Chernoff fusion method based on expectation maximization approximation

Abstract

The invention discloses a Chernoff fusion method based on expectation maximization approximation, which comprises the steps of performing particle filtering on each sensor to obtain a local estimationresult, approximating the local estimation result into Gaussian mixture distribution by adopting an expectation maximization method at the same time, interacting a Gaussian mixture parameter among multiple sensors, then performing preliminary data fusion by using a Chernoff fusion method under a first-order approximation model, enabling the fusion result to act as an importance sampling function,recovering local particle samples of each sensor, calculating corresponding exponential weights at the same time, acquiring an exponential weighting result of each particle sample to act as a new particle sample, then approximating the new particle sample into Gaussian mixture distribution by using the expectation maximization method again, finally performing distributed data fusion according toa Chernoff fusion criterion, and calculating by using the fusion result to obtain an estimation state of the target. The method can achieve the optimal Chernoff fusion and acquire a high-precision conservative distributed data fusion result.

Description

technical field [0001] The invention belongs to the technical field of multi-sensor data fusion, in particular to a Chernoff fusion method based on maximum expectation approximation. 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, because the local estim...

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Problems solved by technology

However, this method introduces some unreasonable assumptions and approximation processes in the implementation process, resulting in large information loss in the fusion results, low fusion accuracy, and suboptimal fusion results.

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