Abstract:

Aiming at the high-precision angle estimation requirement of airborne millimeter-wave radar on unmanned aerial vehicles (UAVs) for "low-slow-small" targets, this paper proposes a three-stage step-by-step angle measurement algorithm of "coarse detection - fine estimation - feedback" integrating digital beamforming (DBF), forward-backward spatial smoothing multiple signal classification (FBSS-MUSIC), and an adaptive feedback mechanism. First, DBF is used to quickly and coarsely scan the large spatial area to lock the target azimuth. Then, FBSS preprocessing is introduced in a local small range to restore the array degrees of freedom under coherent signals, and the Akaike information criterion (AIC)/Bayesian information criterion (BIC) is adaptively applied to estimate the number of signal sources. Finally, based on the eigenvalue gap and the equivalent signal-to-noise ratio (SNR) of the target, a confidence assessment index is constructed. A sliding window is used to dynamically adjust the scanning step size and decision threshold to achieve closed-loop feedback. Simulation experiments show that in the dynamic trajectory tracking of 50 consecutive frames, compared with the DBF + MUSIC combined algorithm, the proposed algorithm reduces the average absolute error and computational load by 38.89% and 47.5%, respectively, achieving a balance between high precision and real-time performance. In the tracking experiments where the target moves at speeds of 1, 2, and 4(°)/s, the angle estimation curve of the proposed algorithm is significantly better than those of DBF, MUSIC, and their combined algorithms, verifying its robustness and engineering applicability in practical operating environments.

Key words: unmanned aerial vehicle, millimeter-wave radar, digital beamforming, improved multiple signal classification, adaptive iterative feedback