A Tracking Method for Multiple Moving Targets in Impulsive Noise Environment

Abstract

The invention aims to provide a method for tracking multiple moving targets under an impact noise environment. The method includes setting a special nonuniform-linear-array array structure; locking the targets within a changing search range; designing a quantum culture wild-goose-group method; utilizing a search mechanism of the quantum culture wild-goose-group method to search an optimal angle value of an extended and weighted signal maximum likelihood equation in the search range. By gradually narrowing the search range and applying the intelligent search mechanism, the calculating quantity problem of a search method is solved. Simulation results show that by the method, instantaneity of a designed method can be guaranteed; the method has array expanding capability and high tracking accuracy, and still has good performance under severe noise environments with strong impact noise.

Description

technical field [0001] The invention relates to a method for tracking a moving target. Background technique [0002] Direction finding, also known as spatial spectrum estimation or DOA estimation, is an important research field of array signal processing and has a wide range of applications in passive detection, radar, sonar and communication. After decades of development, the DOA estimation theory based on Gaussian model assumptions has been gradually perfected and matured. However, in practical applications, many random signals and noises encountered are not Gaussian distributed, such as atmospheric lightning noise, instantaneous spike voice signals on communication lines, marine environmental noise, and various man-made noises. Spikes can be described by SaS processes with different characteristic indices α, and the traditional methods based on second-order and fourth-order statistics cannot obtain satisfactory results. How to find a DOA estimation algorithm that can no...

AI Technical Summary

Problems solved by technology

[0004] Existing literature has shown that in harsh noise environments such as impact noise, the robustness of dynamic tracking is still unresolved

Dynamic tracking in the impact noise environment is a difficult problem, especially it is more difficult to perform dynamic tracking on multi-sources larger than the number of array elements, so the DOA estimation algorithm in the Gaussian noise environment cannot be directly transplanted

Robust dynamic DOA estimation under impact noise should firstly be able to establish a high-performance robust dynamic tracking optimization equation. It is difficult for classical intelligent calculation methods to get rid of the contradiction between convergence speed and convergence performance in the impact noise environment. Under the existing calculation conditions It is difficult to search for the optimal solution in a limited time, so it is necessary to design a new intelligent algorithm to solve the problem of robust dynamic tracking in the impact noise environment, especially in the impact noise environment

[0005] However, one of the main disadvantages of using the maximum likelihood algorithm for angle estimation is that the search process is a complex and time-consuming process with a huge amount of computation.

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