SVM (Support Vector Machine)-based multi-sensor target tracking data fusion algorithm and system thereof

Abstract

The invention discloses a SVM (Support Vector Machine)-based multi-sensor target tracking data fusion algorithm. According to target information acquired by sensors, a compact combination mode is adopted, the SVM serves as a middle layer, an environmental variable and a measurement variance normalized vector serve as input of the SVM, output of the SVM serves as trust of the sensor, a known training sample is used for offline training, real-time filter information is used for online estimation, and a fusion knowledge base performs track fusion through real-time weighting according to the obtained trust of the sensor. According to the SVM-based multi-sensor target tracking data fusion algorithm and the system thereof, the SVM principle is adopted, the algorithm complexity is low, the environmental variable and the measurement variance normalized vector are introduced, the biological robustness and the fault tolerance are strong, extension is easy, and the algorithm and the system thereof are applicable to the field of multi-sensor tracking.

Description

technical field [0001] The invention relates to an SVM-based multi-sensor target tracking data fusion algorithm and a system thereof, belonging to the technical field of network communication. Background technique [0002] A single sensor signal is difficult to guarantee the accuracy and reliability of input information, and cannot satisfy the application system's ability to obtain environmental information and system decision-making. Multi-sensor information fusion technology coordinates the use of multiple sensors through certain technical fusion methods, and integrates the local incomplete measurements provided by multiple homogeneous or heterogeneous sensors distributed in different locations and the relevant information in the associated database. Eliminate the redundancy and contradictions that may exist between multiple sensors, and complement each other to reduce their uncertainty and obtain a consistent description of the object or environment. However, multi-senso...

AI Technical Summary

Problems solved by technology

The traditional maximum likelihood estimation, least squares method, and Kalman filter are suitable for the fusion of original data, but they need to obtain a more accurate mathematical model of the object, which cannot be applied to complex and difficult to establish mathematical models

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