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Journal articles on the topic 'PHD filter'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Yin, Jian Jun, and Jian Qiu Zhang. "Convolution PHD Filtering for Nonlinear Non-Gaussian Models." Advanced Materials Research 213 (February 2011): 344–48. http://dx.doi.org/10.4028/www.scientific.net/amr.213.344.

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A novel probability hypothesis density (PHD) filter, called the Gaussian mixture convolution PHD (GMCPHD) filter was proposed. The PHD within the filter is approximated by a Gaussian sum, as in the Gaussian mixture PHD (GMPHD) filter, but the model may be non-Gaussian and nonlinear. This is implemented by a bank of convolution filters with Gaussian approximations to the predicted and posterior densities. The analysis results show the lower complexity, more amenable for parallel implementation of the GMCPHD filter than the convolution PHD (CPHD) filter and the ability to deal with complex observation model, small observation noise and non-Gaussian noise of the proposed filter over the existing Gaussian mixture particle PHD (GMPPHD) filter. The multi-target tracking simulation results verify the effectiveness of the proposed method.

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2

Xu, Weijun. "Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics." Measurement and Control 54, no. 3-4 (2021): 279–91. http://dx.doi.org/10.1177/0020294021992800.

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Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number. However, inaccurate prior statistics of the random noise will degrade the performance of the PHD filter in many practical applications. This paper presents an adaptive Gaussian mixture PHD (AGM-PHD) filter for the multi-target tracking (MTT) problem in the scenario where both the mean and covariance of measurement noise sequences are unknown. The conventional PHD filters are extended to jointly estimate both the multi-target state and the aforementioned measurement noise statistics. In particular, the Normal-inverse-Wishart and Gaussian distributions are first integrated to represent the joint posterior intensity by transforming the measurement model into a new formulation. Then, the updating rule for the hyperparameters of the model is derived in closed form based on variational Bayesian (VB) approximation and Bayesian conjugate prior heuristics. Finally, the dynamic system state and the noise statistics are updated sequentially in an iterative manner. Simulations results with both constant velocity and constant turn model demonstrate that the AGM-PHD filter achieves comparable performance as the ideal PHD filter with true measurement noise statistics.

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3

Liu, Jiangyi, Chunping Wang, Wei Wang, and Zheng Li. "Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains." Algorithms 12, no. 2 (2019): 31. http://dx.doi.org/10.3390/a12020031.

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Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than the traditional HMC model. A set of weighted particles is used to approximate the probability hypothesis density of multi-targets in the framework of the PMC model, and a particle probability hypothesis density filter based on the PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system. Simulation results show the effectiveness of the PF-PMC-PHD filter and that the tracking performance of the PF-PMC-PHD filter is superior to the particle PHD filter based on the HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.

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4

Cong-An, Xu, Xu Congqi, Dong Yunlong, Xiong Wei, Chai Yong, and Li Tianmei. "A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 6872–77. http://dx.doi.org/10.1166/jctn.2016.5640.

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As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.

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5

Gao, Yiyue, Defu Jiang, and Ming Liu. "Particle-gating SMC-PHD filter." Signal Processing 130 (January 2017): 64–73. http://dx.doi.org/10.1016/j.sigpro.2016.06.017.

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6

Markovic, Ivan, Josip Cesic, and Ivan Petrovic. "Von Mises Mixture PHD Filter." IEEE Signal Processing Letters 22, no. 12 (2015): 2229–33. http://dx.doi.org/10.1109/lsp.2015.2472962.

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7

Ren, Yayun, and Benlian Xu. "A Quantitative Analysis on Two RFS-Based Filtering Methods for Multicell Tracking." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/495765.

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Multiobject filters developed from the theory of random finite sets (RFS) have recently become well-known methods for solving multiobject tracking problem. In this paper, we present two RFS-based filtering methods, Gaussian mixture probability hypothesis density (GM-PHD) filter and multi-Bernoulli filter, to quantitatively analyze their performance on tracking multiple cells in a series of low-contrast image sequences. The GM-PHD filter, under linear Gaussian assumptions on the cell dynamics and birth process, applies the PHD recursion to propagate the posterior intensity in an analytic form, while the multi-Bernoulli filter estimates the multitarget posterior density through propagating the parameters of a multi-Bernoulli RFS that approximates the posterior density of multitarget RFS. Numerous performance comparisons between the two RFS-based methods are carried out on two real cell images sequences and demonstrate that both yield satisfactory results that are in good agreement with manual tracking method.

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8

Zhang, Huanqing, Hongwei Ge, and Jinlong Yang. "Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets." International Journal of Electronics and Telecommunications 63, no. 3 (2017): 247–54. http://dx.doi.org/10.1515/eletel-2017-0033.

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AbstractProbability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.

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9

Tian, Shu Rong, Xiao Shu Sun, and Xi Jing Sun. "Multi-Sensor Interactive Multi-Model PHD Filter for Maneuvering Multi-Target Tracking." Applied Mechanics and Materials 336-338 (July 2013): 200–203. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.200.

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In maneuvering multiple targets tracking problem, Probability Hypothesis Density(PHD) filter can be used to estimate the multi-target state and the number at each time step, but single model method may not provide accurate estimates. In this paper, an interactive multiple model PHD filter is proposed, and then multiple sensor interactive multiple model PHD filter is proposed to improve the tracking of multiple maneuvering targets. PHD particle filter implementation is used to perform the proposed method consisting of multiple maneuvering targets.

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10

Fuse, T., D. Hiramatsu, and W. Nakanishi. "MULTI-TARGET DETECTION FROM FULL-WAVEFORM AIRBORNE LASER SCANNER USING PHD FILTER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 647–52. http://dx.doi.org/10.5194/isprsarchives-xli-b5-647-2016.

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We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions simultaneously. PHD filter overcomes some limitations of conventional Gaussian decomposition method; PHD filter doesn’t require a priori knowledge on the number of targets, assumption of parametric form of the intensity distribution. In addition, it can take a similarity between successive irradiations into account by modelling relative positions of the same targets spatially. Firstly we explain PHD filter and particle filter implementation to it. Secondly we formulate the multi-target detection problem on PHD filter by modelling components and parameters within it. At last we conducted the experiment on real data of forest and vegetation, and confirmed its ability and accuracy.

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11

Fuse, T., D. Hiramatsu, and W. Nakanishi. "MULTI-TARGET DETECTION FROM FULL-WAVEFORM AIRBORNE LASER SCANNER USING PHD FILTER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 647–52. http://dx.doi.org/10.5194/isprs-archives-xli-b5-647-2016.

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We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions simultaneously. PHD filter overcomes some limitations of conventional Gaussian decomposition method; PHD filter doesn’t require a priori knowledge on the number of targets, assumption of parametric form of the intensity distribution. In addition, it can take a similarity between successive irradiations into account by modelling relative positions of the same targets spatially. Firstly we explain PHD filter and particle filter implementation to it. Secondly we formulate the multi-target detection problem on PHD filter by modelling components and parameters within it. At last we conducted the experiment on real data of forest and vegetation, and confirmed its ability and accuracy.

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12

Gong, Yang, and Chen Cui. "A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise." Sensors 21, no. 11 (2021): 3611. http://dx.doi.org/10.3390/s21113611.

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In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.

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13

Gao, Yiyue, Defu Jiang, Chao Zhang, and Su Guo. "A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets." Sensors 21, no. 11 (2021): 3932. http://dx.doi.org/10.3390/s21113932.

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In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

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14

Erdinc, O., P. Willett, and Y. Bar-Shalom. "The Bin-Occupancy Filter and Its Connection to the PHD Filters." IEEE Transactions on Signal Processing 57, no. 11 (2009): 4232–46. http://dx.doi.org/10.1109/tsp.2009.2025816.

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15

Lian, Feng, Chongzhao Han, Jing Liu, and Hui Chen. "Convergence Results for the Gaussian Mixture Implementation of the Extended-Target PHD Filter and Its Extended Kalman Filtering Approximation." Journal of Applied Mathematics 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/141727.

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The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD) filter and its extended Kalman (EK) filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which the two filters satisfy uniform convergence.

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16

Ouyang, Cheng, Xiao-xu Chen, and Yun Hua. "Improved Best-fitting Gaussian Approximation PHD Filter." JOURNAL OF RADARS 2, no. 2 (2013): 239–46. http://dx.doi.org/10.3724/sp.j.1300.2013.13010.

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17

Clark, D. E., and J. Bell. "Convergence results for the particle PHD filter." IEEE Transactions on Signal Processing 54, no. 7 (2006): 2652–61. http://dx.doi.org/10.1109/tsp.2006.874845.

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18

Li, Tiancheng, Shudong Sun, and Tariq Pervez Sattar. "High-speed Sigma-gating SMC-PHD filter." Signal Processing 93, no. 9 (2013): 2586–93. http://dx.doi.org/10.1016/j.sigpro.2013.03.011.

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19

Chen, Jinguang, Xiaoshan Qin, Lili Ma, Bugao Xu, and Xinjuan Zhu. "GM-PHD Filter with State-Dependent Clutter." Asian Journal of Control 18, no. 6 (2016): 2336–42. http://dx.doi.org/10.1002/asjc.1297.

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20

Lian, Feng, Chongzhao Han, and Weifeng Liu. "Estimating Unknown Clutter Intensity for PHD Filter." IEEE Transactions on Aerospace and Electronic Systems 46, no. 4 (2010): 2066–78. http://dx.doi.org/10.1109/taes.2010.5595616.

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21

Wang, Sen, Qinglong Bao, and Zengping Chen. "Refined PHD Filter for Multi-Target Tracking under Low Detection Probability." Sensors 19, no. 13 (2019): 2842. http://dx.doi.org/10.3390/s19132842.

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Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter.

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22

Nabil, M., H. Kamal, and M. Hassan. "Comparison between Kalman Filter and PHD Filter in Multi-target Tracking." International Conference on Electrical Engineering 8, no. 8th (2012): 1–14. http://dx.doi.org/10.21608/iceeng.2012.31375.

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23

Sun, Jie, and Dong Li. "Multiple Model CPHD Filter for Tracking Maneuvering Targets." Applied Mechanics and Materials 556-562 (May 2014): 3238–41. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3238.

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A multiple model cardinalized probability hypothesis density (CPHD) filter is proposed for tracking multiple maneuvering targets. The augmented state is established by combining the target motion mode with the kinematic state. Both the posterior cardinality distribution of the targets and the posterior probability hypothesis density (PHD) of the augmented state are jointly propagated by using CPHD recursion. Simulation results show that the proposed filter improves the estimation accuracy of target number and target states over the multiple model PHD filter and single model CPHD filter respectively.

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24

Tian, Shu Rong, Xiao Shu Sun, and Xi Jing Sun. "PHD Filter for Multi-Radar Multi-Target Tracking." Advanced Materials Research 734-737 (August 2013): 2730–33. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.2730.

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Target tracking with multiple radars is more efficient than tracking with one radar. In this paper, a multi-radar tracking system is proposed even when targets are occluded at radars. Observations from all radars are composed, then, Probability Hypothesis Density (PHD) filter is used to estimate the multi-target state and the number at each time step. PHD particle filter implementation is used to perform the proposed method consisting of multiple mameuvering targets.

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25

Miao, Lu, Xin-xi Feng, and Luo-jia Chi. "Adaptive Target Birth Intensity for ET-PHD Filter." MATEC Web of Conferences 176 (2018): 03010. http://dx.doi.org/10.1051/matecconf/201817603010.

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An adaptive tracking algorithm based on Extended target Probability Hypothesis Density (ETPHD) filter is proposed for extended target tracking problem with priori unknown target birth intensity.The algorithm is implemented by gaussian mixture, where the target birth intensity is generated by measurement-driven, and the persistent and the newborn targets intensity are respectively predicted and updated. The simulation results show that the proposed algorithm improves the performance of the probability hypothesis density filter in the extended target tracking.

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26

Zheng, Jihong, and Meiguo Gao. "Tracking Ground Targets with a Road Constraint Using a GMPHD Filter." Sensors 18, no. 8 (2018): 2723. http://dx.doi.org/10.3390/s18082723.

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The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper.

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27

Clark, Daniel, and Ba-Ngu Vo. "Convergence Analysis of the Gaussian Mixture PHD Filter." IEEE Transactions on Signal Processing 55, no. 4 (2007): 1204–12. http://dx.doi.org/10.1109/tsp.2006.888886.

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28

Zhu, Youqing, Shilin Zhou, Gui Gao, Huanxin Zou, and Lin Lei. "Extended Emitter Target Tracking Using GM-PHD Filter." PLoS ONE 9, no. 12 (2014): e114317. http://dx.doi.org/10.1371/journal.pone.0114317.

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29

Tan, Shun Cheng, Guo Hong Wang, Na Wang, and Hong Bo Yu. "PHD Filter for Multitarget Tracking with Range Ambiguity." Applied Mechanics and Materials 385-386 (August 2013): 1909–12. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1909.

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It is difficult to track multitarget of which the number is unknown and time varying, especially with range ambiguity. The probability hypothesis density (PHD) filter propagates the first order moment of the posterior multitarget density, from which the number of targets as well as their individual states can be extracted. However, when tracking multiple targets via the middle or high pulse repetition frequency (PRF) radar, the problem of range ambiguity has to be solved. In this paper, a novel method for joint range ambiguity resolving and multitarget tracking is proposed. The feasibility and effectiveness of the proposed method are verified by simulation results.

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30

Zhu, Youqing, and Shilin Zhou. "GM-PHD Filter With Signal Features Of Emitter." Asian Journal of Control 17, no. 5 (2014): 1978–83. http://dx.doi.org/10.1002/asjc.1040.

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31

Lin, L., Y. Bar-Shalom, and T. Kirubarajan. "Track labeling and PHD filter for multitarget tracking." IEEE Transactions on Aerospace and Electronic Systems 42, no. 3 (2006): 778–95. http://dx.doi.org/10.1109/taes.2006.248213.

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32

Ouyang, C., and H. B. Ji. "Weight over-estimation problem in GMP-PHD filter." Electronics Letters 47, no. 2 (2011): 139. http://dx.doi.org/10.1049/el.2010.7410.

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33

Ouyang, C., and H. Ji. "Scale unbalance problem in product multisensor PHD filter." Electronics Letters 47, no. 22 (2011): 1247. http://dx.doi.org/10.1049/el.2011.1843.

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34

Krishanth, Krishnan, Xin Chen, Ratnasingham Tharmarasa, Thia Kirubarajan, and Mike McDonald. "The Social Force PHD Filter for Tracking Pedestrians." IEEE Transactions on Aerospace and Electronic Systems 53, no. 4 (2017): 2045–59. http://dx.doi.org/10.1109/taes.2017.2680718.

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35

Xu, Benlian, Huigang Xu, and Jihong Zhu. "Ant clustering PHD filter for multiple-target tracking." Applied Soft Computing 11, no. 1 (2011): 1074–86. http://dx.doi.org/10.1016/j.asoc.2010.02.007.

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36

Li, Tiancheng, and Shudong Sun. "Online Adapting the Magnitude of Target Birth Intensity in the PHD Filter." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2, no. 4 (2014): 31–40. http://dx.doi.org/10.14201/adecaij2013173140.

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Capturing new targets that spontaneously appear in the multi-target tracking (MTT) scene requires a formation of TBI (target birth intensity) item in the PHD (probability hypothesis density) equations. That is, in the particle implementation of the PHD filter, a number of new particles with a certain weight mass are added to the underlying particle set during the propagation of the PHD. In general, TBI is assumed to hold for the same magnitude at all scans. This ad-hoc option is simple but is not always desirable. In this paper, a measurement-driven adaptive mechanism is proposed that determines the magnitude of TBI in real time based on the estimated number of new-born targets, which is calculated by employing the newest measurements. Simulation demonstration of the particle PHD filter has been provided.

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37

Hu, Zhentao, Linlin Yang, Yong Jin, Han Wang, and Shibo Yang. "Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance." Sensors 21, no. 4 (2021): 1126. http://dx.doi.org/10.3390/s21041126.

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Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

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38

Zhang, Guoliang, Chunling Yang, and Yan Zhang. "An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion." Journal of Spectroscopy 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/179039.

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In order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed. Firstly, the nonstationary adaptive suppression method is proposed to remove the background clutter. Based on the multispectral image sequence, the spectral fusion method is used to detect the abnormal targets. Spectral fusion produces the appropriate binary detection model and the computational probability of detection. Secondly, the particle filtering-based adaptive PHD algorithm is developed to detect and track multiple targets. This algorithm can deal with the nonlinear measurement on target state. In addition, the calculated probability of detection substitutes the fixed detection probability in PHD filter. Finally, the synthetic data sets based on various actual background images were utilized to validate the effectiveness of the detection approach. The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances.

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39

Lee, Yeon-Jun, and Seung-Woo Seo. "Performance Improvement of Pedestrian Detection using a GM-PHD Filter." Journal of the Institute of Electronics and Information Engineers 52, no. 12 (2015): 150–57. http://dx.doi.org/10.5573/ieie.2015.52.12.150.

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40

Si, Weijian, Liwei Wang, and Zhiyu Qu. "Multi-Target State Extraction for the SMC-PHD Filter." Sensors 16, no. 6 (2016): 901. http://dx.doi.org/10.3390/s16060901.

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41

Shi, Zhi-Guo, Yunmei Zheng, Xiaomeng Bian, and Zhengde Yu. "THRESHOLD-BASED RESAMPLING FOR HIGH-SPEED PARTICLE PHD FILTER." Progress In Electromagnetics Research 136 (2013): 369–83. http://dx.doi.org/10.2528/pier12120406.

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42

Wu, Wei, and Cheng-you Yin. "An Improved SMC-PHD Filter for Multiple Targets Tracking." JOURNAL OF RADARS 1, no. 4 (2013): 406–13. http://dx.doi.org/10.3724/sp.j.1300.2012.20094.

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43

Zhang, Huanqing, Hongwei Ge, and Jinlong Yang. "Target Birth Intensity Estimation Using Measurement-Driven PHD Filter." ETRI Journal 38, no. 5 (2016): 1019–29. http://dx.doi.org/10.4218/etrij.16.0116.0040.

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YANG, Feng, Yong-Qi WANG, Yan LIANG, and Quan PAN. "A Survey of PHD Filter Based Multi-target Tracking." Acta Automatica Sinica 39, no. 11 (2013): 1944. http://dx.doi.org/10.3724/sp.j.1004.2013.01944.

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Feng, Pengming, Wenwu Wang, Syed Mohsen Naqvi, and Jonathon Chambers. "Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking." IEEE Signal Processing Letters 23, no. 11 (2016): 1592–96. http://dx.doi.org/10.1109/lsp.2016.2611138.

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Habtemariam, Biruk K., R. Tharmarasa, and T. Kirubarajan. "PHD filter based track-before-detect for MIMO radars." Signal Processing 92, no. 3 (2012): 667–78. http://dx.doi.org/10.1016/j.sigpro.2011.09.007.

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Yang, Jinlong, and Hongbing Ji. "A novel track maintenance algorithm for PHD/CPHD filter." Signal Processing 92, no. 10 (2012): 2371–80. http://dx.doi.org/10.1016/j.sigpro.2012.02.010.

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Li, Wenling, Yingmin Jia, Junping Du, and Jun Zhang. "PHD filter for multi-target tracking with glint noise." Signal Processing 94 (January 2014): 48–56. http://dx.doi.org/10.1016/j.sigpro.2013.06.012.

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Jeong, TaIkyeong T. "Particle PHD filter multiple target tracking in sonar image." IEEE Transactions on Aerospace and Electronic Systems 43, no. 1 (2007): 409–16. http://dx.doi.org/10.1109/taes.2007.357143.

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Granstrom, Karl, Christian Lundquist, and Omut Orguner. "Extended Target Tracking using a Gaussian-Mixture PHD Filter." IEEE Transactions on Aerospace and Electronic Systems 48, no. 4 (2012): 3268–86. http://dx.doi.org/10.1109/taes.2012.6324703.

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