The Key Technology Research Of Multi-Sensors Information Fusion

Multi-sensor information fusion technology is the important research project of nation, in recent years, many countries around the world have invested lots of manpower and material in order to researching the multi-source information fusion theory and its application. At present, not only the new technology is mainly used in military field. but also its civilian prospects are very widespread. Thus, we can know the importance of this technology. This article mainly researches some key parts of the multi-sensor information fusion technology, the main work of the paper include outliers elimination, data association, data decision in the data preprocessing technology and the applications of multi-sensor information fusion technology.Firstly, the data preprocessing technology is the premise of improving the fusion system’s accuracy. Due to some factors such as noise interference, the data received by the sensor does not have high accuracy, even some data has serious deviation.Concerning this issue, the paper proposes a kind of outliers detection method based on the changes of innovations. The method detects outliers according to the changes of innovations and utilizes Kalman filter to obtain innovations in order to judge timely whether the measurement is outlier. Meanwhile, the paper solves the problem of outliers through compensating the data points for outliers and the basis of compensation is calculating measurement weight through the weighing function. As a result, the accuracy of the data preprocessing part gets improved. The simulation results show this algorithm is effective.Secondly, This paper focuses on the data association technology. Aiming at the problem of single target tracking owns low accuracy in clutter, the paper proposes the probabilistic data association algorithm based on evidence theory. The algorithm utilizes measurements of sensor and state estimates calculated by probabilistic data association algorithm, then fuses the information with improved evidence theory synthesis algorithm. As a result, the target tracking accuracy gets improved. For multi-target tracking problem in clutter,the paper maks further research on the basic of single target tracking, proposes the joint probabilistic data association algorithm based on evidence theory. The classical data association algorithm for target tracking has poor accuracy at the situation of multi-target and clutter environment. The problem gets solved by the algorithm of this paper.Besides, on the basic of improving the multi-target tracking accuracy, in order to reducing the amount of calculation and improving the real-time of target tracking, this paper proposes the improved data association algorithm based on the maximum fuzzy entropy.The algorithm utilizes the maximum fuzzy entropy to renewedly distribute measurements that are in the tracking gate. Feasibility matrix grows by geometric multiples as the number of goals increases. The proposed algorithm can solve the above problem.At the same time, the algorithm reduces the amount of calculation.The simulation results show the superiority of the proposed algorithm.Thirdly, in data fusion technology, the related algorithms about data decision are limited by priori knowledge and the problem of lacking the ability of dealing with uncertainty information.In this paper, according to the conflict, an improved algorithm based on DS evidence theory is proposed. By analyzing the consistency of the evidence and the importance of determining focus information, the paper solves the existing problem about one-veto veto and the overlarge evidence conflict.It also reduces the uncertainty of judgment result.For the decision problems which need considering the multi-sensor confidence, the paper proposes the improved DS evidence theory algorithm based on confidence of sensors.The method gets the confidence of sensors with grey correlation, then judges the target with the focuss information of sensors and confidence of sensors.Theoretical analysis and experimental simulation indicates that algorithm has a good judgment effect.Finally, in the applications of multi-sensor information fusion, for the information fusion of similar sensors, the paper proposes an improved multi-sensor Kalman filter fusion algorithm. The improved algorithm is based on DS evidence theory in the distribution of weights. Through the algorithm, we can get more accurate fusion information according to processing the information received by sensors. And the paper makes simulations about heterogeneous sensor information fusion systems of radar and infrared, the simulation results show that the method gets higher precision after fusion.