Moving target tracking method based on progressive unscented Kalman

An unscented Kalman and moving target technology, applied in the field of moving target tracking for nonlinear measurement, can solve problems such as poor robustness, improve tracking accuracy and robustness, ensure stability, and ensure computational complexity Effect

Active Publication Date: 2018-04-27
ZHEJIANG UNIV OF TECH
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  • Abstract
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Problems solved by technology

[0004] In order to overcome the lack of poor robustness of the existing moving target tracking methods to linearization errors, the present invention provides a moving ...

Method used

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  • Moving target tracking method based on progressive unscented Kalman
  • Moving target tracking method based on progressive unscented Kalman
  • Moving target tracking method based on progressive unscented Kalman

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Embodiment Construction

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] refer to Figure 1 ~ Figure 4 , a moving target tracking method based on asymptotically unscented Kalman, such as figure 1 As shown, the simulation tracks the target in a wireless sensor environment composed of three ranging sensors. In the wireless sensor environment, the moving target moves in a circle along a fixed trajectory, and the trajectory is as follows: figure 2 shown. The structure diagram of the multi-sensor fusion system of the target tracking system is as follows: image 3 shown. The state space model of the mobile robot target tracking system is shown in equations (1) and (2):

[0021]

[0022]

[0023] Among them, the target state vector x k and y k are the positions of the moving target on the x-axis and y-axis at time k, respectively, is the direction of the moving target at time k. r and V are the radius of the moving target...

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Abstract

The invention discloses a target tracking method based on progressive unscented Kalman. The method considers the truncation error problem in a measurement linearization process and puts forwards a progressive unscented Kalman filter algorithm to better handle linearization error and numerical calculation error increasing problems existing in the measurement linearization process. Firstly, a homotopy function for a system state is constructed according to a Bayes rule; through carrying out iteration on a measurement updating process, the current observation information is introduced progressively, and further, the posterior state of the system is obtained; and besides, an innovation judgment condition is introduced during the progressive process, the estimated errors of the system during the progressive process are ensured to be bounded, and the stability of the filter is ensured. Compared with the existing target tracking method, the method thoroughly considers linearization error influences, and on the premise of ensuring the calculation complexity, the target tracking precision and the robustness are improved.

Description

technical field [0001] The invention belongs to the field of moving target tracking, and relates to a moving target tracking method aimed at nonlinear measurement. Background technique [0002] Target tracking is a basic problem in military and civilian fields, and plays an important role in military defense, environmental monitoring, urban transportation, and home services. At the same time, with the development of microelectronics technology and communication technology, the application of wireless sensor network in the tracking and positioning of moving objects has attracted extensive attention from academia and industry. Wireless sensor networks use a large number of distributed nodes to sense moving targets cooperatively, and provide rich environmental information and accurate positioning services. [0003] In the wireless sensor network environment, the tracking of moving targets usually involves nonlinear filtering problems, such as the target system of multi-sensor ...

Claims

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Application Information

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IPC IPC(8): G01S13/66
CPCG01S13/66
Inventor 俞立郑婷婷杨旭升
Owner ZHEJIANG UNIV OF TECH
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