Method for evaluating uncertainty of track association

Abstract

The invention provides a method for evaluating an uncertainty of track association. The method comprises the following steps of: (1) generating a plurality of track pairs to be evaluated; (2) checking time and space of target track information; (3) constructing a collection of the tracks of the targets taking part in the evaluation; (4) calculating a standardized track likelihood; (5) calculating correction factors used for describing false conditions, omitted conditions, and an undetected area of a sensor; (6) calculating and accounting a standard deviation of a deviation vector; (7) calculating a correction factor used for describing the distribution of the targets; and (8) calculating the uncertainty of the track association of the track pairs. The method has the advantages of comprehensively considering the influence of the factors such as the detection ability of the sensor and the distribution of the targets on the uncertainty of the track association, and being capable of reasonably reflecting the relationships between the uncertainty of the track association and the distance between the targets, the measuring error of the sensor, the correctness of the track association, the detection probability and the like; and in addition, the evaluation results are not sensitive to the systematic error of the sensor, accuracy parameters of the sensor used in the evaluation and the like. The method can be used for constructing self-adaptive, studying or intelligent track data combination systems.

Description

technical field [0001] The invention belongs to C 3 I, command control or information processing technical field, specifically relate to a kind of track association uncertainty assessment method, this method can be used for the multi-source track association processing process judgment associated track originates from the suspicious degree of same target assessment. Background technique [0002] Data fusion is the process of reprocessing data from multiple sensors. Therefore, the characteristics of sensors, targets and their environments are important basis for data fusion system design and parameter tuning, and they play a role in the selection of data fusion algorithms and their control parameters. Key role. In practical applications, since the feature extraction of the target and its environment also depends on the target detection data of the sensor, the detection performance of the sensor and the accuracy of the detection performance of the sensor fundamentally affect...

AI Technical Summary

Problems solved by technology

Due to the need for manual participation, the online estimation of parameters such as sensor system error and accuracy is difficult to automate

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