A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

Pin-Jiao Zhao,Guo-Bing Hu,Li-Wei Wang,Fangqing Wen

doi:10.1155/2021/9705617

Abstract

This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.

Highlights

Direction-of-arrival (DOA) estimation has been a crucial topic in various practical applications, such as radar, navigation, and wireless communication [1,2,3,4], where the antenna arrays are utilized for collecting the spatial sampling of impinging electromagnetic waves
The sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors
We have proposed a sliding window data compression method to reduce the computational burden of high-dimensional data processing for the spatial-time DOA estimation

Summary

Introduction

Direction-of-arrival (DOA) estimation has been a crucial topic in various practical applications, such as radar, navigation, and wireless communication [1,2,3,4], where the antenna arrays are utilized for collecting the spatial sampling of impinging electromagnetic waves. The above spatial-time DOA estimation methods based on SDCA involve high-dimensional data processing of multiple pseudo snapshots, which has high computational load Another limitation of the above methods is that the case of sensor failure [24,25,26] always occurring in the actual direction-finding system has been ignored. E failure of some sensors or receiving channels may destroy the virtual array structure of sparse array and reduce the number of consecutive DOF and the effective array aperture, which results in the performance degradation To tackle these problems, a sliding window data compression method for spatial-time DOA estimation is proposed in this paper. Notations: vectors and matrices are denoted by lowercase and uppercase bold-face letters, respectively. (·)T, (·)H, and (·)∗ and | · | denote transpose, conjugate transpose, conjugate, and the norm of the embraced matrix, respectively. 0m×n represents an m × n null matrix and Im represents an m × m identity matrix. e symbol ⊗ denotes the Kronecker product and ⊙ denotes the Khatri–Rao product

Problem Formulation

DOA Estimation Based on Sliding Window Data Compression

Numerical Simulations

Conclusions