CLC number: TP273.5
On-line Access: 2021-01-11
Received: 2020-06-01
Revision Accepted: 2020-07-17
Crosschecked: 2020-09-11
Cited: 0
Clicked: 4879
Citations: Bibtex RefMan EndNote GB/T7714
Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu. Recent advances in multisensor multitarget tracking using random finite set[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 5-24.
@article{title="Recent advances in multisensor multitarget tracking using random finite set",
author="Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="1",
pages="5-24",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000266"
}
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Abstract: In this study, we provide an overview of recent advances in multisensor multitarget tracking based on the random finite set (RFS) approach. The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements and posterior densities between sensors, respectively. Important properties of each fusion rule including the optimality and sub-optimality are presented. In particular, two robust multitarget density-averaging approaches, arithmetic- and geometric-average fusion, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.
[1]Abbas AE, 2009. A Kullback-Leibler view of linear and log-linear pools. Dec Anal, 6(1):25-37.
[2]Bailey T, Julier S, Agamennoni G, 2012. On conservative fusion of information with unknown non-Gaussian dependence. Proc 15th Int Conf on Information Fusion, p.1876-1883.
[3]Bar-Shalom Y, Willett PK, Tian X, 2011. Tracking and Data Fusion. YBS Publishing, USA.
[4]Battistelli G, Chisci L, Fantacci C, et al., 2013. Consensus CPHD filter for distributed multitarget tracking. IEEE J Sel Top Signal Process, 7(3):508-520.
[5]Battistelli G, Chisci L, Fantacci C, et al., 2014. Distributed peer-to-peer multitarget tracking with association-based track fusion. Proc 17th Int Conf on Information Fusion, p.1-7.
[6]Battistelli G, Chisci L, Laurenzi A, 2017. Random set approach to distributed multivehicle SLAM. IFAC, 50(1):2457-2464.
[7]Beard M, Vo BT, Vo BN, et al., 2017. Void probabilities and Cauchy–Schwarz divergence for generalized labeled multi-Bernoulli models. IEEE Trans Signal Process, 65(19):5047-5061.
[8]Blackman S, Blackman SS, Popoli R, 1999. Design and Analysis of Modern Tracking Systems. Artech House, Norwood, USA.
[9]Boyd S, Ghosh A, Prabhakar B, et al., 2006. Randomized Gossip algorithms. IEEE Trans Inform Theory, 52(6):2508-2530.
[10]Buonviri A, York M, LeGrand K, et al., 2019. Survey of challenges in labeled random finite set distributed multi-sensor multi-object tracking. IEEE Aerospace Conf, p.1-12.
[11]Chen X, Tharmarasa R, Kirubarajan T, 2014. Multitarget multisensor tracking. In: Theodoridis S, Chellappa R (Eds.), Academic Press Library in Signal Processing. Academic Press, USA, p.759-812.
[12]Chen YJ, Zhang Q, Luo Y, et al., 2017. Multi-target radar imaging based on phased-MIMO technique—Part II: adaptive resource allocation. IEEE Sens J, 17(19):6198-6209.
[13]Chong CY, Mori S, Chang K, 1990. Distributed multitarget multisensory tracking. In: Bar-Shalom Y (Ed.), Multitarget-Multisensor Tracking: Advanced Applications. Artech House, Norwood, USA, p.247-295.
[14]Chong CY, Chang KC, Mori S, 2018. A review of forty years of distributed estimation. Proc 21st Int Conf on Information Fusion, p.1-8.
[15]Clark D, Julier S, Mahler R, et al., 2010. Robust multi-object sensor fusion with unknown correlations. Proc Sensor Signal Processing for Defence, p.1-5.
[16]Da K, Li TC, Zhu YF, et al., 2019. Kullback-Leibler averaging for multitarget density fusion. Proc Distributed Computing and Artificial Intelligence, p.253-261.
[17]Da K, Li T, Zhu Y, et al., 2020a. A computationally efficient approach for distributed sensor localization and multitarget tracking. IEEE Commun Lett, 24(2):335-338.
[18]Da K, Li T, Zhu YF, et al., 2020b. Gaussian mixture particle jump-Markov-CPHD fusion for multitarget tracking using sensors with limited views. IEEE Trans Signal Inform Process Netw, 6:605-616.
[19]Delande E, Duflos E, Heurguier D, et al., 2010. Multi-target PHD Filtering: Proposition of Extensions to the Multi-Sensor Case. Research Report No. RR-7337, INRIA, French.
[20]Delande E, Duflos E, Vanheeghe P, et al., 2011. Multi-sensor PHD: construction and implementation by space partitioning. Proc IEEE Int Conf on Acoustics, Speech and Signal Processing, p.3632-3635.
[21]Dias SS, Bruno MGS, 2016. Distributed calBernoulli filters for joint detection and tracking in sensor networks. IEEE Trans Signal Inform Process Netw, 2(3):260-275.
[22]Fantacci C, Papi F, 2016. Scalable multisensor multitarget tracking using the marginalized δ-GLMB density. IEEE Signal Process Lett, 23(6):863-867.
[23]Fantacci C, Vo BT, Papi F, et al., 2015. The marginalized δ-GLMB filter. https://arxiv.org/abs/1501.00926
[24]Fantacci C, Vo BN, Vo BT, et al., 2018. Robust fusion for multisensor multiobject tracking. IEEE Signal Process Lett, 25(5):640-644.
[25]Farina A, Battistelli G, Chisci L, et al., 2017. 40 years of tracking for radar systems: a cross-disciplinary academic and industrial viewpoint. Int Conf on Control, Automation and Information Sciences, p.1-8.
[26]Gan J, Vasic M, Martinoli A, 2016. Cooperative multiple dynamic object tracking on moving vehicles based on sequential Monte Carlo probability hypothesis density filter. Proc IEEE 19th Int Conf on Intelligent Transportation Systems, p.2163-2170.
[27]Gao L, Battistelli G, Chisci L, 2019a. Fusion of labeled RFS densities with minimum information loss. https://arxiv.org/abs/1911.01083
[28]Gao L, Battistelli G, Chisci L, 2019b. Event-triggered distributed multitarget tracking. IEEE Trans Signal Inform Process Netw, 5(3):570-584.
[29]Gao L, Battistelli G, Chisci L, et al., 2020a. Distributed joint sensor registration and multitarget tracking via sensor network. IEEE Trans Aerosp Electron Syst, 56(2):1301-1317.
[30]Gao L, Battistelli G, Chisci L, 2020b. Multiobject fusion with minimum information loss. IEEE Signal Process Lett, 27:201-205.
[31]García-Fernández ÁF, Williams JL, Granström K, et al., 2018. Poisson multi-Bernoulli mixture filter: direct derivation and implementation. IEEE Trans Aerosp Electron Syst, 54(4):1883-1901.
[32]Gostar AK, Hoseinnezhad R, Bab-Hadiashar A, 2017a. Cauchy-Schwarz divergence-based distributed fusion with Poisson random finite sets. Proc Int Conf on Control, Automation and Information Sciences, p.112-116.
[33]Gostar AK, Hoseinnezhad R, Bab-Hadiashar A, et al., 2017b. Sensor-management for multitarget filters via minimization of posterior dispersion. IEEE Trans Aerosp Electron Syst, 53(6):2877-2884.
[34]Gowing R, 2002. Roger Cotes Natural Philosopher. Cambridge University Press, Cambridge, UK.
[35]Granström K, Lundquist C, Orguner U, 2010. A Gaussian mixture PHD filter for extended target tracking. Proc 13th Int Conf on Information Fusion, p.1-8.
[36]Granström K, Willett P, Bar-Shalom Y, 2015. Approximate multi-hypothesis multi-Bernoulli multi-object filtering made multi-easy. IEEE Trans Signal Process, 64(7):1784-1797.
[37]Guldogan MB, 2014. Consensus Bernoulli filter for distributed detection and tracking using multi-static Doppler shifts. IEEE Trans Aerosp Electron Syst, 52(6):2732-2746.
[38]Gunay M, Orguner U, Demirekler M, 2016. Chernoff fusion of Gaussian mixtures based on sigma-point approximation. IEEE Trans Aerosp Electron Syst, 52(6):2732-2746.
[39]He S, Shin HS, Xu SY, et al., 2020. Distributed estimation over a low-cost sensor network: a review of state-of-the-art. Inform Fus, 54:21-43.
[40]Heskes T, 1998. Selecting weighting factors in logarithmic opinion pools. Proc Conf on Advances in Neural Information Processing Systems, p.266-272.
[41]Hlinka O, Slučiak O, Hlawatsch F, et al., 2012. Likelihood consensus and its application to distributed particle filtering. IEEE Trans Signal Process, 60(8):4334-4349.
[42]Hu JW, Zheng BY, Wang C, et al., 2020. A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments. Front Inform Technol Electron Eng, 21(5):675-692.
[43]Hurley MB, 2002. An information theoretic justification for covariance intersection and its generalization. Proc 5th Int Conf on Information Fusion, p.505-511.
[44]Javadi SH, Farina A, 2020. Radar networks: a review of features and challenges. Inform Fus, 61:48-55.
[45]Jiang M, Yi W, Hoseinnezhad R, et al., 2016. Distributed multi-sensor fusion using generalized multi-Bernoulli densities. Proc 19th Int Conf on Information Fusion, p.1332-1339.
[46]Julier SJ, 2006. An empirical study into the use of Chernoff information for robust, distributed fusion of Gaussian mixture models. 9th Int Conf on Information Fusion, p.1-8.
[47]Julier SJ, 2008. Fusion without independence. Proc IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, p.1-4.
[48]Julier SJ, Bailey T, Uhlmann JK, 2006. Using exponential mixture models for suboptimal distributed data fusion. IEEE Nonlinear Statistical Signal Processing Workshop, p.160-163.
[49]Kim H, Granström K, Gao L, et al., 2020. 5G mmwave cooperative positioning and mapping using multi-model PHD filter and map fusion. IEEE Trans Wirel Commun, 19(6):3782-3795.
[50]Li D, Wong KD, Hu YH, et al., 2002. Detection, classification, and tracking of targets. IEEE Signal Process Mag, 19(2):17-29.
[51]Li GC, Battistelli G, Yi W, et al., 2020. Distributed multi-sensor multi-view fusion based on generalized covariance intersection. Signal Process, 166:107246.
[52]Li SQ, Yi W, Hoseinnezhad R, et al., 2017. Robust distributed fusion with labeled random finite sets. IEEE Trans Signal Process, 66(2):278-293.
[53]Li SQ, Battistelli G, Chisci L, et al., 2018. Multi-sensor multi-object tracking with different fields-of-view using the LMB filter. Proc 21st Int Conf on Information Fusion, p.1201-1208.
[54]Li SQ, Battistelli G, Chisci L, et al., 2019. Computationally efficient multi-agent multi-object tracking with labeled random finite sets. IEEE Trans Signal Process, 67(1):260-275.
[55]Li TC, Da K, 2020. Best fit of mixture for distributed Poisson multi-Bernoulli mixture filtering.
[56]Li TC, Hlawatsch F, 2017. A distributed particle-PHD filter with arithmetic-average PHD fusion. http://arxiv.org/abs/1712.06128
[57]Li TC, Corchado JM, Sun SD, et al., 2017a. Clustering for filtering: multi-object detection and estimation using multiple/massive sensors. Inform Sci, 388-389:172-190.
[58]Li TC, Corchado JM, Prieto J, 2017b. Convergence of distributed flooding and its application for distributed Bayesian filtering. IEEE Trans Signal Inform Process Netw, 3(3):580-591.
[59]Li TC, Corchado JM, Sun SD, 2017c. On generalized covariance intersection for distributed PHD filtering and a simple but better alternative. Proc 20th Int Conf on Information Fusion, p.1-8.
[60]Li TC, Prieto J, Fan HQ, et al., 2018a. A robust multi-sensor PHD filter based on multi-sensor measurement clustering. IEEE Commun Lett, 22(10):2064-2067.
[61]Li TC, Corchado JM, Chen HM, 2018b. Distributed flooding-then-clustering: a lazy networking approach for distributed multiple target tracking. Proc 21st Int Conf on Information Fusion, p.2415-2422.
[62]Li TC, Hlawatsch F, Djurić PM, 2019a. Cardinality-consensus-based PHD filtering for distributed multitarget tracking. IEEE Signal Process Lett, 26(1):49-53.
[63]Li TC, Liu ZG, Pan Q, 2019b. Distributed Bernoulli filtering for target detection and tracking based on arithmetic average fusion. IEEE Signal Process Lett, 26(12):1812-1816.
[64]Li TC, Elvira V, Fan HQ, et al., 2019c. Local-diffusion-based distributed SMC-PHD filtering using sensors with limited sensing range. IEEE Sens J, 19(4):1580-1589.
[65]Li TC, Corchado JM, Sun SD, 2019d. Partial consensus and conservative fusion of Gaussian mixtures for distributed PHD fusion. IEEE Trans Aerosp Electron Syst, 55(5):2150-2163.
[66]Li TC, Fan HQ, García J, et al., 2019e. Second-order statistics analysis and comparison between arithmetic and geometric average fusion: application to multi-sensor target tracking. Inform Fus, 51:233-243.
[67]Li TC, Mallick M, Pan Q, 2020a. A parallel filtering-communication based cardinality consensus approach for real-time distributed PHD filtering. IEEE Sens J, in press.
[68]Li TC, Wang XX, Liang Y, et al., 2020b. On arithmetic average fusion and its application for distributed multi-Bernoulli multitarget tracking. IEEE Trans Signal Process, 68:2883-2896.
[69]Lian F, Han C, Liu W, et al., 2011. Joint spatial registration and multi-target tracking using an extended probability hypothesis density filter. IET Radar Sonar Navig, 5(4):441-448.
[70]Lian F, Hou LM, Liu J, et al., 2018a. Constrained multi-sensor control using a multi-target MSE bound and a δ-GLMB filter. Sensors, 18(7):2308.
[71]Lian F, Hou LM, Wei B, et al., 2018b. Sensor selection for decentralized large-scale multi-target tracking network. Sensors, 18(12):4115.
[72]Liggins ME, Hall DL, Llinas J, 2008. Handbook of Multisensor Data Fusion: Theory and Practice. CRC Press, Boca Raton, USA.
[73]Liu WF, Chen YM, Cui HL, et al., 2017. Multi-sensor tracking with non-overlapping field for the GLMB filter. Proc Int Conf on Control, Automation and Information Sciences, p.197-202.
[74]Mahler R, 2000. Optimal/robust distributed data fusion: a unified approach. Proc Signal Processing, Sensor Fusion, and Target Recognition IX, p.128-138.
[75]Mahler R, 2003. Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans Aerosp Electron Syst, 39(4):1152-1178.
[76]Mahler R, 2007a. PHD filters of higher order in target number. IEEE Trans Aerosp Electron Syst, 43(4):1523-1543.
[77]Mahler R, 2007b. Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood, USA.
[78]Mahler R, 2007c. Unified sensor management using CPHD filters. Proc 10th Int Conf on Information Fusion, p.1-7.
[79]Mahler R, 2009a. The multisensor PHD filter: I. General solution via multitarget calculus. Proc Signal Processing, Sensor Fusion, and Target Recognition XVIII, Article 73360E.
[80]Mahler R, 2009b. The multisensor PHD filter: II. Erroneous solution via “Poisson magic”. Proc Signal Processing, Sensor Fusion, and Target Recognition XVIII, Article 73360D.
[81]Mahler R, 2009c. PHD filters for nonstandard targets, I: extended targets. Proc 12th Int Conf on Information Fusion, p.915-921.
[82]Mahler R, 2010. Approximate multisensor CPHD and PHD filters. Proc 13th Int Conf on Information Fusion, p.1-8.
[83]Mahler R, 2012. Toward a theoretical foundation for distributed fusion. In: Hall D, Chong CY, Llinas J, et al. (Eds.), Distributed Data Fusion for Network-Centric Operations. CRC Press, Boca Raton, FL, USA, p.199-224.
[84]Mahler R, 2013. “Statistics 102” for multisource-multitarget detection and tracking. IEEE J Sel Top Signal Process, 7(3):376-389.
[85]Mahler R, 2014. Advances in Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood, USA.
[86]Meyer F, Kropfreiter T, Williams JL, et al., 2018. Message passing algorithms for scalable multitarget tracking. Proc IEEE, 106(2):221-259.
[87]Musicki D, Evans R, 2004. Joint integrated probabilistic data association: JIPDA. IEEE Trans Aerosp Electron Syst, 40(3):1093-1099.
[88]Nagappa S, Clark DE, 2011. On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter. Proc Signal Processing, Sensor Fusion, and Target Recognition XX, Article 80500M.
[89]Nannuru S, Blouin S, Coates M, et al., 2016. Multisensor CPHD filter. IEEE Trans Aerosp Electron Syst, 52(4):1834-1854.
[90]Nielsen F, Bhatia R, 2013. Matrix Information Geometry. Springer, Berlin, Germany.
[91]Olfati-Saber R, Fax JA, Murray RM, 2007. Consensus and cooperation in networked multi-agent systems. Proc IEEE, 95(1):215-233.
[92]Orguner U, Lundquist C, Granström K, 2011. Extended target tracking with a cardinalized probability hypothesis density filter. Proc 14th Int Conf on Information Fusion, p.1-8.
[93]Ouyang C, Ji H, 2011. Scale unbalance problem in product multisensor PHD filter. Electron Lett, 47(22):1247-1249.
[94]Papa G, Repp R, Meyer F, et al., 2018. Distributed Bernoulli filtering using likelihood consensus. IEEE Trans Signal Inform Process Netw, 5(2):218-233.
[95]Pham NT, Huang WM, Ong SH, 2007. Multiple sensor multiple object tracking with GMPHD filter. Proc 10th Int Conf on Information Fusion, p.1-7.
[96]Ramachandran RK, Fronda N, Sukhatme GS, 2020. Resilience in multi-robot multi-target tracking with unknown number of targets through reconfiguration. https://arxiv.org/abs/2004.07197
[97]Reuter S, Vo BT, Vo BN, et al., 2014. The labeled multi-Bernoulli filter. IEEE Trans Signal Process, 62(12): 3246-3260.
[98]Ristic B, Vo BN, Clark D, 2011. A note on the reward function for PHD filters with sensor control. IEEE Trans Aerosp Electron Syst, 47(2):1521-1529.
[99]Ristic B, Clark DE, Gordon N, 2013a. Calibration of multi-target tracking algorithms using non-cooperative targets. IEEE J Sel Top Signal Process, 7(3):390-398.
[100]Ristic B, Vo BT, Vo BN, et al., 2013b. A tutorial on Bernoulli filters: theory, implementation and applications. IEEE Trans Signal Process, 61(13):3406-3430.
[101]Saucan AA, Coates MJ, Rabbat M, 2017. A multisensor multi-Bernoulli filter. IEEE Trans Signal Process, 65(20):5495-5509.
[102]Sayed AH, Djurić PM, Hlawatsch F, 2018. Distributed calKalman and particle filtering. In: Djurić PM, Richard C (Eds.), Cooperative and Graph Signal Processing. Academic Press, Amsterdam, USA, p.169-207.
[103]Shi DW, Chen TW, Shi L, 2014. An event-triggered approach to state estimation with multiple point- and set-valued measurements. Automatica, 50(6):1641-1648.
[104]Si WJ, Zhu HF, Qu ZY, 2020. Multi-sensor calPoisson multi-Bernoulli filter based on partitioned measurements. IET Radar Sonar Nav, 14(6):860-869.
[105]Smith J, Particke F, Hiller M, et al., 2019. Systematic analysis of the PMBM, PHD, JPDA, and GNN multi-target tracking filters. Proc 22th Int Conf on Information Fusion, p.1-8.
[106]Streit R, Degen C, Koch W, 2015. The pointillist family of multitarget tracking filters. https://arxiv.org/abs/1505.08000
[107]Sudderth EB, Ihler AT, Isard M, et al., 2010. Nonparametric belief propagation. Commun ACM, 53(10):95-103.
[108]Tsybakov AB, 2008. Introduction to Nonparametric Estimation. Springer Science & Business Media, New York, USA.
[109]Uhlmann JK, 1996. General data fusion for estimates with unknown cross covariances. Proc Signal Processing, Sensor Fusion, and Target Recognition V, p.536-548.
[110]Üney M, Julier S, Clark D, et al., 2010. Monte Carlo realisation of a distributed multi-object fusion algorithm. Proc Sensor Signal Processing for Defence, p.1-5.
[111]Üney M, Clark DE, Julier SJ, 2011. Information measures in distributed multitarget tracking. Proc 14th Int Conf on Information Fusion, p.1-8.
[112]Üney M, Clark DE, Julier SJ, 2013. Distributed fusion of PHD filters via exponential mixture densities. IEEE J Sel Top Signal Process, 7(3):521-531.
[113]Üney M, Mulgrew B, Clark DE, 2015. A cooperative approach to sensor localisation in distributed fusion networks. IEEE Trans Signal Process, 64(5):1187-1199.
[114]Üney M, Mulgrew B, Clark DE, 2018. Latent parameter estimation in fusion networks using separable likelihoods. IEEE Trans Signal Inform Process Netw, 4(4):752-768.
[115]Üney M, Houssineau J, Delande E, et al., 2019. Fusion of finite-set distributions: pointwise consistency and global cardinality. IEEE Trans Aerosp Electron Syst, 55(6):2759-2773.
[116]Vasic M, Mansolino D, Martinoli A, 2016. A system implementation and evaluation of a cooperative fusion and tracking algorithm based on a Gaussian mixture PHD filter. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.4172-4179.
[117]Vempaty A, Tong L, Varshney PK, 2013. Distributed inference with Byzantine data: state-of-the-art review on data falsification attacks. IEEE Signal Process Mag, 30 (5):65-75.
[118]Vo BN, Ma WK, 2006. The Gaussian mixture probability hypothesis density filter. IEEE Trans Signal Process, 54(11):4091.
[119]Vo BN, Vo BT, 2019. A multi-scan labeled random finite set model for multi-object state estimation. IEEE Trans Signal Process, 67(19):4948-4963.
[120]Vo BN, Singh S, Doucet A, 2005. Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Trans Aerosp Electron Syst, 41(4):1224-1245.
[121]Vo BN, Vo BT, Phung D, 2014. Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Trans Signal Process, 62(24):6554-6567.
[122]Vo BN, Mallick M, Bar-Shalom Y, et al., 2015. Multitarget tracking. In: Webster JG (Ed.), Wiley Encyclopedia of Electrical and Electronics Engineering. John Wiley & Sons, Inc., NJ, USA, p.1-15.
[123]Vo BN, Vo BT, Beard M, 2019. Multi-sensor multi-object tracking with the generalized labeled multi-Bernoulli filter. IEEE Trans Signal Process, 67(23):5952-5967.
[124]Vo BT, 2008. Random Finite Sets in Multi-object Filtering. PhD Thesis, University of Western Australia, Australia.
[125]Vo BT, Vo BN, 2013. Labeled random finite sets and multi-object conjugate priors. IEEE Trans Signal Process, 61(13):3460-3475.
[126]Vo BT, Vo BN, Cantoni A, 2007. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Trans Signal Process, 55(7):3553-3567.
[127]Vo BT, Vo BN, Cantoni A, 2009. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Trans Signal Process, 57(2):409-423.
[128]Vo BT, Vo BN, Hoseinnezhad R, et al., 2013. Robust multi-Bernoulli filtering. IEEE J Sel Top Signal Process, 7(3):399-409.
[129]Wang BL, Yi W, Li S, et al., 2015. Distributed multi-target tracking via generalized multi-Bernoulli random finite sets. Proc 18th Int Conf on Information Fusion, p.253-261.
[130]Wang BL, Yi W, Hoseinnezhad R, et al., 2017. Distributed fusion with multi-Bernoulli filter based on generalized covariance intersection. IEEE Trans Signal Process, 65(1):242-255.
[131]Wei BS, Nener B, Liu WF, et al., 2016. Centralized multi-sensor multi-target tracking with labeled random finite sets. Proc Int Conf on Control, Automation and Information Sciences, p.82-87.
[132]Willett P, Ruan Y, Streit R, 2002. PMHT: problems and some solutions. IEEE Trans Aerosp Electron Syst, 38(3):738-754.
[133]Williams JL, 2015. Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA, and association-based MeMBer. IEEE Trans Aerosp Electron Syst, 51(3):1664-1687.
[134]Xia YX, Granstrcom K, Svensson L, et al., 2017. Performance evaluation of multi-Bernoulli conjugate priors for multi-target filtering. Proc 20th Int Conf on Information Fusion, p.1-8.
[135]Xiang LY, Chen F, Ren W, et al., 2019. Advances in network controllability. IEEE Circ Syst Mag, 19(2):8-32.
[136]Xiao L, Boyd S, 2004. Fast linear iterations for distributed averaging. Syst Contr Lett, 53(1):65-78.
[137]Yan JK, Pu WQ, Zhou SH, et al., 2020a. Optimal resource allocation for asynchronous multiple targets tracking in heterogeneous radar network. IEEE Trans Signal Process, 68:4055-4068.
[138]Yan JK, Pu WQ, Zhou SH, et al., 2020b. Collaborative detection and power allocation framework for target tracking in multiple radar system. Inform Fus, 55:173-183.
[139]Yu JY, Coates M, Rabbat M, 2016. Distributed multi-sensor calCPHD filter using pairwise Gossiping. Proc IEEE Int Conf on Acoustics, Speech and Signal Processing, p.3176-3180.
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