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Multiple-target Bernoulli distribution type fusion method based on weighting negative first-order RD (Renyi Divergence)

A fusion method and multi-objective technology, applied in the field of multi-objective Bernoulli distributed fusion technology and multi-object tracking technology, can solve the problems such as the inability to realize multi-objective Bernoulli filter distributed fusion, and the non-compliance of mixed exponential density.

Active Publication Date: 2014-11-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, after derivation, we found that the mixed exponential density after the fusion of two multi-objective Bernoulli distributions does not obey the multi-objective Bernoulli distribution, so the distributed fusion of multi-objective Bernoulli filters cannot be realized directly by using the CI fusion criterion

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  • Multiple-target Bernoulli distribution type fusion method based on weighting negative first-order RD (Renyi Divergence)
  • Multiple-target Bernoulli distribution type fusion method based on weighting negative first-order RD (Renyi Divergence)
  • Multiple-target Bernoulli distribution type fusion method based on weighting negative first-order RD (Renyi Divergence)

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[0034] The present invention mainly adopts the method of simulation experiment to verify, and all steps and conclusions are verified correctly on Matlab2010. The present invention will be further described in detail with regard to specific embodiments below.

[0035] Step 1: Initialize system parameters

[0036] The initialization system parameters include: radar monitoring range [0,1000m]×[0,1000m], radar range resolution △r=1m, radar scanning period T=1s, total number of observation frames K=50; the number of sensors is 3.

[0037] Step 2: k=0, initialize the born multi-target Bernoulli parameters of sensors 1~S as:

[0038] { ( r Γ ( l , i ) , p Γ ...

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Abstract

The invention discloses a multiple-target Bernoulli distribution type fusion method based on weighting negative first-order RD (Renyi Divergence), and belongs to the field of radar signal processing. The same frame of posteriori multiple-target Bernoulli parameters of a plurality of sensors is transmitted to a fusion center, and the posteriori multiple-target Bernoulli parameters of all sensors are fused in the fusion center. The fusion method comprises the following steps: decomposing the multiple-target Bernoulli parameter into a plurality of Bernoulli components; constructing a cost function based on a weighting negative first-order RD sum to find an optimal matching scheme of the Bernoulli parameters of different sensors; fusing a plurality of matched Bernoulli components in parallel by adopting a CI (Covariance Intersection) fusion rule; approximately realizing the fusion of the multiple-target Bernoulli parameters of different sensors; selecting a plurality of most likely targets from a fusion result; and returning the fused result to each sensor. Therefore, the invention has the effects of being high in tracking precision, low in false alarm rate, strong in robustness, high in reliability, small in communication cost and strong in antijamming capability.

Description

technical field [0001] The invention belongs to the field of radar signal processing, in particular to multi-target tracking technology and multi-target Bernoulli distributed fusion technology based on negative first-order weighted RD sum. Background technique [0002] Due to the stealth design characteristics (or electromagnetic interference environment) of modern targets, the single-view observation of a single-node radar system cannot meet the needs of effective detection of targets, prompting domestic and foreign engineers and researchers to use multi-node radar networking systems to solve stealth target detection difficult question. The multi-node (sensor) radar system obtains the measurement information of the target at different node positions through multi-view observation. In order to effectively use the information provided by each sensor to track the target, it is necessary to design a high-reliability, low-calculation, and easy-to-implement Multi-sensor data fus...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 易伟李溯琪刘睿苟清松崔国龙孔令讲杨晓波
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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