Abstract
Heuristic algorithms are considered to be effective approaches for super-resolution DOA estimations such as Deterministic Maximum Likelihood (DML), Stochastic Maximum Likelihood (SML), and Weighted Subspace Fitting (WSF) which are involved in nonlinear multi-dimensional optimization. Traditional heuristic algorithms usually need a large number of particles and iteration times. As a result, the computational complexity is still a bit high, which prevents the application of these super-resolution techniques in real systems. To reduce the computational complexity of heuristic algorithms for these super-resolution techniques of DOA, this paper proposes three general improvements of heuristic algorithms, i.e., the optimization of the initialization space, the optimization of evolutionary strategies, and the usage of parallel computing techniques. Simulation results show that the computational complexity can be greatly reduced while these improvements are used.
Highlights
Heuristic algorithms are considered to be effective approaches for super-resolution DOA estimations such as Deterministic Maximum Likelihood (DML), Stochastic Maximum Likelihood (SML), and Weighted Subspace Fitting (WSF) which are involved in nonlinear multi-dimensional optimization
It is well known that ML and WSF have the highest accuracy of DOA estimation [13,14,15]
Unlike the one-dimensional optimization, the computational complexity of the estimation of nonlinear multi-dimensional optimization is usually extremely high. at is the reason why DML, SML, and WSF could not be applied in International Journal of Antennas and Propagation real systems [17, 18] in spite of their excellent performance in accuracy. is paper attempts to reduce the computational complexity of the estimation of these algorithms so that they can potentially be applied in practical systems
Summary
Heuristic algorithms are considered to be effective approaches for super-resolution DOA estimations such as Deterministic Maximum Likelihood (DML), Stochastic Maximum Likelihood (SML), and Weighted Subspace Fitting (WSF) which are involved in nonlinear multi-dimensional optimization. Is paper is trying to reduce the computational complexity of heuristic algorithms for the estimation of DML, SML, and WSF which involve nonlinear multi-dimensional optimization. To reduce the computational complexity of heuristic algorithms for DOA estimation, this paper proposes three improvements. Simulation results show that this improvement can greatly reduce the number of initial particles and iteration times. The iterative process satisfies the condition that Single Program uses Multiple Data (SPMD), so it can be applied to parallel computing To be honest, this improvement is not a theoretical innovation, but it is very effective in practical calculation. This improvement is not a theoretical innovation, but it is very effective in practical calculation Note that these improvements of heuristic algorithms are general and independent to each other, and they can be applied to DML, SML, or WSF estimation of DOA alone or together. One should refer [8, 9]
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