Chunning Na, and Yulan Han. "Improved Auxiliary Particle Filter for SINS/SAR Navigation." Mathematical Problems in Engineering 2021 (January 31, 2021): 1–9. http://dx.doi.org/10.1155/2021/6635390.
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In order to obtain the relatively appropriate importance density function and alleviate the problem of particle degradation, a new improved auxiliary particle filter algorithm is proposed. After calculating the auxiliary variable, the adaptive regulator is employed to obtain the state estimation. So, the latest measurement information is efficiently utilized to establish a better importance density function in the importance sampling process. Then, the process of particle weights’ adaptive adjustment and random-weighted calculation can keep the diversity of particles and improve the filter precision; thus, it can better solve the filter problem of nonlinear system model error and noise interference. The simulation and analysis result show that the proposed algorithm can optimize the filter performance and improve the calculation precision in the positioning of the SINS/SAR integrated navigation system, compared with the other two existing filters.
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Zuo, J. ‐Y, Y. ‐N Jia, Y. ‐Z Zhang, and W. Lian. "Adaptive iterated particle filter." Electronics Letters 49, no. 12 (2013): 742–44. http://dx.doi.org/10.1049/el.2012.4506.
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Zhou, Junchuan, Stefan Knedlik, and Otmar Loffeld. "INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter." Journal of Navigation 63, no. 3 (2010): 491–511. http://dx.doi.org/10.1017/s0373463310000068.
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With the rapid developments in computer technology, the particle filter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.
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Yu, Wen Tao, Jun Peng, and Xiao Yong Zhang. "A New Adaptive UPF Algorithm through Improved Relative Entropy." Advanced Materials Research 658 (January 2013): 569–73. http://dx.doi.org/10.4028/www.scientific.net/amr.658.569.
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Unscented particle filter (UPF) has high accuracy of state estimation for nonlinear system with non-Gaussian noise. While the computation of traditional unscented particle filter is huge and this depends on the particle number. In this paper we propose a new adaptive unscented particle filter algorithm AUPF through improved relative entropy which can adaptively adjust the particle number during filtering. Firstly the relative entropy is used to measure the distance between the posterior probability density and the importance proposal and the least number of particles for the next time step is decided according to the relative entropy. Then the least number is adjusted to offset the difference between the importance proposal and the true distribution. This algorithm can effectively reduce unnecessary particles meanwhile reduce the computation. The simulation results show the effectiveness of AUPF.
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Stordal, Andreas S., and Hans A. Karlsen. "Large Sample Properties of the Adaptive Gaussian Mixture Filter." Monthly Weather Review 145, no. 7 (2017): 2533–53. http://dx.doi.org/10.1175/mwr-d-15-0372.1.
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In high-dimensional dynamic systems, standard Monte Carlo techniques that asymptotically reproduce the posterior distribution are computationally too expensive. Alternative sampling strategies are usually applied and among these the ensemble Kalman filter (EnKF) is perhaps the most popular. However, the EnKF suffers from severe bias if the model under consideration is far from linear. Another class of sequential Monte Carlo methods is kernel-based Gaussian mixture filters, which reduce the bias but maintain the robustness of the EnKF. Although many hybrid methods have been introduced in recent years, not many have been analyzed theoretically. Here it is shown that the recently proposed adaptive Gaussian mixture filter can be formulated in a rigorous Bayesian framework and that the algorithm can be generalized to a broader class of interpolated kernel filters. Two parameters—the bandwidth of the kernel and a weight interpolation factor—determine the filter performance. The new formulation of the filter includes particle filters, EnKF, and kernel-based Gaussian mixture filters as special cases. Techniques from particle filter literature are used to calculate the asymptotic bias of the filter as a function of the parameters and to derive a central limit theorem. The asymptotic theory is then used to determine the parameters as a function of the sample size in a robust way such that the error norm vanishes asymptotically, whereas the normalized error is sample independent and bounded. The parameter choice is tested on the Lorenz 63 model, where it is shown that the error is smaller or equal to the EnKF and the optimal particle filter for a varying sample size.
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Sąsiadek, Jurek, and Hamdan Bitlmal. "Optimal State Estimation via Adaptive Fuzzy Particle Filter." Pomiary Automatyka Robotyka 27, no. 4 (2023): 5–12. http://dx.doi.org/10.14313/par_250/5.
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Particle Filters (PF) accomplish nonlinear system estimation and have received high interest from numerous engineering domains over the past decade. The main problem of PF is to degenerate over time due to the loss of particle diversity. One of the essential causes of losing particle diversity is sample impoverishment (most of particle’s weights are insignificant) which affects the result from the particle depletion in the resampling stage and unsuitable prior information of process and measurement noise. To address this problem, a new Adaptive Fuzzy Particle Filter (AFPF) is used to improve the precision and efficiency of the state estimation results. The error in AFPF state is avoided from diverging by using Fuzzy logic. This method is called tuning weighting factor (α) as output membership function of fuzzy logic and input memberships function is the mean and the covariance of residual error. When the motion model is noisier than measurement, the performance of the proposed method (AFPF) is compared with the standard method (PF) at various particles number. The performance of the proposed method can be compared by keeping the noise level acceptable and convergence of the particle will be measured by the standard deviation. The simulation experiment findings are discussed and evaluated.
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Potthast, Roland, Anne Walter, and Andreas Rhodin. "A Localized Adaptive Particle Filter within an Operational NWP Framework." Monthly Weather Review 147, no. 1 (2019): 345–62. http://dx.doi.org/10.1175/mwr-d-18-0028.1.
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Particle filters are well known in statistics. They have a long tradition in the framework of ensemble data assimilation (EDA) as well as Markov chain Monte Carlo (MCMC) methods. A key challenge today is to employ such methods in a high-dimensional environment, since the naïve application of the classical particle filter usually leads to filter divergence or filter collapse when applied within the very high dimension of many practical assimilation problems (known as the curse of dimensionality). The goal of this work is to develop a localized adaptive particle filter (LAPF), which follows closely the idea of the classical MCMC or bootstrap-type particle filter, but overcomes the problems of collapse and divergence based on localization in the spirit of the local ensemble transform Kalman filter (LETKF) and adaptivity with an adaptive Gaussian resampling or rejuvenation scheme in ensemble space. The particle filter has been implemented in the data assimilation system for the global forecast model ICON at Deutscher Wetterdienst (DWD). We carry out simulations over a period of 1 month with a global horizontal resolution of 52 km and 90 layers. With four variables analyzed per grid point, this leads to 6.6 × 106 degrees of freedom. The LAPF can be run stably and shows a reasonable performance. We compare its scores to the operational setup of the ICON LETKF.
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Guo, Yanbing, Lingjuan Miao, and Yusen Lin. "A Novel EM Implementation for Initial Alignment of SINS Based on Particle Filter and Particle Swarm Optimization." Mathematical Problems in Engineering 2019 (February 20, 2019): 1–12. http://dx.doi.org/10.1155/2019/6793175.
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For nonlinear systems in which the measurement noise parameters vary over time, adaptive nonlinear filters can be applied to precisely estimate the states of systems. The expectation maximization (EM) algorithm, which alternately takes an expectation- (E-) step and a maximization- (M-) step, has been proposed to construct a theoretical framework for the adaptive nonlinear filters. Previous adaptive nonlinear filters based on the EM employ analytical algorithms to develop the two steps, but they cannot achieve high filtering accuracy because the strong nonlinearity of systems may invalidate the Gaussian assumption of the state distribution. In this paper, we propose an EM-based adaptive nonlinear filter APF to solve this problem. In the E-step, an improved particle filter PF_new is proposed based on the Gaussian sum approximation (GSA) and the Monte Carlo Markov chain (MCMC) to achieve the state estimation. In the M-step, the particle swarm optimization (PSO) is applied to estimate the measurement noise parameters. The performances of the proposed algorithm are illustrated in the simulations with Lorenz 63 model and in a semiphysical experiment of the initial alignment of the strapdown inertial navigation system (SINS) in large misalignment angles.
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Heilig, Alexander, Ilshat Mamaev, Björn Hein, and Dmitrii Malov. "Adaptive particle filter for localization problem in service robotics." MATEC Web of Conferences 161 (2018): 01004. http://dx.doi.org/10.1051/matecconf/201816101004.
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In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes.
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Prajapati, Ramkhelavan, and Agya Mishra. "DESIGN OF HYBRID ADAPTIVE MODEL FOR IMAGE DENOISING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27371.
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This thesis work proposes a new denoising algorithm based on Particle filter and Wavelet (Curvelet) transform combination, particle filter generates weights through SIR algorithm to cancel the interference of noise present in the image, while curvelet transform is used to shrink the remaining segments of noise, so this method can both remove image blurr and maintain good texture as well. The PF+Clet Image Denoiser is successfully designed and implemented, which is a new approach in image enhancement and Interference cancellation. This thesis concludes that it is quite efficient algorithm among other adaptive filtering techniques. Experimental results also show that proposed algorithm performs extremely well when noise density is increased, as obtained image is completely visible. This approach comprises of generation of particles by performing weight normalization, resampling and update state. Therefore, for large number of particles execution time is more when compared to other adaptive filtering approach. Key Words: Particle Filter(PF), Curvelet(CLet), Wavelet(Wlet), Peak signal to noise Ratio(PSNR), Deblurring.
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Zhang, Jian, and Ling Shen. "Applied Technology in an Adaptive Particle Filter Based on Interval Estimation and KLD-Resampling." Advanced Materials Research 1014 (July 2014): 452–58. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.452.
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Particle filter as a sequential Monte Carlo method is widely applied in stochastic sampling for state estimation in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on the number of particles and the relocating method. The automatic selection of sample size for a given task is therefore essential for reducing unnecessary computation and for optimal performance, especially when the posterior distribution greatly varies overtime. This paper presents an adaptive resampling method (IE_KLD_PF) based on interval estimation, and after interval estimating the expectation of the system states, the new algorithm adopts Kullback-Leibler distance (KLD) to determine the number of particles to resample from the interval and update the filter results by current observation information. Simulations are performed to show that the proposed filter can reduce the average number of samples significantly compared to the fixed sample size particle filter.
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Huo, Youhui, Yaohong Chen, Hongbo Zhang, Haifeng Zhang, and Hao Wang. "Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion." Electronics 11, no. 15 (2022): 2457. http://dx.doi.org/10.3390/electronics11152457.
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Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.
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Han, Sang Sul, Sang Keun Jang, and Sang Jeong Lee. "RADOME COMPENSATION USING ADAPTIVE PARTICLE FILTER." IFAC Proceedings Volumes 40, no. 7 (2007): 43–48. http://dx.doi.org/10.3182/20070625-5-fr-2916.00009.
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Xue, Li, Shesheng Gao, Yongmin Zhong, Reza Jazar, and Aleksandar Subic. "Robust Adaptive Central Difference Particle Filter." International Journal of Robotics Applications and Technologies 2, no. 1 (2014): 19–34. http://dx.doi.org/10.4018/ijrat.2014010102.
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This paper presents a new robust adaptive central difference particle filtering method for nonlinear systems by combining the concept of robust adaptive estimation with the central difference particle filter. This method obtains system state estimate and covariances using the principle of robust estimation. Subsequently, the importance density is obtained by adjusting the state estimate and covariances through the equivalent weight function and adaptive factor constructed from predicted residuals to control the contributions to the new state estimation from measurement and kinematic model. The proposed method can not only minimize the variance of the importance density distribution to resist the disturbances of systematic noises, but it also fully takes advantage of present measurement information to avoid particle degeneration. Experiments and comparison analysis with the existing methods demonstrate the improved filtering accuracy of the proposed method.
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Nummiaro, Katja, Esther Koller-Meier, and Luc Van Gool. "An adaptive color-based particle filter." Image and Vision Computing 21, no. 1 (2003): 99–110. http://dx.doi.org/10.1016/s0262-8856(02)00129-4.
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Hassan, Waqas, Nagachetan Bangalore, Philip Birch, Rupert Young, and Chris Chatwin. "An adaptive sample count particle filter." Computer Vision and Image Understanding 116, no. 12 (2012): 1208–22. http://dx.doi.org/10.1016/j.cviu.2012.09.001.
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Xu, Xiaobin, Minzhou Luo, Zhiying Tan, Min Zhang, and Hao Yang. "Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter." Sensor Review 39, no. 5 (2019): 708–15. http://dx.doi.org/10.1108/sr-10-2018-0255.
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Purpose This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm. Design/methodology/approach A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function. Findings The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement. Originality/value The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.
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Stordal, Andreas S., Hans A. Karlsen, Geir Nævdal, Hans J. Skaug, and Brice Vallès. "Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter." Computational Geosciences 15, no. 2 (2010): 293–305. http://dx.doi.org/10.1007/s10596-010-9207-1.
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Kim, Sun Young, Chang Ho Kang, and Chan Gook Park. "SMC-CPHD Filter with Adaptive Survival Probability for Multiple Frequency Tracking." Applied Sciences 12, no. 3 (2022): 1369. http://dx.doi.org/10.3390/app12031369.
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We propose a sequential Monte Carlo-based cardinalized probability hypothesis density (SMC-CPHD) filter with adaptive survival probability for multiple frequency tracking to enhance the tracking performance. The survival probability of the particles in the filter is adjusted using the pre-designed exponential function related to the distribution of the estimated particle points. In order to ensure whether the proposed survival probability affects the stability of the filter, the error bounds in the prediction process are analyzed. Moreover, an inverse covariance intersection-based compensation method is added to enhance cardinality tracking performance by integrating two types of cardinality information from the CPHD filter and data clustering process. To evaluate the proposed method’s performance, MATLAB-based simulations are performed. As a result, the tracking performance of the multiple frequencies has been confirmed, and the accuracy of cardinality estimates are improved compared to the existing filters.
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Hamza, Dalal, and Tariq Tashan. "Dual channel speech enhancement using particle swarm optimization." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 821. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp821-828.
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Adaptive processing for canceling noise is a powerful technology for signal processing that can completely remove background noise. In general, various adaptive filter algorithms are used, many of which can lack the stability to handle the convergence rate, the number of filter coefficient variations, and error accuracy within tolerances. Unlike traditional methods, to accomplish these desirable characteristics as well as to efficiently cancel noise, in this paper, the cancelation of noise is formulated as a problem of coefficient optimization, where the particle swarm optimization (PSO) is employed. The PSO is structured to minimize the error by using a very short segment of the corrupted speech. In contrast to the recent and conventional adaptive noise cancellation methods, the simulation results indicate that the proposed algorithm has better capability of noise cancelation. The results show great improvement in signal to noise ratio (SNR) of 96.07 dB and 124.54 dB for finite impulse response (FIR) and infinite impulse response (IIR) adaptive filters respectively.
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Hamza, Dalal, and Tariq Tashan. "Dual channel speech enhancement using particle swarm optimization." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 821–28. https://doi.org/10.11591/ijeecs.v23.i2.pp821-828.
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Adaptive processing for canceling noise is a powerful technology for signal processing that can completely remove background noise. In general, various adaptive filter algorithms are used, many of which can lack the stability to handle the convergence rate, the number of filter coefficient variations, and error accuracy within tolerances. Unlike traditional methods, to accomplish these desirable characteristics as well as to efficiently cancel noise, in this paper, the cancelation of noise is formulated as a problem of coefficient optimization, where the particle swarm optimization (PSO) is employed. The PSO is structured to minimize the error by using a very short segment of the corrupted speech. In contrast to the recent and conventional adaptive noise cancellation methods, the simulation results indicate that the proposed algorithm has better capability of noise cancelation. The results show great improvement in signal to noise ratio (SNR) of 96.07 dB and 124.54 dB for finite impulse response (FIR) and infinite impulse response (IIR) adaptive filters respectively.
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Cho, Durkhyun, Sanghoon Lee, and Il Hong Suh. "Facial Feature Tracking Using Adaptive Particle Filter and Active Appearance Model." Journal of Korea Robotics Society 8, no. 2 (2013): 104–15. http://dx.doi.org/10.7746/jkros.2013.8.2.104.
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Liu, Wenjie. "Comparison and Selection of Filters for Target Tracking." Applied and Computational Engineering 127, no. 1 (2025): 122–28. https://doi.org/10.54254/2755-2721/2025.20260.
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In today's fast-paced fields like robotics, computer vision, and autonomous systems, researchers often face the challenge of selecting the most effective filters for accurate and reliable target tracking. Understanding the principles, usage, and appropriate scenarios of different filtering techniques is crucial for making informed decisions. This paper explores how to choose the right filter by focusing on four key types: Kalman filters, particle filters, adaptive correlation filters, and Joint Probabilistic Data Association (JPDA) filters. Each filter offers unique advantages and caters to specific conditions and requirements. Kalman filters are optimal for linear systems with Gaussian noise, while particle filters are well-suited for nonlinear and non-Gaussian systems. Adaptive correlation filters excel at tracking objects whose appearance changes over time, and JPDA filters are effective in multi-target tracking scenarios. By discussing the principles, strengths, limitations, and applicable scopes of these filters, this paper aims to assist researchers in selecting the most suitable filtering method for their specific target tracking needs.
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Gao, She Sheng, Wen Hui Wei, and Li Xue. "Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 931–35. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.931.
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This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.
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Li, Jie, Xiyan Sun, Yuanfa Ji, Jingjing Li, and Long Li. "Multipath Estimation of Navigation Signals Based on Extended Kalman Filter–Genetic Algorithm Particle Filter Algorithm." Applied Sciences 15, no. 7 (2025): 3851. https://doi.org/10.3390/app15073851.
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The particle filter (PF) algorithm has found widespread application in navigation multipath estimation. However, it exhibits significant limitations in complex multipath environments. Its state prediction relies heavily on particle distribution and is prone to particle degeneracy, where the weights of most particles approach zero, and only a few particles contribute significantly to state estimation. These issues result in an inadequate number of effective samples, degrading multipath estimation performance. Therefore, a navigation multipath estimation method based on an EKF-GAPF (Extended Kalman Filter–Genetic Algorithm Particle Filter) algorithm is proposed in this paper. This method utilizes the EKF to calculate the mean and covariance of samples using the latest observation information, providing a more reasonable proposal density for particle filtering and enhancing the accuracy of state prediction. Simultaneously, by introducing the crossover and mutation mechanisms of the adaptive genetic algorithm, particles are continuously evolved during the resampling process, preventing them from falling into local extrema. Experimental results show that EKF-GAPF outperforms EKF, EPF, and PF in amplitude and delay estimation. Under the condition of random initial values, the multipath signal amplitude estimation error converges to 0.002, and the multipath signal time delay estimation error converges to 0.006 (approximately 1.8 m). This method enables high-precision parameter estimation for both direct and multipath signals.
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ZUO, Jun-Yi, Yi-Zhe ZHANG, and Yan LIANG. "Particle Filter Based on Adaptive Part Resampling." Acta Automatica Sinica 38, no. 4 (2012): 647–51. http://dx.doi.org/10.3724/sp.j.1004.2012.00647.
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Zhang, Jinging, Xiaogang Ruan, Pengfei Dong, and Jing Zhou. "Simultaneous Localization and Mapping of Mobile Robot Based on Improved RBPF." MATEC Web of Conferences 160 (2018): 06002. http://dx.doi.org/10.1051/matecconf/201816006002.
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The traditional SLAM based on RBPF has the problem of constructing high-precision map which requires large amounts of particles to make the calculation complexity and the phenomenon of particle depletion caused by particle degradation. Aiming at these problems, an improved RBPF particle filter based on adaptive bacterial foraging optimization algorithm and adaptive resampling is proposed for mobile robot SLAM problem. Firstly, the introduction of adaptive bacterial foraging algorithm to RBPF making the distribution of particles before resampling closer to the real situation. Then use the adaptive resampling method makes the newly generated particles closer to the real movement, thereby increasing the robot position estimation accuracy and map creation accuracy. The experimental results show that this method can improve the practicability of the system, reduce the computational complexity, improve the operation speed and get more effective particles while guaranteeing the accuracy of the grid map.
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Lee, Jong Ki, and Christopher Jekeli. "Improved Filter Strategies for Precise Geolocation of Unexploded Ordnance using IMU/GPS Integration." Journal of Navigation 62, no. 3 (2009): 365–82. http://dx.doi.org/10.1017/s0373463309005360.
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Efficient and precise geolocation can be achieved by integrating a ranging system, such as GPS, with inertial sensors in order to bridge short outages, enhance accuracy degradation, and increase the temporal resolution in the ranging system. Optimal integration depends on appropriate filter methods that can accommodate the particular short-term dynamics experienced by platforms, such as UXO ground-based detection systems. The traditional extended Kalman filter was designed to integrate data from a linearized system excited by Gaussian noise. We compared this filter to modern filters that obviate these prerequisites, including the unscented Kalman filter, the particle filter, and adaptive variations thereof, using simulated IMU/ranging systems that follow a typical trajectory with both straight and curved segments. The unscented filter performed significantly better than the extended Kalman filter, particularly over the curved segments, yielding up to 50% improvement in the position accuracy using medium-grade inertial measurement units. Similar improvement was obtained for the unscented particle filter, and its adaptive variant, over the unscented Kalman filter (which performed comparably to the extended Kalman filter) when the statistical distribution of the IMU noise was non-symmetric (i.e., essentially non-Gaussian). While the few-centimetre geolocation accuracy goal for highly dynamic UXO characterization applications remains a challenge if tactical grade IMUs are integrated with a significantly degraded ranging system, using filters appropriate to the inherent nonlinear dynamics and potential non-Gaussian nature of the sensor noise tend to reduce overall errors compared to the traditional filter.
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Hu, Yunbing, Ao Peng, Biyu Tang, and Hongying Xu. "An Indoor Navigation Algorithm Using Multi-Dimensional Euclidean Distance and an Adaptive Particle Filter." Sensors 21, no. 24 (2021): 8228. http://dx.doi.org/10.3390/s21248228.
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The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy.
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Ratre, Avinash. "GMM-based Imbalanced Fractional Whale Particle Filter for Multiple Object Tracking in Surveillance Videos." International Journal of Computer Network and Information Security 17, no. 2 (2025): 34–50. https://doi.org/10.5815/ijcnis.2025.02.03.
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The imbalanced surveillance video dataset consists of majority and minority classes as normal and anomalous instances in the nonlinear and non-Gaussian framework. The normal and anomalous instances cause majority and minority samples or particles associated with high and low probable regions when considering the standard particle filter. The minority particles tend to be at high risk of being suppressed by the majority particles, as the proposal probability density function (pdf) encourages the highly probable regions of the input data space to remain a biased distribution. The standard particle filter-based tracker afflicts with sample degeneration and sample impoverishment due to the biased proposal pdf ignoring the minority particles. The difficulty in designing the correct proposal pdf prevents particle filter-based tracking in the imbalanced video data. The existing methods do not discuss the imbalanced nature of particle filter-based tracking. To alleviate this problem and tracking challenges, this paper proposes a novel fractional whale particle filter (FWPF) that fuses the fractional calculus-based whale optimization algorithm (FWOA) and the standard particle filter under weighted sum rule fusion. Integrating the FWPF with an iterative Gaussian mixture model (GMM) with unbiased sample variance and sample mean allows the proposal pdf to be adaptive to the imbalanced video data. The adaptive proposal pdf leads the FWPF to a minimum variance unbiased estimator for effectively detecting and tracking multiple objects in the imbalanced video data. The fractional calculus up to the first four terms makes the FWOA a local and global search operator with inherent memory property. The fractional calculus in the FWOA oversamples minority particles to be diversified with multiple imputations to eliminate data distortion with low bias and low variance. The proposed FWPF presents a novel imbalance evaluation metric, tracking distance correlation for the imbalanced tracking over UCSD surveillance video data and shows greater efficacy in mitigating the effects of the imbalanced nature of video data compared to other existing methods. The proposed method also outshines the existing methods regarding precision and accuracy in tracking multiple objects. The consistent tracking distance correlation near zero values provides efficient imbalance reduction through bias-variance correction compared to the existing methods.
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Zhang, Jie. "Application of Quantum Particle Swarm Optimization in Adaptive Notch Filter Design." Advanced Materials Research 482-484 (February 2012): 2466–69. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2466.
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Abstract: adaptive notch filter is a kind of apparatus which can eliminate single frequency or narrow-band interference, normal adaptive algorithm of notch filter is LMS algorithm, but the faster convergence velocity and the smaller steady error are difficult to gain simultaneously. Aimed at the weakness of LMS, the Particle Swarm Optimization (PSO) is studied deeply in the paper, based on the PSO; the quantum mechanic theory is added to improve it. Quantum Particle Swarm Optimization (QPSO) is researched and applied for adaptive notch filter which is proved more efficient in the noise control by MATLAB simulation. The new QPSO algorithm can balance the maladjustment and the searching ability of adaptive filter with a little calculation, the speed of convergence is faster than LMS and normal PSO algorithm.
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Du, Guanglong, Ping Zhang, and Xueqian Wang. "Human-Manipulator Interface Using Particle Filter." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/692165.
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This paper utilizes a human-robot interface system which incorporates particle filter (PF) and adaptive multispace transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. PF is used to estimate the translation of the human hand. Although a PF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an adaptive multispace transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator.
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Li, Zhihao, Junkang Wu, Zhenwu Kuang, et al. "Moving Target Tracking Algorithm Based on Improved Resampling Particle Filter in UWB Environment." Wireless Communications and Mobile Computing 2022 (June 20, 2022): 1–16. http://dx.doi.org/10.1155/2022/9974049.
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In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.
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Kang, Chang Ho, and Sun Young Kim. "Energy-Adaptive SGHSMC: A Particle-Efficient Nonlinear Filter for High-Maneuver Target Tracking." Mathematics 13, no. 10 (2025): 1655. https://doi.org/10.3390/math13101655.
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Tracking targets with nonlinear motion patterns remains a significant challenge in state estimation. We propose an energy-adaptive stochastic gradient Hamiltonian sequential Monte Carlo (SGHSMC) filter that combines adaptive energy dynamics with efficient particle sampling. The proposed method features a novel energy function that automatically adapts to target dynamics while minimizing the need for resampling operations. By integrating Hamiltonian Monte Carlo sampling with stochastic gradient techniques, our approach achieves a 40% reduction in computational overhead compared to traditional particle filters while maintaining particle diversity. We validate the method through both simulation and experimental studies. The simulation employs a univariate nonstationary growth model, demonstrating improvements of 39% in tracking accuracy over the extended Kalman filter (EKF) and 29% over standard sequential Monte Carlo methods. The experimental validation uses a bearing-only tracking scenario with a quadrupedal robot executing complex maneuvers, tracked by high-precision angular measurement systems. In practical tracking scenarios, the SGHSMC filter achieves a 77% better accuracy than EKF while maintaining the computational efficiency suitable for real-time applications. The algorithm demonstrates effectiveness in scenarios involving rapid state changes and irregular motion patterns, offering a robust solution for challenging target tracking problems.
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Straka, Ondřej, and Miroslav Šimandl. "Adaptive particle filter with fixed empirical density quality." IFAC Proceedings Volumes 41, no. 2 (2008): 6484–89. http://dx.doi.org/10.3182/20080706-5-kr-1001.01093.
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Yu, Huapeng, Ziyuan Li, Wentie Yang, Tongsheng Shen, Dalei Liang, and Qinyuan He. "Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology." Journal of Marine Science and Engineering 11, no. 9 (2023): 1739. http://dx.doi.org/10.3390/jmse11091739.
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The geomagnetic field constitutes a massive fingerprint database, and its unique structure provides potential position correction information. In recent years, particle filter technology has received more attention in the context of robot navigation. However, particle degradation and impoverishment have constrained navigation systems’ performance. This paper transforms particle filtering into a particle-matching positioning problem and proposes a geomagnetic localization method based on an adaptive fission particle filter. This method employs particle-filtering technology to construct a geomagnetic matching positioning model. Through adaptive particle fission and sampling, the problem of particle degradation and impoverishment in traditional particle filtering is solved, resulting in improved geomagnetic matching positioning accuracy. Finally, the proposed method was tested in a sea trial, and the results show that the proposed method has a lower positioning error than traditional particle-filtering and intelligent particle-filtering algorithms. Under geomagnetic map conditions, an average positioning accuracy of about 546.44 m is achieved.
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Walia, Gurjit Singh, and Rajiv Kapoor. "Online Object Tracking via Novel Adaptive Multicue Based Particle Filter Framework for Video Surveillance." International Journal on Artificial Intelligence Tools 27, no. 06 (2018): 1850023. http://dx.doi.org/10.1142/s0218213018500239.
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Multicue based object tracking frameworks have been extensively explored due to their numerous applications in the field of computer vision. However, the online adaptive fusion of multicue under scale and illumination variations, partial or full occlusion, background clutters and object deformation remains an open challenge problem. In order to address this, we propose an online visual tracking algorithm using adaptive integration of multicue in a particle filter framework. The particle level fusion process is modelled as Shafer’s model with a power set defined over two focal elements. Partial conflicting masses and conjunctive consensus among three cues are estimated for each evaluated particle. Partial conflicts among cues are redistributed using Dezert-Smarandache Theory (DSmT) based proportional conflict redistribution rules (PCR-6). Additionally, context sensitive transductive cues reliabilities are used for discounting the particle likelihoods for quick adaptation of tracker. In the proposed model, automatic boosting of good particles and suppression of low performing particles not only improves resampling process but also enhances tracker accuracy. Experimental validation over benchmarked video sequences reveals that the proposed multicue tracking framework outperforms state-of-the-art trackers under various dynamic environmental challenges.
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Niu, Jinxing, Zhengyi Liu, Shuo Wang, Jiaxi Huang, and Junlong Zhao. "Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach." Agriculture 15, no. 11 (2025): 1160. https://doi.org/10.3390/agriculture15111160.
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To address the challenge of accurate apple harvesting by orchard robots, which is hindered by dynamic changes in apple position due to wind interference and branch swaying, this study proposes an optimized prediction algorithm based on an integration of the extended Kalman filter (EKF) and an improved particle filter (IPF), built upon initial apple detection and recognition using YOLOv8. The algorithm first employs spatial partitioning according to the cyclical motion patterns of apples to constrain the prediction results. Subsequently, it optimizes the rationality of particle weights within the particle filter (PF) and reduces its computational resource consumption by implementing historical position weighting and an adaptive particle number strategy. Finally, an adaptive error correction mechanism dynamically adjusts the respective weights of the EKF and IPF components, continuously enhancing the algorithm’s prediction accuracy. Experimental results demonstrate that, compared to the classic unscented Kalman filter (UKF) and unscented particle filter (UPF), the proposed EK-IPF algorithm reduces the mean absolute error (MAE) by 22.25% and 10.89%, respectively, and the root mean square error (RMSE) by 23.70% and 13.25%, respectively, indicating a significant improvement in overall prediction accuracy. This research provides technical support for dynamic apple trajectory prediction in orchard environments.
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Lee, Se-Hyeok, and Junho Song. "Regularization-Based Dual Adaptive Kalman Filter for Identification of Sudden Structural Damage Using Sparse Measurements." Applied Sciences 10, no. 3 (2020): 850. http://dx.doi.org/10.3390/app10030850.
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This paper proposes a dual adaptive Kalman filter to identify parameters of a dynamic system that may experience sudden damage by a dynamic excitation such as earthquake ground motion. While various filter techniques have been utilized to estimate system’s states, parameters, input (force), or their combinations, the filter proposed in this paper focuses on tracking parameters that may change suddenly using sparse measurements. First, an advanced state-space model of parameter estimation employing a regularization technique is developed to overcome the lack of information in sparse measurements. To avoid inaccurate or biased estimation by conventional filters that use covariance matrices representing time-invariant artificial noises, this paper proposes a dual adaptive filtering, whose slave filter corrects the covariance of the artificial measurement noises in the master filter at every time-step. Since it is generally impossible to tune the proposed dual filter due to sensitivity with respect to parameters selected to describe artificial noises, particle swarm optimization (PSO) is adopted to facilitate optimal performance. Numerical investigations confirm the validity of the proposed method through comparison with other filters and emphasize the need for a thorough tuning process.
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Liu, Yingjie, and Dawei Cui. "Vehicle dynamics prediction via adaptive robust unscented particle filter." Advances in Mechanical Engineering 15, no. 5 (2023): 168781322311707. http://dx.doi.org/10.1177/16878132231170766.
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Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at the same time. However, it poses a challenge since not all the quantities of interest can be directly measured due to cost and/or technological reasons. Therefore, combining the principle of robust filtering and unscented particle filtering algorithm, a filter estimation method of vehicle state is proposed to estimate driving state parameters of a vehicle. The adaptive robust unscented particle filter (ARUPF) is used to realize the longitudinal and lateral velocity as well as the side slip angle of the vehicle. The CarSim and Matlab/Simulink co-simulation platform is established to verify the estimation algorithm. The results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving states can be estimated, the measurement parameters can be effectively filtered, and the estimation accuracy is higher.
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Ait‐El‐Fquih, Boujemaa, and Ibrahim Hoteit. "A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter." Quarterly Journal of the Royal Meteorological Society 146, no. 727 (2020): 922–37. http://dx.doi.org/10.1002/qj.3716.
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Chang, Bae-Muu, Hung-Hsu Tsai, Xuan-Ping Lin, and Pao-Ta Yu. "Design of median-type filters with an impulse noise detector using decision tree and particle swarm optimization for image restoration." Computer Science and Information Systems 7, no. 4 (2010): 859–82. http://dx.doi.org/10.2298/csis090405029c.
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This paper proposes the median-type filters with an impulse noise detector using the decision tree and the particle swarm optimization, for the recovery of the corrupted gray-level images by impulse noises. It first utilizes an impulse noise detector to determine whether a pixel is corrupted or not. If yes, the filtering component in this method is triggered to filter it. Otherwise, the pixel is kept unchanged. In this work, the impulse noise detector is an adaptive hybrid detector which is constructed by integrating 10 impulse noise detectors based on the decision tree and the particle swarm optimization. Subsequently, the restoring process in this method respectively utilizes the median filter, the rank ordered mean filter, and the progressive noise-free ordered median filter to restore the corrupted pixel. Experimental results demonstrate that this method achieves high performance for detecting and restoring impulse noises, and outperforms the existing well-known methods.
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Zhang, Xinyu, Mengjiao Ren, Jiemin Duan, Yingmin Yi, Biyu Lei, and Shuyue Wu. "An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation." Sensors 23, no. 14 (2023): 6603. http://dx.doi.org/10.3390/s23146603.
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Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles’ initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower.
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Xia, Bizhong, Shengkun Guo, Wei Wang, et al. "A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries." Energies 11, no. 10 (2018): 2755. http://dx.doi.org/10.3390/en11102755.
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A state of charge (SOC) estimation method is proposed. An Adaptive Extended Kalman Particle filter (AEKPF) based on Particle Filter (PF) and Adaptive Kalman Filter (AKF) is used in order to decrease the error and reduce calculations. The second-order resistor-capacitor (RC) Equivalent Circuit Model (ECM) is used to identify dynamic parameters of the battery. After testing (include Dynamic Stress test (DST), New European Driving Cycle (NEDC), Federal Urban Dynamic Schedule (FUDS), Urban Dynamometer Driving Schedules (UDDS), etc.) at different temperatures and times, it was found that the AEKPF exhibits greater tolerance for high system noise (10% or higher) and provides more accurate estimations under common operating conditions.
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Du, Sichun, and Qing Deng. "Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking." Sensors 21, no. 6 (2021): 2236. http://dx.doi.org/10.3390/s21062236.
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Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.