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Nonlinear asynchronous multi-sensor information merging method based on CKF (cubature Kalman filter)

A multi-sensor and sensor technology, applied in the direction of instruments, special data processing applications, electrical digital data processing, etc., can solve problems such as linearization error, filter precision drop, and "dimension disaster"

Inactive Publication Date: 2013-12-18
HARBIN ENG UNIV
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Problems solved by technology

Although EKF is easy to implement and widely used, when the system is strongly nonlinear, it is easy to generate linearization errors, resulting in a decrease in filter accuracy.
Although the filtering accuracy of UKF is higher than that of EKF, it is prone to "dimension disaster" when dealing with high-dimensional systems.

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  • Nonlinear asynchronous multi-sensor information merging method based on CKF (cubature Kalman filter)
  • Nonlinear asynchronous multi-sensor information merging method based on CKF (cubature Kalman filter)
  • Nonlinear asynchronous multi-sensor information merging method based on CKF (cubature Kalman filter)

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Embodiment Construction

[0033] The present invention will be described in detail below in conjunction with specific embodiments.

[0034] (1) Fully preheat the multi-sensor system and collect the measurement information of each sensor i (k i ), where i=1, 2, ..., N (N is the number of sensors), k i is the sampling time of the i-th sensor.

[0035] (2) Establish the nonlinear state equation of the nonlinear asynchronous multi-sensor system and the measurement equation of each sensor.

[0036] (3) Using the subdivision time slice method, the sampling interval of the fusion center is set as the highest precision time unit between sensors, that is, the maximum number that can be divisible by the sampling interval of each sensor at the same time is taken as the sampling interval of the fusion center.

[0037] Let the sampling time of any two sensors be T i ,T j (i, j=1, 2, ..., N, and i≠j), N is the total number of sensors. and have:

[0038]

[0039] Then the sampling interval of the fusion cen...

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Abstract

The invention discloses a nonlinear asynchronous multi-sensor information merging method based on a CKF (cubature Kalman filter). The respective state variables of each sensor are respectively estimated by using the CKF, then, a time breakdown method is adopted for setting the time interval of the information merging center as the highest precision time unit among all sensors, the estimation results of the asynchronous multi-sensor is judged and merged in the corresponding moment, and the more precise state variable estimation results can be obtained. The nonlinear asynchronous multi-sensor information merging method has the advantages that the utilization rate on the asynchronous multi-sensor information can be enhanced, the estimation precision of the state variable in the multi-sensor system is greatly improved, and the survival capability of the system is enhanced.

Description

technical field [0001] The invention relates to a multi-sensor information fusion technology in a nonlinear system, in particular to an information fusion method under the condition of multi-sensor asynchrony. Background technique [0002] Due to the wide application of multi-sensor information fusion technology in military, national defense and high-tech fields, the research on it has never stopped. In the multi-sensor information fusion theory, the redundant and complementary information in the measurement of multiple sensors can be used to effectively improve the system accuracy through multi-source information fusion technology. However, in the process of solving practical problems, it is often encountered that the sensors used have different sampling frequencies, and there are problems such as communication delays and inherent delays between sensors, which are collectively referred to as the asynchronous problem of information fusion. If the problem of asynchronous inf...

Claims

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Application Information

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IPC IPC(8): G06F17/50
Inventor 高伟张亚孙骞石惠文周广涛徐博鲍桂清史宏洋阮双双赵维珩
Owner HARBIN ENG UNIV
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