We consider the problem of precise range estimation between pairs of moving vehicles using periodic broadcast messages. The transmitter and receivers are not time synchronized and one needs to explicitly account for the clock offset and clock drift in addition to the vehicle motion to obtain accurate range estimates. We develop a range estimation algorithm based on a local polynomial smoothing of the vehicle motion and experimentally verify that the performance is close to that obtained using unicast round-trip time ranging. This broadcast approach is of interest particularly in the context of dedicated short-range communications (DSRC) wherein periodic broadcast safety messages are exchanged between vehicles. We propose to exploit these broadcast messages to perform ranging. Our scheme requires additional timestamp information to be transmitted as part of the DSRC messages, and we develop a novel timestamp compression algorithm to minimize the resulting overhead. We validate our proposed algorithm on experimental data and show that it is able to achieve sub-meter ranging accuracies in vehicular scenarios.
Global Navigation Satellite Systems (GNSS) can be used for navigation purposes in vehicular environments. However, the limited accuracy of GNSS makes it unsuitable for applications such as vehicle collision avoidance. Improving the positioning accuracy in vehicular networks, Cooperative Positioning (CP) algorithms have emerged. CP algorithms are based on data communication among vehicles and estimation of the distance between the nodes of the network. Among the variety of radio ranging techniques, Received Signal Strength (RSS) is very popular due to its simplicity and lower cost compared to other methods like Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The main drawback of RSS- based ranging is its inaccuracy, which mostly originates from the uncertainty of the path loss exponent. Without knowing the environment path loss exponent, which is a time-varying parameter in the mobile networks, RSS is effectively useless for distance estimation. There are many approaches and techniques proposed in the literature for dynamic estimation of the path loss exponent within a certain environment. Most of these methods are not functional for mobile applications or their efficiency decreases dramatically with increasing mobility of the nodes. In this paper, we propose a method for dynamic estimation of the path loss exponent and distance based on the Doppler Effect and RSS. Since this method is fundamentally based on the Doppler Effect, it can be implemented within networks with mobile nodes. The higher the mobility of the nodes, the better performance of the proposed technique. This contribution is important because vehicles will be equipped with Dedicated Short Range Communication (DSRC) in the near future. We provide an elaborate discussion on Bluetooth signal parameters with respect to localization, whereby we collectively designate all types of Bluetooth specification parameters that are related to signal strength - such as RSSI, Link Quality, Received and Transmit Power Level - as Bluetooth signal, parameters. According to our analysis and experimental results, "RSSI" and "Transmit Power Level" turn out to be poor candidates for localization, while "Link Quality" has its limitations. On the other hand, "Received Power Level" correlates nicely with distance, which makes it the most desirable Bluetooth signal parameter to be used in location systems. We contend that it is vital to choose the appropriate signal parameter in Bluetooth location systems, and we expect our work to provide useful pointers in any future design of such systems. Existing systems can also benefit by adopting the appropriate Bluetooth signal parameter in their systems, and thereby, improve their location accuracy.  A time-of-arrival (ToA)-based ranging scheme using an ultra-wideband (UWB) radio link is proposed. This ranging scheme implements a search algorithm for the detection of a direct path signal in the presence of dense multipath, utilizing generalized maximum-likelihood (GML) estimation. Models for critical parameters in the algorithm are based on statistical analysis of propagation data and the algorithm is tested on another independent set of propagation measurements. The proposed UWB ranging system uses a correlator and a parallel sampler with a high-speed measurement capability in each transceiver to accomplish two-way ranging between them in the absence of a common clock. We propose a distributed algorithm that uses inter-vehicle distance estimates, made using a radio-based ranging technology, to localize a vehicle among its neighbours. Given that the inter-vehicle distance estimates contain noise, our algorithm reduces the residuals of the Euclidean distance between the vehicles and their measured distances, allowing it to accurately estimate the position of a vehicle within a cluster. In this paper, we show that our proposed algorithm outperforms previously proposed algorithms and present its performance in a simulated vehicular environment. In this paper, we describe a global distributed solution that enables the simultaneous performance of time synchronization and positioning in ultra-wideband (UWB) ad hoc networks. On the one hand, the proposed synchronization scheme basically relies on cooperative two-way-ranging time-of-arrival transactions and a diffusion algorithm that ensures the convergence of clock parameters to average reference values in each node. Although the described solution is generic at first sight, its sensitivity to time-of-arrival accuracy imposes the choice of an impulse-radio ultra-wideband physical layer in the very context. On the other hand, a distributed algorithm coupled with this synchronization scheme mitigates the impact of non-line-of-sight ranging errors on positioning accuracy without any additional protocol hook. More particularly, the realistic UWB ranging error models we use take into account UWB channel effects, as well as detection noises and relative clock drifts. Then, it is demonstrated that a cooperative and distributed maximization of the log-likelihood of range estimates can reduce the uncertainty on estimated positions in comparison with classical distributed weighted least squares approaches. Finally, the proposed distributed maximum log-likelihood algorithm proves to preserve a reasonable level of complexity in each node by approximating asynchronously the positive gradient direction of the log-likelihood function. For both distributed synchronization and positioning algorithms, simulation results are provided to illustrate the relevance of such a solution. There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations. A mobile robot we have developed is equipped with sensors to measure range to landmarks and can simultaneously localize itself as well as locate the landmarks. This modality is useful in those cases where environmental conditions preclude measurement of bearing (typically done optically) to landmarks. Here we extend the paradigm to consider the case where the landmarks (nodes of a sensor network) are able to measure range to each other. We show how the two capabilities are complimentary in being able to achieve a map of the landmarks and to provide localization for the moving robot. We present recent results with experiments on a robot operating in a randomly arranged network of nodes that can communicate via radio and range to each other using sonar. We find that incorporation of inter-node measurements helps reduce drift in positioning as well as leads to faster convergence of the map of the nodes. We find that addition of a mobile node makes the SLAM feasible in a sparsely connected network of nodes A new kind of ad hoc network is hitting the streets: Vehicular Ad Hoc Networks (VANets). In these networks, vehicles communicate with each other and possibly with a roadside infrastructure to provide a long list of applications varying from transit safety to driver assistance and Internet access. In these networks, knowledge of the real-time position of nodes is an assumption made by most protocols, algorithms, and applications. This is a very reasonable assumption, since GPS receivers can be installed easily in vehicles, a number of which already comes with this technology. But as VANets advance into critical areas and become more dependent on localization systems, GPS is starting to show some undesired problems such as not always being available or not being robust enough for some applications. For this reason, a number of other localization techniques such as Dead Reckoning, Cellular Localization, and Image Video Localization has been used in VANets to overcome GPS limitations. A common procedure in all these cases is to use Data Fusion techniques to compute the accurate position of vehicles, creating a new paradigm for localization in which several known localization techniques are combined into a single solution that is more robust and precise than the individual approaches. In this paper, we further discuss this subject by studying and analyzing the localization requirements of the main VANet applications. We then survey each of the localization techniques that can be used to localize vehicles and, finally, examine how these localization techniques can be combined using Data Fusion techniques to provide the robust localization system required by most critical safety applications in VANets.  ABSTRACT Vehicular network with communication among vehicles and between roadside units and vehicles is a recently emerged field of challenge for researchers. Dedicated Short Range Communication (DSRC) is the nominated communication channel specifically designed for this network. Using DSRC, besides sharing information among vehicles, the distances between the nodes of the network can also be estimated for their positioning solutions. In the literature, many algorithms and strategies are presented for radio ranging with less emphasis on constraints and difficulties of distance estimation in vehicular networks. In this paper, different methods of radio ranging like Received Signal Strength (RSS), Time of Arrival (TOA), and Time Difference of Arrival (TDOA) for distance estimation are introduced with emphasis on some important aspects of these methods which should be taken into account by researchers and engineers. Considering some important concerns, related to ranging and positioning with radio signals, some special aspects of each method are presented and discussed. Finally, with regard to the limitations imposed by vehicular networks and DSRC, the preferred ranging approach is proposed. Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions. The Global Positioning System (GPS) is a satellite-based navigation and time transfer system developed by the U.S. Department of Defense. It serves marine, airborne, and terrestrial users, both military and civilian. Specifically, GPS includes the Standard Positioning Service (SPS) which provides civilian users with 100 meter accuracy, and it serves military users with the Precise Positioning Service (PPS) which provides 20-m accuracy. Both of these services are available worldwide with no requirement for a local reference station. In contrast, differential operation of GPS provides 2- to 10-m accuracy to users within 1000 km of a fixed GPS reference receiver. Finally, carrier phase comparisons can be used to provide centimeter accuracy to users within 10 km and potentially within 100 km of a reference receiver. This advanced tutorial will describe the GPS signals, the various measurements made by the GPS receivers, and estimate the achievable accuracies. It will not dwell on those aspects of GPS which are well known to those skilled in the radio communications art, such as spread-spectrum or code division multiple access. Rather, it will focus on topics which are more unique to radio navigation or GPS. These include code-carrier divergence, codeless tracking, carrier aiding, and narrow correlator spacing. Recently, a few efficient timing synchronization protocols for wireless sensor networks (WSNs) have been proposed with the goal of maximizing the accuracy and minimizing the power utilization. This paper proposes novel clock skew estimators assuming different delay environments to achieve energy-efficient network-wide synchronization for WSNs. The proposed clock skew correction mechanism significantly increases the re-synchronization period, which is a critical factor in reducing the overall power consumption. The proposed synchronization scheme can be applied to the conventional protocols without additional overheads. Moreover, this paper derives the Cramer-Rao lower bounds and the maximum likelihood estimators under different delay models and assumptions. These analytical metrics serves as good benchmarks for the thus far reported experimental results