Tracking a maneuvering target using sensors of variable quality

In an environment subject to sudden change, the accuracy of tracking and prediction is strongly influenced both by the sensor architecture and by the quality of the sensors. An image-enhanced algorithm is presented for both path following and covariance estimation in applications where the sensors are subject to sudden and unpredictable variation in quality. For an illustrative trajectory, the performance of the algorithm is contrasted with an extended Kalman filter (EKF) and an image-enhanced algorithm based upon the nominal sensors.