The contents of sensor registration in the multi-sensor data fusion system are introduced, and some existing methods are analyzed. Then, one approach to sensor registration based on BP neural network is proposed. Here...The contents of sensor registration in the multi-sensor data fusion system are introduced, and some existing methods are analyzed. Then, one approach to sensor registration based on BP neural network is proposed. Here the measurements from radar are transformed from the polar coordinate system to the Cartesian coordinate through a BP neural network. With this approach, the systematic errors are removed as well as the coordinate is transformed. The efficiency of this method is demonstrated by simulation, and the result show that this approach could remove the systematic errors effectively and the DAR are closer to real position than DBR.展开更多
The mean shift registration(MSR) algorithm is proposed to accurately estimate the biases for multiple dissimilar sensors.The new algorithm is a batch optimization procedure.The maximum likelihood estimator is used to ...The mean shift registration(MSR) algorithm is proposed to accurately estimate the biases for multiple dissimilar sensors.The new algorithm is a batch optimization procedure.The maximum likelihood estimator is used to estimate the target states,and then the mean shift algorithm is implemented to estimate the sensor biases.Monte Carlo simulations show that the MSR algorithm has significant improvement in performance with reducing the standard deviation and mean of sensor biased estimation error compared with the maximum likelihood registration algorithm.The quantitative analysis and the qualitative analysis show that the MSR algorithm has less computation than the maximum likelihood registration method.展开更多
Multi-target detection,tracking and classification are important problems in aerospace applications,such as reconnaissance,airborne and spaceborne sensing.These problems are correlated but are difficult to be solved s...Multi-target detection,tracking and classification are important problems in aerospace applications,such as reconnaissance,airborne and spaceborne sensing.These problems are correlated but are difficult to be solved simultaneously,especially for systems with multiple sensors.This paper summarized the existing work for multi-target joint detection,tracking and classification based on labeled random finite set.Furthermore,a new algorithm is proposed for multi-sensor multi-target joint detection,tracking and classification problem.A conditional multi-sensor multi-target state estimator is derived,and the optimal solution is then obtained based on the minimum Bayes risk criterion.The numerical simulations are performed,and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set.It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection,tracking and classification.展开更多
文摘The contents of sensor registration in the multi-sensor data fusion system are introduced, and some existing methods are analyzed. Then, one approach to sensor registration based on BP neural network is proposed. Here the measurements from radar are transformed from the polar coordinate system to the Cartesian coordinate through a BP neural network. With this approach, the systematic errors are removed as well as the coordinate is transformed. The efficiency of this method is demonstrated by simulation, and the result show that this approach could remove the systematic errors effectively and the DAR are closer to real position than DBR.
基金the National Basic Research Program ofChina(No.A1420060161)the National Natural ScienceFoundation of China(No.60674107)+1 种基金the Natural ScienceFoundation of Hebei Province(No.F2006000343)the National Aviation Cooperation Research Foundationof China(No.10577012)
文摘The mean shift registration(MSR) algorithm is proposed to accurately estimate the biases for multiple dissimilar sensors.The new algorithm is a batch optimization procedure.The maximum likelihood estimator is used to estimate the target states,and then the mean shift algorithm is implemented to estimate the sensor biases.Monte Carlo simulations show that the MSR algorithm has significant improvement in performance with reducing the standard deviation and mean of sensor biased estimation error compared with the maximum likelihood registration algorithm.The quantitative analysis and the qualitative analysis show that the MSR algorithm has less computation than the maximum likelihood registration method.
基金supported by the National Natural Science Foundation of China(Grant nos.61673262 and 61175028)Shanghai key project of basic research(Grant no.16JC1401100).
文摘Multi-target detection,tracking and classification are important problems in aerospace applications,such as reconnaissance,airborne and spaceborne sensing.These problems are correlated but are difficult to be solved simultaneously,especially for systems with multiple sensors.This paper summarized the existing work for multi-target joint detection,tracking and classification based on labeled random finite set.Furthermore,a new algorithm is proposed for multi-sensor multi-target joint detection,tracking and classification problem.A conditional multi-sensor multi-target state estimator is derived,and the optimal solution is then obtained based on the minimum Bayes risk criterion.The numerical simulations are performed,and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set.It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection,tracking and classification.
