多編隊(duì)目標(biāo)先后出現(xiàn)時(shí)的無先驗(yàn)信息跟蹤方法
doi: 10.11999/JEIT190508 cstr: 32379.14.JEIT190508
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海軍航空大學(xué)信息融合研究所 煙臺(tái) 264001
Tracking Method without Prior Information when Multi-group Targets Appear Successively
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Institute of Information Fusion, Naval Aeronautical University, Yantai 264001, China
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摘要:
針對(duì)多編隊(duì)機(jī)動(dòng)目標(biāo)先后出現(xiàn)時(shí)的跟蹤問題，該文提出了一種基于交互式多模型高斯混合概率假設(shè)密度濾波(IMM-GM-PHD)算法的無先驗(yàn)信息跟蹤方法。首先，在IMM-GM-PHD算法預(yù)測(cè)過程完成的基礎(chǔ)上，引入密度檢測(cè)機(jī)制，利用相關(guān)域?yàn)樗蓄A(yù)測(cè)高斯分量挑選有效量測(cè)，結(jié)合密度聚類(DBSCAN)算法檢測(cè)是否出現(xiàn)新編隊(duì)目標(biāo)。其次，在IMM-GM-PHD算法狀態(tài)更新完成的基礎(chǔ)上，利用更新高斯分量的組成情況完成模型概率的更新。最后，在狀態(tài)估計(jì)優(yōu)化過程中，結(jié)合編隊(duì)目標(biāo)的特點(diǎn)，加入相似度判別技術(shù)，利用杰森-香農(nóng)(JS)散度度量高斯分量間的相似度，剔除沒有相似分量的高斯分量，進(jìn)一步優(yōu)化估計(jì)結(jié)果。仿真結(jié)果表明，該文方法能夠快速有效地跟蹤非同時(shí)出現(xiàn)的多編隊(duì)機(jī)動(dòng)目標(biāo)，具有較好的跟蹤性能。
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關(guān)鍵詞:
- 多編隊(duì)機(jī)動(dòng)目標(biāo) /
- 交互式多模型高斯混合概率假設(shè)密度濾波算法 /
- 相關(guān)域 /
- 密度聚類算法 /
- 杰森-香農(nóng)散度
Abstract:Considering the problem of multi-group maneuvering target tracking, a fast tracking method based on Interactive Multiple Maneuvering Gaussian Mixture Probability Hypothesis Density (IMM-GM-PHD) algorithm is proposed. Firstly, based on the completion of the IMM-GM-PHD algorithm prediction process, the density detection mechanism is added, and the correlation domain is used to select effective measurement for all predicted Gaussian components, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is combined to detect whether a new formation target appears. Secondly, based on the completion of the state update of the IMM-GM-PHD algorithm, the update of the model probability is completed by updating the composition of the Gaussian component. Finally, in the process of state estimation optimization, combined with the characteristics of formation targets, the similarity discrimination technique is added, and the Jensen-Shannon (JS) divergence is used to measure the similarity between Gaussian components, and the Gaussian components without similar components are eliminated, and the estimation results are further optimized. The simulation results show that the proposed algorithm can track multi-group maneuvering targets quickly and effectively, and has better tracking performance.
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表 1 不同雜波密度下平均OSPA距離比較
算法 雜波密度 ${\rm{\lambda }} = 1$ ${\rm{\lambda }} = 10$ ${\rm{\lambda }} = 50$ IMM-GM-PHD算法 29.755 32.129 44.609 本文算法 21.821 28.617 43.996 
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