Guidance tool error identification method based on particle swarm optimization algorithm

Abstract

The invention provides a guidance tool error identification method based on particle swarm algorithm, which comprises the following steps: 1 acquiring data information, wherein the data information comprises flight trajectory data, prior information data and impact point deviation information data; 2, preprocessing that data information obtained in the step 1; 3, setting parameters; 4, identifyingerror coefficients of that guidance tool through the particle swarm algorithm; 5, if that termination condition is satisfied, re-checking the result and outputting the identification result of the error coefficient of the guidance tool; otherwise, returning to the step 4. By applying the method of the invention, the error coefficient of the guidance tool is obtained by forward searching calculation based on the trajectory recurrence instead of linear inverse solution, the technical bottleneck of seriously ill-conditioned environment function matrix in the parameter estimation method of the linear model can be avoided, and the method has the advantages of efficiency and robustness.

Description

technical field [0001] The invention relates to the field of aerospace technology and the field of weapon precision analysis and evaluation technology, in particular to a guidance tool error identification method based on particle swarm algorithm. Background technique [0002] Traditional linear model parameter estimation methods for guidance tool error identification, such as least squares method, principal component method, ridge estimation method, etc., are based on linearization and matrix theory, so they are deeply bound by correlation. The identification effect of complex situations such as noise pollution is often poor. In order to avoid this technical bottleneck, consider avoiding the reverse solution of the linearization model, and directly select the appropriate error coefficient of the guidance tool within the constraints of the physical background, so that it can fit the deviation of the remote external measurement data calculated by the error model The value an...

AI Technical Summary

Problems solved by technology

In this way, limited information can only be used as the terminal target for reproduction (such as time parameters on feature points, position parameters, total accuracy of each segment, etc.). Although it can reveal the main contradiction of the problem, there are many human factors and periodic , large amount of calculation, low ballistic reproduction accuracy and other deficiencies

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