Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Wireless Location in CDMA Cellular Radio Systems
Wireless Location in CDMA Cellular Radio Systems
Localization from Connectivity in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Ordinal MDS-based localisation for wireless sensor networks
International Journal of Sensor Networks
A supplement to multidimensional scaling framework formobile location: a unified view
IEEE Transactions on Signal Processing
A multidimensional scaling framework for mobile location using time-of-arrival measurements
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A Generalized Subspace Approach for Mobile Positioning With Time-of-Arrival Measurements
IEEE Transactions on Signal Processing
An accurate algebraic solution for moving source location using TDOA and FDOA measurements
IEEE Transactions on Signal Processing
A Novel Weighted Multidimensional Scaling Analysis for Time-of-Arrival-Based Mobile Location
IEEE Transactions on Signal Processing - Part I
Wideband TDOA/FDOA processing using summation of short-time CAF's
IEEE Transactions on Signal Processing
On bending invariant signatures for surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The pre-image problem in kernel methods
IEEE Transactions on Neural Networks
PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map
IEEE Transactions on Neural Networks
| Hi-index | 35.69 |
A new framework for positioning a moving target is introduced by utilizing time differences of arrival (TDOA) and frequency differences of arrival (FDOA) measurements collected using an array of passive sensors. It exploits the multidimensional scaling (MDS) analysis, which has been developed for data analysis in the field such as physics, geography and biology. Particularly,we present an accurate and closed-form solution for the position and velocity of a moving target. Unlike most passive target localization methods focusing on minimizing a loss function with respect to the measurement vector, the proposed method is based on the optimization of a cost function related to the scalar product matrix in the classical MDS framework. It is robust to the large measurement noise. The bias and variance of the proposed estimator is also derived. Simulation results show that the proposed estimator achieves better performance than the spherical-interpolation (SI) method and the two-step weighted least squares (WLS) approach, and it attains the Cramér-Rao lower bound at a sufficiently high noise level before the threshold effect occurs. Moreover, for the proposed estimator the threshold effect, which is a result of the nonlinear nature of the localization problem, occurs apparently later as the measurement noise increases for a near-field target.