Target feature-assisted multi-source data correlation method

Abstract

The invention provides a target feature-assisted multi-source data correlation method, aiming to provide a correlation method with high utilization rate of measurement parameters and capable of improving the correlation accuracy of a radar and electronic support measures (ESM). The target feature-assisted multi-source data correlation method is realized through the following technical scheme thataccording to the correlation between heterogeneous features, a correlation classification rule of heterogeneous sensor data is determined, a mapping correlation model of a target motion feature space,a target recognition feature space and a target type space is established, a category identification frame is established, K neighbors being at a distance from target features are found according toa K-K-nearest neighbor-NN rule, and trust assignment is constructed based on the distance between a target and the neighbors of the target, an acceptance threshold value and a rejection threshold value; the features of the target at each sampling time are obtained, and then BK-NN training is carried out on the target features at each t time, local static evidences at corresponding times of the categories are obtained and are integrated to generate a static criterion; and the comprehensive results of dynamic classification of different features are calculated, and the correlation filtering results are obtained.

Description

technical field [0001] The invention relates to a method for associating radar and electronic support measures (ESM) based on auxiliary features in the field of information fusion. In particular, the method of linking targets to radar tracks. Background technique [0002] The correlation between radar and electronic support measures is very important in the multi-sensor information fusion system. The data correlation between active radar and electronic support measures (ESM) is the premise of radar and ESM data fusion. In multi-sensor data fusion, the data fusion of heterogeneous sensors is an important issue. Data correlation between radar and electronic support measures (ESM) sensors is a typical heterogeneous sensor data correlation. The distance, speed, angle and phase of the radar at time k become a key issue. Radar and ESM are important airborne sensors, and they are used in combination to fuse their detection information. During information fusion, the correlation...

AI Technical Summary

Problems solved by technology

In the association of radar and ESM data, the azimuth information is mainly used for association. Since only the azimuth information is considered, the distance information in the radar data is not used, which will affect the accuracy of the association.

Only using the single information of orientation for correlation, the basis of information is limited, and the probability of correlation is not accurate enough

Traditional association algorithms only use azimuth angle information. At this time, the information used for association has few dimensions and low quality, which leads to low association accuracy in complex environments.

In the traditional correlation algorithm, only the motion feature information of radar and ESM is used, and since the ESM can only provide angle measurement information, or relatively rough positioning results, the traditional position statistical correlation is extremely prone to error correlation, thereby reducing the The Correlation Accuracy Rate of Radar and Electronic Support Measures ESM

In order to solve the problem that in the traditional correlation between radar and ESM, when the target is in a dense, intersecting scene, the accuracy of the correlation is low because the ESM only has angle measurement information or the positioning accuracy is very poor.

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