Passive multiple disjoint sources localization using TDOAs and GROAs in the presence of sensor location uncertainties

Abstract

Passive source localization has been the focus of considerable research efforts due to its usefulness in various applications. This paper performs a fundamental investigation of whether the gain ratios of arrival (GROAs) can be utilized in conjunction with the time differences of arrival (TDOAs) to improve the multiple sources localization accuracy with erroneous sensor positions. It's an urgent need to find a closedform solution with good localization accuracy for this challenging problem. This paper takes the advantage that the TDOAs and GROAs from different sources have the same sensor position displacements and proposes an estimator that jointly estimates the unknown sources and sensor positions. In order to establish an closed-form solution for multiple sources location estimates using GROAs and TDOAs, we use the idea of hypothesized source locations in the algorithm development to enable the formulation of pseudo-linear equations. The proposed algebraic solution does not require initialization and does not have divergence problem. Our conclusion is that the improvement from GROAs is obvious for multiple sources localization compared with methods only using TDOAs. Numerical simulations are included to support and corroborate the theoretical developments.

Geolocation of a Known Altitude Target Using TDOA and GROA in the Presence of Receiver Location Uncertainty

“…But they require that the target transmit power is known, which renders them unsuitable for the passive localization of uncooperative targets. On the other hand, noting that the signal energies received at different receivers would be different, the utilization of the gain difference of arrival (GROA) measurement has been recognized to be useful for passive localization [11][12][13]. It needs the reciprocal of the received signal amplitude with respect to a reference receiver only to locate a target.…”

“…The contribution of the GROA measurements to the improvement of target localization accuracy was studied. Different from the study in [11], Hao et al [12,13] considered the practical scenario where the known sensor positions have errors. They proposed in [12] a new closed-form algorithm that estimates both the unknown source and the sensor positions from TDOA and GROA measurements [12].…”

Effective bias reduction methods for passive source localization using TDOA and GROA

“…Passive source localization is a fundamental problem that has found numerous practical applications in many fields, such as sensor networks [1][2][3][4], wireless communications [5][6][7], microphone arrays [8,9], sonar [10] and many others. Time Differences of Arrival (TDOA) is maybe the most common technique for passive localization using a number of spatially separated sensors.…”

“…Time Differences of Arrival (TDOA) is maybe the most common technique for passive localization using a number of spatially separated sensors. We need to solve a set of nonlinear equations to estimate the source location [2]. This paper considers the indirect localization approach based on TDOA measurements and Gain Ratio of Arrival (GROA) measurements with a Gaussian signal model.…”

“…When the source signals are captured at the sensors distributed in different locations, both time delay and signal strength information can be measured for passive localization. Due to the spatial separation of the sensors, the source signal will arrive at the sensors with different energies so that the energy differences can be utilized for source localization [2]. Based on energy measurements using a sensor network, Blatt et al [11] have proposed different algorithms for locating one or multiple sources.…”