New Tracking Filter Algorithm Using Input Parameter Estimation

A new method for the design of tracking filters for maneuvering targets, based on kinematic models and input signals estimation, is developed. The input signal\u27s level, u is considered a continuous variable and consequently the input estimation problem is posed as a purely parameter estimation problem. Moreover, the application of the new tracking filter algorithm is not contingent on distinguishing maneuvering and non-maneuvering targets, and does not require the detection of maneuver onset. The filter will automatically detect the onset of a maneuver. Furthermore, an estimate of the target\u27s acceleration is also obtained with reasonable precision. This opens the door to the employment of advanced Augmented Proportional Navigation Missile guidance laws, which require an estimate of the target\u27s state acceleration, recognizing the precision is not as good as that for position and velocity estimates. When the target dynamics and measurement are linear and input u is constant, then an unbiased estimate of u of u and of the target state is obtained, provided that an observability condition holds. It is shown that the critical observability condition holds for kinematic target motion models of interest