Method and Information Retention System for Probability Hypothesis Density Filter Target Information

Abstract

The invention is suitable for the field of multi-sensor information fusion and provides a probability hypothesis density filter target information maintaining method. The probability hypothesis density filter target information maintaining method includes: step 1, forecasting posterior moments and Gaussian items at the current moment according to posterior moments and Gaussian items of the last moment; step 2, updating the posterior moments and the Gaussian items according to the posterior moments and the Gaussian items of the current moment and a measurement set of the current moment; 3, cutting down or combining the updated Gaussian items; step 4, extracting a weight Gaussian item as output of a filter according to the cut down and combined Gaussian items, wherein means and variances in the corresponding Gaussian items are state estimation and error estimation of a survival target. By means of the hypothesis density filter target information maintaining method, information of a target with detection leaked is retained in a posteriori updating moment by amending an updating function of a probability hypothesis density filter, so that information of the target with detection leaked cannot be missing, effectiveness in target number estimation and target state extraction is improved, and further a multi-target tracking capability of the Gaussian probability hypothesis density filter is improved.

Description

technical field

[0001] The invention belongs to the technical field of multi-sensor information fusion, and in particular relates to a method for maintaining target information of a probability hypothesis density filter and an information maintenance system. Background technique

[0002] In the presence of false alarms, missed detections and unknown number of targets, the probability hypothesis density filter proposed by Mahler is a new method to solve target detection and tracking. The probability hypothesis density filter avoids the direct correlation between the observed value and the state value, and its biggest advantage is that the target number can be estimated from the posterior moment. In order to solve the problem that the integral operation in the prediction and update equation of the probability hypothesis density filter is difficult to handle, Vo et al. proposed the particle probability hypothesis density filter and the Gaussian mixture probability hypothesis de...

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