Research On Multi-Platform Multi-Sensor Registration Algorithm

In multi-platform multi-sensor systems, each sensor is independent to work, and the data ratio is not completely same. The pretreatments of measurements are implemented in local coordinate of each sensor. Information fusion is not a process to accumulate the data from multiple sensors. In order to achieve the aim of information fusion, the temporal and spatial calibrations are performed to transform the measurements into uniform information in a common coordinate. Sensor registration is a precondition of information fusion, and is a key technique to improve the system performance. The sensor calibration has been applied in weapon systems, remote sensing, navigation, intelligent traffic, robot, security examination, other martial and civil areas.In this dissertation, multi-platform multi-sensor registration algorithms are studied. The research focuses on solving the problems of homologous / dissimilar sensor registration, dynamic bias estimation, and asynchronous sensor registration. The main contributions of this dissertation are summarized as follows:1. Gaussian mean shift and the sufficient condition for convergence are presented. The process that mean shift is derived from kernel density estimator, the convergence of mean shift, and its smooth trajectory property are studied firstly. Gaussian mean shift is derived from maximum likelihood density function, then the convergent theorem of Gaussian mean shift is proved. The theorem extends the current theorem from a convex kernel to a piece-wise convex and concave kernel, which widens the applied areas of Gaussian mean shift and provides the theoretic base for the latter chapters. 2. Modified maximum likelihood registration algorithm based on information fusion is proposed. Aiming at the space registration in two-observer passive tracking system, the reason that target is not observable in the blind spot is analyzed when there are registration biases in the system. The principle of redundant information compensation, based on the background of projects and information fusion, is proposed. The range information in the blind spot is updated by using the redundant information, and the registration model for bias compensation is rebuilt. The target location updated in the blind spot is used to estimate the sensor biases. The modified maximum likelihood registration algorithm achieves the aims of bias compensation and target location in multi-platform passive sensors system, which improves the estimation precision and the tracking performance of passive tracking system.3. Mean shift registration algorithm for dissimilar sensors is presented. The target state is calculated by maximum likelihood estimate, then mean shift is used to estimate the sensor biases. The computation complexity of the proposed algorithm is also analyzed. The algorithm is applied to estimate the biases of dissimilar sensors. The simulation results show that the new method improves the bias estimation precision, and furthermore, it reduces the complex computation.4. Gaussian mean shift registration algorithm for dynamic bias estimate is proposed. Based on the theoretic study on mean shift in the second chapter, Gaussian mean shift is derived with the assumption that the measurement noises are Gaussian. The basic idea of the proposed algorithm is that Gaussian mean shift combined with extended Kalman filter is implemented to estimate the dynamic biases. The algorithm complexity is also analyzed, and the computation of the proposed algorithm is less than other methods. The algorithm has better estimation performance than other methods, especially for that the number of targets is several.5. Asynchronous sensors registration algorithm is proposed. Firstly, the measurements from asynchronous sensors are expressed by the true target state in same time index. Then the linear function of the measurements is constructed to cancel the target state, and the pseudomeasurement equation of sensor biases is obtained. Thirdly, the equivalent bias state equation is derived from the bias dynamic equation. Finally, Kalman filter is used to estimate the biases of asynchronous sensors. The algorithm is effective to calibrate the asynchronous sensors without the limit of the data ratio and the number of sensors, and the autocorrelated measurement noises are not introduced in the pseudomeasurement equation. The proposed algorithm reduces the computation and improves the estimation precision.