Fusion estimation algorithm of selectively measured data of multiple sensors

A measurement data, multi-sensor technology, applied in the field of multi-sensor selective measurement data fusion estimation algorithm, can solve problems such as reducing the data transmission volume of nodes, and achieve good tracking estimation effect, low data transmission volume, and good tracking effect. Effect

Active Publication Date: 2018-10-09
ZHEJIANG UNIV
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Problems solved by technology

This algorithm only selects a more valuable part of the original measurement data and submits it to the fusion center.

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  • Fusion estimation algorithm of selectively measured data of multiple sensors
  • Fusion estimation algorithm of selectively measured data of multiple sensors
  • Fusion estimation algorithm of selectively measured data of multiple sensors

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[0028] The implementation of the present invention will be described in detail below in conjunction with the accompanying drawings, and specific operation modes and implementation steps will be given. The present invention can also be realized through other different specific examples.

[0029] The examples here adopt computer simulation to generate training data and test data, which are respectively applied to the offline algorithm and the online algorithm of the algorithm of the present invention. Here it is assumed that there are n homogeneous sensors with the same observation field and registration has been completed, and each sensor obtains n x ×m y The image information Z k . It is assumed that the measurement information comes from two parts: the target signal and the measurement noise. Generate the motion state of the target at each moment through computer simulation, and then generate the measurement data Z of each sensor according to the actual state of the target...

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Abstract

The invention belongs to the field of fusion estimation of multiple sensors, and discloses a fusion estimation algorithm of selectively measured data of multiple sensors. The current conventional datafusion algorithm has the defect of large data transmission quantity when being used in target tracking estimation. The invention solves the above problem. According to the fusion estimation algorithm, some valuable data in the measured data of each sensor can be selected and submitted to a fusion center, so that the data use efficiency is improved, and the data transmitted between nodes is greatly reduced while a certain target estimation precision is achieved. The algorithm combines the artificial neural network and the genetic algorithm to select measured data, so that a multi-sensor tracking system reaches a good target tracking estimation effect through genetic algorithm optimization when a total data bandwidth is fixed. The algorithm has practical significance for solving an actual target tracking estimation problem.

Description

technical field [0001] The invention belongs to the field of multi-sensor fusion estimation, and relates to a multi-sensor selective measurement data fusion estimation algorithm. Background technique [0002] The multi-sensor fusion estimation problem is the combination of traditional target state estimation and data fusion theory, and target tracking estimation based on the measurement results of multiple sensors. For target tracking in the underwater environment, since the measurement accuracy of commonly used sensors such as sonar is not high, adding multi-sensor data fusion technology can effectively improve the target estimation accuracy. In addition, the communication between multiple sensor nodes in the underwater environment is often limited. Therefore, for the target tracking problem, it is studied how to fuse multi-sensor measurement data under the condition that the data transmission bandwidth between sensor nodes is limited. To ensure a better tracking effect is...

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Application Information

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IPC IPC(8): H04W4/029H04W4/38
CPCH04W4/029H04W4/38
Inventor 刘妹琴黄志成张森林樊臻何衍
Owner ZHEJIANG UNIV
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