Multiple Search Particle Probability Hypothesis Density Filtering Method for Multiple Target Tracking

A probability hypothesis density, multi-target tracking technology, applied in the field of radar tracking of multi-targets, can solve problems such as target loss

Active Publication Date: 2017-08-18
NAVAL AVIATION UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of this invention is to propose a multi-target tracking method of multiple search particle probability hypothesis density filtering (MS-PPHDF), which solves the problem that the target is lost easily when the target detection probability is low in the general PPHDF method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Multiple Search Particle Probability Hypothesis Density Filtering Method for Multiple Target Tracking
  • Multiple Search Particle Probability Hypothesis Density Filtering Method for Multiple Target Tracking
  • Multiple Search Particle Probability Hypothesis Density Filtering Method for Multiple Target Tracking

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0065] The MS-PPHDF method proposed by the present invention will be described in detail below in conjunction with the accompanying drawings.

[0066] Without loss of generality, a two-dimensional simulation scene is set, the monitoring area S=[-60km, 60km]×[-60km, 60km], and the total simulation time K=50s. Assuming that the target can appear and disappear randomly in the monitoring area, the average target appearance probability γ k = 0.2, the initial distribution D of the target appearance 0 subject to mean x 0 and covariance Q b The normal distribution of , here take x 0 =[30km0.2km / s 30km-0.1km / s] T and Q b =diag([1km 0.5km / s 1km 0.5km / s]), the standard deviation of the process noise in the x direction and y direction is 0.01km, the probability that the target persists is e k|k-1 = 0.95 and has nothing to do with the target state. The radar is located at point (0km, -10km), and the detection probability is P D =0.75, the average number of clutter per frame is λ k...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a multi-target tracking method through multi-search particle probability hypothesis density filter, and belongs to the field of radar data processing. The multi-target tracking method based on particle probability hypothesis density filter has an obvious defect of rapid degeneration of particle diversity caused by resampling when leak detection of targets occurs and then the phenomenon of target loss occurs, and thus the algorithm is difficult to meet multi-target tracking of low target detection probability. The multi-search particle probability hypothesis density filter aims at solving the problem. The multi-target tracking method is simple in structure, rapid in computation and simple in hardware realization; meanwhile, the limitation of the application based on the general particle probability hypothesis density filter method is overcome, and adaptability to a nonlinear and non-Gaussian system is high so that the multi-target tracking method has relatively high engineering application value and popularization prospect.

Description

technical field [0001] The invention relates to a radar data processing method, in particular to a radar tracking method for multiple targets under the condition of low detection probability. Background technique [0002] Particle probability hypothesis density filter (PPHDF) is an effective method for tracking multiple targets in dense clutter environment. By modeling the measurement and target state as a random set, PPHDF can easily estimate the time-varying and unknown target state from the time-varying number of measurements, and can simultaneously calculate the number of targets and the target state At the same time, PPHDF can avoid the correlation problem between the target and the measurement, which greatly reduces the complexity and calculation amount of the multi-target tracking algorithm. Therefore, PPHDF has received extensive attention and research in the field of multi-target tracking. This method is mainly realized through the following steps: [0003] (1) I...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/02G01S13/56G01S13/66
CPCG01S7/02G01S13/56G01S13/66
Inventor 谭顺成王国宏吴巍于洪波
Owner NAVAL AVIATION UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products