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Multi-frame nearest rapid data correlation method based on ripple transformation

A motion state and target technology, applied in computing, radio wave measurement system, image data processing, etc., can solve the problems of limited real-time use, large amount of calculation, etc., and achieve reduced computational complexity, small amount of calculation, and improved correlation effect Effect

Inactive Publication Date: 2007-06-13
SHANGHAI JIAOTONG UNIV
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Problems solved by technology

However, the soft logic data association method needs to use all the echoes in the tracking gate to update the target state, and perform a weighted combination of the state update results of each echo, so it has a large amount of calculation, which limits the application of this method in systems with high real-time requirements. use in

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  • Multi-frame nearest rapid data correlation method based on ripple transformation
  • Multi-frame nearest rapid data correlation method based on ripple transformation
  • Multi-frame nearest rapid data correlation method based on ripple transformation

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Embodiment Construction

[0018] In order to better understand the technical solution of the present invention, the implementation steps of the present invention will be further described below in conjunction with the examples.

[0019] In order to facilitate the analysis of the association effect of the multi-frame nearest neighbor data association method, the simulation experiment does not consider the target maneuvering situation for the time being. Assuming that the target is moving in a straight line at a uniform speed in a two-dimensional plane, the initial state is X(0)=[0m 200m / s 0m200m / s]'. The standard deviation of the motion process noise is 1m / s2, the standard deviation of the measurement noise on each axis is 150m, the motion time is 180 seconds, and the sampling period is 1s. The track is initiated in a clutter-free environment, and clutter is added at t=3ls. The clutter density is 1.0×10 -6 / m 2 , 2.0×10 -6 / m 2 , 3.0×10 -6 / m 2 , 4.0×10 -6 / m 2 , 5.0×10 -6 / m 2 , 6.0×10 -6 / ...

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Abstract

The present invention relates to a multiframe nearest neighbour fast data association method based on wavelet transformation. Said method utilizes the wavelet transformation introduction and multirate motion model application to make the nearest neighbour method be popularized into multiframe condition. Firstly, mapping measurement prediction in pattern space into measurement space by using wavelet inverse transformation method to form equence measurement prediction in measurement space, then using nearest neighbour method to make data association in sequene frame, compressing associated measurement data and mapping it into pattern space to form measurement data or multirate motion model, then using said multirate motion model to make state refreshment, mapping the refreshed result into measurement space by means of wavelet inverse transformation method to form state refreshment in measurement space.

Description

technical field [0001] The invention relates to a method for estimating the motion state of a target in a clutter environment, which solves the technical problem that the association effect of the nearest neighbor data association method is not ideal in a high clutter density environment, and can be widely used in robots, intelligent transportation, air traffic control and Aerospace, aviation, navigation and other fields. Background technique [0002] The nearest neighbor method is a data association method widely used in the actual target tracking system. This method has the characteristics of clear concept and small amount of calculation. However, the association effect of the nearest neighbor method is often unsatisfactory in the environment of high clutter density. [0003] In order to solve this problem, a variety of data association methods have been proposed in the tracking field, and the main methods can be classified into two categories: the probabilistic data asso...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/10G01S7/41
Inventor 敬忠良田宏伟胡士强
Owner SHANGHAI JIAOTONG UNIV