Sidelobe suppression with constraint for MIMO radar via chaotic whale optimisation

Abstract

The beam system of multiple input and multiple output (MIMO) radar needs lower peak sidelobe level for reducing interference, in addition, broad nulls placed at undesired directions are also an alternative method. A hybrid method based on the whale optimisation algorithm is proposed to implement sidelobe suppression and broad nulls. To the best of the authors' knowledge, relative works based on the whale optimisation algorithm applied to MIMO radar are not reported so far. By introducing chaotic optimisation into the whale optimisation algorithm scheme, performance of origin algorithm is improved substantially in suppressing peak sidelobe level with constraint of broad nulls. Furthermore, it is found in the experiment that the proposed algorithm can avoid the premature of origin algorithm effectively, and only takes a short time during the optimum accomplishment. Finally, simulation results demonstrate the good performance of the proposed method.

Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

“…Swarm intelligence algorithms have been widely applied to various practical engineering issues in recent years. Whale optimization algorithm, which was proposed in 2016, has been cited more than 1600 times as of April 2020 based on Google Scholar indicators and has been widely used in different fields such as energy [10,11], computer networks [12,13], cloud computing [14], image processing and machine vision [15,16], electronics and electrical engineering [17,18], antenna design [19], feature selection [20][21][22], aerospace [23], path planning [24], and structural optimization [25,26]. Moreover, GOA has been successfully applied to the trajectory optimization of multiple solar-powered UAVs [27], short-term load forecasting [28], electrical characterization of proton exchange membrane fuel cell stacks [29], data clustering [30], scheduling of thermal system [31], analyzing vibration signals from rotating machinery [32], short-term wind electric power forecasting [33], optional distribution system reconfiguration and distributed generation placement [34], classification for biomedical purpose [35], design of linear antenna arrays [36], automatic seizure detection of EEG signals [37], designing automatic voltage regulator systems [38], training neural network [39], and other issues.…”

Two-Way Pattern Synthesis of Mimo Radar With Sidelobe Reduction and Null Control via Improved Whale Optimization Algorithm

“…Presently, some evolutionary algorithms have been used to solve the problems of sidelobe suppression, deep null, and capacity of system associated with the array configuration, such as generic algorithm (GA) [2,15], differential particle swarm optimization (DPSO)) [3], galaxybased search algorithm [16], chaotic differential evolution(DE) [17], and chaotic whale optimization algorithm(CWOA) [18]. For the optimization of system capacity, some evolutionary algorithms have also been utilized to improve the capacity of system by optimizing the array configuration [15,16,19,20].…”

“…The basic idea of this algorithm is to make full use of the diversity property of chaotic optimization to reduce the risk of trapping into local optimality, further to avoid the premature of algorithm. Due to the emerging of WOA, the CWOA integrating the chaotic optimization with WOA is used to optimize the problem of sidelobe suppression [18].…”