Guidance tool error identification method based on particle swarm optimization algorithm

A particle swarm algorithm and identification method technology, which is applied in the field of guidance tool error identification based on particle swarm algorithm, can solve the problems of many human factors, large amount of calculation, and low trajectory reproduction accuracy

Active Publication Date: 2018-12-21
NAT UNIV OF DEFENSE TECH
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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

Method used

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  • Guidance tool error identification method based on particle swarm optimization algorithm
  • Guidance tool error identification method based on particle swarm optimization algorithm
  • Guidance tool error identification method based on particle swarm optimization algorithm

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Experimental program
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Embodiment 1

[0056] see figure 1 , a guidance tool error identification method based on particle swarm optimization, including the following steps:

[0057] Step 1. Obtain data information;

[0058] Step 2. Preprocessing the data information obtained in Step 1;

[0059] Step 3. Set parameters;

[0060] Step 4, identify the guidance error coefficient by the particle swarm algorithm (specifically, the particle swarm algorithm optimization and reproduction), wherein: during the identification process, each particle will call the ballistic simulation program and return the objective function;

[0061] Step 5. If the termination condition is met, then perform a result review and output the identification result of the error coefficient of the guidance tool; otherwise, return to Step 4.

[0062] The data acquired in the above step 1 includes flight trajectory data, pre-test information data and landing point deviation data. Among them, the flight trajectory data includes the telemetry and ex...

Embodiment 2

[0127] Embodiment 2 differs from Embodiment 1 only in that: the weighted residual error of the landing point deviation fitting is incorporated into the objective function, and the new composite objective function after the combination is set as expression 4):

[0128]

[0129] Where: F 合 is the composite objective function; F is the sum of the sum of the squares of the apparent velocity heterodyne fitting residuals of the sampling points in each direction; ΔL is the longitudinal deviation of the landing point; ΔH is the horizontal deviation of the landing point; j is the error coefficient of the j-th guidance tool, is the error coefficient k of the guidance tool for the j-th item of the longitudinal deviation of the landing point j partial derivative of is the error coefficient k of the guidance tool for the j-th item traverse deviation of the landing point j Partial derivative of , j=1,2,...M; ω 1 is the weighting coefficient of the trajectory of the active segment; ...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/00
CPCG06N3/006G06F30/15G06F30/20
Inventor 蒋小勇孟云鹤陈琪锋王子鉴
Owner NAT UNIV OF DEFENSE TECH
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