Multi-target tracking method in case of loss of measurement data

A multi-target tracking and measurement data technology, applied in the field of target tracking, can solve problems such as high computational complexity and low computational efficiency, and achieve the effects of solving data loss, improving robustness, and reducing the amount of calculation.

Active Publication Date: 2017-02-15
NANJING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, when the Gaussian mixture probability hypothesis density filter (GM-CPHD) is used for target tracking, there is a blind spot in the radar Doppler, and the calculation complexity is too large and the calculation efficiency is low.

Method used

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  • Multi-target tracking method in case of loss of measurement data
  • Multi-target tracking method in case of loss of measurement data
  • Multi-target tracking method in case of loss of measurement data

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Experimental program
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Embodiment 1

[0160] Effect of the present invention can be further illustrated by following simulation experiments:

[0161] 1. Simulation conditions

[0162] Hypothetical target state The unit of position is m, and the unit of speed is m / s. There are four targets in this simulation, each target motion model is a CA model, and the initial states of the four targets are as follows:

[0163] x t1 =[300m, 300m, 40m, -20m / s, -20m / s, -1m / s, 0m / s 2 ,0m / s 2 ,-2m / s 2 ] T ;

[0164] x t2 =[40m, -300m, -300m, 1m / s, 20m / s, 20m / s, 2m / s 2 ,0m / s 2 ,0m / s 2 ] T ;

[0165] x t3 =[200m, 300m, 300m, -1m / s, -20m / s, -20m / s, -2m / s 2 ,0m / s 2 ,0m / s 2 ] T ;

[0166] x t4 =[-200m, 40m, -200m, 20m / s, -1m / s, 20m / s, 0m / s 2 ,-2m / s 2 ,0m / s 2 ] T ;

[0167] The equation of motion of the target:

[0168] The simulation experiment assumes that the birth time of target 1 is 0s, the death time is 7s, the birth time of target 2 is 7s, the death time is 25s, the birth time of target 3 is 11s, the d...

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Abstract

The invention discloses a multi-target tracking method in the case of loss of measurement data. The multi-target tracking method includes the following steps: initializing probability hypothetical density and potential distribution of a target state set; predicting the probability hypothetical density and potential distribution of the target state set; updating and converting a deviation matrix and a covariance matrix for measurement; introducing a scaling factor, and changing a gain matrix; using an ellipsoid threshold to remove irrelevant measurement data for the weight of a target strength function; performing trimming and merging, and extracting a target state; determining if a target exists or not, returning to the step of predicting the probability hypothetical density and potential distribution of the target state set if so, otherwise ending target tracking. According to the invention, the scaling factor is introduced during updating of a CPHD filter to regulate the filter gain matrix, and the robustness of the filter can be improved; a self-adaption threshold can simplify a measurement set, the observation number of a current observation set is reduced, all the measurement data irrelevant with a detected target is removed, and finally, the computing amount is reduced.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to a multi-target tracking method in the case of loss of measurement data. Background technique [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. The PHD filter method tran...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 吴盘龙邓宇浩李星秀王超尘王雪冬薄煜明
Owner NANJING UNIV OF SCI & TECH
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