Javier Rodas

Bluetooth Sensor Network Positioning System with Dynamic Calibration

Positioning systems based on sensor networks is one of the most promising fields in mobile computing. This paper considers the Bluetooth standard to locate devices using the RSSI (Received Signal Strength Indicator). The major problem of working with this parameter is its fluctuation that happens very fast due to changes in the environment. These changes can be caused by humidity or temperature, presence and movement of people, opening and closing of doors, multipath effect, etc... This paper introduces an innovative approach that uses the RSSI information between several fixed wireless beacons to improve the reliability of a Bluetooth positioning system. This information is used to calibrate the sensor responses. The results of several experiments illustrate how the real time calibration improves the precision and the stabilization of the position estimations. Moreover, we show the improvement obtained when increasing the number of beacons.

A methodology for repeatable, off-line, closed-loop wireless communication system measurements at very high velocities of up to 560 km/h

The impact of high velocities on the physical layer downlink performance of mobile radio communication systems is generally measured by placing a receiver in a car, train, or similar vehicle. While these so-called drive test measurements produce valuable results, they lack the flexibility, repeatability, and controllability usually required for initial testing of ideas and algorithms. In this paper, we present a methodology that allows for repeat-able, closed-loop, off-line-processed measurements at velocities up to 560 km/h (350 mph). The proposed laboratory set-up allows for precise controlling of velocity and average signal-to-noise ratio. For increased convenience during initial testing, the apparatus can be even used indoors.

Bayesian filtering for a bluetooth positioning system

Positioning systems are one of the multiple applications of the wireless sensor networks. These networks are very adequate in environments where other positioning technologies, as satellite systems, do not work. Bluetooth is a promising technology, since it is present in any kind of portable devices. By using the received signal strength indicator (RSSI) it is possible to make an estimation of the distance between a transmitter and a receiver. By using this information, it is possible to develop an algorithm that estimates positions, even with the bluetooth constraints when the RSSI is obtained. Our experimental results show that our algorithm, based on a particle filter, can achieve a good performance.

Architecture for Multi-Technology Real-Time Location Systems

The rising popularity of location-based services has prompted considerable research in the field of indoor location systems. Since there is no single technology to support these systems, it is necessary to consider the fusion of the information coming from heterogeneous sensors. This paper presents a software architecture designed for a hybrid location system where we can merge information from multiple sensor technologies. The architecture was designed to be used by different kinds of actors independently and with mutual transparency: hardware administrators, algorithm developers and user applications. The paper presents the architecture design, work-flow, case study examples and some results to show how different technologies can be exploited to obtain a good estimation of a target position.

A multi-model particle filtering algorithm for indoor tracking of mobile terminals using RSS data

In this paper we address the problem of indoor tracking using received signal strength (RSS) as a positiondependent data measurement. This type of measurements are very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.

A multi-model sequential Monte Carlo methodology for indoor tracking: Algorithms and experimental results

In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. Although various models have been proposed in the literature, they often require the use of very large collections of data in order to fit them and display great sensitivity to changes in the radio propagation environment. In this work we advocate the use of switching multiple models that account for different classes of target dynamics and propagation environments and propose a flexible probabilistic switching scheme. The resulting state-space structure is termed a generalized switching multiple model (GSMM) system. Within this framework, we investigate two types of models for the RSS data: polynomial models and classical logarithmic path-loss representation. The first model is more accurate however it demands an offline model fitting step. The second one is less precise but it can be fitted in an online procedure. We have designed two tracking algorithms built around a Rao-Blackwellized particle filter, tailored to the GSMM structure and assessed its performances both with synthetic and experimental measurements.

Multiple Antennas Bluetooth System for RSSI Stabilization

In this paper we introduce a Bluetooth system which achieves an important improvement in the RSSI stability thanks to the use of multiple antennas. The main advantage of our system is that it reduces the effects produced by fading in the received signal power, that is obtained by the RSSI (received signal strength indicator). In most positioning systems the RSSI stability is very important, because it is the main reference for obtaining the desired positions. Using spatial diversity our system tries to stabilize this RSSI measurement. We present a study of this important improvement in which we compare different combination/selection techniques which use the RSSIs obtained from the received signals. We have carried out an empirical evaluation of this multiple antenna Bluetooth system, and we have verified how these spatial diversity techniques reduce the mean square error of the measured RSSI with respect to a theoretical propagation model.

Joint Estimation of Position and Channel Propagation Model Parameters in a Bluetooth Network

Wireless sensor networks are a promising solution for indoor location systems. However, many of these systems rely on algorithms that use parametric models of channel propagation where the parameters can be time variant. This paper introduces a new technique based on a Bayesian filtering method that estimates network node positions at the same time that propagation model parameters are extracted. Experimental results show the location estimation improvement of the proposed technique.

Cross measurement process with a ZigBee sensor network

Location systems based on Wireless Sensor Networks (WSN) and Receiver Signal Strength (RSS) mainly depend on how the measurements are obtained. Usually, there are some mobile nodes to be located that periodically send beacon frames, which are captured by an anchor sensor network. After combining this information, the system estimates node positions. However, it is well-known that these systems suffer from big inaccuracy problems due to environmental changes (obstacles, people, loose of line-of-sight, etc.), that produce fluctuations on RSS level. We introduce a cross measurement process to obtain more RSS data, getting information among the anchors and making possible to detect these changes, to perform appropriate fixes and calibrations.

A model-switching sequential Monte Carlo algorithm for indoor tracking with experimental RSS data

In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.