A new algorithm for general asynchronous sensor bias estimation in multisensor-multitarget systems

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 data reported by the sensors are usually not time-coincident or synchronous due to the different data. This paper deals with these problems and presents a new algorithm for estimation of both constant and dynamic biases in asynchronous multisensor systems. We use the measurements from asynchronous sensors into pseudo measurements of the sensor biases with additive noises that are zero-mean, white and with easily calculated covariances. This algorithm is a Kahnan 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 asynchronous sensor bias. The Simulation results show the Cramer-Rao Lower Bound (CRLB) is achievable. This means the proposed estimation algorithm is statistically efficient.