Exploiting geometry for improved hybrid AOA/TDOA-based localization

In this paper we examine the geometrically constrained optimization approach to localization with hybrid bearing (angle of arrival, AOA) and time difference of  arrival (TDOA) sensors. In particular, we formulate a constraint on the measurement errors which is then used along with constraint-based optimization tools in order to estimate the maximum likelihood values of the errors given an appropriate cost function. In particular we focus on deriving a localization algorithm for stationary target localization in the so-called adverse localization geometries where the relative positioning of the sensors and the target do not readily permit accurate or convergent localization using traditional approaches. We illustrate this point via simulation and we compare our approach to a number of different techniques that are discussed in the literature.

TDOA based transmitter localization with minimum number of teceivers and power measurements

This paper investigates the problem of localizing a wireless transmitter using a minimum number of receivers and other readily available means in a time difference of arrival (TDOA) setting. Using the necessary and sufficient conditions for unique solution, we use power measurements to compensate for the reduced number of receivers. In other words, if the transmitter is not located in the unique solution area, we provides a technique to find the true location via the measured received signal power. Our approach neither requires the knowledge of the transmission power nor the path loss exponent.

Optimal range-difference-based localization considering geometrical constraints

This paper proposes a new type of algorithm aimed at finding the traditional maximum-likelihood (TML) estimate of the position of a target given time-difference-of-arrival (TDOA) information, contaminated by noise. The novelty lies in the fact that a performance index, akin to but not identical with that in maximum likelihood (ML), is a minimized subject to a number of constraints, which flow from geometric constraints inherent in the underlying problem. The minimization is in a higher dimensional space than for TML, and has the advantage that the algorithm can be very straightforwardly and systematically initialized. Simulation evidence shows that failure to converge to a solution of the localization problem near the true value is less likely to occur with this new algorithm than with TML. This makes it attractive to use in adverse geometric situations.

Localization with ghost elimination of emitters via time-delay-of-arrival measurements

This paper investigates the theoretical requirements for unique localization of multiple emitters using time delay of arrival(TDoA) subjected to the data-association problem. Specifically, an examination is carried out to find the necessary fundamental requirements to solve the so-called ghost node problem pertaining to sensor arrays.