Surface multi-beam forming method based on hybrid adaptive particle swarm optimization

Abstract

The invention belongs to the technical field of radar signal processing, and particularly relates to a surface multi-beam forming method based on hybrid adaptive particle swarm optimization, which comprises the following steps: obtaining related parameters of a forming reference beam by an analytical method; selecting an appropriate fitness function and an appropriate cost function; initializing aparticle swarm, an individual optimal particle, a global optimal particle and a non-inferior solution set; using hybrid adaptive particle swarm optimization for updating to obtain a new swarm of which the scale is three times of the original scale; taking the optimal particle which is 1 time of the original scale as a new generation of swarm; updating the individual optimal particle, the global optimal particle and the non-inferior solution set; updating related parameters in the iteration process; determining an optimal result or terminating iteration; and outputting the non-inferior solution set and a related result graph.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and in particular relates to an area multi-beam forming method based on a hybrid adaptive particle swarm algorithm. Background technique [0002] Multi-beam forming means that the radar antenna system transmits multiple parallel beams at the same time, and the two-dimensional pointing and main lobe shape of each beam is controlled by adjusting the weighted value of the antenna array. Compared with traditional single beam forming, multi-beam forming has the advantages of coverage Wide range and beam parameters can be controlled and so on. The traditional multi-beam forming mostly adopts the method of multi-beam forming, that is, the pattern synthesis of the array antenna. The basic principle is to compensate the phase difference of each beam pointing down by iteratively optimizing the amplitude excitation and phase weighting of the array antenna elements. , and finally form multip...

AI Technical Summary

Problems solved by technology

[0005] The traditional particle swarm optimization algorithm uses the sharing of information by individuals in the population. The entire search and update process follows the current global and individual optimal solutions to complete the evolution process from disorder to order, but there is insufficient ability to change, and premature entry case of local convergence

The genetic algorithm directly evaluates the pros and cons of individuals in the population based on the fitness function that reflects the value of the objective function, and determines the overall search direction, but there are problems of being too random and insufficient memory

When performing simultaneous multi-beam synthesis on a planar array, it is necessary to optimize the amplitude excitation and phase of the array antenna elements at the same time, in order to control multiple main lobe beam shapes, it is necessary to select multiple fitness functions, which make it easier to optimize the search space range. Wider and more complex

However, there has not been an algorithm that combines the particle swarm optimization algorithm using adaptive inertia weights, the genetic algorithm that controls the crossover mutation probability according to the convergence degree, and the method of dynamically weighting the fitness degree to apply to the simultaneous multi-beam forming of the planar array. related research

Images