Probability hypothesis density filtering and smoothing method based on segmentation RTS (Rauch-Tung-Striebel)

A probability hypothesis density, filtering smoothing technology, applied in the field of multi-target tracking, can solve the problem of not finding the PHD filtering effect and so on

Inactive Publication Date: 2018-10-30
XI'AN POLYTECHNIC UNIVERSITY
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Problems solved by technology

The current research focuses on using the forward and backward smoothing algorithm to improve the PHD filtering effect, but there is no report on using RTS to improve the PHD filtering effect

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  • Probability hypothesis density filtering and smoothing method based on segmentation RTS (Rauch-Tung-Striebel)
  • Probability hypothesis density filtering and smoothing method based on segmentation RTS (Rauch-Tung-Striebel)
  • Probability hypothesis density filtering and smoothing method based on segmentation RTS (Rauch-Tung-Striebel)

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Embodiment Construction

[0105] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0106] The present invention is based on the probability assumption density filter smoothing method of subsection RTS, specifically implements according to the following steps:

[0107] Step 1. In multi-target tracking, the state value of multiple targets at time k is N k is the number of targets, Indicates the state value of the i-th target at time k; the measured value is m k To measure the number, Indicates the jth quantity measurement received by the sensor at time k;

[0108] random set Indicates the state of the target at time k, and the i-th target state vector is preset as Its dynamic equation is as follows:

[0109]

[0110] In formula (1), F k is the dynamic transition matrix of the target, is the covariance Q k the process noise, the F k As a linear Gaussian model, the ordinary Kalman filter algorithm is used ...

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Abstract

The invention discloses a probability hypothesis density filtering and smoothing method based on segmentation RTS (Rauch-Tung-Striebel). Through combination of a probability hypothesis density (PHD) filter and an RTS smoother, a probability hypothesis density filtering and smoothing algorithm based on the RTS is provided. The fact that a relatively high output delay problem exists in a smoothing process is taken into consideration, so a segmentation thought is employed, and the probability hypothesis density filtering and smoothing algorithm based on the segmentation RTS is provided. Evaluation values needing to be smoothed is segmented; track-evaluation association is carried out through adoption of a Hungary algorithm; and RTS smoothing is carried out on associated evaluation values segment by segment. Compared with a PHD filtering result, the probability hypothesis density filtering and smoothing method based on the segmentation RTS provided by the invention has the advantages thata target state can be evaluated precisely, and the problem that the timeliness is poor resulting from directly applying the RTS smoothing can be effectively avoided.

Description

technical field [0001] The invention belongs to the field of multi-target tracking, and in particular relates to a probability hypothesis density filtering and smoothing method based on segmented RTS. This method can estimate the target state more accurately, and at the same time can improve the problem of poor real-time performance caused by the direct use of RTS smoothing, and can be used in traffic control, robots, video surveillance, etc. Background technique [0002] Multi-target tracking refers to the combination of measurement information received by sensors and prior knowledge, real-time prediction and estimation of target movement, identification of target attributes, analysis of target intentions and situation estimation, so as to achieve the purpose of target estimation and tracking . At present, target tracking technology is widely used, such as: 1. In the field of video surveillance: the camera can be used to track people or objects in the monitoring area, espe...

Claims

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Application Information

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IPC IPC(8): G06T7/207G06T7/277G06T5/00
CPCG06T7/207G06T7/277G06T2207/30241G06T5/70
Inventor 陈金广王星辉马丽丽张馨东巩林明
Owner XI'AN POLYTECHNIC UNIVERSITY
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