Clutter Mitigation for Joint RadCom Systems Based On Spatial Modulation

TL;DR

This paper introduces a novel clutter mitigation technique for joint RadCom systems using spatial modulation, combining time and spatial domain filtering to improve radar performance amidst spectrum sharing challenges.

Contribution

It proposes a reduced dimension spatial temporal adaptive processing (RD-STAP) method with an eigen-decomposition algorithm based on Power method for effective clutter mitigation in joint RadCom systems.

Findings

RD-STAP effectively mitigates clutter in joint RadCom systems.

Eigen-decomposition with Power method outperforms SVD in this context.

Simulation results confirm the method's effectiveness.

Abstract

Joint radar and communication (RadCom) systems have been proposed to integrate radar and communication into one platform and achieve spectrum sharing in recent years. However, the joint RadCom systems cause the clutter modulation and the performance degradation of radar. Therefore, it's very essential to improve the performance of radar when designing joint RadCom systems. This paper deals with the clutter mitigation for joint RadCom systems based on spatial modulation. The communication information embedding introduces variation of transmit beampatterns in a coherent processing interval (CPI) and causes the clutter modulation and spreading of the clutter spectrum. This paper propose a reduced dimension spatial temporal adaptive processing (RD-STAP) method, i.e., we firstly perform time domain filtering and then perform spatial domain filtering on received data. As for time domain filtering, this paper mitigates the extended clutter by subspace projection (SP) and proposes a more effective eigen-decomposition algorithm based on Power method than singular value decomposition (SVD) to obtain clutter subspace basis vectors. And the matched filter is performed on the data after clutter mitigation to display the moving target. And receive beamforming is utilized for spatial domain filtering. Simulation results highlight the effectiveness of the proposed RD-STAP method and the eigen-decomposition algorithm.