Robust Sparse Array-Based DOA Tracking in Impulse Noise

For robust DOA tracking under impulsive noise, a robust DOA tracking method is proposed by leveraging capability of a sparse array to virtualize a uniform linear array and extend the array aperture, we construct an extended infinite norm fractional-order covariance matrix to effectively suppress the impulsive noise. Furthermore, we derive the maximum likelihood tracking equation for this extended matrix. To efficiently solve this equation, we introduce an immune moth-flame algorithm. Additionally, we design a dynamic updating strategy for the search space to further reduce the computational complexity of the algorithm. Through simulations, we demonstrate that the proposed method achieves excellent tracking performance while maintaining a low computational complexity, and it is capable of tracking coherent sources without the need for any decoherence methods.