Application Research Of Prediction Filtering Technology On Optoelectronic Target Tracking

Combined control is an important method for acquiring fast moving targets and improving tracking precision by prediction filtering parameters of target. The no difference grade of control system is increased by it. The lag errors of velocity and acceleration can also be removed. Combined control provides greatly superior performance than conventional control system. Furthermore, it can reduce some disturbances.Combined control must obtain the signals of target's velocity and acceleration by prediction filtering technology. So target's parameter estimation is important for tracking system.Usually a Cartesian coordinate frame is well suited to describe the target dynamics, in this description, the dynamics equations are often linear and uncoupled .Because measurements of the target location are expressed as nonlinear equations in Cartesian coordinates, the tracking problem is connected with nonlinear estimation. In this dissertation, applications of nonlinear filtering algorithms to target tracking are studied.At first, linear dynamic models are proposed based on white noise and colour noise. Observation models are built in Cartesian coordinates and Polar coordinates respectively.Extended kalman filter (EKF) is simple and practical. But the EKF has the disadvantage of model linearization error. This filer is always divergence. The author analyzed the relationship between tracking accuracy and linearization error and presents an algorithm that can estimate the statistic properties of measurements noise, it also compensates for the errors of model linearization to overcome this disadvantage. It was founded nonlinearities of the range measurements influence on the tracking accuracy. But nonlinearities of two angle measurements do not appear significant.A new filtering algorithm for improving tracking performance is developed in mixed coordinate system which includes correct evaluation of the measurement error covariance. The target dynamics in inertial rectangular coordinate is modeled. Theestimation of target's trajectory and calculation of gain are performed in it. While residual is calculated in polar coordinates. The compensation of linearization error is included in MEKF. The range measurement nonlinearity is treated as additional measurement error. The measurement error variance for the range is calculated adaptively at each time. The algorithm reduces nonlinearity effect. So the performance of this filter is better than EKF.In order to reduce computational complexity, it is preferable to sequentially process scalar component of the measurement vector instead of processing it as a single data. The advantage of sequential processing is that instead of requiring the matrix inversion, which leads to considerable computational savings. Furthermore, in the case of nonlinear measurements, sequential processing of the measurement vector in particular order may produce additional benefit of improving estimation accuracy. .Simulation results show that the proposed method offers superior performance.An improved adaptive filtering algorithm based on current statistical model is presented by using the relationship between maneuvering acceleration and its covariance. The acceleration of a maneuvering target is considered as a time-correlation random process with non-zero mean values. The prediction of acceleration is as mean value of maneuvering acceleration and its covariance varies adaptively. The simulation results show that adaptive algorithm can estimate the position, velocity and acceleration of target and require less computation, no matter the target is maneuvering at any way.In the end, the algorithms discussed in this dissertation are tested in experiments for different targets and input noises. The results verify the proposed algorithms.