Multi-sensor distributed data fusion method based on Chernoff fusion criterion

Abstract

The invention discloses a multi-sensor distributed data fusion method based on the Chernoff fusion criterion. According to the method, particle filtering is performed on each sensor to obtain local estimation results firstly, the local estimation results are approximated as Gaussian mixture distribution through adoption of the maximum expectation algorithm, Gaussian mixture parameters are interacted among the multiple sensors, initial fusion results of the multiple sensors are obtained through utilization of a Chernoff fusion method in the case of a first-order approximation model, importance sampling is performed on the results, a multivariate optimization function with a constraint is built, the optimization function is solved through adoption of a particle swarm optimization algorithm, index values of the multiple sensors under the Chernoff fusion criterion are obtained through calculation, Chernoff fusion is finally performed by utilizing particle samples and the optimal index weight of the Chernoff fusion, and then an estimation state of a target is obtained through calculation. The method theoretically, approximately and optimally solves the problem that ideal distributed fusion results for the multiple sensors are difficult to obtain in the case that correlation between the local estimation results exists.

Description

technical field [0001] The invention belongs to the technical field of multi-sensor data fusion, and in particular relates to a multi-sensor distributed data fusion method based on Chernoff fusion criterion. Background technique [0002] With the increasing complexity of the modern battlefield environment, the urgent need for stealth and anti-stealth, confrontation and anti-confrontation, and the emergence of problems such as strong maneuverability, high clutter, low detection rate and high false alarm rate, using multi-sensor data fusion to obtain more Comprehensive, accurate and reliable environmental situation information has attracted more and more people's attention. Among them, distributed data fusion has been greatly developed due to its many advantages such as low communication volume, strong scalability, and good robustness, and has been widely used in many fields such as area monitoring, target tracking, and target positioning. [0003] For distributed data fusion...

AI Technical Summary

Problems solved by technology

The prior art proposes a Chernoff fusion method under the first-order approximation model, which can finally obtain a convergent fusion result by performing exponentially weighted fusion on the estimated results of different sensors; however, this method introduces Some unreasonable assumptions and approximation processes lead to large information loss in fusion results and low fusion accuracy; the existing technology also proposes a Chernoff fusion method based on importance sampling, which is also based on exponential weighting Therefore, it can effectively solve the problem of correlation of local estimation results; in addition, this method adopts the strategy of importance sampling without any approximation process, so there is almost no loss of accuracy; however, this method is only applicable to Fusion between two sensors, and the iterative fusion of two sensors for multi-sensor fusion will lead to the index weight of Chernoff fusion as a suboptimal solution, and the accuracy of fusion results will be greatly reduced

Therefore, this method is not suitable for distributed fusion of any number of sensor network systems

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