本文選題：檢測前跟蹤 + 勢概率假設(shè)密度�。� 參考：《控制與決策》2017年11期

【摘要】：針對未知目標(biāo)數(shù)條件下多弱小目標(biāo)檢測前跟蹤(TBD)算法魯棒性較低、運算量較大等問題,提出一種基于高斯粒子勢概率假設(shè)密度(CPHD)濾波的多目標(biāo)檢測前跟蹤算法.運用高斯函數(shù)近似目標(biāo)狀態(tài)的后驗概率密度,采取粒子濾波的方法迭代更新CPHD中各高斯項的均值與協(xié)方差,無需重采樣,避免了粒子退化和采樣枯竭等問題;同時結(jié)合檢測前跟蹤算法的實際情況,得出粒子權(quán)值的更新表達式.仿真實驗表明,與現(xiàn)有算法相比,所提出算法在降低復(fù)雜度的同時,可以更為可靠地傳遞目標(biāo)勢分布信息,從而提高多弱小目標(biāo)數(shù)目和狀態(tài)估計的準(zhǔn)確性和穩(wěn)定性.
[Abstract]:In order to solve the problems of low robustness and large computational complexity in multi-dim target pre-tracking algorithm with unknown number of targets, a multi-target detection pre-tracking algorithm based on Gao Si particle potential probability assumption density (Gao Si) filter is proposed. Using the Gao Si function to approximate the posterior probability density of the target state, the particle filter method is used to iteratively update the mean and covariance of each Gao Si term in the CPHD without re-sampling, and the problems of particle degradation and sampling depletion are avoided. At the same time, according to the actual situation of the tracking algorithm before detection, the update expression of particle weight is obtained. Simulation results show that compared with the existing algorithms, the proposed algorithm can transfer the target potential distribution information more reliably while reducing the complexity, thus improving the accuracy and stability of the number of small and weak targets and the state estimation.
【作者單位】： 空軍工程大學(xué)信息與導(dǎo)航學(xué)院;95806部隊;
【基金】：國家自然科學(xué)基金項目(61571458) 陜西省自然科學(xué)基金項目(2011JM8023)
【分類號】：TN713

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1 歐陽成;姬紅兵;郭志強;;改進的多模型粒子PHD和CPHD濾波算法[J];自動化學(xué)報;2012年03期

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本文編號：2022577