Cognitive radar waveform optimization method based on particle swarm optimization

Abstract

The invention belongs to the field of cognitive radars, and proposes a waveform optimization method based on particle swarm optimization. Compared with the existing methods, the method is simpler andmore convenient, and can acquire a better effect. According to the technical scheme provided by the invention, the cognitive radar waveform optimization method based on particle swarm optimization comprises the steps that a radar receiver uses received waveform information to estimate a target scattering coefficient, and the estimation method is to use maximum posterior probability estimation anduse Kalman filtering for iterative estimation; the estimated target scattering coefficient is compared with an actual target scattering coefficient; the minimum mean square error between two coefficients is calculated as the objective function of particle swarm optimization; and a waveform to be optimized is used as a variable to the most accurately estimate the target information. The method provided by the invention is mainly applied to the design and manufacture of cognitive radars.

Description

technical field [0001] The invention belongs to the field of cognitive radar, and is an algorithm for optimizing the self-adaptive waveform in the cognitive radar based on the particle swarm optimization algorithm to improve the estimation accuracy of the scattering coefficient of the target. Background technique [0002] The cognitive radar system composed of a transmitter, a receiver and the environment is a dynamic closed loop [1], which can adaptively adjust the radar transceiver system to adapt to the current environment by sensing the complex and changeable environment, and greatly improve the Radar system performance. The receiver feeds back the received environmental information to the transmitter, and the transmitter optimizes the transmitted waveform according to the feedback information, thereby continuously improving the estimation and detection performance of the system. Therefore, waveform optimization is the most important problem in cognitive radar. [0003...

AI Technical Summary

Problems solved by technology

However, these problems are generally non-convex [8] problems, which can only be transformed into convex optimization problems and solved by semidefinite relaxation method (SDR) [9]

Although SDR is an effective method to solve optimization problems, some constraints are often discarded to reduce the accuracy of the results, so we have to try to find other methods

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