Pattern synthesis of MIMO radar using differential particle swarm optimization algorithm

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

“…Similar to the transmitter, the receiver has f R (u) = f R (−u), f R (1 + Δu) = f R (1 − Δu). The two-way pattern of MIMO radar [3,7] associated with u can be written as…”

“…In recent years, MIMO radar has received much attention due to superior characteristics [1][2][3][4][5][6][7][8][9][10][11][12]. Unlike a traditional phased array system, MIMO radar allows emitting orthogonal or noncoherent waveforms at transmitter, and these waveforms can be extracted at receiver by a set of matched filters.…”

“…However, interference is always an inevitable problem in practice= and would cause the distortion of signals, ultimately degrade the performance of entire system. Generally in terms of antenna array, there are two approaches available to overcome this problem: one is to suppress the peak sidelobe level (Psll) [3,13,14], and the other is to form the deep nulls at the directions of interferences in the radiation pattern [13]. Herein we only focus on the two-way pattern [2] of MIMO radar.…”

“…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].…”

“…This method obtains a desired Psll, but suffers from a heavier computational cost. On the basis of [2], a hybrid algorithm called differential particle swarm optimization (DPSO) algorithm is proposed in [3], in which the crossover and mutual operator from the differential evolution algorithm are integrated into particle swarm optimization scheme, to use the superiority of DE operators to improve the diversity of population and avoid premature of PSO. Its experimental results demonstrate that the ability of global optimization is greatly improved, and the diversity of population is enhanced.…”