易伟,,
孔令讲
电子科技大学信息与通信工程学院 成都 611731
基金项目:国家自然科学基金(61771110)，****奖励计划(B17008)，中央高校基本科研基金(ZYGX2016J031)
详细信息
作者简介:张国鑫(1996–)，男，山西大同人。现为电子科技大学信息与通信工程学院博士研究生。研究方向为雷达信号处理及目标定位
易伟：易 伟(1983–)，男，四川雅安人。现为电子科技大学教授，教育部青年****。研究方向为雷达信号处理、微弱目标探测技术、目标跟踪、多传感器数据融合及资源智能管控
孔令讲(1974–)，男，河南南阳人。现为电子科技大学教授，博士生导师，********。研究方向为新体制雷达、统计信号处理、优化理论和算法、雷达信号处理、非合作信号处理技术和自适应阵列信号处理
通讯作者:易伟 kusso@uestc.edu.cn
责任主编：王鼎 Corresponding Editor: WANG Ding中图分类号:TN953
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出版历程
收稿日期:2021-05-13
修回日期:2021-09-01
网络出版日期:2021-09-08
刊出日期:2021-12-28
Direct Position Determination for Massive MIMO System with One-bit Quantization
ZHANG Guoxin,YI Wei,,
KONG Lingjiang
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Funds:The National Natural Science Foundation of China (61771110), The Chang Jiang Scholars Program (B17008), The Fundamental Research Funds of Central Universities (ZYGX2016J031)
More Information
Corresponding author:YI Wei, kusso@uestc.edu.cn
摘要
摘要:1比特量化技术在大规模MIMO雷达系统中的应用使得系统成本、功耗及传输带宽显著降低。但这同时也对如何从1比特量化后的数据中提取目标高精度信息提出了严峻挑战。针对基于1比特量化的二次定位算法在低信噪比下定位精度低、鲁棒性差的问题，该文提出了一种基于1比特量化的大规模MIMO雷达系统目标直接定位算法。首先，通过将接收信号进行1比特量化，并推导基于1比特信号的概率分布，建立了关于目标位置的代价函数；其次，通过证明代价函数的凸性，利用梯度下降算法求解了回波中未知的信号参数；最后，根据最大似然估计实现了目标直接定位。仿真实验分析了所提算法的定位性能，结果表明，所提算法仅需传输相较于高精度采样(16比特为例)直接定位算法6.25%的通信带宽，同时其功耗仅为前者的0.1%。此外，与基于1比特量化的二次定位算法相比，所提算法在低信噪比下便可实现对目标位置的有效估计，并且其定位性能在低信噪比和低MIMO天线数量下均明显优于前者。同时，其性能会随着过采样技术的应用进一步提升。
关键词:直接定位/
最大似然估计/
1比特量化/
MIMO雷达/
二次定位
Abstract:The application of one-bit quantization technology in a massive MIMO radar system significantly reduced the system cost, power consumption, and transmission bandwidth. However, it also poses a severe challenge to extract high-precision target information from one-bit quantized data. To address the problem of low positioning accuracy and poor robustness of secondary positioning based on one-bit quantization under low Signal-to-Noise Ratio (SNR), this paper proposes a multi-station radar target direct position determination algorithm based on one-bit quantization. First, by quantizing the received signal with one bit, and deriving the probability distribution based on the one-bit signal, the cost function about the target position is established. Second, by proving the convexity of the cost function, the maximum likelihood estimation and gradient descent algorithm are used to solve the unknown signal parameters in the echo. Finally, the direct positioning of the target is achieved according to the maximum likelihood estimation. Simulation experiments were performed to analyze the positioning performance of the proposed algorithm, and the results showed that the proposed algorithm only needed to transmit 6.25% of the communication bandwidth compared with the high-bit sampling (e.g., 16 bits) direct position determination algorithm, and its power consumption is only 0.1% of the former. In addition, compared with the secondary positioning algorithm based on one-bit quantization, the proposed algorithm can achieve an effective estimation of the target position under a low SNR. In addition, its localization performance is significantly better than the former under low SNR and a low number of MIMO antennas. Simultaneously, its performance will be further improved with the application of oversampling technology.
Key words:Direct Position Determination (DPD)/
Maximum Likelihood estimation (ML)/
One-bit quantization/
MIMO radar/
Secondary localization
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