2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSAM-7.2
Paper Title ONE-BIT AUTOCORRELATION ESTIMATION WITH NON-ZERO THRESHOLDS
Authors Chun-Lin Liu, Zi-Min Lin, National Taiwan University, Taiwan
SessionSAM-7: Detection and Estimation 1
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Sensor Array and Multichannel Signal Processing: [SSP-PARE] Parameter Estimation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract One-bit quantization has received attention due to its simplicity, low cost, and ability to recover the autocorrelation of the unquantized data. In the past, the autocorrelation estimation from one-bit data can be done in two separate stages. One is the power estimation by using one-bit quantizers of non-zero thresholds, while the other focuses on estimating the normalized autocorrelation with one-bit quantizers of a zero threshold. However, the overall hardware cost increases in this approach. This paper presents an autocorrelation estimator based on a one-bit quantizer with a non-zero threshold. The proposed method depends purely on a one-bit quantizer and its output data. Our method first infers the power information and then estimates the normalized autocorrelation by polynomial root-finding. The autocorrelation estimate is obtained by combining the power and the normalized autocorrelation. Numerical simulations show that the proposed method exhibits similar behavior to an estimator based on the unquantized data, with a 5dB loss in the estimation error.