基于紅外壓縮成像的點(diǎn)目標(biāo)跟蹤方法研究

李少毅; 梁爽; 張凱; 董敏周; 閆杰

doi:10.11999/JEIT141324

基于紅外壓縮成像的點(diǎn)目標(biāo)跟蹤方法研究

doi: 10.11999/JEIT141324

1.
(西北工業(yè)大學(xué)航天學(xué)院西安 710072)
2.
(空軍工程大學(xué)理學(xué)院西安 710051)

基金項(xiàng)目:

國家自然科學(xué)基金(60974149)和航天科技創(chuàng)新基金(CASC201104)

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出版歷程
- 收稿日期: 2014-10-15
- 修回日期: 2015-01-09
- 刊出日期: 2015-07-19

Research of Infrared Compressive Imaging Based Point Target Tracking Method

摘要

摘要: 目前壓縮測量的應(yīng)用研究主要集中在重構(gòu)圖像方面，但是很多應(yīng)用中最終目的是檢測和跟蹤。直接基于壓縮測量的檢測和跟蹤問題尚未解決。該文首次建立一種壓縮域到空間域的映射模型，并提出一種無需重構(gòu)任何圖像且直接從低維壓縮測量中經(jīng)解碼進(jìn)行目標(biāo)跟蹤的方法，并分析其應(yīng)用于天基紅外探測的可能性。該方法利用Hadamard測量矩陣構(gòu)建紅外壓縮成像系統(tǒng)，采用自適應(yīng)壓縮背景差分法從低維壓縮測量信息中分離背景和前景，再從壓縮前景信息中解碼目標(biāo)空間位置，并結(jié)合數(shù)據(jù)關(guān)聯(lián)和Kalman濾波算法解決了雜波環(huán)境下點(diǎn)目標(biāo)跟蹤問題。理論分析和仿真實(shí)驗(yàn)結(jié)果表明，該方法能利用少量壓縮測量實(shí)現(xiàn)目標(biāo)跟蹤任務(wù)，并減小探測器規(guī)格及相關(guān)算法的計(jì)算復(fù)雜度和存儲(chǔ)代價(jià)。
- 目標(biāo)跟蹤 /
- 數(shù)據(jù)關(guān)聯(lián) /
- Kalman濾波 /
- 壓縮成像 /
- 壓縮背景差分
Abstract: Currently the application research of compressive measurements is still focused on the image recovery, but the ultimate purpose is a task of target detection and tracking in many special applications. And the issue performing target detection and tracking based on compressive measurements is not yet solved. The mapping model is firstly exploited to locate the target in the spatial domain through the measurements in the compressive domain. Further, a method tracking point targets through decoding targets location in the low-dimensional compressive measurements without reconstructed image is proposed for the possible application in space based infrared detection. The method uses the Hadamard matrix to design infrared compressive imaging system, and separates the background and foreground image from the low-dimensional compressive measurements by the adaptive compressive background subtraction. With the mapping relation from the compressive domain into the spatial domain, the target location is possibly decoded. Then the task of point target tracking in the clutter environment can be done by the associated data association and Kalman filtering algorithm. The theoretical analysis and numerical simulations demonstrate the approach proposed is able to accomplish a task of target tracking only by using less compressive measurements, and reduce detector scale, computation complexity and storage cost.
- Target tracking /
- Data association /
- Kalman filtering /
- Compressive imaging /
- Compressive background subtraction

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參考文獻(xiàn)(20)

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