Method for improving multisensor system data fusion precision

Abstract

The invention provides a method for improving multisensor system data fusion precision and aims to effectively inhibit influence of the local bad information on global estimation. The method comprises steps that a weight factor lambda is set in a local estimation component of a sensor system which has relatively poor estimation precision or relatively large interference, parameters alpha and beta are solved according to the lambda, and a matched parameter fuzzy tuning device is set; each sensor acquires measurement points, after local Kalman filtering, acquired local state estimation 1-L is inputted to a data fusion pre-processing module, first-row first-column components of each local estimation error autocorrelation matrix are extracted by the data fusion pre-processing module according to definition of the Kalman filtering estimation error autocorrelation matrix, an error ratio coefficient r, an error ratio coefficient change rate rc and the weight factor lambda which are used for quantitatively describing each local estimation error magnitude relationship are solved online, and the value of the lambda is adjusted; during data fusion, the lambda is called for resolution, and final global state estimation is realized.

Description

technical field [0001] The invention relates to a multi-sensor system data fusion technology in the technical field of target tracking, and is a design method for improving tracking precision by adding an adaptive weighting factor of parameter fuzzy setting into a classical data fusion method. Background technique [0002] Target positioning and tracking is based on the principle of best estimation, using the calculation method of digital filtering, processing the measurement received by the sensor, and estimating the data processing process of the moving elements of the target. Measurements are noise-contaminated sensor observations about the state of a target, including other information such as slant range, azimuth, elevation, and time difference. Target motion elements generally refer to parameters such as target state and heading. The target state mainly refers to the motion components of the target (such as position, velocity, acceleration, etc.). Usually, target pos...

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Problems solved by technology

However, experiments have shown that P alone (i) (n) The adjustment strength is relatively limited. If the error of a certain sensor is particularly large, its estimation result will still participate in the weighted sum calculation, resulting in the loss of other better local estimation information, and the global estimation accuracy will inevitably be greatly reduced. pull down

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