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Dual radar modified sequential Gaussian mixture probability hypothesis density filtering method

A technology of Gaussian mixture probability and hypothetical density, which is applied in radio wave measurement system, radio wave reflection/reradiation, utilization of reradiation, etc., can solve problems such as target loss

Active Publication Date: 2018-11-02
THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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Problems solved by technology

[0007] In order to solve the problem of target loss caused by sequential GM-PHD filtering when the target is located in a non-common measurement area in a dual radar system, the present invention provides a method for keeping targets only measured by a single radar

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  • Dual radar modified sequential Gaussian mixture probability hypothesis density filtering method
  • Dual radar modified sequential Gaussian mixture probability hypothesis density filtering method
  • Dual radar modified sequential Gaussian mixture probability hypothesis density filtering method

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Embodiment Construction

[0075] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0076] The corrected sequential GM-PHD filtering in the dual radar system of the present invention can give the Gaussian term as the output of the filter, and after the filter prediction, updating, cutting and merging and maintaining the fusion, the filter outputs the target state. In this embodiment, the system equation is:

[0077] x k =F k *X k-1 +w k-1 (Formula 1)

[0078] in Represents the target state vector at time k, each component corresponds to the position and velocity of the target, and the system noise w k ~N(0,Q k ), the state transition matrix and the noise variance matrix are respectively

[0079]

[0080] The measurement equations are

[0081]

[0082] in measurement noise

[0083] Such as figure 1 As shown, the present invention mainly includes: an initialization module, a radar 1 Gaussian component prediction ...

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Abstract

The invention discloses a dual radar modified sequential Gaussian mixture probability hypothesis density (GM-PHD) filtering method. The traditional dual radar GM-PHD is only suitable for the case thata measurement target is located in a common measurement region of two radars, when the target is not in the common measurement region, a target loss problem is prone to occur. The dual radar modifiedsequential GM-PHD filtering method is based on a finite statistical theory, performs prediction, updating, trimming fusion, maintaining fusion and target state extraction on Gaussian components corresponding to each radar measured values, realizes multi-target tracking, cannot loss a target in a non-common measurement region, and expands the application range of sequential GM- PHD. Compared to traditional methods, the computational complexity of the dual radar modified sequential GM-PHD filtering method does not change much.

Description

technical field [0001] The invention relates to the technical field of multi-sensor multi-target tracking, in particular to a dual-radar correction sequential Gaussian mixture probability hypothesis density filtering method. Background technique [0002] In the 1950s, with the complexity of the radar application environment, it was required that the radar can track multiple targets at the same time, and the concept of multi-target tracking was proposed. After decades of research, the theory of multi-target tracking technology has developed rapidly, and more and more excellent algorithms have been proposed and widely used in various fields of military and civil affairs, such as military intelligence collection, enemy early warning, industrial process monitoring, air traffic, etc. control etc. [0003] The commonly used multi-target tracking algorithms in engineering mainly include Nearest Neighbor (NN), Joint Probabilistic Data Association (JPDA) and Multiple Hypothesis Trac...

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Application Information

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IPC IPC(8): G01S13/66G01S7/02
CPCG01S7/02G01S13/66
Inventor 樊小龙许建黄放明黄志良孙裔申卜卿沈海平王汉斌
Owner THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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