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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbas AE, 2009. A Kullback-Leibler view of linear and log-linear pools. Dec Anal, 6(1):25–37. https://doi.org/10.1287/deca.1080.0133
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.
Bar-Shalom Y, Willett PK, Tian X, 2011. Tracking and Data Fusion. YBS Publishing, USA.
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. https://doi.org/10.1109/JSTSP.2013.2250911
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.
Battistelli G, Chisci L, Laurenzi A, 2017. Random set approach to distributed multivehicle SLAM. IFAC, 50(1):2457–2464. https://doi.org/10.1016/j.ifacol.2017.08.410
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. https://doi.org/10.1109/TSP.2017.2723355
Blackman S, Blackman SS, Popoli R, 1999. Design and Analysis of Modern Tracking Systems. Artech House, Norwood, USA.
Boyd S, Ghosh A, Prabhakar B, et al., 2006. Randomized Gossip algorithms. IEEE Trans Inform Theory, 52(6):2508–2530. https://doi.org/10.1109/TIT.2006.874516
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. https://doi.org/10.1109/AERO.2019.8742216
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. https://doi.org/10.1016/B978-0-12-396500-4.00015-6
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. https://doi.org/10.1109/JSEN.2017.2740038
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.
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. https://doi.org/10.23919/ICIF.2018.8455318
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. https://doi.org/10.1049/ic.2010.0233
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. https://doi.org/10.1007/978-3-030-23887-2_29
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. https://doi.org/10.1109/LCOMM.2019.2954888
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. https://doi.org/10.1109/TSIPN.2020.3016478
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.
Delande E, Duflos E, Vanheeghe P, et al., 2011. Multisensor PHD: construction and implementation by space partitioning. Proc IEEE Int Conf on Acoustics, Speech and Signal Processing, p.3632–3635. https://doi.org/10.1109/ICASSP.2011.5947137
Dias SS, Bruno MGS, 2016. Distributed Bernoulli filters for joint detection and tracking in sensor networks. IEEE Trans Signal Inform Process Netw, 2(3):260–275. https://doi.org/10.1109/TSIPN.2016.2580460
Fantacci C, Papi F, 2016. Scalable multisensor multitarget tracking using the marginalized δ-GLMB density. IEEE Signal Process Lett, 23(6):863–867. https://doi.org/10.1109/LSP.2016.2557078
Fantacci C, Vo BT, Papi F, et al., 2015. The marginalized δ-GLMB filter. https://arxiv.org/abs/1501.00926
Fantacci C, Vo BN, Vo BT, et al., 2018. Robust fusion for multisensor multiobject tracking. IEEE Signal Process Lett, 25(5):640–644. https://doi.org/10.1109/LSP.2018.2811750
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. https://doi.org/10.1109/iccais.2017.8217557
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. https://doi.org/10.1109/itsc.2016.7795906
Gao L, Battistelli G, Chisci L, 2019a. Fusion of labeled RFS densities with minimum information loss. https://arxiv.org/abs/1911.01083
Gao L, Battistelli G, Chisci L, 2019b. Event-triggered distributed multitarget tracking. IEEE Trans Signal Inform Process Netw, 5(3):570–584. https://doi.org/10.1109/TSIPN.2019.2924196
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. https://doi.org/10.1109/TAES.2019.2929972
Gao L, Battistelli G, Chisci L, 2020b. Multiobject fusion with minimum information loss. IEEE Signal Process Lett, 27:201–205. https://doi.org/10.1109/LSP.2019.2963817
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. https://doi.org/10.1109/TAES.2018.2805153
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. https://doi.org/10.1109/iccais.2017.8217559
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. https://doi.org/10.1109/TAES.2017.2718280
Gowing R, 2002. Roger Cotes Natural Philosopher. Cambridge University Press, Cambridge, UK.
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. https://doi.org/10.1109/ICIF.2010.5711885
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. https://doi.org/10.1109/TSP.2015.2500884
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. https://doi.org/10.1109/TAES.2016.150403
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. https://doi.org/10.1109/TAES.2016.150403
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. https://doi.org/10.1016/j.inffus.2019.06.026
Heskes T, 1998. Selecting weighting factors in logarithmic opinion pools. Proc Conf on Advances in Neural Information Processing Systems, p.266–272.
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. https://doi.org/10.1109/TSP.2012.2196697
Hu JW, Zheng BY, Wang C, et al., 2020. A survey on multisensor fusion based obstacle detection for intelligent ground vehicles in off-road environments. Front Inform Technol Electron Eng, 21(5):675–692. https://doi.org/10.1631/FITEE.1900518
Hurley MB, 2002. An information theoretic justification for covariance intersection and its generalization. Proc 5th Int Conf on Information Fusion, p.505–511. https://doi.org/10.1109/ICIF.2002.1021196
Javadi SH, Farina A, 2020. Radar networks: a review of features and challenges. Inform Fus, 61:48–55. https://doi.org/10.1016/j.inffus.2020.03.005
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.
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. https://doi.org/10.1109/ICIF.2006.301755
Julier SJ, 2008. Fusion without independence. Proc IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, p.1–4. https://doi.org/10.1049/ic:20080050
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. https://doi.org/10.1109/NSSPW.2006.4378844
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. https://doi.org/10.1109/TWC.2020.2978479
Li D, Wong KD, Hu YH, et al., 2002. Detection, classification, and tracking of targets. IEEE Signal Process Mag, 19(2):17–29. https://doi.org/10.1109/79.985674
Li GC, Battistelli G, Yi W, et al., 2020. Distributed multisensor multi-view fusion based on generalized covariance intersection. Signal Process, 166:107246. https://doi.org/10.1016/j.sigpro.2019.107246
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. https://doi.org/10.1109/TSP.2017.2760286
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. https://doi.org/10.23919/ICIF.2018.8455250
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. https://doi.org/10.1109/TSP.2018.2880704
Li TC, Da K, 2020. Best fit of mixture for distributed Poisson multi-Bernoulli mixture filtering. https://doi.org/10.36227/techrxiv.12351710
Li TC, Hlawatsch F, 2017. A distributed particle-PHD filter with arithmetic-average PHD fusion. http://arxiv.org/abs/1712.06128
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. https://doi.org/10.1016/j.ins.2017.01.028
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. https://doi.org/10.1109/TSIPN.2016.2631944
Li TC, Corchado JM, Sun SD, 2017c. On generalized co-variance intersection for distributed PHD filtering and a simple but better alternative. Proc 20th Int Conf on Information Fusion, p.1–8. https://doi.org/10.23919/ICIF.2017.8009732
Li TC, Prieto J, Fan HQ, et al., 2018a. A robust multisensor PHD filter based on multi-sensor measurement clustering. IEEE Commun Lett, 22(10):2064–2067. https://doi.org/10.1109/LCOMM.2018.2863387
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. https://doi.org/10.23919/icif.2018.8455759
Li TC, Hlawatsch F, Djurić PM, 2019a. Cardinality-consensus-based PHD filtering for distributed multitarget tracking. IEEE Signal Process Lett, 26(1):49–53. https://doi.org/10.1109/LSP.2018.2878064
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. https://doi.org/10.1109/LSP.2019.2950588
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. https://doi.org/10.1109/jsen.2018.2882084
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. https://doi.org/10.1109/TAES.2018.2882960
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. https://doi.org/10.1016/j.inffus.2019.02.009
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. https://doi.org/10.1109/JSEN.2020.3004068
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. https://doi.org/10.1109/TSP.2020.2985643
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. https://doi.org/10.1049/iet-rsn.2010.0057
Lian F, Hou LM, Liu J, et al., 2018a. Constrained multisensor control using a multi-target MSE bound and a δ-GLMB filter. Sensors, 18(7):2308. https://doi.org/10.3390/s18072308
Lian F, Hou LM, Wei B, et al., 2018b. Sensor selection for decentralized large-scale multi-target tracking network. Sensors, 18(12):4115. https://doi.org/10.3390/s18124115
Liggins ME, Hall DL, Llinas J, 2008. Handbook of Multisensor Data Fusion: Theory and Practice. CRC Press, Boca Raton, USA.
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. https://doi.org/10.1109/ICCAIS.2017.8217575
Mahler R, 2000. Optimal/robust distributed data fusion: a unified approach. Proc Signal Processing, Sensor Fusion, and Target Recognition IX, p.128–138. https://doi.org/10.1117/12.395064
Mahler R, 2003. Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans Aerosp Electron Syst, 39(4):1152–1178. https://doi.org/10.1109/TAES.2003.1261119
Mahler R, 2007a. PHD filters of higher order in target number. IEEE Trans Aerosp Electron Syst, 43(4):1523–1543. https://doi.org/10.1109/TAES.2007.4441756
Mahler R, 2007b. Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood, USA. Mahler R, 2007c. Unified sensor management using CPHD filters. Proc 10th Int Conf on Information Fusion, p.1–7. https://doi.org/10.1109/ICIF.2007.4407996
Mahler R, 2009a. The multisensor PHD filter: I. General solution via multitarget calculus. Proc Signal Processing, Sensor Fusion, and Target Recognition XVIII, Article 73360E. https://doi.org/10.1117/12.818024
Mahler R, 2009b. The multisensor PHD filter: II. Erroneous solution via “Poisson magic”. Proc Signal Processing, Sensor Fusion, and Target Recognition XVIII, Article 73360D. https://doi.org/10.1117/12.818025
Mahler R, 2009c. PHD filters for nonstandard targets, I: extended targets. Proc 12th Int Conf on Information Fusion, p.915–921.
Mahler R, 2010. Approximate multisensor CPHD and PHD filters. Proc 13th Int Conf on Information Fusion, p.1–8. https://doi.org/10.1109/ICIF.2010.5711984
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. https://doi.org/10.1201/b10124-8
Mahler R, 2013. “Statistics 102” for multisource-multitarget detection and tracking. IEEE J Sel Top Signal Process, 7(3):376–389. https://doi.org/10.1109/JSTSP.2013.2253084
Mahler R, 2014. Advances in Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood, USA.
Meyer F, Kropfreiter T, Williams JL, et al., 2018. Message passing algorithms for scalable multitarget tracking. Proc IEEE, 106(2):221–259. https://doi.org/10.1109/jproc.2018.2789427
Musicki D, Evans R, 2004. Joint integrated probabilistic data association: JIPDA. IEEE Trans Aerosp Electron Syst, 40(3):1093–1099. https://doi.org/10.1109/TAES.2004.1337482
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. https://doi.org/10.1117/12.884618
Nannuru S, Blouin S, Coates M, et al., 2016. Multisensor CPHD filter. IEEE Trans Aerosp Electron Syst, 52(4):1834–1854. https://doi.org/10.1109/TAES.2016.150265
Nielsen F, Bhatia R, 2013. Matrix Information Geometry. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-642-30232-9
Olfati-Saber R, Fax JA, Murray RM, 2007. Consensus and cooperation in networked multi-agent systems. Proc IEEE, 95(1):215–233. https://doi.org/10.1109/JPROC.2006.887293
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.
Ouyang C, Ji H, 2011. Scale unbalance problem in product multisensor PHD filter. Electron Lett, 47(22):1247–1249. https://doi.org/10.1049/el.2011.1843
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. https://doi.org/10.1109/TSIPN.2018.2881718
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. https://doi.org/10.1109/ICIF.2007.4408087
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
Reuter S, Vo BT, Vo BN, et al., 2014. The labeled multi-Bernoulli filter. IEEE Trans Signal Process, 62(12): 3246–3260. https://doi.org/10.1109/TSP.2014.2323064
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. https://doi.org/10.1109/TAES.2011.5751278
Ristic B, Clark DE, Gordon N, 2013a. Calibration of multitarget tracking algorithms using non-cooperative targets. IEEE J Sel Top Signal Process, 7(3):390–398. https://doi.org/10.1109/JSTSP.2013.2256877
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. https://doi.org/10.1109/TSP.2013.2257765
Saucan AA, Coates MJ, Rabbat M, 2017. A multisensor multi-Bernoulli filter. IEEE Trans Signal Process, 65(20):5495–5509. https://doi.org/10.1109/TSP.2017.2723348
Sayed AH, Djurić PM, Hlawatsch F, 2018. Distributed Kalman and particle filtering. In: Djurić PM, Richard C (Eds.), Cooperative and Graph Signal Processing. Academic Press, Amsterdam, USA, p.169–207. https://doi.org/10.1016/B978-0-12-813677-5.00006-7
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. https://doi.org/10.1016/j.automatica.2014.04.004
Si WJ, Zhu HF, Qu ZY, 2020. Multi-sensor Poisson multi-Bernoulli filter based on partitioned measurements. IET Radar Sonar Nav, 14(6):860–869. https://doi.org/10.1049/iet-rsn.2019.0510
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.
Streit R, Degen C, Koch W, 2015. The pointillist family of multitarget tracking filters. https://arxiv.org/abs/1505.08000
Sudderth EB, Ihler AT, Isard M, et al., 2010. Nonparametric belief propagation. Commun ACM, 53(10):95–103. https://doi.org/10.1145/1831407.1831431
Tsybakov AB, 2008. Introduction to Nonparametric Estimation. Springer Science & Business Media, New York, USA. https://doi.org/10.1007/b13794
Uhlmann JK, 1996. General data fusion for estimates with unknown cross covariances. Proc Signal Processing, Sensor Fusion, and Target Recognition V, p.536–548. https://doi.org/10.1117/12.243195
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. https://doi.org/10.1049/ic.2010.0232
Üney M, Clark DE, Julier SJ, 2011. Information measures in distributed multitarget tracking. Proc 14th Int Conf on Information Fusion, p.1–8.
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. https://doi.org/10.1109/JSTSP.2013.2257162
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. https://doi.org/10.1109/TSP.2015.2493981
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. https://doi.org/10.1109/TSIPN.2018.2825599
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. https://doi.org/10.1109/TAES.2019.2893083
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. https://doi.org/10.1109/IROS.2016.7759614
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. https://doi.org/10.1109/MSP.2013.2262116
Vo BN, Ma WK, 2006. The Gaussian mixture probability hypothesis density filter. IEEE Trans Signal Process, 54(11):4091. https://doi.org/10.1109/TSP.2006.881190
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. https://doi.org/10.1109/TSP.2019.2928953
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. https://doi.org/10.1109/TAES.2005.1561884
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. https://doi.org/10.1109/TSP.2014.2364014
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. https://doi.org/10.1002/047134608X.W8275
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. https://doi.org/10.1109/TSP.2019.2946023
Vo BT, 2008. Random Finite Sets in Multi-object Filtering. PhD Thesis, University of Western Australia, Australia. Vo BT, Vo BN, 2013. Labeled random finite sets and multi-object conjugate priors. IEEE Trans Signal Process, 61(13):3460–3475. https://doi.org/10.1109/TSP.2013.2259822
Vo BT, Vo BN, Cantoni A, 2007. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Trans Signal Process, 55(7):3553–3567. https://doi.org/10.1109/TSP.2007.894241
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. https://doi.org/10.1109/TSP.2008.2007924
Vo BT, Vo BN, Hoseinnezhad R, et al., 2013. Robust multi-Bernoulli filtering. IEEE J Sel Top Signal Process, 7(3):399–409. https://doi.org/10.1109/JSTSP.2013.2252325
Wang BL, Yi W, Li S, et al., 2015. Distributed multitarget tracking via generalized multi-Bernoulli random finite sets. Proc 18th Int Conf on Information Fusion, p.253–261.
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. https://doi.org/10.1109/TSP.2016.2617825
Wei BS, Nener B, Liu WF, et al., 2016. Centralized multisensor multi-target tracking with labeled random finite sets. Proc Int Conf on Control, Automation and Information Sciences, p.82–87. https://doi.org/10.1109/ICCAIS.2016.7822440
Willett P, Ruan Y, Streit R, 2002. PMHT: problems and some solutions. IEEE Trans Aerosp Electron Syst, 38(3):738–754. https://doi.org/10.1109/TAES.2002.1039396
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. https://doi.org/10.1109/TAES.2015.130550
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. https://doi.org/10.23919/ICIF.2017.8009710
Xiang LY, Chen F, Ren W, et al., 2019. Advances in network controllability. IEEE Circ Syst Mag, 19(2):8–32. https://doi.org/10.1109/MCAS.2019.2909446
Xiao L, Boyd S, 2004. Fast linear iterations for distributed averaging. Syst Contr Lett, 53(1):65–78. https://doi.org/10.1016/j.sysconle.2004.02.022
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. https://doi.org/10.1109/TSP.2020.3007313
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. https://doi.org/10.1016/j.inffus.2019.08.010
Yu JY, Coates M, Rabbat M, 2016. Distributed multi-sensor CPHD filter using pairwise Gossiping. Proc IEEE Int Conf on Acoustics, Speech and Signal Processing, p.3176–3180. https://doi.org/10.1109/ICASSP.2016.7472263
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the Key Laboratory Foundation of National Defence Technology, China (No. 61424010306), the Joint Fund of Equipment Development and Aerospace Science and Technology, China (No. 6141B0624050101), and the National Natural Science Foundation of China (Nos. 61901489 and 62071389)
Contributors
Kai DA and Tiancheng LI designed the research. Kai DA drafted the manuscript. Tiancheng LI revised the manuscript. Qiang FU and Yongfeng ZHU edited the manuscript and added references. Tiancheng LI and Hongqi FAN reviewed and finalized the paper.
Compliance with ethics guidelines
Kai DA, Tiancheng LI, Yongfeng ZHU, Hongqi FAN, and Qiang FU declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Da, K., Li, T., Zhu, Y. et al. Recent advances in multisensor multitarget tracking using random finite set. Front Inform Technol Electron Eng 22, 5–24 (2021). https://doi.org/10.1631/FITEE.2000266
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2000266