Multi-target tracking method based on adaptive extended Kalman probability hypothesis density filter

A probability hypothesis density, extended Kalman technology, applied in the field of multi-target tracking, can solve the problem that the EK-PHD filter cannot track the target, and achieve the effect of avoiding participation in the update process and overcoming the inability to track the target

Active Publication Date: 2021-02-05
HARBIN ENG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to propose a multi-target tracking method based on the adaptive extended Kalman probability hypothesis density filter to solve the problem that the EK-PHD filter cannot track the target when the strength of the new target is unknown

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  • Multi-target tracking method based on adaptive extended Kalman probability hypothesis density filter
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  • Multi-target tracking method based on adaptive extended Kalman probability hypothesis density filter

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Embodiment Construction

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0051] refer to figure 1 Shown, a kind of multi-target tracking method based on adaptive extended Kalman probability hypothesis density filter, described tracking method comprises the following steps:

[0052] Step 1. Initialize the new strength function v 0 (x);

[0053] Step 2, according to the newborn intensity function v 0 (x) predicted survival target strength function v s,k|k-1 (x) and the nascent target strength function v γ,k|k-1 (x);

[0054] Step...

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Abstract

The invention discloses a multi-target tracking method based on an adaptive extended Kalman probability hypothesis density filter, and belongs to the technical field of multi-target tracking. Firstly,a two-point difference algorithm is utilized to initialize new target strength, and then a target maximum speed constraint algorithm is utilized to eliminate wrong new target strength. Besides, in order to eliminate the interference of clutter measurement values, an improved measurement value classification algorithm is utilized to respectively extract a survival target measurement value and a new target measurement value from the measurement value set, and then the survival target measurement value and the new target measurement value are respectively utilized to update the survival target and the new target, so that the precision of the algorithm is improved. According to the method, the problem that the EK-PHD filter cannot track the target under the condition that the strength of thenew target is unknown is solved.

Description

technical field [0001] The invention relates to a multi-target tracking method based on an adaptive extended Kalman probability hypothesis density filter, and belongs to the technical field of multi-target tracking. Background technique [0002] With the development of modern information science and technology, multi-target tracking technology is widely used and plays an important role in the fields of vision, radar and sonar tracking, and vehicle tracking. In the multi-target tracking scenario, not only the state of the target will change with time, but also the number of targets will change with the appearance and disappearance of the target. Research hotspots and difficult issues. The traditional method to solve the problem of multi-target tracking is data association technology, including multiple hypothesis tracking (Multiple Hypothesis Tracking, MHT) algorithm and joint probability data association (Joint Probability Data Association, JPDA) algorithm. However, these ...

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Application Information

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IPC IPC(8): G06F17/15G01S13/72G01S13/88G05D1/12
CPCG06F17/15G01S13/726G01S13/88G05D1/12
Inventor 齐滨梁国龙张博宇付进张光普邹男
Owner HARBIN ENG UNIV
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