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Creators/Authors contains: "Rawat, Ankit S"

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  1. ; ; (, Thirty-Ninth Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 31, 2025
  2. ; ; ; ; (, Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024)
    Accepted Manuscript Full Text Available
  3. ; ; (, IEEE Global Conference on Signal and Information Processing (GlobalSIP))
    Many direction-of-arrival (DOA) estimation algo- rithms require accurate measurements from all sensing elements on an antenna array. However, in various practical settings, it becomes imperative to perform DOA estimation even in the presence of faulty elements. In this work, we develop an algorithm that can jointly estimate the DOA of sources and the locations of the faulty elements. This is achieved by introducing weights that describe the degree of outlierness of each element. Further, for situations where only single snapshots are available, we propose a new snapshot diversity formulation for which our algorithm can still be applied. Simulation results over four different fault models demonstrate that the proposed algorithm robustly estimates DOAs and accurately identifies the faulty elements. 
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