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Motion parameter estimation combination smoothing filtering method based on GM-PHD

A technology of smooth filtering and motion parameters, applied in the field of information fusion, can solve problems such as inaccurate target state estimation, influence and interference, sensor perception link errors, etc.

Active Publication Date: 2021-10-22
XIAN UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] At present, in practical applications, it is found that the GM-PHD filtering method generally has the following problems: 1. The GM-PHD filtering method usually shows good tracking performance when the preset speed is close to the real speed of the target, but the actual situation If the target motion speed is unknown or has a large difference from the real one, the target motion state parameters will not match, and the target state estimation will be inaccurate; 3. Even if the preset speed is known, errors and random deviations may be introduced by the target’s motion actuator and sensor perception link, and these errors and random deviations will cause the actual execution speed to differ from the preset speed in each specific Deviations occur at any time, which will affect and interfere with the target state estimation of "velocity known GM-PHD", and then affect the estimation accuracy; 4. The above situation is common in various PHD filtering methods and has similarities. For the widely used Kalman A similar problem exists with filters
[0009] The defects above the prior art limit the improvement of the performance of the GM-PHD filtering method, resulting in the fact that the currently available effective information is not fully utilized, the positioning accuracy is not effectively explored, and the tracking performance is unstable
Thus affecting the application effect of GM-PHD filtering method

Method used

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  • Motion parameter estimation combination smoothing filtering method based on GM-PHD

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Embodiment 1

[0035] The concept of multi-target tracking was proposed as early as 1955, and gradually developed into an important branch in the field of multi-source information fusion. With the emergence of radar, sonar, infrared and other sensors and the continuous development of technology, multi-target tracking technology has been widely used in military and civilian fields, such as ballistic missile defense system, military reconnaissance and early warning, precision guidance, video surveillance , air traffic control, intelligent robots.

[0036] The existing GM-PHD filtering method has been widely used in recent years. This method usually shows good tracking performance when the preset speed is close to the real speed of the target. However, in actual situations, the tracking environment is complex and the speed information It is often impossible to know. If the speed is unknown or has a large difference from the real one, the state parameters will not match and the state estimation ...

Embodiment 2

[0062] The motion parameter estimation combined smoothing filter method based on GM-PHD is the same as embodiment 1, and the target speed estimate value of the differential calculation i target described in step 3 includes the following steps:

[0063] 3.1 Obtain the target state position information at time k-1 and time k: use the Gaussian mixture GM component of the target state at time k-1 of target i and the Gaussian mixture GM component of the target state at time k Among them, only from Extract the state position information of i target k-1 time from and from Extract the state position information of i target k time from

[0064] The present invention extracts the relatively accurate state position information of the same target at different times, and prepares for the subsequent acquisition of a more accurate target speed estimation value. The state position information is distinguished according to the Cartesian three-dimensional coordinate direction, and is...

Embodiment 3

[0077] The motion parameter estimation combined smoothing filtering method based on GM-PHD is the same as embodiment 1-2, and the initial target speed estimate value described in step 4 is smoothed, wherein, the 3-point linear smoothing window selects 3 regressions of the Hamming window. Normalize the non-zero weights 0.25, 0.5, 0.25 as the value of ω(m).

[0078] The selection of the 3-point linear smoothing window function in the present invention can have a variety of optional windows, such as rectangular windows, Hanning windows, Hamming windows, Blackman windows, Gaussian windows and the like.

[0079] In this example, the Hamming window is used. Considering that the combined smoothing filter system should respond as quickly as possible to environmental speed changes, the window length should not be set too long, so three normalized non-zero weights 0.25, 0.5, and 0.25 are selected as ω(m ), a better smoothing effect is obtained.

[0080] The invention discloses a GM-PHD...

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Abstract

The invention discloses a motion parameter estimation combination smoothing filtering method based on GM-PHD, and solves the technical problem that the GM-PHD filtering method does not fully utilize the existing information. Implementations include using a GM-PHD filter; obtaining target state information; obtaining a preliminary target speed estimation value through difference; carrying out parallel processing according to the speed to obtain a smoothed speed estimation value; acquiring a smooth difference value of the speed estimation value; calculating a compensation value of the smoothed speed estimation value; obtaining combined smoothing filtering speed estimation; and updating the obtained target state information in real time, and completing real-time motion parameter estimation combined smoothing filtering of the target state information. According to the method, the speed estimation value is obtained through position difference, the speed estimation value is combined and smoothed, the target state speed information in the GM-PHD filter is updated, the speed estimation value closer to the reality is obtained, the method is used for detecting and tracking the target state in real time, and the tracking accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of information fusion, and mainly relates to moving object tracking and state estimation, specifically a GM-PHD-based motion parameter estimation combined smoothing filter method, which is used for state estimation, tracking, and positioning of moving objects. Background technique [0002] Information fusion is an interdisciplinary subject based on a variety of cognitive means to understand external things and their changing laws. At present, information fusion theory and technology have been widely used in many military and civilian fields such as navigation, remote sensing, target detection, tracking and identification, environmental monitoring, etc. [0003] In the 1970s, the target tracking theory attracted a lot of attention. Since then, a variety of classic target tracking methods have been proposed. Malher combined the point process theory with the expert system theory and proposed a random set RFS (R...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S13/72G01S7/41
CPCG01S13/726G01S7/415G01S7/418G01S7/414
Inventor 黄庆东李晓瑞王梅
Owner XIAN UNIV OF POSTS & TELECOMM
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