基于多伯努利概率假設(shè)密度的擴(kuò)展目標(biāo)跟蹤方法

李文娟; 顧紅; 蘇衛(wèi)民

doi:10.11999/JEIT160372

基于多伯努利概率假設(shè)密度的擴(kuò)展目標(biāo)跟蹤方法

doi: 10.11999/JEIT160372

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61471198)

計(jì)量
- 文章訪問(wèn)數(shù): 1368
- HTML全文瀏覽量: 120
- PDF下載量: 331
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-04-18
- 修回日期: 2016-08-25
- 刊出日期: 2016-12-19

Extended Target Tracking Method Based on Multi-BernoulliProbability Hypothesis Density

LI Wenjuan¹,
GU Hong^{1
, ,},
SU Weimin¹

Funds:

The National Natural Science Foundation of China (61471198)

摘要

摘要: 高分辨率雷達(dá)系統(tǒng)中，擴(kuò)展目標(biāo)一般會(huì)產(chǎn)生多個(gè)量測(cè)?，F(xiàn)有隨機(jī)有限集(RFS) 類算法一般假定擴(kuò)展目標(biāo)的量測(cè)數(shù)目服從泊松分布，然而這個(gè)假設(shè)與實(shí)際情況不符。針對(duì)這一問(wèn)題，該文提出一種多伯努利擴(kuò)展目標(biāo)概率假設(shè)密度(MB-ET-PHD)跟蹤算法。該算法首先假設(shè)擴(kuò)展目標(biāo)的量測(cè)數(shù)目服從多伯努利分布，然后通過(guò)有限集統(tǒng)計(jì)(FISST)理論的多目標(biāo)微積分推導(dǎo)得到校正等式，最后給出了高斯混合(GM)框架的仿真結(jié)果。仿真結(jié)果表明該算法能夠獲得比泊松ET-PHD算法更好的跟蹤性能。
- 擴(kuò)展目標(biāo)跟蹤 /
- 概率假設(shè)密度 /
- 多伯努利
Abstract: Extended targets usually generate multiple measurements in high resolution radar systems. Existing algorithms of the Random Finite Set (RFS) assume that the measurement number of extended targets follows Poisson distribution in a general way. However, this assumption is inconsistent with actual situations. Considering this issue, a Multi-Bernoulli Extended Target Probability Hypothesis Density (MB-ET-PHD) tracking method is proposed. First, this method assumes that the measurement number of extended targets is Multi-Bernoulli (MB) distributed. Then, its update equation is derived by using the FInite Set STatistics (FISST) multi-target calculus. Finally, simulated results of Gaussian Mixture (GM) framework are given. The simulation results show that the proposed method can obtain better tracking performance compared with the Poisson ET-PHD method.
- Extended target tracking /
- Probability Hypothesis Density (PHD) /
- Multi-Bernoulli (MB)

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參考文獻(xiàn)(20)

GILHOLM K and SALMOND D. Spatial distribution model for tracking extended objects[J]. IET Radar, Sonar Navigation, 2005, 152(5): 364-371. doi: 10.1049/ip-rsn: 20045114.

MAHLER R. PHD filters for nonstandard targets I: extended targets[C]. International Conference on Information Fusion, Seattle, WA, USA, 2009: 915-921.

MAHLER R. Statistical Multisource-Multitarget Information Fusion[M]. Artech House, Norwood, MA, 2007: 193-360.

MAHLER R. Multi target Bayes filtering via first-order multi target moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178. doi: 10.1109/ TAES.2003.1261119.

VO B, SINGH S, and DOUCENT A. Sequential Monte Carlo methods for multi-target filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245. doi: 10.1109/TAES.2005.1561884.

胡子軍, 張林讓, 張鵬, 等. 基于高斯混合帶勢(shì)概率假設(shè)密度濾波器的位置雜波下多機(jī)動(dòng)目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報(bào), 2015, 37(1): 116-122. doi: 10.11999/JEIT140218.

HU Zijun, ZHANG Linrang, ZHANG Peng, et al. Gaussian mixture cardinalized probability hypothesis density filter for multiple maneuvering target tracking under unknown clutter situation[J]. Journal of Electronics Information Technology, 2015, 37(1): 116-122. doi: 10.11999/JEIT140218.

吳衛(wèi)華, 江晶, 馮訊, 等. 基于高斯混合勢(shì)化概率假設(shè)密度的脈沖多普勒雷達(dá)多目標(biāo)跟蹤算法[J].電子與信息學(xué)報(bào), 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232.

WU Weihua, JIANG Jing, FENG Xun, et al. Multi-target tracking algorithm based on Gaussian mixture cardinalized probability hypothesis density for pulse Doppler radar[J]. Journal of Electronics Information Technology, 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232.

VO B and MA W. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104. doi: 10.1109/TSP.2006. 881190.

GRANSTROM K, LUNDQUIST C, and ORGUNER U. Extended target tracking using a Gaussian mixture PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3268-3286. doi: 10.1109/TAES.2012. 6324703.

GRANSTROM K, LUNDQUIST C, and ORGUNER U. A Gaussian mixture PHD filter for extended target tracking[C]. International Conference on Information Fusion, Edinburgh, Scotland, UK, 2010: 1-8. doi: 10.1109/ICIF.2010.5711885.

LAN Jian and LI Xiaorong. Tracking of maneuvering non-ellopsoidal extended ojectct or target group using random matrix[J]. IEEE Transactions on Signal Processing, 2014, 62(9): 1042-1059. doi: 10.1109/TSP.2014.2309561.

FELDMANN M, FRANKEN D, and KOCH W. Tracking of extended objects and group targets using random matrices[J]. IEEE Transactions on Signal Processing, 2011, 59(4): 1409-1420. doi: 10.1109/TSP.2010.2101064.

GRANSTROM K and ORGUNER U. A PHD filter for tracking multiple extended targets using random matrices[J]. IEEE Transactions on Signal Processing, 2012, 60(11): 5657-5671. doi: 10.1109/TSP.2012.2212888.

GENNARELLI G, VIVONE G, BRACA P, et al. Multiple extended target tracking for through-wall radars[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6482-6494. doi: 10.1109/TGRS.2015.2441957.

WAHLSTROM N and OZKAN E. Extended target tracking using Gaussian processes[J]. IEEE Transactions on Signal Processing, 2015, 63(16): 4165-4178. doi: 10.1109/TSP. 2015.2424194.

GRANSTROM K, NATALE A, BRACA P, et al. Gamma Gaussian inverse Wishart probability hypothesis density for extended target tracking using X-band marine radar data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6617-6631. doi: 10.1109/TGRS.2015.2444794.

BEARD M, REUTER S, GILHOLM K, et al. Multiple extended target tracking with labeled random finite sets[J]. IEEE Transactions on Signal Processing, 2016, 64(7): 1638-1653. doi: 10.1109/TSP.2015.2505683.

MA Dongdong, LIAN Feng, and LIU Jing. Sequential Monte Carlo implementation of cardinality balanced multi-target multi-Bernoulli filter for extended target tracking[J]. IET Radar, Sonar Navigation, 2016, 10(2): 272-277. doi: 10.1049/iet-rsn.2015.0081.

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