Kotaro Hirasawa

A target tracking algorithm with range rate under the color measurement environment

In this paper a new observation model is presented to improve the state estimation and prediction in a target tracking problem. Comparing with conventional approaches, the following are distinguished points of the approach. First, the measurement equation is set up in the polar coordinate and even combines the range rate measurement with the usual position measurements (i.e. range, azimuth, elevation angle, range rate). Second, the observation noise of sensor data is considered as a colored one, so new linear state and observation equation can be obtained by incorporating the noise vector into the state vector, which satisfy the requirement of Kalman filter. As a result, the accuracy of both the measurement and the prediction will be increased.

Use of pseudo measurement to real-time target tracking

In this paper, a pseudo measurement model is adopted to deal with the Kalman filter problem with color measurement noise. It is assumed that the measurement noise is color, and is described by an AR(n) model. The state equation is set up according to Singers model. The measurement equation is set up in polar coordinate and the range rate measurement is combined with ordinary data (i.e., there are four measurement data: range r, azimuth /spl theta//sub a/, elevation /spl theta//sub e/ and range rate r/spl dot/). By means of proper coordinate transformation, the matrix of Kalman gain and the matrix of filter error covariance can be decoupled. Thus the computing requirement of the algorithm is reduced and can be performed in real time.

Improving generalization ability of universal learning networks with superfluous parameters

The parameters in large scale neural networks can be divided into two classes. One class is necessary for a certain purpose while another class is not directly needed. The parameters in the latter are defined as superfluous parameters. How to use these superfluous parameters effectively is an interesting subject. It is studied how the generalization ability of dynamic systems can be improved by use of networks superfluous parameters. A calculation technique is proposed which uses second order derivatives of the criterion function with respect to superfluous parameters. So as to investigate the effectiveness of the proposed method, simulations of modeling a nonlinear robot dynamics system is studied. Simulation results show that the proposed method is useful for improving the generalization ability of neural networks, which may model nonlinear dynamic systems.