认知雷达的未知目标检测

邹鲲

doi:10.11999/JEIT170254

认知雷达的未知目标检测

doi: 10.11999/JEIT170254

邹鲲¹

基金项目:

国家自然科学基金(61571456)，陕西省自然科学基金(2016JM0644)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-03-29
- 修回日期: 2017-10-23
- 刊出日期: 2018-01-19

Unknown Target Detection for Cognitive Radar

ZOU Kun¹

Funds:

The National Natural Science Foundation of China (61571456), The Natural Science Foundation of Shaanxi Province (2016JM0644)

摘要

摘要: 认知雷达可以在探测过程中不断优化自身系统参数，实现与当前探测环境的匹配，从而能够改善雷达的探测性能。针对未知目标的探测问题，利用当前回波数据更新目标分量的估计值及其协方差矩阵，基于目标相关信息优化下一次探测所需的发射机波形和接收机滤波器，并构成一个闭环处理过程。该文提出了两种优化途径，第1种途径利用目标分量的估计仅优化下一次探测波形，在接收端采用广义匹配滤波器；第2种途径将估计误差等效为信号依赖的噪声，联合优化发射波形与接收机滤波器。计算机仿真分析表明，采用闭环迭代优化的方法是渐进等效的，并可以在相干累积获得的性能增益基础上，进一步改善雷达的探测性能。
- 认知雷达 /
- 目标检测 /
- 闭环处理 /
- 凸优化
Abstract: For the cognitive radar system, the system parameters can be optimized to match the current environment during the detection procedure, so as to improve the radar detection performance. For the unknown target detection problem, the useful signal component estimate and its covariant matrix are updated using the current returns, then the transmit waveform and receive filter are optimized based on the information of the useful signal. A closed loop processing is formed. The two optimization approaches are proposed. For the first approach, the transmit waveform design is based on the estimate of the useful signal, and the generalized match filter is used at the receiver. For the second approach, the estimate error of the useful signal is equivalent to the signal- dependent noise, and the transmit waveform and receive filter are jointly designed. The computer simulation result show that, the proposed methods are asymptotically equivalent, and they can improve the detection performance further, compared with the performance gain of the coherent accumulation.
- Cognitive radar /
- Target detection /
- Close loop process /
- Convex optimization

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参考文献(15)

HAYKIN S. Cognitive Dynamic Systems[M]. London: Cambridge University Press, 2012, 122-125.

BELL K L, BAKER C J, and SMITH G E. Cognitive radar framework for target detection and tracking[J]. IEEE Journal of Selected Toptics in Signal Processing, 2015, 9(8): 1427-1439. doi: 10.1109/JSTSP.2015.2465304.

HAO C, ORLANDO D, FOGLIA G, et al. Adaptive radar detection and range estimation with oversampled data for partially homogeneous environment[J]. IEEE Signal Processing Letters, 2015, 22(9): 1359-1363. doi: 10.1109/LSP. 2015.2404923.

SMITH G E, CAMMENGA Z, MITCHELL A, et al. Experiments with cognitive radar[J]. IEEE AE System Magazine, 2016, 31(12): 34-46. doi: 10.1109/CAMSAP.2015. 7383794.

KAY S M. Fundamentals of Statistical Signal Processing: Detection Theory[M]. Englewood Cliffs, NJ: Prentice-Hall, 1998.

BLUNT S D and MOKOLE E L. Overview of radar waveform diversity[J]. IEEE AE System Magazine, 2016, 31(11): 2-42. doi: 10.1109/MAES.2016.160071.

KARBASI S M, AUBRY A, MAIO A De, et al. Robust transmit code and receive filter design for extended targets in clutter[J]. IEEE Transactions on Signal Processing, 2015, 63(2): 1965-1976. doi: 10.1109/TSP.2015.2404301.

AUBRY A, CAROTENUTO V, MAIO A De, et al. Optimization theory-based radar waveform design for spectrally dense environments[J]. IEEE AE Systems Magazine, 2016, 31(12): 14-25. doi: 10.1109/MAES.2016. 150216.

CHENG X, AUBRY A, CIUONZO D, et al. Robust waveform and filter bank design of polarimetric radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(1): 370-384. doi: 10.1109/TAES.2017.2650619.

DE MAIO A, DE NICOLA S, HUANG Y, et al. Code design to optimize radar detection performance under accuracy and similarity constraints[J]. IEEE Transactions on Signal Processing, 2008, 56(11): 5618-5629. doi: 10.1109/TSP.2008. 929657.

AUBRY A, DE MAIO A, JIANG B, et al. Ambiguity function shaping for cognitive radar via complex quartic optimization[J]. IEEE Transactions on Signal Processing, 2013, 61(22): 5603-5620. doi: 10.1109/TSP.2013.2273885.

LUO Z Q, MA W K, SO A M, et al. Semidefinite relaxation of quadratic optimization problems, from its practical deployments and scope of applicability to key theoretical results[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20-34. doi: 10.1109/MSP.2010.936019.

AI W, HUANG Y, and ZHANG S. New results on Hermitian matrix rank-one decomposition[J]. Mathematical Programming, Series A, 2011, 128(1): 253-283. doi: 10.1007/ s10107-009-0304-7.

AUBRY A, MAIO A De, and WICKS M. Knowledge-aided (potentially cognitive) transmit signal and receive filter design in signal-dependent clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 93-115. doi: 10.1109/TAES.2013.6404093.

CHARNES A and COOPPER W W. Programming with linear fractional functions[J]. Naval Research Logistics Quarterly, 1962, 9: 181-186. doi: 10.1002/nav.3800100123.

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