Robust Decoding from 1-Bit Compressive Sampling with Least Squares

@inproceedings{Huang2017RobustDF,
  title={Robust Decoding from 1-Bit Compressive Sampling with Least Squares},
  author={Jian Huang and Yuling Jiao and Liping Zhu},
  year={2017}
}
In 1-bit compressive sensing (1-bit CS) where target signal is coded into a binary measurement, one goal is to recover the signal from noisy and quantized samples. Mathematically, the 1-bit CS model reads: y = η ⊙ sign(Ψx + ǫ), where x ∈ R, y ∈ R, Ψ ∈ R, and ǫ is the random error before quantization and η ∈ R is a random vector modeling the sign flips. Due to the presence of nonlinearity, noise and sign flips, it is quite challenging to decode from the 1-bit CS. In this paper, we consider least… CONTINUE READING

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