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. In order to effectively use each sensor data, these bias errors must be removed from the sensor data before the fusion process takes place. 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 rates and deriving a common reference time for bias estimation is often difficult. This paper deals with these problems and presents a new algorithm for multi-sensor bias estimation in asynchronous sensors. We use the measurements from asynchronous sensors into pseudomeasurements of the sensor biases with additive noises that are zero-mean, white and with easily calculated covariances. This algorithm is a 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 asynchronous sensor bias. The simulation results show the Cramer-Rao lower bound (CRLB) is achievable. This means the proposed estimation algorithm is statistically efficient