Range tracking method based on adaptive “current” statistical model with velocity prediction

Abstract

This study considers the problem of range tracking of a maneuvering target by an airborne digital array radar system. In the classic “current” statistical (CS) model, the maneuvering frequency α is not adaptive, and an acceleration limit has to be set. First, a new range tracking method called “adaptive CS model with velocity prediction” (ACSMVP) is proposed. This method utilizes the velocity prediction algorithm of cross product automatic frequency control with loop filter to measure the radial acceleration between the radar and the target. With the acceleration been obtained, the maneuvering frequency is derived, which is calculated in every tracking period. Second, an improved algorithm called “Sage–Husa-based ACSMVP” (SH-ACSMVP) is also proposed. In this algorithm, an online measuring noise covariance matrix is achieved by a simplified Sage–Husa estimator. Third, a Cramér-Rao lower bound of pulse-Doppler pulse train signal for range estimation is derived to assess the range tracking performance of the proposed algorithms. Simulations highlight the superior performance of our proposed algorithms over existing techniques.