A unified estimation method for target number and angle-range parameters in FDA-MIMO radar under impulse noise environments

Abstract

In the presence of impulse noise, estimating both the target number and angle-range parameters of frequency diverse array multiple-input multiple-output (FDA-MIMO) radars is a recognized challenge. In this paper, a novel unified method is introduced for simultaneously estimating both the angle-range parameter and target number of FDA-MIMO radars under impulse noise. To mitigate the effects of impulse noise, a novel adaptive low-order covariance (ALC) method that requires no prior information is proposed. Additionally, a two-dimensional spatial spectrum function is derived using the ALC-MVDR method, leveraging the ability of the minimum-variance distortionless response (MVDR) beamformer to provide a two-dimensional spatial spectrum when the target number is unknown. To resolve the two-dimensional spatial spectrum function, the multimodal quantum sunflower optimization algorithm (MQSFOA) is introduced, which can effectively identify the number of spectral peaks and estimate the peak location without quantization errors. The interrelated Cramér–Rao bound (CRB) is then deduced for evaluating the developed method under conditions of impulse noise. Comparative simulation studies demonstrate the significant performance improvements of the presented method.