Direction Finding Based on Spatial Sparsity of Cumulative Slicing for Circular Array in Correlated Noise and Multipath

The performance of direction finding for uniform circular array (UCA) is degraded with low precision and typical false-alarm result from the correlated noise and coherent interference. To address this issue, a novel direction-finding algorithm based on spatial sparsity representation of high-order cumulative slicing is presented. Spatial sparsity reconstruction based on sparse Bayesian inference of high-order cumulative slicing is constructed to simultaneously mitigate the correlated noise introduced from the environment and the coherent interference caused by multipath. Meanwhile, the multiple interpolation is utilized to overcome the quantization error inherent in sparsity representation. The implementation of high-precision direction-finding is achieved by optimizing each individual component rather than relying on global fitting. The simulation results demonstrate that superior direction-finding accuracy and computational efficiency are provided for UCA, even with concurrent occurrences of environmentally correlated noise and coherent interference.