Schedule-based sequential localization in asynchronous wireless networks

Abstract

In this paper, we consider the schedule-based network localization concept, which does not require synchronization among nodes and does not involve communication overhead. The concept makes use of a common transmission sequence, which enables each node to perform self-localization and to localize the entire network, based on noisy propagation-time measurements. We formulate the schedule-based localization problem as an estimation problem in a Bayesian framework. This provides robustness with respect to uncertainty in such system parameters as anchor locations and timing devices. Moreover, we derive a sequential approximate maximum a posteriori (AMAP) estimator. The estimator is fully decentralized and copes with varying noise levels. By studying the fundamental constraints given by the considered measurement model, we provide a system design methodology which enables a scalable solution. Finally, we evaluate the performance of the proposed AMAP estimator by numerical simulations emulating an impulse-radio ultra-wideband (IR-UWB) wireless network.

A Bayesian Algorithm for Distributed Network Localization Using Distance and Direction Data

“…However, a carefully designed schedule can ensure the convergence for an specific problem. In the problem of cooperative localization, an schedule can be designed in advance by analyzing the network connectivity, or it can be done dynamically by each node [43]- [45]. A dynamic scheduling mechanism employed by the MPHL, as described in the following.…”

Schedule based self localization of asynchronous wireless nodes with experimental validation

“…As shown in the TWR case, the biggest error term in a scheduled based self localisation measurement is the skew error RD that appears as a bias. Considering a minimal length valid schedule, S is then full rank and S ∈ R N (N −1)/2+N −1×(N −1)/2+N [6], and by construction u = [0 12 , · · · , 0 N-1N , 1 L1 , · · · , 1 LN ] T ∈ ker S, hence as dim(kerS) = 1, ker S = span(u) leading all the distances between the anchors to be identifiable in our measurements. Let S + be the Moore-Penrose pseudo inverse of S and Π = I N (N −1) 2 0 0 0 ,we have ΠS + S = Π, then under our previous assumptions…”

“…Clock error mitigation from the measurements reduces the measurement model in (6) to the approximate model…”

“…Schedule based positioning has been proposed and analysed in [3,4,5,6], as a method allowing asynchronous nodes in a network to perform cooperative localization without communication overhead. In scheduled based positioning, every node takes turn to broadcast its messages as per a schedule.…”

Synchronization and Localization in Wireless Networks

“…Practical implementations and the combination of recently proposed methods from both fields are analyzed. In Chapter 6, an overview of simultaneous approaches is presented, with a distinction in centralized and distributed computation [1,19,26,35,36,37,75,89,124,129,137,138,139,141,143,144]. In this review we provide a comprehensive orientation in this novel topic.…”