A Simulation Tool for Real-Time Hybrid-Cooperative Positioning Algorithms

A Simulation Tool for Real-Time Hybrid-Cooperative Positioning Algorithms

Francesco Sottile (Istituto Superiore Mario Boella, Italy), Mauricio A. Caceres (Istituto Superiore Mario Boella, Italy) and Maurizio A. Spirito (Istituto Superiore Mario Boella, Italy)
DOI: 10.4018/jertcs.2012070105


The authors propose a simulation tool (ST) able to test real-time hybrid GNSS/terrestrial and cooperative positioning algorithms that fuse both pseudorange measurements from satellites and terrestrial range measurements based on radio frequency communication performed between nodes of a wireless network. In particular, the ST simulates devices belonging to a peer-to-peer (P2P) wireless network where peers, equipped also with a GNSS receiver, cooperate among them by exchanging aiding data in order to improve both positioning accuracy and availability. Furthermore, the authors propose a method to increase the robustness of cooperative algorithms based on the estimated position covariance matrix. In particular, the proposed approach assures a faster estimation convergence and improved accuracy while lowering computational complexity and network traffic. Finally, the authors tested the sensitivity of the implemented positioning algorithms through the ST in two different scenarios, first in presence of high level of pseudorange noise and then in presence of a malicious peer in the P2P network.
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Nowadays, various services in wireless communication networks depend on mobile positioning. In fact, information collected or communicated by a wireless node is often useless if it is not associated to the node’s location. For example, a generic WSN monitoring application (Garcia-Hernandez, Ibarguengoytia-Gonzalez, & Perez-Diaz, 2007), where nodes collect data such as temperature, humidity, etc., requires the nodes’ position to recognize where the data come from. Moreover, location-based technology allows the deployment of many other applications, including asset tracking, intruder detection (Yan, He, & Stankovic, 2003), healthcare monitoring (Budinger, 2003), emergency 911 services (Caffery & Stuber, 1995) and so forth (Liu, Darabi, Banerjee, & Liu, 2007).

Usually, positioning techniques infer the position of unknown nodes by applying a two-step estimation process (Caffery & Stuber, 1998). In the first step, ranging estimation is performed by means of various techniques such as received signal strength (RSS) (Hashemi, 1993; Laitinen, Juurakko, Lahti, Korhonen, & Lahteenmaki, 2007), time-of-arrival (ToA) or time difference-of-arrival (TDoA) (Caffery & Stuber, 1998). Then, the position of the unknown peers are inferred in a two or three dimensional plane by applying a localization algorithm which takes as inputs the range measurements and the positions of anchor peers, whose coordinates are known a priori.

Recently, much attention is focused on the signal-of-opportunity (SoO) approach (Yang & Nguyen, 2009) which consists in the exploitation of terrestrial communication systems (e.g., wireless sensor network (WSN), ultra wide-band (UWB), Wi-Fi, cellular networks, etc.) with a purpose other than navigation in order to guarantee and improve global navigation satellite systems (GNSS)-based services and enhance the robustness of the overall GNSS end-user performance. In general, GNSS provide accurate position estimation when the receiver’s antenna is able to acquire at least four satellites (Del Re, 2011). This condition is generally verified in open sky outdoor environments. On the contrary, in GNSS-challenged environments such as urban canyons, under dense foliage and indoors, the line-of-sight (LoS) between satellites and the receiver’s antenna is often obstructed and the GNSS-based localization performance degrades or fails completely. Therefore, in such environments, hybrid and cooperative localization methods, which fuse both pseudorange measurements from GNSS satellites and terrestrial based ranging measurements from neighbouring peers, have been recently proposed in the literature to improve both availability and positioning accuracy (Sottile, Wymeersch, Caceres, & Spirito, 2011; Caceres, Penna, Wymeersch, & Garello, 2010; Caceres, Sottile, Garello, & Spirito, 2010) .

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