TY - GEN
T1 - On direction of arrival estimation with 1-bit quantizer
AU - Yoffe, Ilia
AU - Regev, Nir
AU - Wulich, Dov
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Designing and implementing analog front-end circuits is a complex problem and thus, is the cornerstone of any radar system design. We propose removing the gain control block, as well as reducing the complexity by introducing a 1-bit analog to digital converter (ADC) at the receiving path. Nevertheless, this nonlinear quantization operation distorts the signal in a way that does not preserves its Gaussianity, rendering the common Maximum Likelihood (ML) based Direction of Arrival (DOA) estimation methods non-optimal. We derive the ML optimal DOA estimator for the 1-bit ADC and propose suboptimal, yet, effective estimator to reduce the complexity of the ML estimator. We benchmark the performance of the proposed estimators derived in this paper against the derived Cramér-Rao lower bound and investigate the case of a known and unknown transmitted signals. We show that the proposed algorithms attain the bound under various conditions as well as outperform a naïve ML approach for the 1-bit ADC problem.
AB - Designing and implementing analog front-end circuits is a complex problem and thus, is the cornerstone of any radar system design. We propose removing the gain control block, as well as reducing the complexity by introducing a 1-bit analog to digital converter (ADC) at the receiving path. Nevertheless, this nonlinear quantization operation distorts the signal in a way that does not preserves its Gaussianity, rendering the common Maximum Likelihood (ML) based Direction of Arrival (DOA) estimation methods non-optimal. We derive the ML optimal DOA estimator for the 1-bit ADC and propose suboptimal, yet, effective estimator to reduce the complexity of the ML estimator. We benchmark the performance of the proposed estimators derived in this paper against the derived Cramér-Rao lower bound and investigate the case of a known and unknown transmitted signals. We show that the proposed algorithms attain the bound under various conditions as well as outperform a naïve ML approach for the 1-bit ADC problem.
KW - 1-bit Quantizer
KW - DOA
KW - Expectation Maximization
UR - http://www.scopus.com/inward/record.url?scp=85073103499&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2019.8835785
DO - 10.1109/RADAR.2019.8835785
M3 - Conference contribution
AN - SCOPUS:85073103499
T3 - 2019 IEEE Radar Conference, RadarConf 2019
BT - 2019 IEEE Radar Conference, RadarConf 2019
PB - Institute of Electrical and Electronics Engineers
T2 - 2019 IEEE Radar Conference, RadarConf 2019
Y2 - 22 April 2019 through 26 April 2019
ER -