Salp swarm optimization in hybrid beamforming for MIMO radar

Abstract

The Multi Input Multi Output (MIMO) radar waveform diversity Significantly improves parameter identifiably than phased-array radar performance. Precoding, combining and spatial multiplexing techniques improves the data throughput and reliability of the transmission in MIMO systems. But increment in transmit and receive elements in MIMO antenna array induces considerable increase in required power for hardware and computation cost. Hybrid beamforming employs fewer RF-to-baseband chains. With conscious selection of the weights for pre-coding and combining, hybrid beamforming establishes perfect trade-off between complexity, performance, cost, and power consumption in practical applications. Performance of MIMO radar system can be improved using newly developed bio inspired metaheuristic algorithms as compared to conventional and adaptive beamforming algorithms. In this work the Salp Swarm algorithm (SSA) is implemented to optimize the performance of hybrid beamforming using Raleigh channel and considering the bit error rate and normalized array power parameters. The swarming behavior of salps when navigating and foraging in oceans is the inspiration behind the SSA optimization algorithm. The obtained results are compared with the conventional phase-shift as well as adaptive linearly constrained minimum variance beamforming algorithms on simulation platform with standard considerations. It is observed that this new approach of Salp swarm algorithm is having improved and much better performance with the considered parameters.

Hybrid Beamforming for Dual Functioning MIMO Radar Using EnhancedSocial Ski Drivers Algorithm

“…However, the algorithm's limitations include limited exploration of the search space, slow convergence, and a large number of iterations. [21,22]. According to studies, Monarch butterflies have several migration theories to address specific optimization problems but have issues of population degeneration and time complexity.…”