# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 1 Introduction
Among the algorithms without specific modulation restriction, the extensive cancellation algorithm (ECA) derived from the least-squares (LS) estimation [31; 32] is chosen in this work for its excellent convergence and suppression performance. Compared with the algorithms based on adaptive filters such as [26; 33], the effect of ECA is determined by more intuitive factors such as the clutter extension range or Doppler resolution rather than the step size, which enhances the robustness of parameter selection. However, as mentioned in [23; 26], the high computational cost of ECA brings a massive bottleneck to the real-time system. Many efforts have been made to break the hindrance: the batch version (ECA-B) [31] reduces the processing delay via signal segmentation. However, the ineluctable notch broadening limits the maximum batch number, and the periodic modulation of slow targets restricts its usage. Although these limitations can be overcome via the sliding process, which was introduced by sliding ECA (ECA-S) [34], the computational burden is more severe as the total Flops increase sharply with the batch number. ECA-expectation (ECA-E) and its simplified version (ECA-ES) [35] were developed for better real-time performance via more radical segmentation, making batch-level parallel computing implementable. However, as mentioned later, the constant weight limits the notch width formed via ECA-E (S), and the moving clutters need to be rejected by other algorithms, such as ECA-B. In this paper, PBR based on a digital audio broadcast (DAB) satellite is selected to detect UAVs in which the illuminator, which is also located at synchronous orbit, works at a frequency band close to GNSS while having a much higher ground power density. An advanced version of ECA is derived based on the orthogonality between clutter subspaces of different Doppler frequencies to reduce the processing overhead and to improve the detectability of low-speed targets. The algorithm first segments the sampled data thoroughly, and each batch's clutter coefficients are then estimated. After being smoothed with a low-pass filter, these coefficients regenerate the clutter component to perform cancellation. Theoretical analysis and numerical simulation demonstrate the advantage in slow target retention. Additionally, the efficiency improvement and memory usage reduction, which benefit from the compatibility with precompression, are verified for the close-range monitoring. Three groups of experimental data are obtained to validate the detectability of small UAVs, and the influence from target altitude and bistatic geometry is also discussed. The rest of this paper is organized as follows. The signal model of PBR based on a DAB satellite and the algorithm derivation are given in Sections 2 and 3. The computational cost and cancellation capability are evaluated theoretically and compared with existing ECA versions in Section 4. In Sections 5 and 6, the proposed algorithms' effectiveness is verified by simulation and experimental data, respectively. A further discussion about the experiments is presented in Section 7. Finally, Sections 8 concludes the paper with some critical remarks.