A study of bias error estimation method by KGBE

Data fusion uses observations from networked multiple sensors and generates an integrated track. It achieves wide surveillance area and high accuracy, by minimizing error covariance matrix. In ideal environment, a fundamental assumption is that sensor biases are zero. However, the bias errors are not zero in real environment. As a result, the accuracy of integrated tracks deteriorate, even if the all sensors observe the same target. In this paper, we propose a new bias estimation algorithm is based on kalman filter bias estimator with grid search method. It is called the KGBE method (Kalman filter with Grid search Bias Estimator). As the result, we confirmed that the KGBE achieves higher accuracy than conventional algorithms.