Precise tracking method based on nerve network for moving target

A neural network and maneuvering target technology, applied in the field of precise tracking of maneuvering targets, to achieve the effect of improving tracking accuracy, improving fault tolerance performance, effective and simple maneuvering and non-maneuvering target adaptive tracking methods

Inactive Publication Date: 2002-12-04
SHANGHAI JIAO TONG UNIV
View PDF0 Cites 33 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In order to overcome this difficulty, the interactive multi-model adaptive tracking method (Blom, H.A.P., and Bar-Shalom, Y., The interacting multiple model algorithms for systems with Markovian switchingcoefficients. IEEE Tran. Automatic Control, 19

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Precise tracking method based on nerve network for moving target
  • Precise tracking method based on nerve network for moving target
  • Precise tracking method based on nerve network for moving target

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0017] Embodiment 1, state feature extraction

[0018] In order to overcome the influence of noise on the feature quantity, for the state information fusion algorithm, F 1 and F 2 The error norm estimated by the filter at the current moment is used as the input of the neural network NN, and its components are In the formula and for the filter F 1 and F 2 Based on the echo at time k (filtering the position, velocity and acceleration of the target t. 2. Network training

[0019] The selected feature samples are shown in Table 1.

[0020] input

(0，0，0)

(0，0，1)

(0，1，0)

(0，1，1)

(1，0，0)

(1，0，1)

(1，1，0)

(1，1，1)

net 0

0.10

0.75

0.60

0.85

0.40

0.75

0.60

0.95

[0021] Because the three-layer BP network has the learning ability to approximate any nonlinear function, a BP network with one hidden layer is selected, in which the...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invented method for accurate tracking the maneuvering target based on neural net adopts double parallel structure being composed of two filters. The difference of states between the parallel double filters is picked-up as the character vector of the state of target to be estimate, which is input to the neutral net system. Based on the character vector input, the neural net system, which has been trained off-line before tracking estimation, outputs the predicted value of the acceleration variance of the target to be estimated at present time. Adaptive filtering the said predicted value based on the current statistical model obtains the fast and accurate tracking the target to be estimate. The invented method possesses favorable adaptive tracking ability.

Description

Technical field: [0001] The invention relates to a precise tracking method of maneuvering targets based on neural network (NN), which is used for high-precision positioning and prediction of maneuvering targets in systems such as intelligent transportation, robots, avionics, anti-ballistic missile defense and precision guidance, and belongs to intelligent information processing technology field. Background technique: [0002] For the precise tracking of maneuvering single targets, many maneuvering target models and adaptive tracking algorithms have been proposed at home and abroad since the 1980s, including differential polynomial models, uniform velocity and uniform acceleration models, time-dependent models, semi-Markov models, and maneuvering target models. "Current" statistical model (Zhou, H. and K.S.P. Kumar. A current statistical model and adaptive algorithm for estimating maneuvering targets. ALAA Journal of Guidance, Control, and Dynamics, 1984, 7(5), 596-602.). Th...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01S7/292G01S7/41G01S13/66
Inventor 敬忠良李建勋
Owner SHANGHAI JIAO TONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap