To address the problems of time-varying measurement noise, non-Gaussian outlier interference, and model-switching delay in tracking low-altitude, slow-speed, and small targets with airborne millimeter-wave radar, this paper proposes an Adaptive Interacting Multiple Model–Unscented Kalman Filter (ADIMM-UKF) algorithm based on the statistical characteristics of radar point clouds. Under a three-model IMM-UKF framework including constant velocity (CV), constant acceleration (CA), and coordinated turn (CT) models, an adaptive measurement noise covariance matrix is constructed from the spatial distribution of point clouds, while the Median Absolute Deviation (MAD) criterion is used to remove abnormal scattering points, thereby improving measurement quality. In addition, a Student’s t-distribution is introduced into the UKF sub-filters for robust innovation weighting, reducing the influence of outlier measurements on state estimation. A transient activation mechanism based on Sigmoid nonlinear mapping is further designed to update the model transition probability matrix online, improving model-switching responsiveness and matching accuracy under abrupt maneuvers. Experiments using both radar target simulator data and real UAV point-cloud data show that, compared with AIMM-UKF, the proposed algorithm reduces the root mean square errors of position and velocity by 18.7% and 14.3% in a two-dimensional simulated scenario, and by 22.9% and 12.4% in a three-dimensional real-world scenario, respectively. These results demonstrate the effectiveness and robustness of the proposed algorithm in practical applications.