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              基于代價參考粒子濾波器組的多目標檢測前跟蹤算法

              盧錦 馬令坤 呂春玲 章為川 Sun Chang-Ming

              盧錦, 馬令坤, 呂春玲, 章為川, Sun Chang-Ming. 基于代價參考粒子濾波器組的多目標檢測前跟蹤算法. 自動化學報, 2024, 50(4): 851?861 doi: 10.16383/j.aas.c220635
              引用本文: 盧錦, 馬令坤, 呂春玲, 章為川, Sun Chang-Ming. 基于代價參考粒子濾波器組的多目標檢測前跟蹤算法. 自動化學報, 2024, 50(4): 851?861 doi: 10.16383/j.aas.c220635
              Lu Jin, Ma Ling-Kun, Lv Chun-Ling, Zhang Wei-Chuan, Sun Chang-Ming. A multi-target track-before-detect algorithm based on cost-reference particle filter bank. Acta Automatica Sinica, 2024, 50(4): 851?861 doi: 10.16383/j.aas.c220635
              Citation: Lu Jin, Ma Ling-Kun, Lv Chun-Ling, Zhang Wei-Chuan, Sun Chang-Ming. A multi-target track-before-detect algorithm based on cost-reference particle filter bank. Acta Automatica Sinica, 2024, 50(4): 851?861 doi: 10.16383/j.aas.c220635

              基于代價參考粒子濾波器組的多目標檢測前跟蹤算法

              doi: 10.16383/j.aas.c220635
              基金項目: 國家自然科學基金(61801281)資助
              詳細信息
                作者簡介:

                盧錦:陜西科技大學電子信息與人工智能學院講師. 主要研究方向為目標檢測與跟蹤.E-mail: lj491216@163.com

                馬令坤:陜西科技大學電子信息與人工智能學院教授. 主要研究方向為數字信號處理. 本文通信作者.E-mail: malingkun@sust.edu.cn

                呂春玲:施耐德(西安)創新技術有限公司工程師. 主要研究方向為數字信號處理. E-mail: chunling.lv@se.com

                章為川:格里菲斯大學集成與智能系統研究所研究員. 主要研究方向為圖像信號處理. E-mail: zwc2003@163.com

                Sun Chang-Ming:聯邦科學與工業研究組織Data61中心研究員. 主要研究方向為圖像信號處理. E-mail: changming.sun@csiro.au

              A Multi-target Track-before-detect Algorithm Based on Cost-reference Particle Filter Bank

              Funds: Supported by National Natural Science Foundation of China (61801281)
              More Information
                Author Bio:

                LU Jin Lecturer at the School of Electrical Information and Artifici-al Intelligence, Shaanxi University of Science & Technology. Her main research interest is target detection and tracking

                MA Ling-Kun Professor at the School of Electrical Information and Artificial Intelligence, Shaanxi University of Science & Technology. His main research interest is digital signal processing. Corresponding author of this paper

                LV Chun-Ling Engineer at Sch-neider (Xi'an) Innovation & Technology Company Limited. Her main research interest is digital signal processing

                ZHANG Wei-Chuan Researcher at the Institute of Integrated and Intelligent Systems, Griffith University. His main research interest is image signal processing

                SUN Chang-Ming Researcher at the Data61, Commonwealth Scien-tific and Industrial Research Organization. His main research interest is image signal processing

              • 摘要: 針對圖像序列中多目標檢測和跟蹤算法結構復雜、計算量大、性能降低等問題, 提出一種基于代價參考粒子濾波器組的多目標檢測前跟蹤(Cost-reference particle filter bank based multi-target track-before-detect, CRPFB-MTBD)算法, 將多目標跟蹤問題轉換為序貫地檢測和跟蹤多個單目標的問題. 首先, 采用代價參考粒子濾波器組序貫地估計所有可能單目標狀態序列; 其次, 基于所有可能單目標狀態序列的歐氏距離和累積代價確定目標數量; 最后, 根據累積代價判斷每個目標出現和消失的具體時刻. 仿真實驗驗證了CRPFB-MTBD的優良性能, 與基于傳統粒子濾波的多目標檢測前跟蹤算法(Particle filter based multi-target track-before-detect, PF-MTBD)、基于概率假設密度的檢測前跟蹤算法(Probability hypothesis density based track-before-detect, PHD-TBD)和基于伯努利濾波的檢測前跟蹤算法(Bernoulli based track-before-detect, Bernoulli-TBD) 相比, CRPFB-MTBD的目標狀態序列和數量估計結果最佳, 且平均單次運行時間極短.
              • 圖  1  CRPFB的基本結構

                Fig.  1  Basic structure of CRPFB

                圖  2  原始先驗信息與表1先驗信息的對比

                Fig.  2  Comparison of original prior information and the prior information in table 1

                圖  3  CRPFB-MTBD算法基本框架

                Fig.  3  Basic structure of CRPFB-MTBD

                圖  4  估計可能的目標狀態序列

                Fig.  4  Estimation of all possible targets' state sequences

                圖  5  估計目標數量

                Fig.  5  Estimation of target number

                圖  6  判斷各個目標存在的具體時刻

                Fig.  6  Determination of the specific moments when each target existing

                圖  7  當SNR = 10 dB時的一次觀測

                Fig.  7  One observation when SNR = 10 dB

                圖  8  當SNR = 6 dB, 3個目標時, 4種方法的OSPA

                Fig.  8  Comparison of OSPAs resulted from 4 algorithms when SNR = 6 dB and 3 targets

                圖  9  當SNR = 8 dB, 3個目標時, 4種方法的OSPA

                Fig.  9  Comparison of OSPAs resulted from 4 algorithms when SNR = 8 dB and 3 targets

                圖  10  當SNR = 8 dB, 3個目標時, CRPFB-MTBD的目標狀態估計結果

                Fig.  10  State estimation of CRPFB-MTBD when SNR = 8 dB and 3 targets

                圖  11  當SNR = 8 dB, 3個目標時, CRPFB-MTBD的目標數量估計結果

                Fig.  11  Estimation of target number provided by CRPFB-MTBD when SNR = 8 dB and 3 targets

                圖  12  當SNR = 6 dB時, 目標數量對CRPFB-MTBD性能的影響

                Fig.  12  Impact of target number on the performance of CRPFB-MTBD when SNR = 6 dB

                圖  13  當SNR = 6 dB, 3個目標時, CRPF數量對CRPFB-MTBD性能的影響

                Fig.  13  Impact of the number of CRPFs on the performance of CRPFB-MTBD when SNR = 6 dB and 3 targets

                圖  14  當SNR = 6 dB, 5個目標時, CRPF數量對CRPFB-MTBD性能的影響

                Fig.  14  Impact of the number of CRPFs on the performance of CRPFB-MTBD when SNR = 6 dB and 5 targets

                圖  15  當SNR = 6 dB, 3個目標時, 門限$ V_{2}$對CRPFB-MTBD性能的影響

                Fig.  15  Impact of threshold $ V_{2}$ on the performance of CRPFB-MTBD when SNR = 6 dB and 3 targets

                圖  16  當SNR = 6 dB, 5個目標時, 門限$ V_{2}$對CRPFB-MTBD性能的影響

                Fig.  16  Impact of threshold $ V_{2}$ on the performance of CRPFB-MTBD when SNR = 6 dB and 5 targets

                表  1  第$l$個目標的先驗信息

                Table  1  Apriori information for the $l\text{-} {\rm th}$ target

                先驗信息$x $方向$y $方向
                初始位置$x_{l,1}=x_{m_{s}}$$y_{l,1}=y_{m_{s}}$
                速度范圍$-\dfrac{x_{m_{s} } }{K\triangle T}\leq \dot{x}_{l}\leq \dfrac{N\triangle_{x}-x_{m_{s} } }{K\triangle T}$$-\dfrac{y_{m_{s} } }{K\triangle T}\leq \dot{y}_{l}\leq \dfrac{M\triangle_{y}-y_{m_{s} } }{K\triangle{T} }$
                $k$時刻位置范圍$x_{m_{s}}-(k-1)\dfrac{-x_{m_{s}}}{K\triangle T} \leq x_{m_{s}}+(k-1)\dfrac{N\triangle_{x}-x_{m_{s}}}{K\triangle T}$$y_{m_{s}}-(k-1)\dfrac{-y_{m_{s}}}{K\triangle T} \leq y_{m_{s}}+(k-1)\dfrac{M\triangle_{y}-y_{m_{s}}}{K\triangle T}$
                下載: 導出CSV

                表  2  4種算法的平均單次運行時間 (s)

                Table  2  Average single running time of 4 algorithms (s)

                算法名稱運行時間
                PHD-TBD506.8180
                PF-MTBD131.0574
                Bernoulli-TBD6.6079
                CRPFB-MTBD0.0116
                下載: 導出CSV
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                        • 網絡出版日期:  2023-04-24
                        • 刊出日期:  2024-04-26

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