TY - JOUR
T1 - Efficient Velocity Estimation in Distributed RF Sensing
AU - Wang, Fangzhou
AU - Zhang, Xudong
AU - Li, Hongbin
AU - Himed, Braham
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we examine the problem of velocity estimation for a moving object using distributed measurements. The system employs a non-cooperative transmitter and multiple receivers to collect targets echoes. The problem is formulated by modelling the unknown transmitted waveform as a deterministic process. The exact maximum likelihood estimator (MLE) is developed which requires a multi-dimensional search procedure. To reduce the computational load, an efficient two-step estimator (TSE) is proposed. The TSE first finds the maximum likelihood estimates of pairwise differences of the Doppler frequencies observed by the receivers. Then, the target velocity can be estimated from the frequency differences in closed-form. We show that the maximum likelihood estimation of each frequency difference reduces to a cross-correlation process followed by peak finding, which can efficiently be implemented by the fast Fourier transform (FFT). As a result, the TSE is significantly more efficient than the MLE. Numerical results show the TSE achieves a similar estimation accuracy as that of the MLE except for very low signal-to-noise ratio (SNR) scenarios.
AB - In this paper, we examine the problem of velocity estimation for a moving object using distributed measurements. The system employs a non-cooperative transmitter and multiple receivers to collect targets echoes. The problem is formulated by modelling the unknown transmitted waveform as a deterministic process. The exact maximum likelihood estimator (MLE) is developed which requires a multi-dimensional search procedure. To reduce the computational load, an efficient two-step estimator (TSE) is proposed. The TSE first finds the maximum likelihood estimates of pairwise differences of the Doppler frequencies observed by the receivers. Then, the target velocity can be estimated from the frequency differences in closed-form. We show that the maximum likelihood estimation of each frequency difference reduces to a cross-correlation process followed by peak finding, which can efficiently be implemented by the fast Fourier transform (FFT). As a result, the TSE is significantly more efficient than the MLE. Numerical results show the TSE achieves a similar estimation accuracy as that of the MLE except for very low signal-to-noise ratio (SNR) scenarios.
KW - Multistatic passive radar
KW - efficient implementation
KW - maximum likelihood
KW - target velocity estimation
UR - http://www.scopus.com/inward/record.url?scp=85146200281&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146200281&partnerID=8YFLogxK
U2 - 10.1109/RadarConf2248738.2022.9764288
DO - 10.1109/RadarConf2248738.2022.9764288
M3 - Conference article
AN - SCOPUS:85146200281
SN - 1097-5764
JO - Proceedings of the IEEE Radar Conference
JF - Proceedings of the IEEE Radar Conference
T2 - 2022 IEEE Radar Conference, RadarConf 2022
Y2 - 21 March 2022 through 25 March 2022
ER -