Multiobjective Bilevel Evolutionary Approach for Off-Grid Direction-of-Arrival Estimation
This addresses a specific issue in signal processing for applications like radar or sonar, but appears incremental as it builds on existing DOA estimation methods with a novel algorithmic twist.
The paper tackles the problem of inaccurate source number identification in direction-of-arrival estimation under challenging conditions like low SNR or impulsive noise, by proposing a multiobjective bilevel evolutionary approach that simultaneously identifies source numbers and estimates DOAs, achieving improved performance in simulations.
The source number identification is an essential step in direction-of-arrival (DOA) estimation. Existing methods may provide a wrong source number due to inferior statistical properties (in low SNR or limited snapshots) or modeling errors (caused by relaxing sparse penalties), especially in impulsive noise. To address this issue, we propose a novel idea of simultaneous source number identification and DOA estimation. We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation. In particular, the source number is properly exploited by the $l_0$ norm of impinging signals without relaxations, guaranteeing accuracy. Furthermore, we design a multiobjective bilevel evolutionary algorithm to solve the proposed model. The source number identification and sparse recovery are simultaneously optimized at the on-grid (lower) level. A forward search strategy is developed to further refine the grid at the off-grid (upper) level. This strategy does not need linear approximations and can eliminate the off-grid gap with low computational complexity. Simulation results demonstrate the outperformance of our method in terms of source number and root mean square error.