The widespread use of small Unmanned Aerial Vehicles (UAVs) makes the task of their tracking highly relevant, especially in conditions where objects are at close ranges and their trajectories intersect. Frequency-Modulated Continuous-Wave (FMCW) radar is a modern tool for detecting and tracking small UAVs, allowing for a significant reduction in peak transmit power, thus lowering energy consumption and improving the weight, size, and cost characteristics of the system. Small UAVs have extremely low radar cross-section values. Increasing the detection range of small UAVs by FMCW radar can be achieved by lowering the detection threshold, which, however, leads to a significant increase in the probability of false alarms. To improve the efficiency of multitarget tracking using FMCW radar data in the presence of a significant number of false alarms, the Amplitude-Aided Joint Probabilistic Data Association Filter (AA-JPDAF) algorithm has been developed. This algorithm proposes the use of decision statistics (amplitude information) from the output of the optimal primary signal processing receiver as additional information. This information is utilised at the data association stage based on the Joint Probabilistic Data Association (JPDA) method. Target motion parameter estimation for each trajectory is performed using the Extended Kalman Filter (EKF). The analysis of the AA-JPDAF algorithm and its comparison with the conventional JPDAF were conducted via statistical simulation for scenarios involving intersecting trajectories and long-term parallel motion of targets at close ranges.
Kovtun et al. (2026) studied this question.