配準(zhǔn)誤差下的多基地雷達(dá)目標(biāo)檢測算法
doi: 10.11999/JEIT160207 cstr: 32379.14.JEIT160207
基金項(xiàng)目:
國家自然科學(xué)基金(61372134, 61401329)
Target Detection Algorithm for Multistatic Radar with Registration Errors
Funds:
The National Natural Science Foundation of China (61372134, 61401329)
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摘要: 在多基地雷達(dá)系統(tǒng)中，即使進(jìn)行了空間配準(zhǔn)處理，也很難實(shí)現(xiàn)完美的空間配準(zhǔn)。該文研究了分布式MIMO雷達(dá)系統(tǒng)存在配準(zhǔn)誤差時的目標(biāo)檢測問題。根據(jù)是否利用已知先驗(yàn)配準(zhǔn)誤差信息對目標(biāo)位置信息進(jìn)行估計(jì)，給出了MAP-GLRT和ML-GLRT兩種檢測器。由于MAP-GLRT檢測器利用了先驗(yàn)信息，因此其檢測性能優(yōu)于ML-GLRT檢測器。在配準(zhǔn)誤差條件下，兩種檢測器的性能要優(yōu)于傳統(tǒng)的融合檢測算法。通過仿真實(shí)驗(yàn)驗(yàn)證了所提算法的有效性。
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關(guān)鍵詞:
- 多基地雷達(dá) /
- 配準(zhǔn)誤差 /
- 目標(biāo)檢測
Abstract: In a multistatic radar system, perfect registration is unavailable in practice even after a registration process. In this paper, a target detection problem for a distributed Multiple-Input Multiple-Output (MIMO) radar with registration errors is considered. To estimate target positions by weather using a knowing a priori information of registration errors or not, a Maximum A Posteriori Generalized Likelihood Ratio Test (MAP-GLRT) detector and a Maximum Likelihood GLRT (ML-GLRT) detector are proposed. The MAP-GLRT detector outperforms the ML-GLRT detector due to the prior information. The two proposed algorithms have better detection performance over the conventional detection fusion algorithm with registration errors. Simulation results verify the effectiveness of the proposed detection algorithms.-
Key words:
- Multistatic radar /
- Registration errors /
- Target detection
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LIU Jun, LI Hongbin, and HIMED B. Persymmetric adaptive target detection with distributed MIMO radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 372-382. doi: 10.1109/TAES.2014.130652. HAIMOVICH A, BLUM R, CIMINI L, et al. MIMO radar with widely separated antennas[J]. IEEE Signal Processing Magazine, 2008, 25(1): 116-129. doi: 10.1109/MSP.2008. 4408448. FRANKFORD M T, STEWART K B, MAJUREC N, et al. Numerical and experimental studies of target detection with MIMO radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1569-1577. doi: 10.1109/ TAES.2014.120180. 金鎮(zhèn), 謝良貴, 文樹梁. 分布式MIMO雷達(dá)單脈沖測角[J]. 雷達(dá)學(xué)報(bào), 2014, 3(4): 474-479. doi: 10.3724/SP.J.1300.2014. 13077. JIN Z, XIE L G, and WEN S L. Distributed MIMO radar monopulse angular estimation[J]. Journal of Radars, 2014, 3(4): 474-479. doi: 10.3724/SP.J.1300.2014.13077. LI Hongbin, WANG Zhe, LIU Jun, et al. Moving target detection in distributed MIMO radar on moving platforms[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1524-1535. doi: 10.1109/JSTSP.2015.2467355. RADMARD M, CHITGARHA M M, MAJD M N, et al. Antenna placement and power allocation optimization in MIMO radar detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1468-1478. doi: 10.1109/ TAES.2014.120776. LIU Hongwei, ZHOU Shenghua, SU Hongtao, et al. Detection performance of spatial-frequency diversity MIMO radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(4): 3137-3155. doi: 10.1109/TAES.2013.120040. HU Qinzhen, SU Hongtao, ZHOU Shenghua, et al. Two-stage constant false alarm rate detection for distributed multiple- input multiple-out radar[J]. IET Radar, Sonar Navigation, 2016, 10(2): 264-271. doi: 10.1049/iet-rsn.2015.0026. ZHOU Y F and LEUNG H. An exact maximum likelihood registration algorithm for data fusion[J]. IEEE Transactions on Signal Processing, 1997, 45(6): 1560-1572. doi: 10.1109/ 78.599998. OKELLO N and RISTIC B. Maximum likelihood registration for multiple dissimilar sensors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(3): 1074-1083. doi: 10.1109/TAES.2003.1238759. HUANG D L, LEUNG H, and BOSSE E. A pseudo- measurement approach to simultaneous registration and track fusion[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2315-2331. doi: 10.1109/ TAES.2012.6237594. RISTIC B, CLARK D, and GORDON N. Calibration of multi-target tracking algorithms using non-cooperative targets[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 390-398. doi: 10.1109/TSTSP.2013. 2256877. HAO Mengxi, YUAN Xianghui, and HAN Chongzhao. Recursive joint track-to-track association and sensor nonlinear bias estimation based on generalized Bayes risk[C]. 18th International Conference on Information Fusion, Washington, DC, 2015: 1519-1525. THOMOPOULOS S and OKELLO N. Distributed and centralized multisensor detection with misaligned sensors[J]. Information Sciences, 1994, 77(1): 293-323. doi: 10.1016/ 0020-0255(94)9006-X. TAJER A, JAJAMOVICH G H, WANG X D, et al. Optimal joint target detection and parameter estimation by MIMO radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(1): 127-145. doi: 10.1109/TSTSP.2010. 2040104. MOUSTAKIDEX G V, JAJAMOVICH G H, TAJER A, et al. Joint detection and estimation: optimum tests and applications[J]. IEEE Transactions on Information Theory, 2012, 58(7): 4215-4229. doi: 10.1109/TIT.2012.2184260. HU Qinzhen, SU Hongtao, ZHOU Shenghua, et al. Target detection in distributed MIMO radar with registration errors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 438-450. doi: 10.1109/TAES.2015. 140479. BHARGAVA R and KHATRI C. The distribution of product of independent beta random variables with application to multivariate analysis[J]. Annals of the Institute of Statistical Mathematics, 1981, 33(1): 287-296. doi: 10.1007/ BF02480942. -
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