Bias estimation for angle-only sensors in distributed multi-target tracking systems
Sean R. Martin, Cameron K. Peterson · 2017
This paper describes a method of automatic sensor bias calculation for angle only sensor models in a target tracking scenario. It is assumed that separate Kalman filters are employed by each sensor and no measurements of known landmarks are available. Accurate bias estimation is achieved through the use of pseudo measurements of slant range from each sensor to the target and pseudo measurements of each sensor's bias. The covariance intersection (CI) algorithm is used to produce a pseudo measurement of slant range. This pseudo measurement of range allows pseudo measurements of sensor bias to be calculated based on each sensor's residuals and Kalman gains. Substantially improved tracking performance is demonstrated when estimating and accounting for constant biases on each sensor.