Cooperative Localization Bounds for Indoor Ultra-Wideband Wireless Sensor Networks

Abstract

Recommended by L. MucchiIn recent years there has been growing interest in ad-hoc and wireless sensor networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the main sought after parameter. The cooperative localization performance of WSNs is constrained by the behavior of the utilized ranging technology in dense cluttered indoor environments. Recently, ultra-wideband (UWB) Time-of-Arrival (TOA) based ranging has exhibited potential due to its large bandwidth and high time resolution. The performance of its ranging and cooperative localization capabilities in dense indoor multipath environments, however, needs to be further investigated. Of main concern is the high probability of non-line of sight (NLOS) and Direct Path (DP) blockage between sensor nodes which biases the TOA estimation and degrades the localization performance. In this paper, based on empirical models of UWB TOA-based Outdoor-to-Indoor (OTI) and Indoor-to-Indoor (ITI) ranging, we derive and analyze cooperative localization bounds for WSNs in different indoor multipath environments: residential, manufacturing floor, old office and modern office buildings. First, we highlight the need for cooperative localization in indoor applications. Then we provide comprehensive analysis of the factors affecting localization accuracy such as network and ranging model parameters.

NLOS channel identification and mitigation in Ultra Wideband ToA-based Wireless Sensor Networks

“…The nodes with unknown location are referred to as "sensor nodes" hereafter and the set is denoted as S. The localization process is to estimate the location of all nodes in S based on all or some of the available range estimates between nodes in both S and A. In either networks, the impact of corrupted or biased ToA-based ranging have shown in [10] to yield significant localization error and many instances useless location information. We will show that when our identification and mitigation algorithm is integrated in both types of networks, substantial performance gains can be achieved.…”

“…The algorithm used in the simulation is the iterative distributed localization described in [12], [13]. In this type of cooperative localization the network is composed of blind sensor nodes and small fraction of anchor nodes.…”

“…For a typical WSN connected with its nodes connected with R links, the performance of localization is highly dependent on the condition of the propagation channel of these R range measurements [10]. For range measurements under LOS channel conditions, the Direct Path (DP) is usually the strongest and UWB has been shown to offer excellent ranging accuracy [3], [5].…”

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

“…Range-free localization methods do not use physical parameters but work with the content of a message and are also not treated here. There are different technologies used for indoor localization: ultrasonic [10], infrared [11], Bluetooth [12], RFID [11,12], WiFi [13,14], UWB [15][16][17], and a combination of technologies [18,19]. These technologies all have not only their respective advantages, but also shortcomings.…”

Hybrid WiFi/UWB, cooperative localization using Particle Filter

“…The Particle Filter is chosen for this work to deal with such an environment. We analyze the performance of the Particle Filter versus CRLB by using the theoretical, IEEE 802.11 channel model for RSS and the empirical one for UWB ranging error as presented in [7]. For our performance platform, we leverage off the previous findings in [3], where we used eight fixed anchors (FAs) along with three moving objects (MOs).…”