Compressed Sampling for Spectrum Measurement and DOA Estimation With Array Cooperative MWC
Siyi Jiang, Ning Fu, Zhiliang Wei, Zihao Lian, Liyan Qiao, Xiyuan Peng
Abstract
Spectrum measurement and direction of arrival (DOA) estimation methods based on traditional Nyquist theory lead to high sampling rate and a large amount of data, which bring challenges to hardware and signal processing. An array cooperative modulated wideband converter (ACMWC) sub-Nyquist sampling system for spectrum measurement and DOA estimation is proposed in this work. Based on a linear array (LA) cooperative MWC sampling structure with an arbitrary array element position, we construct a unified space-frequency domain sparse model by combining the modulation information of different sensors and the spatial phase information of the array to estimate carrier and DOA simultaneously. Combining multi-sensor data to jointly reconstruct spectrum and estimate DOA can improve data utilisation and estimation accuracy. In addition, we also give a spatial separation processing scheme based on the sparsity characteristics of the signal spatial distribution, which can greatly reduce the computational complexity. Compared with the similar approaches, the problem of source ambiguity and coherent signals in the array parameter estimation can be solved by the time-space distribution characteristics of spectrum and DOA. Moreover, the proposed method relaxes the restrictions on array manifolds and can be used for LA with arbitrary sensor positions with wider application prospects. The effectiveness of the proposed method is verified by simulation and hardware results.