Asynchronous time-of-arrival-based source localization

Abstract

In this paper, asynchronous wireless source localization using timeof-arrival (TOA) measurements is studied. In TOA localization, the travel time of the signal between the source node and anchor nodes is measured and used to estimate range. In synchronous networks, the anchor nodes know when the source node starts transmission. In asynchronous networks, however, the source transmit time is unknown and TOA measurements have a positive bias due to the synchronization error which could lead to a large localization error. One way to tackle this problem is to use time-difference-of-arrival (TDOA) measurements which do not depend on the source transmission time. However, in this work, applying an alternative approach, we estimate the source transmit time as a nuisance parameter jointly with the source location. The optimal maximum likelihood (ML) estimator is derived. To avoid the ML convergence problem, a novel semidefinite programming (SDP) technique is proposed by converting the noncovex ML problem into a convex one. Computer simulations showing superior performance of the proposed SDP estimator are conducted.Index Terms-time-of-arrival (TOA), source localization, semidefinite programming (SDP), asynchronous networks.

Robust TDOA Localization Algorithm for Asynchronous Wireless Sensor Networks

“…The performance of the synchronous TDOA algorithm in [7] is presented for comparison in Figure 9. The dotted red lines with markers " " and "+" represent RCRLB and RMSE of [7], respectively.…”

“…Clock skews in [4,7] are treated as constant. Furthermore, it is assumed that all anchors are synchronized and their positions are known in [7]; it is also assumed that there are ( ≥ 3) anchors with known timing and locations in [4].…”

“…Many protocols have been proposed to resolve the synchronization problem of WSN, such as the reference broadcast synchronization (RBS) protocol [13], the timing-sync protocol for sensor networks (TPSN) [14], and flooding time synchronization protocol (FTSP) [15]. On the other hand, clock synchronization can also be handled by estimating the relative clock offsets and skews among sensor nodes using maximum likelihood estimator (MLE) [3][4][5]7]; the theoretical performance limits are evaluated by Cramér-Rao lower bound (CRLB).…”

TDOA-Based Source Collaborative Localization via Semidefinite Relaxation in Sensor Networks

“…However, the TDOA measurements are not available 2 International Journal of Distributed Sensor Networks for noncooperative sources, for example, enemy target in a battlefield. One way to tackle this problem is to jointly estimate the initial signal transmission time and the source location by using available TOA measurements and known sensors coordinates [8,9]. Another alternative method is to utilize the difference of pairwise TOA measurements, that is, TDOA, where the initial transmission time is not required.…”

“…To localize the source, different measurement models are defined based on the information such as time of arrival (TOA) [7][8][9][10], time difference of arrival (TDOA) [11][12][13][14][15][16][17][18][19], angle of arrival (AOA) [20], received signal strength (RSS) [21][22][23], distance measurement (DM) [16,24,25], and a combination of them [26,27]. Distance measurement is widely employed in the scenario of cooperative localization for sensor networks, for example, sensor node self-localization [4,28].…”