Improved Different Dimensional Sensors Combined Space Registration Algorithm

Yuming Bo,Zhimin Chen,Mingfeng Yin,Tianxiong Wang

doi:10.1155/2015/289825

Yuming Bo, Zhimin Chen + Show 2 more

Open Access

https://doi.org/10.1155/2015/289825

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Abstract

To address the problem of deviation and registration of 3D radar and infrared sensor, this paper presents and improves a method based on the state value and space deviation of federated filtering of unscented Kalman filter and standard Kalman filter, which conduces to real time registering of system deviation of radar and IF sensors. In the method presented here, a covariance matching criteria-based approach was employed for judgment of filtering divergent trend, while self-adaptive attenuation factor was introduced for correction of the predicted error covariance so as to inhibit the divergent phenomenon. The experiment results indicated that the method presented here conduces to improvement of the precision and speed of space registration, showing practical value in deviation registration of 3D radars and infrared sensors.

Highlights

To address the problem of deviation and registration of 3D radar and infrared sensor, this paper presents and improves a method based on the state value and space deviation of federated filtering of unscented Kalman filter and standard Kalman filter, which conduces to real time registering of system deviation of radar and IF sensors
Heterogeneous multisensor system overcomes the defect of single sensor that can provide unilateral information of the tracking object image [1, 2]
To address the issues stated above, this paper employed an improved federated filter based on unscented and standard Kalman filters, the former wherein was used for accuracy estimation relying on its self-adaptive divergence-inhibition ability, the later wherein was used for deviation vector estimation depending on its high speed, thereby actualizing accurate registration of space deviation

Summary

Introduction

Heterogeneous multisensor system overcomes the defect of single sensor that can provide unilateral information of the tracking object image [1, 2]. Employment of heterogeneous multisensor system in measurement requires calibration of the data information of such sensors. Due to the fact that the transferred data form, narration, and description of the environment by every sensor vary [2,3,4], if the space deviation undergoes information fusion directly without registration, it may result in obvious tracking error or even the presence of multiple false points [5,6,7,8]. Z (tk) = h (X (tk)) + V (tk) , Mathematical Problems in Engineering where in Xk+1 is the state vector of the system, F is the state transition matrix, Wk is system process noise with Q(tk) as variance, and Vk is measurement noise with R(tk) as variance. After sigma point sampling of the system state vector, the process noise and measurement noise can be separated.

Improved Unscented Kalman Filter

Establishment of Improved Heterogeneous Sensor Deviation Registration Model

Heterogeneous Sensor Deviation Registration Steps

Simulation Experiment

Conclusions