TY - GEN
T1 - Model order identification and parameters estimation using array processings for small sample support
AU - Rong, Yu
AU - Samuel, Alphonso A.
AU - Bliss, Daniel
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/7
Y1 - 2017/6/7
N2 - We propose a sequential algorithm for determining the number of narrow band source signals, and estimation of the parameters associated with the signal (angles and powers) and of the receiver noise power using the noisy sample observed at the receiver sensor arrays. Previous parameter estimates (or initial estimates) are refined multiple times in our sequential approach and also the Newton-based refinement gives continuous-valued estimates so the estimation performance is not limited to the grid resolution. By benchmarking against the Cramer Rao Lower Bound (CRLB), the estimation performance for all parameters of the proposed algorithm achieves near optimal performance even in the low SNR and small sample support region, in which, the sample size can be smaller than the number of sensors in the array. At the same time, the detection (or model order identification) performance outperforms other relevant algorithms.
AB - We propose a sequential algorithm for determining the number of narrow band source signals, and estimation of the parameters associated with the signal (angles and powers) and of the receiver noise power using the noisy sample observed at the receiver sensor arrays. Previous parameter estimates (or initial estimates) are refined multiple times in our sequential approach and also the Newton-based refinement gives continuous-valued estimates so the estimation performance is not limited to the grid resolution. By benchmarking against the Cramer Rao Lower Bound (CRLB), the estimation performance for all parameters of the proposed algorithm achieves near optimal performance even in the low SNR and small sample support region, in which, the sample size can be smaller than the number of sensors in the array. At the same time, the detection (or model order identification) performance outperforms other relevant algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85021400005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021400005&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2017.7944380
DO - 10.1109/RADAR.2017.7944380
M3 - Conference contribution
AN - SCOPUS:85021400005
T3 - 2017 IEEE Radar Conference, RadarConf 2017
SP - 1165
EP - 1169
BT - 2017 IEEE Radar Conference, RadarConf 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Radar Conference, RadarConf 2017
Y2 - 8 May 2017 through 12 May 2017
ER -