Improved Gaussian mixed potential probability hypothesis density filtering method

Abstract

The invention discloses an improved Gaussian mixed potential probability hypothesis density filtering method. The method comprises the following steps: 1) forming a target state set and a target strength function; 2) initializing probability hypothesis density and potential distribution of an initial target; 3) carrying out predication on the probability hypothesis density and potential distribution of the target state set at the time of k+1 to obtain probability hypothesis density and potential distribution at the time of k+1; 4) updating the probability hypothesis density and potential distribution of the target state set at the time of k+1 to obtain probability hypothesis density and potential distribution at the time of k+1, carrying out unbiased conversion on a true covariance matrix and true deviation, and setting an ellipsoid threshold value to simplify a measurement set and reduce observation number of a current observation set; 5) carrying out trimming and combining on Gaussian items of the target strength function, and extracting target state estimation and carrying out performance evaluation; and 6) repeating the steps 3)-5), and tracking the target until the target disappears. The method facilitates direct application of radar data information, and reduces calculation amount of a filter.

Description

1. Technical field [0001] The invention relates to the technical field of target tracking, in particular to an improved Gaussian mixed potential probability hypothesis density filtering method. 2. Background technology [0002] With the continuous emergence of non-traditional defense and security challenges, the research on multi-target tracking algorithms has become a hot spot. In the multi-target tracking algorithm, there are two main methods, data association (such as PDA, JPDA) and direct processing bypassing association (such as PHD, CPHD). When the number of targets is large and contains clutter, the calculation amount of data association (PDA, etc.) will be very large, which is not conducive to engineering applications. In recent years, Professor Ronald P.S. Mahler, an expert in multi-target tracking research, proposed the random finite set (RFS) theory based on finite set statistics (FISST), and on this basis, the Probability Hypothesis Density (PHD )filter. [00...

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Problems solved by technology

[0003] The PHD filter method transforms the set function integration method into the integral of a single target. It first tracks the entire target group, and then detects each variable. However, there are some problems in the PHD filter, such as missing detection sensitivity, no number distribution, etc.

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