一種改進的高斯逆威沙特概率假設密度擴展目標跟蹤算法
doi: 10.11999/JEIT170883
基金項目:
國家自然科學基金(61471198, 61671246),江蘇省自然科學基金(BK20160847, BK20170855)
Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking
Funds:
The National Natural Science Foundation of China (61471198, 61671246), The Natural Science Foundation of Jiangsu Province (BK20160847, BK20170855)
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摘要: 假設擴展目標(ET)的擴展和量測數(shù)目分別為橢圓和泊松模型,高斯逆威沙特概率假設密度(GIW-PHD)能夠估計擴展目標的運動和擴展狀態(tài)。然而,該濾波器對空間鄰近目標的數(shù)目、非橢圓目標和受到遮擋目標的擴展估計不夠準確。針對這些問題,該文提出一種改進的GIW-PHD。首先,假設目標擴展為一個相同尺寸的參考橢圓,通過設計新的散射矩陣得到改進的隨機矩陣(RM)方法。然后,將改進的RM方法與假設量測數(shù)目服從多伯努利分布的ET-PHD結(jié)合,得到改進的GIW-PHD濾波器。仿真和實驗結(jié)果表明,與傳統(tǒng)GIW-PHD相比,改進的GIW- PHD估計的目標數(shù)目和量測數(shù)目較多,擴展較大的橢圓和非橢圓目標的擴展更準確。
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關鍵詞:
- 擴展目標跟蹤 /
- 高斯逆威沙特概率假設密度 /
- 隨機矩陣 /
- 多伯努利分布
Abstract: Assumed that extension and measurement number of Extended Targets (ET) are respectively modeled as ellipse and Poisson, a Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter can estimate kinematic and extension states. However, for the number of spatially close targets and the extensions of non-ellipsoidal and occluded targets, the results estimated by this filter are not accurate enough. In view of these problems, an improved GIW-PHD filter is proposed in this paper. Firstly, assumed that target extension is modeled as a reference ellipse of the same size, a modified Random Matrix (RM) method is obtained by devising a new scatter matrix. Then, combining the improved RM method with the ET-PHD based on a measurement number multi-Bernoulli model, the improved GIW-PHD filter is obtained. Simulated and experimental results show that, compared with the traditional GIW-PHD, the improved GIW-PHD filter can obtain more accurate estimates in target number and the extensions of ellipsoidal and non-ellipsoidal targets with large measurement number and extensions. -
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