基于高斯混合勢化概率假設(shè)密度的脈沖多普勒雷達(dá)多目標(biāo)跟蹤算法
doi: 10.11999/JEIT141232
基金項(xiàng)目:
國家自然科學(xué)基金(61102168)資助課題
Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis
-
摘要: 為在新興的隨機(jī)有限集(RFS)框架下充分利用多普勒信息跟蹤雜波環(huán)境下的多目標(biāo),該文提出基于高斯混合勢化概率假設(shè)密度(GM-CPHD)的脈沖多普勒雷達(dá)多目標(biāo)跟蹤(MTT)算法。該算法在標(biāo)準(zhǔn)GM-CPHD基礎(chǔ)上,在使用位置量測更新狀態(tài)后,再利用多普勒量測進(jìn)行序貫更新,可獲得更精確的似然函數(shù)和狀態(tài)估計(jì)。仿真結(jié)果驗(yàn)證了該算法的有效性,表明在GM-CPHD基礎(chǔ)上引入目標(biāo)的多普勒信息可有效抑制雜波,顯著改善跟蹤性能。
-
關(guān)鍵詞:
- 多目標(biāo)跟蹤 /
- 隨機(jī)有限集 /
- 概率假設(shè)密度 /
- 高斯混合勢化概率假設(shè)密度 /
- 脈沖多普勒雷達(dá)
Abstract: In order to take full advantage of Doppler information for Multi-Target Tracking (MTT) in the clutter environment under the framework of emerging Random Finite Sets (RFS), an MTT algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD) for pulse Doppler radar is proposed. Based on the standard GM-CPHD, the target states are updated sequentially using Doppler measurements after updating them using position measurements, then more accurate likelihood function and state estimation are obtained. Simulation results show the effectiveness of the proposed algorithm, and the introduced Doppler information can effectively suppress clutter and evidently improve tracking performance. -
Mahler R. Statistical Multisource-multitarget Information Fusion[M]. Norwood: Artech House, 2007. Reuter S, Wilking B, Wiest J, et al.. Real-time multi-object tracking using random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2666-2678. Zhou G, Pelletier M, Kirubarajan T, et al.. Statically fused converted position and Doppler measurement Kalman filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 300-318. Ulmke M, Erdinc O, and Willett P. GMTI tracking via the Gaussian mixture cardinalized probability hypothesis density filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 1821-1833. Yoon J H, Kim D Y, Bae S H, et al.. Joint initialization and tracking of multiple moving objects using Doppler information[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3447-3452. Vo B-N and Ma W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104. Vo B T, Vo B N, and Antonio C. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3553-3567. Ouyang C, Ji H, and Tian Y. Improved Gaussian mixture CPHD tracker for multitarget tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2): 1177-1191. [9] 歐陽成, 姬紅兵, 張俊根. 一種改進(jìn)的CPHD多目標(biāo)跟蹤算法[J]. 電子與信息學(xué)報(bào), 2010, 32(9): 2112-2118. Ouyang C, Ji H, and Zhang J. Improved CPHD filter for multitarget tracking[J]. Journal of Electronics Information Technology, 2010, 32(9): 2112-2118. Beard M, Vo B T, Vo B N, et al.. A partially uniform target birth model for Gaussian mixture PHD/CPHD filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2835-2844. Ristic B, Clark D, Vo B N, et al.. Adaptive target birth intensity for PHD and CPHD filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1656-1668. Beard M, Vo B-T, and Vo B-N. Multitarget filtering with unknown clutter density using a bootstrap GMCPHD filter[J]. IEEE Signal Processing Letters, 2013, 20(4): 323-326. Ristic B, Clark D, and Gordon N. Calibration of multi-target tracking algorithms using non-cooperative targets[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 390-398. Battistelli G, Chisci L, Fantacci C, et al.. Consensus CPHD filter for distributed multitarget tracking[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 508-520. Uney M, Clark D, and Julier S. Distributed fusion of PHD filters via exponential mixture densities[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 521-531. Ristic B, Vo B-N, Clark D, et al.. A metric for performance evaluation of multi-target tracking algorithms[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3452-3457. -
計(jì)量
- 文章訪問數(shù): 1556
- HTML全文瀏覽量: 132
- PDF下載量: 418
- 被引次數(shù): 0