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Research on Dissimilar Sensor Data Fusion Methods

Author: LuoZhiBin
Tutor: LiuXianSheng
School: Henan University
Course: Control Theory and Control Engineering
Keywords: Heterogeneous Sensor Innovation Displacement prediction covariance
CLC: TP20
Type: Master's thesis
Year: 2009
Downloads: 181
Quote: 1
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Abstract


Heterogeneous sensor data fusion technology is the use of different types of sensors to detect the target in order to obtain a wide range of information to be more accurate than a single sensor target state estimation. In heterogeneous sensor target tracking system tracking precision, this paper proposes some solutions. The paper's main work is as follows: 1. Elaborated the concept of data fusion, the basic principles of its development. Then the state under different criteria the estimated theoretical basis. In the actual target tracking system, due to various factors such as environmental, mechanical failure, such that the statistical characteristics of the sensors actual statistical characteristics of the measured with the a priori set larger difference, if the variance of measurements based upon the a priori statistical characteristics, will inevitably cause the state estimates lead to considerable error. Orthogonal characteristics of the new interest rate on the variance of the measurement adaptively adjusted to meet the new interest rate orthogonal characteristics to obtain the optimal gain, and thus to amend the state prediction estimates, the estimation error variance is the minimum. Simulation results show that the algorithm is able to overcome the adverse impact of outliers predicted optimal state estimates, target tracking accuracy. 3. Sensor undetected phenomenon may occur for the target tracking system, the trust value of each sensor measurement based on the predictive value of the multi-sensor fusion measurement calculated similarity between any two sensor measurement values ??using probability source combining theory and the theory of non-negative matrix feature vectors for each of the sensors the measured values ??the integrated similarity with other sensors, in order to determine the weight of each sensor. Fusion weights, the algorithm in real time to adjust the measured values ??of the respective sensors to effectively suppress the adverse effects of the undetected sensor measurement value for measurement fusion value, increase the tracking accuracy of the system. \The algorithm uses the extended Kalman filter Heterogeneous Sensor fusion; while avoiding the adverse effects of acceleration preset limits on state estimation. Simulation results show that the proposed method improves the accuracy of maneuvering target tracking. Preset mobile heterogeneous sensor measurement variance in the actual environment impact on the precision of the filter, this paper proposes a mobile platform in real time based on actual innovation covariance estimated fixed radar and mobile infrared measurement noise variance heterogeneity sensor fusion algorithm. The algorithm is based on radar and infrared on the distance of the target, the azimuth and elevation estimates and estimate covariance asynchronous fusion, and then extended Kalman filter target state estimate. The simulation results show that the improved maneuvering target tracking accuracy.

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