Volume 10 Issue 6
Dec.  2021
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ZHANG Guoxin, YI Wei, and KONG Lingjiang. Direct position determination for massive MIMO system with one-bit quantization[J]. Journal of Radars, 2021, 10(6): 970–981. doi: 10.12000/JR21062
Citation: ZHANG Guoxin, YI Wei, and KONG Lingjiang. Direct position determination for massive MIMO system with one-bit quantization[J]. Journal of Radars, 2021, 10(6): 970–981. doi: 10.12000/JR21062
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Direct Position Determination for Massive MIMO System with One-bit Quantization

DOI: 10.12000/JR21062

  • 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)
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  • Corresponding author: YI Wei, kusso@uestc.edu.cn
  • Received Date: 2021-05-13
  • Rev Recd Date: 2021-09-01
    • Available Online: 2021-09-08
  • Publish Date: 2021-09-24
    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.

     

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