We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subspace of streaming data over time. αFAPI is fast and it outperforms many RST methods while only having a low complexity linear to the data dimension.

• Run files "demo_xyz.m" for synthetic experiments.

• FAPI: Badeau, Roland, Bertrand David, and Gaël Richard. "Fast approximated power iteration subspace tracking." IEEE Trans. Signal Process. 53.8 (2005): 2931-2941.

• LORAF: Strobach, Peter. "The fast recursive row-Householder subspace tracking algorithm." Signal Process. 89.12 (2009): 2514-2528.

• GYAST: Arjomandi-Lari, Mostafa, and Mahmood Karimi. "Generalized YAST algorithm for signal subspace tracking." Signal Process. 117 (2015): 82-95.

• ROBUSTA: Linh-Trung, Nguyen, et al. "Low-complexity adaptive algorithms for robust subspace tracking." IEEE J. Sel. Topics Signal Process. 12.6 (2018): 1197-1212.

• RYAST: Nguyen, Viet-Dung, Nguyen Linh Trung, and Karim Abed-Meraim. "Robust subspace tracking algorithms using fast adaptive Mahalanobis distance." Signal Process. 195 (2022): 108402.

• TRPAST: A. M. Rekavandi, A.-K. Seghouane, and K. Abed-Meraim, “TRPAST: A tunable and robust projection approximation subspace tracking method,” IEEE Trans. Signal Process. (2022).

• Effect of p and alpha

• Tracking in Contaminated Environments

• DOA Tracking

If you use this code, please cite the following paper.

[1] L.T. Thanh, A.M. Rekavandi, S. Abd-Krim, & K. Abed-Meraim. “Robust Subspace Tracking with Contamination Mitigation via Alpha-Divergence”. Proc. 48th IEEE ICASSP, 2023. [PDF].