角閃爍下基于變分貝葉斯-交互式多模型的目標(biāo)跟蹤

許紅; 袁華東; 謝文沖; 劉維建; 王永良

doi:10.11999/JEIT171025

角閃爍下基于變分貝葉斯-交互式多模型的目標(biāo)跟蹤

doi: 10.11999/JEIT171025 cstr: 32379.14.JEIT171025

^①(海軍工程大學(xué) 武漢 430033) ^②(空軍預(yù)警學(xué)院武漢 430019)

基金項(xiàng)目:

國家自然科學(xué)基金(61501505, 61501506)

詳細(xì)信息

作者簡介:
許紅：許紅：男，1991年生，博士生，研究方向?yàn)槔走_(dá)數(shù)據(jù)處理、信息融合. 袁華東：男，1985年生，博士生，研究方向?yàn)槔走_(dá)數(shù)據(jù)處理、陣列信號處理. 謝文沖：男，1978年生，副教授，主要研究方向?yàn)闄C(jī)載雷達(dá)信號處理、空時(shí)自適應(yīng)信號處理等. 劉維建：男，1982年生，講師，主要研究方向?yàn)榭諘r(shí)自適應(yīng)檢測、陣列信號處理. 王永良：男，1965年生，中國科學(xué)院院士，主要研究方向?yàn)槔走_(dá)信號處理、空時(shí)自適應(yīng)信號處理等.

中圖分類號: TN953
計(jì)量
- 文章訪問數(shù): 1222
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-11-02
- 修回日期: 2018-04-03
- 刊出日期: 2018-07-19

Variational Bayesian-interacting Multiple Model Tracking Filter with Angle Glint Noise

XU Hong^① YUAN Huadong^② XIE Wenchong^② LIU Weijian^② WANG Yongliang^②

Funds:

The National Natural Science Foundation of China (61501505, 61501506)

摘要

摘要: 開展角閃爍噪聲下的目標(biāo)跟蹤研究對提升傳感器的探測性能具有重要意義，其中角閃爍噪聲具有的分布未知和非平穩(wěn)特性是長期困擾研究者的難點(diǎn)。針對該問題，該文首先給出角閃爍下基于變分貝葉斯參數(shù)學(xué)習(xí)的跟蹤濾波理論框架。其次，提出一種聯(lián)合估計(jì)運(yùn)動狀態(tài)和閃爍噪聲分布的變分貝葉斯-交互式多模型(VB-IMM)算法，該算法通過設(shè)計(jì)多個(gè)并行的跟蹤模型處理角閃爍的跟蹤問題，同時(shí)利用變分貝葉斯方法實(shí)現(xiàn)閃爍噪聲分布參數(shù)的在線學(xué)習(xí)，并反饋給跟蹤模型，實(shí)時(shí)調(diào)整跟蹤模型參數(shù)。最后，設(shè)計(jì)了仿真實(shí)驗(yàn)對算法在閃爍噪聲分布未知和非平穩(wěn)條件下的跟蹤性能進(jìn)行了驗(yàn)證，同時(shí)對算法的計(jì)算復(fù)雜度進(jìn)行了仿真分析。仿真結(jié)果表明，在量測噪聲分布未知和非平穩(wěn)條件下，VB-IMM具有較高的跟蹤精度，且算法復(fù)雜度較小，易于實(shí)現(xiàn)。
- 目標(biāo)跟蹤 /
- 角閃爍噪聲 /
- 非平穩(wěn) /
- 變分貝葉斯 /
- 交互式多模型
Abstract: Research on target tracking with glint noise is important to improve detection performance of sensor, in which the glint noise’s unknown distribution and non-stationary property puzzle researchers for a long time. In order to solve this problem, the tracking theoretical framework of variational Bayesian parameter learning with glint noise is firstly introduced. Then, a novel algorithm called Variational Bayesian-Interacting Multiple Model (VB-IMM) is proposed to estimate the system states as well as the unknown glint noise’s distribution. The proposed algorithm designs a bank of tracking filters in parallel with different measurement noise. Moreover, the algorithm utilizes variational Bayesian method to learn distribution parameters of the glint noise online and feed these parameters back to the tracking filters to revise the filters. In order to validate the performance of this algorithm, comparative experiments are carried out from two aspects of tracking accuracy and computational complexity. Simulation results verify good performance of tracking error and low computational complexity of the proposed algorithm.
- Target tracking、Angle glint noise、Non-stationary、Variational Bayesian、Interacting Multiple Model (IMM) /

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參考文獻(xiàn)(27)

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