Wideband off-grid direction-of-arrival estimation based on the improved finite rate of innovation method

Off-grid direction-of-arrival estimation is a crucial research area in multi-sensor array signal processing to achieve accurate estimation in a finite sparse grid. However, current off-grid estimation methods primarily focus on narrowband processing, which may not be suitable for practical passive estimation scenarios where the targets of interest are wideband signals with various steering vectors and varying signal-to-noise ratios across frequency bins. First, we propose an improved weighting-based wideband joint finite rate of innovation algorithm to address this limitation. This algorithm extends the narrowband approach by approximating the wideband array data as multiple observations at the difference frequency using extended frequency difference weighting. Additionally, we propose an estimation method under non-ideal weighting conditions to mitigate bias caused by deviations in initial weight values through linear fitting of multiple estimation results obtained on a sparse grid. Simulation results demonstrate that our proposed algorithm outperforms existing methods by providing more accurate estimates and lower computational complexity for wideband off-grid multi-targets at low signal-to-noise ratio while unrestricted by grid limitations. Furthermore, experimental data collected from the South China Sea validate our proposed algorithm's effectiveness and superior performance for direction-of-arrival estimation of wideband off-grid targets.