【摘要】：針對(duì)標(biāo)準(zhǔn)粒子濾波算法在機(jī)動(dòng)目標(biāo)波達(dá)方向(direction of arrival,DOA)隨時(shí)間快速變化導(dǎo)致跟蹤精度下降、實(shí)時(shí)性變差及多目標(biāo)跟蹤誤差大等不足的問(wèn)題,本文提出了一種改進(jìn)粒子濾波(particle filter,PF)算法。該算法依據(jù)陣列信號(hào)處理模型和勻速(constant velocity,CV)模型,建立了機(jī)動(dòng)目標(biāo)跟蹤的狀態(tài)方程和觀測(cè)方程作為狀態(tài)空間模型,并在此基礎(chǔ)上,借鑒多重信號(hào)分類(multiple signal classification,MUSIC)算法譜函數(shù)修改了粒子濾波的似然函數(shù),實(shí)現(xiàn)了對(duì)目標(biāo)方位的實(shí)時(shí)動(dòng)態(tài)跟蹤。仿真結(jié)果表明,與傳統(tǒng)子空間類跟蹤算法和標(biāo)準(zhǔn)粒子濾波算法相比,本文方法跟蹤精度更高,收斂速度更快,抗噪能力及魯棒性更強(qiáng),對(duì)軌跡交叉的多目標(biāo)跟蹤性能也更優(yōu)。
[Abstract]:In this paper, an improved particle filter (particle filter) algorithm is proposed to solve the problem that the fast change of the standard particle filter algorithm in the direction of arrival of maneuvering targets leads to the decrease of tracking accuracy, the real time variation and the large tracking error of multiple targets. In this paper, an improved particle filter (particle filter) algorithm is proposed. Based on array signal processing model and constant velocity CV model, the state equation and observation equation of maneuvering target tracking are established as state space model. The likelihood function of particle filter is modified by using the spectral function of (multiple signal classification music algorithm, and the real-time dynamic tracking of target azimuth is realized. The simulation results show that compared with the traditional subspace tracking algorithm and the standard particle filter algorithm, this method has higher tracking accuracy, faster convergence speed, stronger anti-noise ability and better robustness, and better performance for multi-target tracking with track crossing.
【作者單位】： 哈爾濱工程大學(xué)水聲技術(shù)重點(diǎn)實(shí)驗(yàn)室;哈爾濱工程大學(xué)水聲工程學(xué)院;解放軍92985部隊(duì);
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(51279043);國(guó)家自然科學(xué)基金項(xiàng)目(61201411);國(guó)家自然科學(xué)基金項(xiàng)目(51209059) 國(guó)家“863”計(jì)劃資助項(xiàng)目(2013AA09A503) 黑龍江省普通高校青年學(xué)術(shù)骨干支持計(jì)劃(1253G019)
【分類號(hào)】：TN911.23

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