Direction-of-Departure and Direction-of-Arrival Estimation Algorithm Based on Compressive Sensing: Data Fitting

Ji Woong Paik,Joon-Ho Lee,Wooyoung Hong

doi:10.3390/rs12172773

Abstract

In this paper, a compressive sensing-based data fitting direction-of-departure/direction-of-arrival (DOD/DOA) estimation algorithm is proposed to apply the superior performance of compressive sensing method to the bistatic MIMO sonar systems. The algorithm proposed in this paper optimizes the output data via convex optimization-based sparse recovery, so that it is possible to estimate the DOD and the DOA for each target accurately. In order to minimize the amount of computation, the cost function with constraint condition is implemented in this paper. Furthermore, the constraint condition parameter of the cost function is analytically derived. Through various simulations, it is shown that the superior DOD and DOA estimation performance of the proposed algorithm and that the analytical derivation of the constraint condition parameter is useful for determination of regularization parameter.

Highlights

Conventional direction-of-arrival (DOA) estimation algorithms estimate the DOA of the signal source using the phase difference between the elements constituting the sensor array
In order to take advantage of the superior performance of the DOA estimation based on compressive sensing in the bistatic multiple input multiple output (MIMO) sonar system, this paper proposes a compressive sensing-based direction-of-departure/ direction-of-arrival (DOD/DOA) estimation algorithm in the bistatic MIMO sonar system
To show the superior performance of the DOD/DOA estimation of the proposed scheme, the second case illustrates the spectrum of the proposed scheme and the spectrum of conventional DOD/DOA estimation method

Summary

Introduction

Conventional direction-of-arrival (DOA) estimation algorithms estimate the DOA of the signal source using the phase difference between the elements constituting the sensor array. The CS-based DOA estimation method estimates DOAs by optimizing a sparse output signal data with two constraints of sparsity reduction and sensor data model fitting. The bistatic multiple input multiple output (MIMO) sonar system is the solution to the problem of detecting the low TS submarines or vessels This is a system in which two sensor arrays are divided and performed by a transmitter and a receiver, rather than performing both transmission and Remote Sens. In order to detect the low TS submarines or vessels, a number of DOA estimation algorithms implemented in bistatic MIMO sonar systems have been extensively studied. The proposed algorithm in this paper optimizes the output data as sparse by searching the solution through convex optimization, so that it is possible to estimate the DOD and the DOA for each target accurately. The reduced-dimension cost functions based on fast Fourier transform (FFT) for moving targets was derived

Signal Modeling of Bistatic MIMO Sonar System

Numerical Results

Checking the Analytically Derived Parameter of the Constraint Condition

Conclusions