基于广义Rao检验的单/多比特MIMO雷达运动目标检测方法

doi:10.12305/j.issn.1001-506X.2024.01.12

摘要/Abstract

摘要：

通道数的增加在提高多输入多输出(multiple-input multiple-output, MIMO)雷达目标检测性能的同时, 也显著增加了数据的传输量和处理负担。针对运动目标的集中式MIMO雷达检测问题, 首先对雷达回波数据进行比特量化, 然后再进行融合检测处理。由于广义似然比检验(generalized likelihood ratio test, GLRT)需要对未知参数进行最大似然估计(maximum likelihood estimation, MLE), 而上述问题中未知参数的MLE没有闭合解, 导致相应的检验统计量的计算量较大。采用了一种新颖的广义Rao(generalized Rao, G-Rao)检验方法, 由于不需要求解未知参数的MLE, 相应的检验统计量有闭合解, 显著降低了检验统计量的计算量。此外, 为改善检测性能, 运用粒子群优化算法对量化门限进行了优化。最后, 实验结果在验证G-Rao检测器有效性的同时, 表明: 相比单比特量化而言, 少量多比特量化在有效降低信号传输和处理负担的同时, 其检测性能高于单比特量化方式。

关键词: 多输入多输出雷达, 比特量化, 目标检测, 广义Rao检验, 粒子群方法

Abstract:

The increase in the channel number of multiple-input multiple-output (MIMO) radar significantly raises the amount of data transmission and processing burden while improving the target detection performance. In view of the problem of colocated MIMO radar detection of moving targets, firstly, the radar echo data is bit-quantized, and then fusion detection processing is performed. Since the generalized likelihood ratio test (GLRT) requires maximum likelihood estimation (MLE) for the unknown parameters, and there is no closed solution for the MLE of the unknown parameters in the problem above, resulting in a large computational effort for the corresponding test statistics. In this paper, a novel generalized Rao (G-Rao) test is applied, which remarkably reduces the computational effort of the test statistics since there is no need to solve the MLE of the unknown parameters and the corresponding test statistics have a closed-form solution. In addition, to improve the detection performance, the quantization thresholds are optimized using the particle swarm optimization algorithm (PSOA). Finally, experiment results not only verify the effectiveness of the G-Rao detector but also show that, compared with single-bit quantization, the detection performance of a small number of multi-bit quantization is superior to that of the single-bit quantization method while effectively reducing the signal transmission and processing burden.

Key words: multiple input multiple output (MIMO) radar, bit quantization, target detection, generalized Rao test, particle swarm approach

中图分类号:

TN95

黄广佳, 程旭, 饶彬, 王伟. 基于广义Rao检验的单/多比特MIMO雷达运动目标检测方法[J]. 系统工程与电子技术, 2023, 46(1): 105-112.

Guangjia HUANG, Xu CHENG, Bin RAO, Wei WANG. One/multi-bit MIMO radar detection of a moving target based on generalized Rao test[J]. Systems Engineering and Electronics, 2023, 46(1): 105-112.

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参考文献 30

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量化位数q	量化门限向量τ_k
q=1	[－∞, 0, +∞]
q=2	[－∞, －0.981 6, 0, 0.981 6, +∞]
q=3	[－∞, －1.747 9, －1.05, －0.500 6, 0, 0.500 5, 1.05, 1.747 9, +∞]
q=4	[－∞, －2.400 8, －1.843 5, －1.71, －1.099 3, －0.799 5, －0.522 4, －0.258 2, 0, 0.258 2, 0.522 4, 0.799 6, 1.099 3, 1.437 1, 1.843 5, 2.400 8, +∞]

量化位数q	平均每次耗时/s
量化位数q	G-Rao检验	GLRT
1	0.001 40	0.647 11
2	0.001 42	0.673 88
3	0.001 45	0.719 18
4	0.001 49	0.728 35
未量化	-	0.413 42¹⁾

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