Log in

Relevant bibliographies by topics / PHD filter / Journal articles

To see the other types of publications on this topic, follow the link: PHD filter.

Journal articles on the topic 'PHD filter'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'PHD filter.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

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.

Full text

Abstract:

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 obser

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 mult

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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,

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 imp

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 distribu

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 a

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 probab

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 respect

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 coordinate

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 determin

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 previou

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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 ad

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!