Martin Klepal

Indoor PDR performance enhancement using minimal map information and particle filters

For professional users such as firefighters and other first responders, GNSS positioning technology (GPS, assisted GPS) can satisfy outdoor positioning requirements in many instances. However, there is still a need for high-performance deep indoor positioning for use by these same professional users. This need has already been clearly expressed by various communities of end users in the context of WearIT@Work, an R&D project funded by the European Communitys Sixth Framework Program. It is known that map matching can help for indoor pedestrian navigation. In most previous research, it was assumed that detailed building plans are available. However, in many emergency / rescue scenarios, only very limited building plan information may be at hand. For example a building outline might be obtained from aerial photographs or cataster databases. Alternatively, an escape plan posted at the entrances to many building would yield only approximate exit door and stairwell locations as well as hallway and room orientation. What is not known is how much map information is really required for a USAR mission and how much each level of map detail might help to improve positioning accuracy. Obviously, the geometry of the building and the course through will be factors consider. The purpose of this paper is to show how a previously published Backtracking Particle Filter (BPF) can be combined with different levels of building plan detail to improve PDR performance. A new in/out scenario that might be typical of a reconnaissance mission during a fire in a two-story office building was evaluated. Using only external wall information, the new scenario yields positioning performance (2.56 m mean 2D error) that is greatly superior to the PDR-only, no map base case (7.74 m mean 2D error). This result has a substantial practical significance since this level of building plan detail could be quickly and easily generated in many emergency instances. The technique could be used to mitigate heading errors that result from exposing the IMU to extreme operating conditions. It is hoped that this mitigating effect will also occur for more irregular paths and in larger traversed spaces such as parking garages and warehouses.

A testbed for evaluating human interaction with ubiquitous computing environments

Core to ubiquitous computing environments are adaptive software systems that adapt their behavior to the context in which the user is attempting the task the system aims to support. This context is strongly linked with the physical environment in which the task is being performed. The efficacy of such adaptive systems is thus highly dependent on the human perception of the provided system behavior within the context represented by that particular physical environment and social situation. However, effective evaluation of human interaction with adaptive ubiquitous computing technologies has been hindered by the cost and logistics of accurately controlling such environmental context. This paper describes TATUS, a ubiquitous computing simulator aimed at overcoming these cost and logistical issues. Based on a 3D games engine, the simulator has been designed to maximize usability and flexibility in the experimentation of adaptive ubiquitous computing systems. We also describe how this simulator is interfaced with a testbed for wireless communication domain simulation.

A Backtracking Particle Filter for fusing building plans with PDR displacement estimates

It is known that Particle Filter and Map Filtering techniques can be used to improve the performance of positioning systems, such as Pedestrian Dead Reckoning (PDR). In previous research on indoor navigation, it was generally assumed that detailed building plans were available. However, in many emer gency / rescue scenarios, there may be only limited building plan information on hand. The purpose of this paper is to show how a novel Backtracking Particle Filter (BPF) can be combined with different levels of building plan detail to improve PDR performance. We use real PDR stride length and blunder-prone stride azimuth data which were collected from multiple walks along paths in and out of a small office building. The PDR displacement data is input to the BPF estimator that in turn uses the building plan information to constrain particle motions. The BPF can take advantage of long-range (geometrical) constraint information and yields excellent positioning performance (1.32 m mean 2D error) with detailed building plan information. More significantly, this same filter using only external wall information produces dramatically improved positioning performance (1.89 m mean 2D error) relative to a PDR-only, no map base case (8.04 m mean 2D error). This effect may very well occur for many other realistic wall layouts and path geometries. Moreover, this result has a substantial practical significance since this level of building plan detail could be quickly and easily generated in many emergency instances.

Influence of Predicted and Measured Fingerprint on the Accuracy of RSSI-based Indoor Location Systems

WLAN indoor location that is based on received signal strength indication (RSSI) technique needs extensive calibration to build a signal fingerprint. Re-calibration is also needed if there is a major change in the propagation environment. The use of propagation models to predict signal fingerprint becomes an interesting preposition. This paper will investigate the influence of predicted fingerprint on the accuracy of indoor location. They include empirical propagation models (i.e. one-slope model and multi-wall model) and a semi-deterministic model. A framework for indoor location with the nearest-neighbour and particle filter are developed to evaluate predicted and measured fingerprints. In order to take advantage of environment description, a map-filtering technique is also elaborated.

A novel backtracking particle filter for pattern matching indoor localization

Particle Filter (PF) techniques has been widely used in indoor localization systems. They are often used in conjunction with pattern matching based on Received Signal Strength Indication (RSSI) fingerprinting. Several variants of the particle filter within a generic framework of the Sequential Importance Sampling (SIS) algorithm have been described. The purpose of this paper is to show how a variant of PF, the so-called Backtracking Particle Filter (BPF), can be used to improve indoor localization performance. The BPF is a technique for refining state estimates based on exclusion of invalid particle trajectories. Categorization of invalid trajectory determined during importance sampling step of the PF. The BPF can also take advantage of available building plan information using the so-called Map Filtering (MF) technique. The incorporation of MF allows the BPF to exploit long-range geometrical constraints. This paper evaluates BPF with indoor localization based on WLAN RSSI fingerprinting. The filtering schema is evaluated using the propagation simulation in an office building, a typical environment for fingerprinting technique. Favorable result are obtained, showing positioning performance (1.34 m mean 2D error) superior to the PF-only no MF case (1.82 m mean 2D error), or up to 25% improvement. It is also shown that the performance is far better than the position estimates from conventional Nearest-Neighbour (NN) and Kalman Filter (KF) approaches using the same RSSI measurements.

Mobile Phone-Based Displacement Estimation for Opportunistic Localisation Systems

The accelerometers integrated in today’s phones can be used to estimate the distance travelled from the accelerations made while walking. The placement of the sensor on the body is important to take into consideration. In this paper, the accelerations recorded with a daily-used phone in the trouser pocket were processed on a mobile device to detect steps and estimate the distance travelled. The outcome of the distance estimates shows an error of 0.05 metres per one metre and can be improved through calibration. This distance was applied in the motion model of a particle filter, and fused with a map of the building. The results establish that the estimates of the algorithm are valuable when fusing with other technologies or environment information, to aid the estimation of the location.

Influence of people shadowing on optimal deployment of WLAN access points

With their low cost and high-speed data rate capabilities, installations of IEEE 802.11-based wireless local area networks (WLANs) are growing exponentially. Although many organizations have started using WLANs, there are still very few tools available that can help the design of WLAN networks. As a result, the current deployment remains ad-hoc in nature. The objective of this work is to develop modeling tools for performance optimization of WLAN networks and WLAN access points. In particular, propagation models are available that can predict the signal strength and interference in a WLAN system by taking into account environment specific parameters such as the structure of the building, presence or absence of stationary obstacles etc. This paper investigates the influence of moving obstacles, such as people, on radio wave propagation inside a building and the effect on received signal quality in a WLAN. Our findings suggest that the presence of moving obstacles seriously affects the performance of the system by introducing heavy variations in the received signal strength.

A Data Dissemination Strategy for Cooperative Vehicular Systems

Cooperative systems in transportation can bring new intelligence for vehicles, roadside systems, operators and individuals by creating a communications platform allowing vehicles and infrastructure to share information. The performance of this underlying communication system has a major impact on the effectiveness of the emerging applications for intelligent transportation systems. Similarly, the approach taken to the dissemination of relevant information throughout the vehicular setting is influenced by the network performance characteristics. This paper investigates the concept of data dissemination in a heterogeneous vehicular wireless environment. A communications architecture which consists of infrastructure based transmission for cooperative vehicular systems is described. Following this, a simple, policy-based solution to establish how best to disseminate the data for an envisaged ITS application is presented. This policy considers the application requirements and the quality of the wireless carrier in determining how the information can be propagated to the relevant recipients in the most effective and efficient manner.

Virtual lifeline: Multimodal sensor data fusion for robust navigation in unknown environments

We present a novel, multimodal indoor navigation technique that combines pedestrian dead reckoning (PDR) with relative position information from wireless sensor nodes. It is motivated by emergency response scenarios where no fixed or pre-deployed global positioning infrastructure is available and where typical motion patterns defeat standard PDR systems. We use RF and ultrasound beacons to periodically re-align the PDR system and reduce the impact of incremental error accumulation. Unlike previous work on multimodal positioning, we allow the beacons to be dynamically deployed (dropped by the user) at previously unknown locations. A key contribution of this paper is to show that despite the fact that the beacon locations are not known (in terms of absolute coordinates), they significantly improve the performance of the system. This effect is especially relevant when a user re-traces (parts of) the path he or she had previously travelled or lingers and moves around in an irregular pattern at single locations for extended periods of time. Both situations are common and relevant for emergency response scenarios. We describe the system architecture, the fusion algorithms and provide an in depth evaluation in a large scale, realistic experiment.

A Bayesian Approach for RF-Based Indoor Localisation

The proliferation of Wireless LAN and Wireless Sensor Network make the technologies become an attractive proposition for indoor localisation. Both technologies have provided communication infrastructure and hence RF-based localisation with WLAN and WSN becomes a software-only solution. WLAN-based localisation generally provides room accuracy, therefore sensor data fusion with WSN is proposed when better location accuracy is needed. This paper will describe a Bayesian approach for indoor localisation. A suboptimal sequential Bayesian method of Particle Filter combined with Map Filtering technique is used for sensor data fusion between WLAN and WSN. The location system performance also will be evaluated.