Probabilistic logics combine the ability to reason about complex scenes, with a rigorous approach to uncertainty. This paper explores the construction of probabilistic spatial logics through the combination of established qualitative spatial calculi together with Markov logic networks (MLNs). Qualitative spatial calculi provide the basis for automated representation and reasoning with complex spatial scenes; MLNs provide a rigorous basis for handling uncertainty and driving probabilistic inference. Our approach focuses specifically on the combination of an uncertain knowledge base with a certain spatial reasoning rule-base. The experiments explore how uncertain knowledge propagates through certain qualitative spatial inferences, using the specific example of reasoning with cardinal directions. The results provide a template for probabilistic qualitative spatial reasoning more generally, with applications to a wide range of common scenarios for situational awareness and automated reasoning under uncertainty.

The paper investigates whether the methods chosen for representing uncertain geographic information aid or impair decision-making in the context of wildfire hazard. Through a series of three human subject experiments, utilizing 180 subjects and employing increasingly difficult tasks, this research evaluates the effect of five different visualizations and a text-based representation on decision-making under uncertainty. Our quantitative experiments focus specifically on the task of decision-making under uncertainty, rather than the task of reading levels of uncertainty from the map. To guard against the potential for generosity and risk seeking in decision-making under uncertainty, the experimental design uses performance-based incentives. The experiments showed that the choice of representation makes little difference to performance in cases where subjects are allowed the time and focus to consider their decisions. However, with the increasing difficulty of time pressure, subjects performed best using a spectral color hue-based representation, rather than more carefully designed cartographic representations. Text-based and simplified boundary encodings were among the worst performers. The results have implications for the performance of decision-making under uncertainty using static maps, especially in the stressful environments surrounding an emergency.

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required.

We consider the problem of estimating the path taken by an object in a road network from sparse, noisy position measurements. Path estimation is posed in a Bayesian framework which allows the incorporation of prior information about vehicle movements. A carefully designed importance sampler is used to approximate the posterior path probabilities. The algorithm is demonstrated on simulated data.

Fingerprinting is the prevailing positioning method for location based service (LBS) and indoor positioning applications when compared with other methods such as cell of origin (CoO) and trilateration. It is especially more suitable for complicated indoor environments. However, higher positioning accuracy is still expected for it to match the capabilities of other mature techniques such as GPS. This paper presents a new algorithm for improving the positioning accuracy of the Nearest Neighbour (NN) algorithm from a Wi-Fi-based fingerprinting method. The new algorithm initially used the NN algorithm to identify the initial position estimate of the user being tracked. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated through differencing two calculated distances which were derived from a similar environment. The new algorithm was tested and the results evaluated against that of the NN algorithm. The preliminary results from 24 test points showed that the positioning accuracy of the new approach has improved consistently and the root mean square accuracy improved to 3.4 m from 3.8 m with the NN algorithm.

Wi-Fi and smartphone based location tracking technologies have been widely applied in location based services (LBS). This research is a part of an Australian Research Council (ARC) project required by a large global shopping mall company located in Australia. It aims to develop an effective customer tracking approach for acquiring shopping behavior of customers and providing them better services. Currently, the log data provided by the company only recorded one Wi-Fi connection at a time for one smartphone user. This makes most of the conventional tracking and positioning methods inapplicable. In order to solve this problem, a new approach was developed, which takes advantage of both the cell of origin (CoO) method and the fingerprinting method. Its implementation mainly includes two phases: 1) the cell determination phase, including partitioning of the floors of the shopping mall using a Voronoi diagram and determining a unique cell for each wireless access point (AP), adjusting the cells manually according to the real floor layout, obstructions as well as an on-site observing and calibrating process. An AP-shop/room matching table and other relevant information are stored in a database; and 2) the user location tracking phase, including determination of the cell that the customer locates in according to the detected AP linked with the customer's mobile phone. A back-tracing method is also used to improve the tracking performance. This new approach was tested in a large shopping-mall-like space covering an area of 1000 square meters, containing 8 simulated shops and 11 APs. The results showed a 96% correct detection rate whilst the 4% erroneous detections occurred when a user was near the cell boundaries. This new approach is also easy to implement and integrate with other location tracking systems.

This article provides a decentralized and coordinate-free algorithm, called decentralized gradient field (DGraF), to identify critical points (peaks, pits, and passes) and the topological structure of the surface network connecting those critical points. Algorithms that can operate in the network without centralized control and without coordinates are important in emerging resource-constrained spatial computing environments, in particular geosensor networks. Our approach accounts for the discrepancies between finite granularity sensor data and the underlying continuous field, ignored by previous work. Empirical evaluation shows that our DGraF algorithm can improve the accuracy of critical points identification when compared with the current state-of-the-art decentralized algorithm and matches the accuracy of a centralized algorithm for peaks and pits. The DGraF algorithm is efficient, requiring O(n) overall communication complexity, where n is the number of nodes in the geosensor network. Further, empirical investigations of our algorithm across a range of simulations demonstrate improved load balance of DGraF when compared with an existing decentralized algorithm. Our investigation highlights a number of important issues for future research on the detection of holes and the monitoring of dynamic events in a field.

Cooperative positioning (CP) is an approach for positioning and/or positioning enhancement among a number of participants, which communicate and fuse their position-related information. Due to the shortcomings of Global Navigation Satellite Systems (GNSSs), modern CP approaches are considered for improving vehicular positioning where the GNSS cannot address the requirements of the specific applications such as collision avoidance or lane-level positioning. An inertial navigation system (INS) has not been considered for CP in the literature. The hybrid INS/GNSS methods used for positioning enhancement in standalone nodes cannot be classified as CP because the position-related data are not communicated between at least two independent entities. In this paper, we present a novel CP technique to improve INS-based positioning in vehicular networks. This cooperative inertial navigation (CIN) method can be used to enhance INS-based positioning in difficult GNSS environments, such as in very dense urban areas and tunnels. In the CIN method that is proposed, vehicles communicate their inertial measurement unit (IMU) and INS-based position data with oncoming vehicles traveling in the opposite direction. Each vehicle fuses the received data with those locally observed and the carrier frequency offset (CFO) of the received packets to improve the accuracy of its position estimates. The proposed method is analyzed using simulations and is also experimentally verified. The experimental results show up to 72% improvement in positioning over the standalone INS-based method.

Relative positioning among vehicles is a fundamental parameter for advanced applications of intelligent transportation systems such as collision avoidance and road safety. However, the level of positioning accuracy achievable using Global Navigation Satellite Systems does not meet the requirements of these applications. Cooperative positioning (CP) techniques can be used for improving the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). The tight integration of Global Positioning System (GPS) data among communicating vehicles has already been introduced by the authors as a tight CP approach with specific advantages over differential GPS (DGPS) for relative positioning. In this paper, we propose an enhanced tight CP technique adding low-cost inertial navigation sensors and GPS Doppler shifts. Based on analytical and experimental results, the new method outperforms its predecessor and DGPS by 10% and 24%, respectively.

Cooperative Positioning (CP) techniques are used to enhance the performance of positioning through sharing position-related data among a number of agents. These agents are usually users with mobility and, possibly, the infrastructure node(s). In CP systems, position-related data are shared among the participating nodes using a communication medium. Inter-node distance is a common parameter considered in CP techniques, especially for those in the environments with limited Global Navigation Satellite System (GNSS) visibility including dense urban areas and indoor environments. Radio ranging based on Received Signal Strength (RSS) is popular among researchers for its simplicity. However, the accuracy of this method is far beyond the requirements of CP systems. Here, we introduce Carrier Frequency Offset (CFO) as a potential observable to improve RSS ranging. Regardless of the content of the data communicated among the agents, RSS and CFO always exist in the communication signal. Therefore, the results of this work can be applied to improve the performance of any other CP method. Here, a hybrid CFO-RSS ranging method is presented to improve the accuracy of RSS ranging. The experimental results show up to 80% accuracy improvement over RSS using the proposed hybrid CFO-RSS technique. Although the examples used here are for outdoor situations, the outcomes are directly applicable indoors or any situation where GNSS signals are not available.

An integrated positioning solution termed ‘collaborative positioning’ employs multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UAV's, team of robots, emergency crews and first responders. The stakeholders of the solution (i.e., mobile sensors, users, fixed stations and external databases) are involved in an iterative algorithm to estimate or improve the accuracy of each node's position based on statistical models. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple-sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation, and partially indoor navigation. For this purpose different sensor platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMU's, MEMS-based IMU's, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans, a personal navigator and a foot tracker unit. In terms of the tests, the data from the different platforms are recorded simultaneously. Several field experiments conducted in a week at the University of Nottingham are described and investigated in the paper. The personal navigator and a foot tracker unit moved on the building roof, then trough the building down to where it logged data simultaneously with the vans, all of them moving together and relative to each other. The platforms then logged data simultaneously covering various accelerations, dynamics, etc. over longer trajectories. Promising preliminary results of the field experiments showed that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms.