Mahler R P S. Multitarget Bayes fltering via frst-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178
[2]
Bar-Shalom Y. Tracking and Data Association. San Diego: Academic Press, 1988
[3]
Musicki D, Evans R. Joint integrated probabilistic data association: JIPDA. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3): 1093-1099
[4]
Erdinc O, Willett P, Bar-Shalom Y. A physical-space ap-proach for the probability hypothesis density and cardinal-ized probability hypothesis density flters. In: Proceedings of the 2006 Signal and Data Processing of Small Targets. Orlando, FL: SPIE, 2006. 623619-623619-12
[5]
Mahler R. A theory of PHD flters of higher order in tar-get number. In: Proceedings of the 2006 Signal and Data Processing of Small Targets. Orlando, FL: SPIE, 2006.62350K-62350K-12
[6]
Vo B T, Vo B N, Cantoni A. Performance of PHD based multi-target flters. In: Proceedings of the 9th Interna-tional Conference on Information Fusion. Florence: IEEE,2006. 1-8
[7]
Mahler R, Lockheed Martin M S, Eagan M N. Second-generation PHD/CPHD flters and multitarget calculus. In: Proceedings of the 2009 Signal and Data Processing of Small Targets. Orlando, FL: SPIE, 2009, 7445: 74450I
[8]
Vo B N, Ma W K. The Gaussian mixture probability hy-pothesis density flter. IEEE Transactions on Signal Pro-cessing, 2006, 54(11): 4091-4104
[9]
Ouyang C, Ji H B. Weight over-estimation problem in GMP-PHD flter. Electronics Letters, 2011, 47(2):139-141
[10]
Melzi M, Ouldali A. Joint multiple target tracking and classifcation using the unscented Kalman particle PHD fl-ter. In: Proceedings of the 9th International New Circuits and Systems Conference. Bordeaux: IEEE, 2011. 534-537
[11]
Clark D E, Bell J. Convergence results for the particle PHD flter. IEEE Transactions on Signal Processing, 2006,54(7): 2652-2661
[12]
Clark D E, Vo B N. Convergence analysis of the Gaussian mixture PHD flter. IEEE Transactions on Signal Process-ing, 2007, 55(4): 1204-1212
[13]
Pace M, Del Moral P, Caron F. Comparison of implemen-tations of Gaussian mixture PHD flters. In: Proceedings of the 13th International Conference on Information Fu-sion. Edinburgh, UK: IEEE, 2010. 1-8
[14]
Mahler R. Linear-complexity CPHD flters. In: Proceed-ings of the 13th International Conference on Information Fusion. Edinburgh, UK: IEEE, 2010. 1-8
[15]
Clark D, Ristic B, Vo B N, Vo B T. Bayesian multi-object fltering with amplitude feature likelihood for unknown ob-ject SNR. IEEE Transactions on Signal Processing, 2010,58(1): 26-37
[16]
Panta K, Vo B N, Singh S, Doucet A. Probability hypoth-esis density flter versus multiple hypothesis tracking. In: Proceedings of the 2004 SPIE Conference on Signal Pro-cessing, Sensor Fusion and Target Recognition. Orlando, FL: SPIE, 2004. 284-295
[17]
Lin L, Bar-Shalom Y, Kirubarajan T. Track labeling and PHD flter for multitarget tracking. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 778-795
[18]
Clark D E, Bell J. Multi-target state estimation and track continuity for the particle PHD flter. IEEE Transac-tions on Aerospace and Electronic Systems, 2007, 43(4):1441-1453
[19]
Dunne D, Kirubajaran T. Weight partitioned probability hypothesis density flters. In: Proceedings of the 14th In-ternational Conference on Information Fusion. Chicago, IL: IEEE, 2011. 1-8
[20]
Streit R L. Multisensor multitarget intensity flter. In: Pro-ceedings of the 11th International Conference on Informa-tion Fusion. Cologne: IEEE, 2008. 1-8
[21]
Mahler R. The multisensor PHD flter: II. Erroneous solu-tion via `Poisson magic'. In: Proceedings of the 2009 SPIE Defense, Security, and Sensing on International Society for Optics and Photonics. Orlando, FL: SPIE, 2009. 73360D-73360D-12
[22]
Liu W F, Wen C L. A linear multisensor PHD flter using the measurement dimension extension approach. In: Pro-ceedings of the 2nd International Conference on Advances in Swarm Intelligence. Berlin, Heidelberg: Springer, 2011.486-493
[23]
Meng F B, Hao Y L, Xia Q X, Ouyang T S, Zou W. A particle PHD flter for multi-sensor multi-target tracking based on sequential fusion. In: Proceedings of the 2009 International Conference on Information Engineering and Computer Science. Wuhan, China: IEEE, 2009. 1-5
[24]
Anderson B D O, Moore J B. Optimal Filtering. Engle-wood Cliffs, NJ: Prentice-Hall, 1979
[25]
Bar-Shalom Y, Li X R. Multitarget-Multisensor Tracking: Principles and Techniques. Storrs, CT: University of Con-necticut, 1995
[26]
Clark D. Joint target-detection and tracking smoothers. In: Proceedings of the 2009 SPIE Defense, Security, and Sensing on International Society for Optics and Photonics. Orlando, FL: SPIE, 2009. 73360G-73360G-11
[27]
Vo B T. Random Finite Sets in Multi-Object Filtering [Ph. D. dissertation], University ofWestern Australia, Aus-tralia, 2008
[28]
Clark D E, Vo B T, Vo B N. Forward-backward sequen-tial Monte Carlo smoothing for joint target detection and tracking. In: Proceedings of the 12th International Con-ference of Information Fusion. Seattle, WA: IEEE, 2009.899-906
[29]
Nandakumaran N, Kirubarajan T. Maneuvering target tracking using probability hypothesis density smoothing. In: Proceedings of the 2009 SPIE conference on Signal Pro-cessing, Sensor Fusion, and Target Recognition. Orlando, FL: SPIE, 2009, 7336: 73360F-1
[30]
Nagappa S, Clark D E. Fast sequential Monte Carlo PHD smoothing. In: Proceedings of the 14th International Con-ference of Information Fusion. Chicago, IL: IEEE, 2011.1-7
[31]
Hoffman J R, Mahler R P S. Multitarget miss distance via optimal assignment. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2004,34(3): 327-336
[32]
Ristic B, Vo B N, Clark D. Performance evaluation of multi-target tracking using the OSPA metric. In: Proceed-ings of the 13th International Conference of Information Fusion. Edinburgh: IEEE, 2010. 1-7
[33]
Nagappa S, Clark D E, Mahler R. Incorporating track uncertainty into the OSPA metric. In: Proceedings of the 14th International Conference of Information Fusion. Chicago, IL: IEEE, 2011. 1-8
[34]
Sidenbladh H. Multi-target particle fltering for the proba-bility hypothesis density. In: Proceedings of the 6th Inter-national Conference of Information Fusion. Cairns, Aus-tralia: IEEE, 2003. 800-806
[35]
Kohlleppel R. Ground moving target tracking of PAMIR detections with a Gaussian mixture-PHD flter. In: Pro-ceedings of the 2011 International Radar Symposium. Leipzig: IEEE, 2011. 193-198
[36]
Punithakumar K, Kirubarajan T, Sinha A. A sequential Monte Carlo probability hypothesis density algorithm for multitarget track-before-detect. In: Proceedings of the2005 SPIE. San Diego, California: SPIE, 2005, 5913:59131S
[37]
Habtemariam B K, Tharmarasa R, Kirubarajan T. PHD flter based track-before-detect for MIMO radars. Signal Processing, 2012, 92(3): 667-678
[38]
Pasha A, Vo B, Tuan H D, Ma W K. Closed form PHD fltering for linear jump Markov models. In: Proceedings of the 9th International Conference on Information Fusion. Sunnyvale, CA: IEEE, 2006. 1-8
[39]
Wood T M. Interacting methods for manoeuvre handling in the GM-PHD flter. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(4): 3021-3025
[40]
Clark D, Godsill S. Group target tracking with the Gaus-sian mixture probability hypothesis density flter. In: Pro-ceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. Melbourne, Qld.: IEEE, 2007. 149-154
[41]
Gilholm K, Godsill S, Maskell S, Salmond D. Poisson mod-els for extended target and group tracking. In: Proceedings of the 2005 SPIE conference on Signal and Data Process-ing on Small Targets. San Diego, California, USA: SPIE,2005, 5913: 59130R
[42]
Subramaniam M, Tharmarasa R, McDonald M, Kirubara-jan T. Passive tracking with sensors of opportunity using passive coherent location. In: Proceedings of the 2008 In-ternational Society for Optics and Photonics. Orlando, FL: SPIE, 2008. 69691F-69691F-12
[43]
Tobias M, Lanterman A D. Probability hypothesis density-based multitarget tracking with bistatic range and Doppler observations. IEE Proceedings-Radar, Sonar, and Naviga-tion, 2005, 152(3): 195-205
[44]
Maggio E, Taj M, Cavallaro A. E±cient multitarget visual tracking using random fnite sets. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(8):1016-1027
[45]
Juang R R, Levchenko A, Burlina P. Tracking cell motion using GM-PHD. In: Proceedings of the 2009 IEEE Inter-national Symposium on Biomedical Imaging: From Nano to Macro. Boston, MA: IEEE, 2009. 1154-1157
[46]
Guerriero M, Coraluppi S, Willett P. Analysis of scan and batch processing approaches to static fusion in sensor net-works. In: Proceedings of the 2008 International Soci-ety for Optics and Photonics. Orlando, FL: SPIE, 2008.69690Z-69690Z-10
[47]
Mahler R. Sensor management with non-ideal sensor dy-namics. In: Proceedings of the 2004 International Confer-ence on Information Fusion. Sunnyvale, CA: IEEE, 2004
[48]
Zatezalo A, El-Fallah A, Mahler R K, Pham K. Joint search and sensor management for geosynchronous satel-lites. In: Proceedings of the 2008 International Society for Optics and Photonics. Orlando, FL: SPIE, 2008. 69680O-69680O-12
[49]
Zatezalo A, El-Fallah A, Mahler R K, Brown J. Dispersed and disparate sensor management for tracking low earth orbit satellites. In: Proceedings of the 2009 International Society for Optics and Photonics. Orlando, FL: SPIE,2009. 73360I-73360I-12
[50]
Tian Shu-Rong, Wang Guo-Hong, He You. Multi-target tracking with probability hypothesis density particle flter. Journal of Naval Aeronautical Engineering Institute, 2007,22(4): 417-420, 430 (田淑荣, 王国宏, 何友. 多目标跟踪的概率假设密度粒子滤波. 海军航空土程学院学报, 2007, 22(4): 417-420, 430)
[51]
Yin Y J, Zhang J Q, Zhuang Z S. Gaussian sum PHD flter-ing algorithm for nonlinear non-Caussian models. Chinese Journal of Aeronautics, 2008, 21(4): 341-351
[52]
Lian Feng, Han Chong-Zhao, Liu Wei-Feng, Yuan Xiang-Hui. Multiple-model probability hypothesis density smoother. Acta Automatica Sinica, 2010, 30(4): 939-950 (连峰, 韩崇绍, 刘伟峰, 元向辉. 多模型概率假设密度平滑器. 自动化学报, 2010, 30(4): 939-950)
[53]
Wu Jing-Jing, Hu Shi-Qiang. Probability hypothesis den-sity flter based multi-target visual tracking. Control and Decision, 2010, 25(12): 1861-1865 (吴静静, 胡士强. 基于概率假设密度的多目标视频跟踪算法. 控制与决策, 2010, 25(12): 1861-1865)
[54]
Tan Shun-Cheng, Wang Guo-Hong, Wang Na, Jia Shu-Yi. Multi-target tracking based on PHD flter and data association. Systems Engineering and Electronics, 2011,33(4): 734-737 (谭顺成, 王国宏, 王娜, 贾舒宜. 基于PHD 滤波和数据关联的多目标跟踪. 系统工程与电子技术, 2011, 33(4): 734-737)
[55]
Yan Xiao-Xi, Han Chong-Zhao. Multiple target tracking algorithm based on online estimation of target birth inten-sity. Acta Automatica Sinica, 2011, 37(8): 963-972 (闫小喜, 韩崇昭. 基于目标出生强度在线估计的多目标跟踪算法. 自动化学报, 2011, 37(8): 963-972)
[56]
Lian Feng, Han Chong-Zhao, Liu Wei-Feng, Yuan Xiang-Hui. Convergence analysis of the Gaussian mixture extended-target probability hypothesis density flter. Acta Automatica Sinica, 2012, 38(8): 1343-1352 (连峰, 韩崇昭, 刘伟峰, 元向辉. 高斯混合扩展目标概率假设密度滤波器的收敛性分析. 自动化学报, 2012, 38(8): 1343-1352)
[57]
Mahler R P. Statistical Multisource-Multitarget Informa-tion Fusion. Norwood: Artech House, 2007
[58]
Bar-Shalom Y, Kirubarajan T, Lin X. Probabilistic data association techniques for target tracking with applications to sonar, radar and EO sensors. IEEE Aerospace and Elec-tronic Systems Magazine, 2005, 20(8): 37-56
[59]
Blackman S S. Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine, 2004, 19(1): 5-18
[60]
Erdinc O, Willett P, Bar-Shalom Y. Probability hypoth-esis density flter for multitarget multisensor tracking. In: Proceedings of the 8th International Conference on Infor-mation Fusion. Philadelphia, PA: IEEE, 2005. 25-29
[61]
Mahler R. PHD flters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems,2007, 43(4): 1523-1543
[62]
Mahler R. The multisensor PHD flter: I. General solution via multitarget calculus. In: Proceedings of the 2009 SPIE Defense, Security, and Sensing on International Society for Optics and Photonics. Orlando, FL: SPIE, 2009. 73360D-73360D-12
[63]
Vo B N, Singh S, Boucet A. Sequential Monte Carlo meth-ods for multi-target fltering with random fnite sets. IEEE Transactions on Aerospace and Electronic Systems. 2005,41(4): 1224-1245
[64]
Clark D, Vo B T, Vo B N. Gaussian particle implemen-tations of probability hypothesis density flters. In: Pro-ceedings of the 2007 IEEE Aerospace Conference. Big Sky, MT: IEEE, 2007. 1-11
[65]
Nandakumaran N, Sutharsan S, Tharmarasa R, Lang T, Kirubarajan T, Kirubarajan T. Rao-blackwellised approx-imate conditional mean probability hypothesis density fl-tering. In: Proceedings of the 2009 Signal and Data Pro-cessing of Small Targets. Orlando, FL: SPIE, 2009, 7445:74450J-1
[66]
Pace M, Zhang H L. Grid based PHD fltering by fast Fourier transform. In: Proceedings of the 14th Interna-tional Conference on Information Fusion. Chicago, IL: IEEE, 2011. 1-8
[67]
Johansen A M, Singh S S, Doucet A, Vo B N. Convergence of the SMC implementation of the PHD flter. Methodol-ogy and Computing in Applied Probability, 2006, 8(2):265-291
[68]
Ristic B, Clark D E, Vo B N. Improved SMC implemen-tation of the PHD flter. In: Proceedings of the 13th In-ternational Conference on Information Fusion. Edinburgh, UK: IEEE, 2010. 1-8
[69]
Fearnhead P, Clifford P. On-line inference for hidden Markov models via particle flters. Journal of the Royal Statistical Society: Series B (Statistical Methodology),2003, 65(4): 887-899
[70]
Macagnano D, Freitas de Abreu G T. Adaptive gating for multitarget tracking with Gaussian mixture flters. IEEE Transactions on Signal Processing, 2012, 60(3):1533-1538
[71]
Lin L, Bar-Shalom Y, Kirubarajan T. Data association combined with the probability hypothesis density flter for multitarget tracking. In: Proceedings of the the 2004 SPIE conference on Signal and Data Processing on Small Tar-gets. Orlando, Florida: SPIE, 2004. 464-475
[72]
Papi F, Battistelli G, Chisci L, Morrocchi S, Farina A, Graziano A. Multitarget tracking via joint PHD fltering and multiscan association. In: Proceedings of the 12th International Conference on Information Fusion. Seattle, WA: IEEE, 2009. 1163-1170
[73]
Danu D G, Lang T, Kirubarajan T. Assignment-based par-ticle labeling for PHD particle flter. In: Proceedings of the2009 SPIE conference on Signal and Data Processing on Small Targets. Orlando, FL: SPIE, 2009, 7445: 74450D-1
[74]
Erdinc O, Willett P, Bar-Shalom Y. The bin-occupancy flter and its connection to the PHD flters. IEEE Trans-actions on Signal Processing, 2009, 57(11): 4232-4246
[75]
Panta K, Clark D E, Vo B N. Data association and track management for the Gaussian mixture probability hypoth-esis density flter. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 1003-1016
[76]
Streit R L, Stone L D. Bayes derivation of multitarget in-tensity flters. In: Proceedings of the 11th International Conference on Information Fusion. Cologne, Germany: IEEE, 2008. 1-8
[77]
Schikora M, Bender D, Koch W, Cremers D. Multitarget, multisensor localization and tracking using passive anten-nas and optical sensors on UAVs. In: Proceedings of the2010 Security and Defence on International Society for Op-tics and Photonics. Bellingham, WA: SPIE, 2010. 783305-783305-9
[78]
Delande E, Duflos E, Vanheeghe P, Heurguier D. Multi-sensor PHD: construction and implementation by space partitioning. In: Proceedings of the 2011 IEEE Interna-tional Conference on Acoustics, Speech, and Signal Pro-cessing. Prague, Czech Republic: IEEE, 2011. 3632-3635
[79]
Mahler R. Approximate multisensor CPHD and PHD fl-ters. In: Proceedings of the 13th International Conference on Information Fusion. Edinburgh, UK: IEEE, 2010. 1-8
[80]
Harvey A C. Forecasting, Structural Time Series Models and the Kalman Filter. New York: Cambridge University Press, 1989
[81]
Bar-Shalom Y, Li X R, Kirubarajan T. Estimation with Applications to Tracking and Navigation. New York: Wi-ley, 2001
[82]
Mahler R P S. Statistical Multisource-Multitarget Infor-mation Fusion. Norwood: Artech House, 2007
[83]
Vo B N, Vo B T, Mahler R P S. Closed-form solutions to forward-backward smoothing. IEEE Transactions on Sig-nal Processing, 2012, 60(1): 2-17
[84]
Nandakumaran N, Punithakumar K, Kirubarajan T. Im-proved multi-target tracking using probability hypothesis density smoothing. In: Proceedings of the 2007 Signal and Data Processing of Small Targets. San Diego, CA: SPIE,2007, 6699: 66990M
[85]
Clark D E. First-moment multi-object forward-backward smoothing. In: Proceedings of the 13th International Con-ference of Information Fusion. Edinburgh: IEEE, 2010.1-6
[86]
Rothrock R L, Drummond O E. Performance metrics for multiple-sensor multiple-target tracking. In: Proceedings of the 2000 International Society for Optics and Photonics. Orlando, FL: SPIE, 2000. 521-531
[87]
Schuhmacher D, Vo B T, Vo B N. A consistent met-ric for performance evaluation of multi-object flters. IEEE Transactions on Signal Processing, 2008, 56(8):3447-3457
[88]
Ristic B, Vo B N, Clark D, Vo B T. A metric for per-formance evaluation of multi-target tracking algorithms. IEEE Transactions on Signal Processing, 2011, 59(7):3452-3457
[89]
Granstrom K, Lundquist C, Orguner U. A Gaussian mix-ture PHD flter for extended target tracking. In: Proceed-ings of the 13th International Conference on Information Fusion. Edinburgh: IEEE, 2010. 1-8
[90]
Ulmke M, Erdinc O, Willett P. GMTI tracking via the Gaussian mixture cardinalized probability hypothesis den-sity flter. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 1821-1833
[91]
Sidenbladh H, Wirkander S L. Tracking random sets of vehicles in terrain. In: Proceedings of the 2003 Conference on Computer Vision and Pattern Recognition Workshop. Madison, Wisconsin, USA: IEEE, 2003. 98
[92]
Tong H S, Zhang H, Meng H D, Wang X Q. Multitarget tracking before detection via probability hypothesis den-sity flter. In: Proceedings of the 2010 International Con-ference on Electrical and Computer Engineering. Wuhan, China: IEEE, 2010. 1332-1335
[93]
Punithakumar K, Kirubarajan T, Sinha A. A multiple-model probability hypothesis density flter for tracking ma-neuvering targets. In: Proceedings of the 2004 Signal and Data Proceedings of Small Targets. Orlando, FL: SPIE,2004. 113-121
[94]
Pasha S A, Vo B N, Tuan H D, Ma W K. A Gaussian mixture PHD flter for jump Markov system models. IEEE Transactions on Aerospace and Electronic Systems, 2009,45(3): 919-936
[95]
Ahlberg S, H?rling P, J?red K, M?rtenson C, Neider G, Schubert J, Sidenbladh H, Svenson P, Svensson P, Undén, Walter J. The IFD03 information fusion demonstrator. In: Proceedings of the 7th International Conference on Infor-mation Fusion. Sunnyvale, CA: IEEE, 2004. 936-943
[96]
Wang Y D, Wu J K, Kassim A A, Huang W M. Tracking a variable number of human groups in video using probabil-ity hypothesis density. In: Proceedings of the 18th Inter-national Conference on Pattern Recognition. Hong Kong, China: IEEE, 2006. 1127-1130
[97]
Mahler R. PHD flters for nonstandard targets, I: extended targets. In: Proceedings of the 12th International Con-ference on Information Fusion. Seattle, WA: IEEE, 2009.915-921
[98]
Kemper J, Hauschildt D. Passive infrared localization with a probability hypothesis density flter. In: Proceedings of the 7th Workshop on Positioning Navigation and Commu-nication. Dresden: IEEE, 2010. 68-76
[99]
Balakwmar B, Sinha A, Kirubarajan T, Reilly J P. PHD fltering for tracking an unknown number of sources using an array of sensors. In: Proceedings of the 13th Workshop on Statistical Signal Processing. Novosibirsk: IEEE, 2005.43-48
[100]
Pham N T, Huang W M, Ong S H. Tracking multiple ob-jects using probability hypothesis density flter and color measurement. In: Proceedings of the 2007 IEEE Inter-national Conference on Multimedia and Expo. Beijing, China, IEEE, 2007. 1511-1514
[101]
Battistelli G, Chisci L, Morrocchi S, Papi F, Benavoli A, Di Lallo A, Farina A, Graziano A. Tra±c intensity estimation via PHD fltering. In: Proceedings of the 2008 European Radar Conference. Amsterdam: IEEE, 2008. 340-343
[102]
Mahler R. Multitarget sensor management of dispersed mobile sensors. Theory and Algorithms for Cooperative Systems, New York: Springer, 2005
[103]
El-Fallah A, Zatezalo A, Mahler R K, Donatelli D. Space-based sensor management and geostationary satellites tracking. In: Proceedings of the 2007 International Soci-ety for Optics and Photonics. Orlando, FL: SPIE, 2007.65670R-65670R-12
[104]
El-Fallah A, Zatezalo A, Mahler R K, Donatelli D. Dy-namic sensor management of dispersed and disparate sen-sors for tracking resident space objects. In: Proceedings of the 2008 International Society for Optics and Photonics. Orlando, FL: SPIE, 2008. 69680P-69680P-11
[105]
Mahler R, El-Fallah A. Unifed sensor management in un-known dynamic clutter. In: Proceedings of the 2010 Inter-national Society for Optics and Photonics. Orlando, FL: SPIE, 2010. 769811-769811-12
[106]
Zhuang Ze-Sen, Zhang Jian-Qiu, Yin Jian-Jun. Rao-Blackwellized particle probability hypothesis density fl-ter. Acta Aeronautica et Astronautica Sinica, 2009, 30(4):698-705 (庄泽森, 张建秋, 尹建君. Rao-Blackwellized 粒子概率假设密度滤波算法. 航空学报, 2009, 30(4): 698-705)
[107]
Zhou Cheng-Xing, Liu Gui-Xi, Hou Lian-Yong, Zhong Xing-Zhi. Modifed Gaussian particle probability hypothe-sis density fltering algorithm. Control Theory and Appli-cations, 2011, 30(4): 1005-1008 (周承兴, 刘贵喜, 侯连勇, 钟兴质. 改进的高斯粒子概率假设密度滤波算法. 控制理论与应用, 2011, 30(4): 1005-1008)
[108]
Meng Fan-Bin, Hao Yan-Ling, Zhou Wei-Dong, Sun Feng. Sequential particle PHD flter algorithm based on radar and infrared sensor. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2010,38(4): 14-17 (孟凡彬, 郝燕玲, 周卫东, 孙枫. 基于雷达和红外的序贯粒子PHD 滤波融合算法. 华中科技大学学报(自然科学版), 2010, 38(4):14-17)
[109]
Huang Zhi-Bei, Sun Shu-Yan, Wu Jian-Kang. Multiple hy-potheses detection with Gaussian mixture probability hy-pothesis density flter for multi-target trajectory tracking. Journal of Electronics and Information Technology, 2010,32(6): 1289-1294 (黄志蓓, 孙树岩, 吴健康. 多元假设检验GMPHD 轨迹跟踪. 电子与信息学报, 2010, 32(6): 1289-1294)
[110]
Wang Xiao, Han Chong-Zhao. A probability hypothesis density flter with multiple models for maneuvering tar-get tracking. Journal of Xi0an Jiaotong University, 2011,45(12): 1-5 (王晓, 韩崇昭. 用于机动目标跟踪的多模型概率假设密度滤波器. 西安交通大学学报, 2011, 45(12): 1-5)