A New Algorithm for Synchronous Sensor Bias Estimation in Nonlinear Multi-Sensor Multi-Target Systems

摘要

Errors due to sensor bias are often present in sensor data and can reduce the tracking accuracy and stability of multi-sensor systems. The other practical problem is that the target dynamic is nonlinear in many situations special ly when target direction change or when it accelerate or decelerate. In all of the previous works on bias estimation in multi-sensor multi-target tracking problems a simple linear model for the target is considered but really targets have nonlinear dynamics.If dynamics of these targets are considered linear or linearized about operating point, some kinds of errors appear and thtus tracking accuracy may decrease. This problem is in conjunction with the prime purpose of bias estimation (increasing tracking accuracy by removing biases). This paper deals with these problems and presents a new algorithm for estimation of both constant and synchronous multi-sensor systems. We use the measurements from synchronous sensors into pseudomeasurements of the sensor biases. This algorithm is an Unscented Kalman filter based technique to estimate both the range and offset biases and is implemented recursively which is computationally efficient and provided real time estimation of synchronous sensor bias. The Simulation results show the Cramer-Rao Lower Bound (CRLB) is achievable. This means the proposed estimation algorithm is statistically efficient.