MIMO Radar Array Optimization Using Intelligent Algorithms

The channels of MIMO radar systems can be increased considerably as the transmit channels can be separated at receiver. This increased channels are equal to array element in colocated MIMO radar systems, which are called virtual elements.The optimal beam pattern can be obtained by optimizing the virtual elements. The difference of array optimization between traditional phased arrays and MIMO radar is that the pattern of the virtual elements is optimized by arranging the position of real array in MIMO radar. The elements overlap each other for transmit-receive community array with uniform linear. If the array is nonuniform, virtual array aperture would be extend and redundancy virtual elements would be reduced for linear array. Aim to the virtual array optimization of MIMO radar, using Main-to-Side Lobe Ratio of virtual array beamform as cost function, MIMO radar array optimization using intelligent Optimization algorithm is studied in this thesis. The details of this thesis are organized as follows.1. An improved differential evolution algorithm(DE) is used to optimize MIMO radar array. The basical DE is introduced firstly. A difference of the population center with respect to individuals is added to the existing mutation operator to accelerate the algorithm speed.To avoid the trap of a local optimum and get balance between the convergence and the global search ability,a simplified simulated annealing(SA)selection strategy is adopted. The possibility of the trap of a local optimum is reduced more by using the adaptive mutation operator Finally the improved DE is used to optimize MIMO array, which does not need full search and heavy computation burden, compared to minimum redundancy array in current literatures.2. An improved particle swarm optimization(PSO) algorithm based MIMO radar array optimization method is proposed. The basical PSO is introduced firstly. Based on current literatures, an improved particle swarm optimization algorithm based on adaptive nonlinear inertial weight is proposed. An exponential parameter of adaptive inertial weight is adopted to adjust the speed of the particles and the speed of the best particle is set to zero. The proposed algorithm make for plunging local primary and accelerate the convergence speed at the some time. Finally the improved PSO is used to optimize MIMO array, Unlike GA and DE, which does not have the procession of selection, cross and aberrance, can realizes easily and need less count.