In maneuvering target tracking, the target motion model serves as a fundamental component that ideally captures various movement states during target maneuvers. Commonly used models include Constant Velocity (CV) model, Constant Acceleration (CA) model, time-correlated Singer model, and the "Current" Statistical model for maneuvering targets. These models characterize target maneuvers using a maneuver frequency parameter. In practical applications, a fixed maneuver frequency is typically employed, implying constant maneuver duration. However, actual target maneuver durations vary continuously, meaning the maneuver frequency changes dynamically. Using a fixed maneuver frequency inevitably introduces tracking errors. When the sampling period ranges from 0.5 to 2 seconds, lower maneuver frequencies yield higher tracking accuracy [1]. This description highlights the need for adaptive frequency adjustment algorithms that can dynamically optimize tracking performance through neural network implementations.
MATLAB
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