Dynamic deviation estimation method based on gauss average value mobile registration

A mean value shifting and dynamic deviation technology, applied in the field of target tracking, can solve problems such as the decrease of estimation accuracy

Inactive Publication Date: 2008-07-16
SHANGHAI JIAO TONG UNIV
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However, this method is based on multiple frames and multiple targets, and as the numbe

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  • Dynamic deviation estimation method based on gauss average value mobile registration
  • Dynamic deviation estimation method based on gauss average value mobile registration
  • Dynamic deviation estimation method based on gauss average value mobile registration

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Embodiment Construction

[0041] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following the described embodiment.

[0042] As shown in Figure 2, the coordinates of the two sensors in this embodiment are: (0,0), (0,50), unit: km, the initial state of the target is X 0 =[30000m, 40m / s, 30000m, 40m / s]', the measurement and prediction covariance matrix is ​​P(0|0)=diag[(1000m) 2 , (50m / s) 2 , (1000m) 2 , (50m / s) 2 ]. In this embodiment, both the sensor and the target (radiation source) are in the same plane XY, and each sensor can measure the distance and azimuth of the target. The initial value of the dynamic deviation of the two sensors is b 1 =b 2 =[50m, 8mrad]', the standard...

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Abstract

The invention discloses a dynamic deviation estimation method based on the Gaussian average value movement registration and comprises the following steps: step one, an objective state is measured by a sensor in a multi-platform system; step two, a Gaussian density estimator of the method of Gaussian average value movement is constructed; step three, the objective state is estimated by an extended Kalman filter; step four, an estimation value of a measurement value of the objective state is computed; step five, an estimation value of the dynamic deviation of the sensor is computed; step six, the estimation value of the dynamic deviation is calibrated; step seven, the convergence judgment of the estimation value of the dynamic deviation is calibrated. By only using the measurement data of one target, the method of the invention quickly and accurately estimates the dynamic deviation of the sensor. The method is simple, effective and easy to implement, and can be widely used for various fields such as robots, intelligent transportation, aerial transportation supervision, space flights, aviation and sailing, etc.

Description

technical field [0001] The invention relates to a method in the technical field of target tracking, in particular to a dynamic deviation estimation method based on Gaussian mean moving registration. Background technique [0002] In a multi-sensor target tracking system, information fusion technology can improve the ability to detect, identify and track targets. At the same time, the use of multiple sensors also brings some problems, such as the registration of sensors. The combination of unregistered sensors may lead to worse system performance than that of a single sensor, deteriorating tracking performance, and even generating false targets. Therefore, before the fusion of multi-sensor measurement data, it is necessary to register the sensors. [0003] The deviation of the sensor is usually fixed, or changes very slowly, and the registration of the fixed deviation can be divided into two cases: offline registration and online registration. Offline registration methods m...

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Application Information

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IPC IPC(8): G01S7/00G06F17/17
Inventor 敬忠良祁永庆胡士强赵海涛
Owner SHANGHAI JIAO TONG UNIV
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