The bistatic frequency diverse array (FDA) radar system is designed to exploit the beam autoscanning of FDA radar, providing a novel solution to address spatial synchronization challenges in bistatic radar architecture, unleashing bistatic radar’s advantage in low-observable target detection, main-lobe jamming (MLJ) suppression, etc. To lay the theoretical foundation for subsequent research on bistatic FDA radar systems, this study develops a generalized ambiguity function (GAF) framework, jointly characterizing target velocity, range, and angular parameters, which can provide a reference for transmitted signal optimization and bistatic geometric configuration design. This paper derives the mathematical model of the bistatic FDA radar system’s GAF and validates that its structure not only depends on the transmitted signal but also exhibits strong geometric dependency, where baseline length and target position jointly reshape the bistatic triangle through numerical simulations.

An adaptive constant acceleration (ACA) model is proposed for the maneuvering target tracking problem. Based on the Taylor series expansion of acceleration, we establish the relationship between the Jerk and the velocity as well as the acceleration so that the maneuvering acceleration variance is approximated by the components in the state error covariance matrix. Then, the latter one is connected with the process noise, and the adaptive adjustment of the ACA model is realized. Combining with the strong tracking square-root cubature filter (ST-SCKF) in our previous work, an ACA-ST-SCKF is developed. The simulation results show that the proposed filter possesses better adaptability, tracking accuracy and lower computational complexity compared with the adaptive current statistical (ACS) model-based ST-SCKF, the modified CS (MCS) model-based ST-SCKF, and the IMM-based STF-SCKF.