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Multi-target track-before-detect method based on probability hypothesis density filtering

A probability hypothesis density and multi-target technology, which is applied in the field of multi-target tracking before detection based on probability hypothesis density filtering, can solve the problems of large amount of calculation, poor real-time performance, missed detection or wrong detection, etc.

Active Publication Date: 2016-09-28
ZHEJIANG UNIV
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Problems solved by technology

[0003] Although the above-mentioned traditional PHD-TBD method has achieved some results, it still has many deficiencies. There are two main points: first, it has low accuracy in estimating the number of targets, and often missed or wrongly detected, resulting in poor detection and tracking effects. Ideal; the second is that it has a large amount of calculation, which leads to poor real-time performance and cannot be practically applied

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  • Multi-target track-before-detect method based on probability hypothesis density filtering
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  • Multi-target track-before-detect method based on probability hypothesis density filtering

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[0087] The objective equation of motion is uniform linear motion x k+1 =Fx k +v k ; v k is Gaussian white noise with zero mean, and its covariance matrix is ​​Q. The sensor continuously observes T O The data of =30 moments, it has n * m = 35 * 35 sensing units, the length and width Δx=Δy=1 of the sensing unit block, the time interval T=1, the standard deviation σ=1 of the noise, the sensor’s Measurement error Σ = 0.7. Target 1 appears at the 2nd moment and disappears at the 18th moment; target 2 appears at the 12th moment and disappears at the 27th moment. Each target is represented by 2048 particles, and the number of new particles per moment is J=1024. The survival probability of the target is 0.95, the derived probability is 0, and the newborn probability is 0.2. Set the threshold probability p * = 0.98.

[0088] F = 1 ...

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Abstract

The invention discloses a multi-target track-before-detect method based on probability hypothesis density filtering. The method deeply analyzes the problem of a traditional method and points out the essential reason of the problem of the traditional method that the traditional method considers that one target influences the observation of a whole area and also considers that the number of each frame of false alarm can be approximated into one constant value, i.e. the traditional method does not comply with two pieces of basic hypothesis for realizing PHD (Probability Hypothesis Density) filtering: firstly, one target only can generate one observation, and secondly, the number of each frame of false alarm must submit to Poisson distribution on an aspect of time. In order to solve the problems, the invention puts forward the multi-target track-before-detect method based on the probability hypothesis density filtering, can improve the accuracy of target number estimation, enhances detection and tracking performance, and also achieves an effect on lowering a calculated amount.

Description

technical field [0001] The invention belongs to the technical field of target detection and tracking, and in particular relates to a multi-target tracking method before detection based on probability hypothesis density filtering. Background technique [0002] In the current research on weak target detection and tracking methods in the strong clutter background, the Track Before Detect (TBD) method is unanimously believed by domestic and foreign scholars that it can greatly improve the detection and tracking performance of weak targets. The main feature of the TBD method is that there is no threshold for single-frame observation. Since it takes the entire original signal as the observation input, it retains the target information to the greatest extent and avoids the loss of single-frame detection. Therefore, it can improve the detection and tracking performance of faint targets. Probability Hypothesis Density (PHD) is a filter based on the framework of random set theory. It...

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 陈积明陈瑞勇史治国罗欣杨超群
Owner ZHEJIANG UNIV
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