TY - JOUR
T1 - Maximum Likelihood and IRLS Based Moving Source Localization with Distributed Sensors
AU - Zhang, Xudong
AU - Wang, Fangzhou
AU - Li, Hongbin
AU - Himed, Braham
N1 - Publisher Copyright:
© 1965-2011 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - In this article, we consider the problem of estimating the location and velocity of a moving source using a distributed passive radar sensor network. We first derive the maximum likelihood estimator (MLE) using direct sensor observations, when the source signal is unknown and modeled as a deterministic process. Since the MLE obtains the source location and velocity estimates through a search process over the parameter space and is quite computationally intensive, we also developed an efficient algorithm to solve the problem using a two-step approach. The first step finds the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimates for each sensor with respect to a reference sensor by using a two-dimensional fast Fourier transform and interpolation, while the second step employs an iterative reweighted least square (IRLS) approach with a varying weighting matrix to determine the source location and velocity. To benchmark the performance of the proposed methods, a constrained Cramér-Rao bound (CRB) for the considered source localization problem is derived. Numerical results show that the IRLS approach has a lower signal-to-noise ratio threshold phenomenon compared with several recent TDOA/FDOA-based methods, especially when the source is considerably farther away from some sensors than others, creating a larger disparity in the quality of sensors observations.
AB - In this article, we consider the problem of estimating the location and velocity of a moving source using a distributed passive radar sensor network. We first derive the maximum likelihood estimator (MLE) using direct sensor observations, when the source signal is unknown and modeled as a deterministic process. Since the MLE obtains the source location and velocity estimates through a search process over the parameter space and is quite computationally intensive, we also developed an efficient algorithm to solve the problem using a two-step approach. The first step finds the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimates for each sensor with respect to a reference sensor by using a two-dimensional fast Fourier transform and interpolation, while the second step employs an iterative reweighted least square (IRLS) approach with a varying weighting matrix to determine the source location and velocity. To benchmark the performance of the proposed methods, a constrained Cramér-Rao bound (CRB) for the considered source localization problem is derived. Numerical results show that the IRLS approach has a lower signal-to-noise ratio threshold phenomenon compared with several recent TDOA/FDOA-based methods, especially when the source is considerably farther away from some sensors than others, creating a larger disparity in the quality of sensors observations.
KW - Constrained Cramér-Rao bound (CRB)
KW - IRLS
KW - distributed sensors
KW - frequency difference of arrival (FDOA)
KW - linear weighted least squares (LWLS)
KW - maximum likelihood estimator (MLE)
KW - moving source localization
KW - time difference of arrival (TDOA)
UR - http://www.scopus.com/inward/record.url?scp=85101438778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101438778&partnerID=8YFLogxK
U2 - 10.1109/TAES.2020.3021809
DO - 10.1109/TAES.2020.3021809
M3 - Article
AN - SCOPUS:85101438778
SN - 0018-9251
VL - 57
SP - 448
EP - 461
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
IS - 1
M1 - 9188024
ER -