GASA-based MIMO radar orthogonal coded signal optimization method

Abstract

The invention belongs to the radar technical field and discloses a GASA-based MIMO radar orthogonal coded signal optimization method. The method comprises the following steps: establishing an orthogonal polyphase coded signal module of an MIMO radar; generating an initial population according to the established orthogonal polyphase coded signal model, and carrying out initialization on annealing temperature, iteration times and population generation number; carrying out optimization on the tth generation of population to generate a new population; judging whether the iteration times reach preset iteration times; if not, letting the iteration times and population generation number add 1 respectively and carrying out optimization on the new population, which serves as the tth generation of population, until the iteration times reach the preset iteration times; and if so, determining an optimal individual in the population and obtaining an orthogonal polyphase coded signal according to the optimal individual. The method can avoid trapping in local optimum and can obtain an excellent-performance global optimal solution.

Description

technical field [0001] The invention belongs to the technical field of radar, and relates to a multiple-input multiple-output (Multiple-Input Multiple-Output, MIMO) radar quadrature multi-phase encoding signal optimization method, specifically a hybrid genetic simulated annealing algorithm (Genetic Algorithm and Simulated Annealing, GASA) ) quadrature polyphase coded signal optimization method for MIMO radar. Background technique [0002] MIMO radar uses multiple transmitting antennas to simultaneously transmit mutually orthogonal signals to irradiate the target, and uses multiple receiving antennas to receive target echo signals and process them to obtain information such as the spatial position and motion state of the target. In order to avoid the cross-correlation of the transmitted signals of multiple transmitting antennas, which will cause the transmitted signals to interfere with each other and reduce the target detection performance of the radar system, it is required...

AI Technical Summary

Problems solved by technology

However, although the genetic algorithm has a strong overall ability to grasp the search process, its local search ability is poor; and although the simulated annealing algorithm has a strong local search ability, it does not know much about the status of the entire search space, which is not conducive to making the search process enter The most promising search area, resulting in a decrease in computational efficiency

Images