# One-Bit-Aided Modulo Sampling for DOA Estimation

arXiv: https://arxiv.org/abs/2309.04901 · HTML: https://mlsanity.com/paper/2309.04901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [On Unlimited Sampling and Reconstruction](https://mlsanity.com/paper/1905.03901/) (2019)
- [Unlimited Sampling from Theory to Practice: Fourier-Prony Recovery and Prototype ADC](https://mlsanity.com/paper/2105.05818/) (2021)
- [UNO: Unlimited Sampling Meets One-Bit Quantization](https://mlsanity.com/paper/2301.10155/) (2022)
- [Above the Nyquist Rate, Modulo Folding Does Not Hurt](https://mlsanity.com/paper/1903.12289/) (2019)
- [Residual Recovery Algorithm For Modulo Sampling](https://mlsanity.com/paper/2110.03335/) (2021)
- [On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays](https://mlsanity.com/paper/2012.14051/) (2020)
- [The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments](https://mlsanity.com/paper/2111.12675/) (2021)
- [Blind Modulo Analog-to-Digital Conversion](https://mlsanity.com/paper/2108.08937/) (2021)
- [Time Encoding via Unlimited Sampling: Theory, Algorithms and Hardware Validation](https://mlsanity.com/paper/2208.10234/) (2022)
- [Line Spectral Estimation via Unlimited Sampling](https://mlsanity.com/paper/2210.15811/) (2022)
- [Blind Unwrapping of Modulo Reduced Gaussian Vectors: Recovering MSBs from LSBs](https://mlsanity.com/paper/1901.10396/) (2019)
- [Robust Unlimited Sampling Beyond Modulo](https://mlsanity.com/paper/2206.14656/) (2022)
- [Unlimited Sampling of Bandpass Signals: Computational Demodulation via Undersampling](https://mlsanity.com/paper/2307.04917/) (2023)
- [Blind Modulo Analog-to-Digital Conversion of Vector Processes](https://mlsanity.com/paper/2110.06183/) (2021)

## Abstract (arXiv)

Modulo sampling has recently drawn a great deal of attention for cutting-edge applications, due to overcoming the barrier of information loss through sensor saturation and clipping. This is a significant problem, especially when the range of signal amplitudes is unknown or in the near-far case. To overcome this fundamental bottleneck, we propose a one-bit-aided (1bit-aided) modulo sampling scheme for direction-of-arrival (DOA) estimation. On the one hand, one-bit quantization involving a simple comparator offers the advantages of low-cost and low-complexity implementation. On the other hand, one-bit quantization provides an estimate of the normalized covariance matrix of the unquantized measurements via the arcsin law. The estimate of the normalized covariance matrix is used to implement blind integer-forcing (BIF) decoder to unwrap the modulo samples to construct the covariance matrix, and subspace methods can be used to perform the DOA estimation. Our approach named as 1bit-aided-BIF addresses the near-far problem well and overcomes the intrinsic low dynamic range of one-bit quantization. Numerical experiments validate the excellent performance of the proposed algorithm.