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)
Research of Infrared Compressive Imaging Based Point Target Tracking Method
-
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)。
-
關(guān)鍵詞:
- 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. -
Takhar D, Laska J N, Wakin M B, et al.. A new compressive imaging camera architecture using optical-domain compression[C]. Proceedings of SPIE 6065 Computational Imaging IV, San Jose, USA, 2006: 606509. August Y, Vachman C, Rivenson Y, et al.. Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains[J]. Applied Optics, 2013, 52(10): D46-D54. Kuiteing S K, Coluccia G, Barducci A, et al.. Compressive hyperspectral imaging using progressive total variation[C]. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014: 7794-7798. Wagadarikar A, John R, Willett R, et al.. Single disperser design for coded aperture snapshot spectral imaging[J]. Applied Optics, 2008, 47(10): B44-B51. Slinger C W, Gilholm K, Gordon N, et al.. Adaptive coded aperture imaging in the infrared: towards a practical implementation[C]. Proceedings of SPIE Adaptive Coded Aperture Imaging and Non-imaging Sensors II, San Diego, USA, 2008: 709609. Mahalanobis A, Reyner C, Patel H, et al.. IR performance study of an adaptive coded aperture diffractive imaging system employing MEMS eyelid shutter technologies[C]. Proceedings of SPIE Adaptive Coded Aperture Imaging and Non-Imaging Sensors, San Diego, USA, 2007: 67140D. Cevher V, Sankaranarayanan A, Duarte M F, et al.. Compressive Sensing for Background Subtraction[M]. Berlin: Springer, 2008: 155-168. Mei X and Ling H. Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2259-2272. Li H, Shen C, and Shi Q. Real-time visual tracking using compressive sensing[C]. Proceedings of 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, USA, 2011: 1305-1312. Shujuan G, Insuk K, and Seong T J. Sparse representation based target detection in frared image[J]. International Journal of Energy, Information and Communications, 2013, 4(6): 21-28. Neifeld M A and Ke J. Optical architectures for compressive imaging[J]. Applied Optics, 2007, 46(22): 5293-5303. Willett R M, Marcia R F, and Nichols J M. Compressed sensing for practical optical imaging systems: a tutorial[J]. Optical Engineering, 2011, 50(7): 072601. Hayashi K, Nagahara M, and Tanaka T. A user,s guide to compressed sensing for communications systems[J]. IEICE Transactions on Communications, 2013, 96(3): 685-712. Keil K H and Hupfer W. Simulation of signal and data processing for a pair of GEO IR sensors[C]. Preoceedings of SPIE Signal and Data Processing of Small Targets, San Diego, USA, 2007: 1-12. Aziz A M. A new nearest-neighbor association approach based on fuzzy clustering[J]. Aerospace Science and Technology, 2013, 26(1): 87-97. Dallil A, Oussalah M, and Ouldali A. Sensor fusion and target tracking using evidential data association[J]. IEEE Sensors Journal, 2013, 13(1): 285-293. 李正周, 金鋼, 董能力. 基于改進(jìn)概率數(shù)據(jù)關(guān)聯(lián)濾波的紅外小運(yùn)動(dòng)目標(biāo)跟蹤[J]. 電子與信息學(xué)報(bào), 2008, 30(4): 954-956. Li Zheng-zhou, Jin Gang, and Dong Neng-li. A novel method for tracking and recognizing infrared dim and small moving target based on modified probabilistic data associating filter[J]. Journal of Electronics Information Technology, 2008, 30(4): 954-956. Habtemariam B, Tharmarasa R, Thayaparan T, et al.. A multiple-detection joint probabilistic data association filter[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 461-471. -
計(jì)量
- 文章訪問數(shù): 1247
- HTML全文瀏覽量: 99
- PDF下載量: 640
- 被引次數(shù): 0