In the signal processing procedure,an inverse Kalman filter is used to restore the gravity anomaly signal and high frequency noises.

Then we studied the tracking problem of multiple maneuvering targets by using the combination of interacting multi-model and unscented Kalman filter algorithm.

Unscented Kalman filter was chosen as the relative navigation filter.

The unscented Kalman filter(UKF) is applied to the radar registration and a new algorithm for the multi-radar azimuth registration is presented.

The problem of target motion analysis based on bearing and Doppler measurements(BDO-TMA)is studied contrastively by means of unscented kalman filtering(UKF)and extended kalman filtering(EKF)algorithms.

Simulation study based on unscented kalman filtering for the impact of data rate in the bearing-only target motion analysis;

The unscented Kalman filter(UKF) model for the system is built up,and a numerical simulation is performed with the software Matlab.

We aim to eliminate these shortcomings as much as possible with a different and we believe better method by using the unscented Kalman filter(UKF) based SLAM(simultaneous localization and mapping) technique.

Its core consists of:(1) we design a variable structure sliding-mode speed controller and a variable structure sliding -mode current controller to replace the traditional speed PI controller and two current PI controllers;(2) we design the unscented Kalman filter(UKF) observer to estimate direct-axis current,quadrature-axis current,load torque,rotor position and speed simultaneously.

In view of the problem that determination of relative attitude between formation satellites is difficult,a modified Unscented Kalman Filter(UKF)was adopted to design the Filter of system in the paper.

The Standard Kalman Filter can not solve the nonlinear mathematic model,and the Extending Kalman Filter(EKF) will consume much more calculation,so Unscented Kalman Filter(UKF) is adopted in this system.