Gridless Cooperative Coevolutionary Algorithm for Real-Time DOA Estimation in Automotive Radar

In automotive radar systems, super-resolution angle-of- arrival estimation is required to distinguish neighboring targets for good imaging results. In order to adapt to the rapidly changing dynamic driving scenarios, the angle of arrival estimation must meet the real-time computational requirements and can be realized based on single snapshot data. Existing gridless methods based on atomic norm minimization are suitable for super-resolved angle estimation for single snapshot signals and avoid the grid mismatch problem. But the atomic norm acts as a convex relaxation of the atomicl0norm, and it leads to a degradation of the angle resolution. Moreover, it has high computational complexity and cannot meet the real-time requirement. In order to avoid the convex relaxation and satisfy the real-time solution requirements at the same time, we propose a single snapshot multi-objective DOA estimation model. It minimizes both the measurement error term and the atomicl0norm, and directly deals with the conflict between angle measurement accuracy and signal sparsity to jointly achieve target detection and angle estimation. To efficiently solve this NP-hard model, we design a cooperative coevolutionary crayfish optimization algorithm. The novelty is two-fold: one is the proposal of a new cooperative co-evolutionary decomposition strategy for multiple populations to efficiently decompose the multi-objective DOA estimation model into multiple single-objective problems. The crayfish optimization algorithm is applied to each population to solve the multi-objective problem with variable dimensions based on multiple swarm cooperative co-evolutionary techniques. The other innovation is the proposal of a variable-length neighboring orthogonal crossover operator for information exchange between populations. This mechanism effectively speeds up the convergence of the algorithm. Simulation results and hardware experiments show that, compared with the spatially smooth MUSIC algorithm, the atomic-norm-based gridless algorithm and its various variants, and the variational Bayesian-based gridless algorithm, our method exhibits superior performance in terms of angle measurements and target number detection. Moreover, it can realize angle measurement within 20ms, which is the only gridless algorithm among these gridless super-resolution algorithms that can meet the real-time computation requirements of automotive radar.