Spatial filter measurement matrix design for interference/jamming suppression in colocated compressive sensing MIMO radars

Abstract

A new methodology for interference/jamming suppression employing a spatial filter measurement matrix (SFMM) is presented. Different from the previous Capon beamforming method, the proposed method is not dependent on accurate prior information of targets' directions-of-arrival (DOAs). The proposed SFMM suppresses the noise and interferences outside the searching area, thus apparently improves detection performance. The experimental results demonstrate the effectiveness of the proposed SFMM.

SFMM design in colocated CS‐MIMO radar for jamming and interference joint suppression

“…Under the same assumption as in [14], the target, interfering sources and jammers are static, so the Doppler frequency is not considered in our signal model. Then, the received echo of the n th receiver at the fast time t within the th pulse can be expressed as [9] where is the slow‐time (pulse) index, is the complex amplitude of the k th source with variance . It is emphasised that is assumed to be constant during the whole pulse, but varies independently from pulse to pulse, i.e.…”

“…It has been shown that the colocated MIMO radars have some advantages over PA radars such as the extended virtual array aperture, the increased upper limit on the number of detectable targets, improved parameter identifiability and angular resolution and so on [1–5]. In the last decade, by exploiting the target sparsity in the target space/scene, compressive sensing (CS) theory has been successfully applied to MIMO radars and has shown great potentials in target parameter estimation [6], Synthetic Aperture Radar (SAR) imaging [7], waveform design [8] and interference attenuation [9]. CS‐MIMO radar systems have been proposed in [6–8] and the beamforming techniques for colocated CS‐MIMO radars have also been studied to achieve processing gain or the desired beampattern to suppress the target‐like interference [9, 10].…”

“…In the last decade, by exploiting the target sparsity in the target space/scene, compressive sensing (CS) theory has been successfully applied to MIMO radars and has shown great potentials in target parameter estimation [6], Synthetic Aperture Radar (SAR) imaging [7], waveform design [8] and interference attenuation [9]. CS‐MIMO radar systems have been proposed in [6–8] and the beamforming techniques for colocated CS‐MIMO radars have also been studied to achieve processing gain or the desired beampattern to suppress the target‐like interference [9, 10]. Colocated CS‐MIMO radar can achieve either the same localisation performance as traditional methods with significantly fewer measurements or significantly improved performance with the same number of measurements [10].…”

“…The transformed target signal would be also attenuated while minimising the power of the transformed interference and jamming. Similarly, Tao et al [9] proposed a spatial filter measurement matrix (SFMM) design method for colocated CS‐MIMO radar based on the pass‐band beamforming for spatial target sectors and lower gain beamforming for out‐of‐sector areas to suppress the possible interference or clutter. However, it can be seen that the attenuation levels for spatial areas out‐of‐sector are limited.…”

Antenna placement optimisation for compressed sensing‐based distributed MIMO radar

“…Since the placement optimisation for the MIMO radar is mainly considered in this paper, for the simplification, we assume that the scattering coefficients of the different targets are different, but keep the same for different view angles of the same target. This assumption has also been considered in [15, 24–26]. τ m , n , k denotes the delay of the k th target, and can be expressed as where , and denote, respectively, the positions of the m th TX, the n th RX and the k th target, and c denotes the speed of electromagnetic wave.…”