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Multi Strategy - PSO Optimized Cyclic-MUSIC Algorithm for Enhanced DOA Estimation in MIMO Radar Systems

International Journal of Electrical and Electronics Engineering
© 2025 by SSRG - IJEEE Journal
Volume 12 Issue 12
Year of Publication : 2025
Authors : N. V. S. V. Vijay Kumar, P. Rajesh Kumar
10.14445/23488379/IJEEE-V12I12P114
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How to Cite?

N. V. S. V. Vijay Kumar, P. Rajesh Kumar, "Multi Strategy - PSO Optimized Cyclic-MUSIC Algorithm for Enhanced DOA Estimation in MIMO Radar Systems," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 12, pp. 175-185, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I12P114

Abstract:

In contemporary Multiple-Input Multiple-Output (MIMO) radar and wireless communication systems, Direction of Arrival (DOA) estimation with high accuracy is still a crucial challenge, particularly in unfavorable propagation conditions like multipath fading, low Signal-to-Noise Ratio (SNR), and closely spaced sources. Although the classical Multiple Signal Classification (MUSIC) algorithm has a high-resolution DOA estimation capability, its limited resolution for closely spaced sources and susceptibility to noise cause significant performance degradation in actual propagation environments. In this work, we compare three sophisticated DOA estimation techniques: Cyclic-MUSIC, which is based on cyclic correlation; Basic Particle Swarm Optimization (PSO)-Cyclic-MUSIC, which combines PSO with Wavelet Packet Decomposition (WPD) preprocessing; and Enhanced PSO-Cyclic-MUSIC, which offers additional optimization performance enhancements like dynamic acceleration coefficient tuning and adaptive inertia weight control. The suggested Enhanced PSO method incorporates fuzzy logic-based parameter adaptation, scenario-specific acceleration coefficient adjustment, and first-order Taylor series expansion for inertia weight optimization to overcome the premature convergence limitation in complex optimization landscapes. The Enhanced PSO-Cyclic-MUSIC performs better and achieves average Root Mean Square Error (RMSE) improvements of 41.7% over classical approaches, according to extensive simulations conducted under four difficult scenarios. It also demonstrates an improved convergence rate and can function robustly under low SNR conditions (-10 dB) and with closely spaced source configurations of 2o separation. By providing high-precision spatial localization under difficult propagation conditions, this suggested enhancement strategy makes scalable solutions for real-time applications in MIMO radar possible.

Keywords:

Acceleration coefficient tuning, Cyclic-MUSIC, Particle Swarm Optimization, Inertia Weight Optimization, MIMO radar, Direction of Arrival estimation, Wavelet packet decomposition, DOA resolution enhancement.

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