Maarten Weyn

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.

Survey of the DASH7 Alliance Protocol for 433 MHz Wireless Sensor Communication

433 MHz is getting more attention for Machine-to-Machine communication. This paper presents the DASH7 Alliance Protocol, an active RFID alliance standard for 433 MHz wireless sensor communication based on the ISO/IEC 18000-7. First, the major differences of 433 MHz communication compared to more frequently used frequencies, such as 2.4 GHz and 868/920 MHz are explained. Subsequently, the general concepts of DASH7 Alliance Protocol are described, such as the BLAST networking topology and the different OSI layer implementations, in a top-down method. Basic DASH7 features such as the advertising protocol, ad-hoc synchronization and query based addressing are used to explain the different layers. Finally, the paper introduces a software stack implementation named OSS-7, which is an open source implementation of the DASH7 alliance protocol used for testing, rapid prototyping, and demonstrations.

A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

This paper presents results from comparing different Wi-Fi fingerprinting algorithms on the same private dataset. The algorithms where realized by independent teams in the frame of the off-site track of the EvAAL-ETRI Indoor Localization Competition which was part of the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). Competitors designed and validated their algorithms against the publicly available UJIIndoorLoc database which contains a huge referenceand validation data set. All competing systems were evaluated using the mean error in positioning, with penalties, using a private test dataset. The authors believe that this is the first work in which Wi-Fi fingerprinting algorithm results delivered by several independent and competing teams are fairly compared under the same evaluation conditions. The analysis also comprises a combined approach: Results indicate that the competing systems where complementary, since an ensemble that combines three competing methods reported the overall best results.

DASH7 alliance protocol 1.0: Low-power, mid-range sensor and actuator communication

This paper presents the DASH7 Alliance Protocol 1.0. It is an industry alliance standard for wireless sensor and actuator communication using the unlicensed sub-1 GHz bands. The paper explains its historic relation to active RFID standards ISO 18000-7 for 433 MHz communication, the basic concepts and communication paradigms of the protocol. Since the protocol is a full OSI stack specification, the paper discusses the implementation of every OSI layer.

OLS: opportunistic localization system for smart phones devices

In this paper, we describe the opportunistic localization, which enables localization services that works seamlessly in heterogeneous environments including indoors as oppose to GPS based outdoor-only systems.

Localization performance quantification by conditional entropy

The performance of a localization algorithm is usually expressed as its mean error distance. We argue that this assumes a unimodal distribution of the localization posterior, which is not always appropriate. We propose to additionally quantify the localization posterior distribution by its conditional entropy. This informs us of the uncertainty over the position after a measurement, which must be processed by the localization algorithm. Our example measurement model was ranked in the Evaluating Ambient Assisted Living competition, for which we present the results. Furthermore, we discuss the conditional entropy of our measurement model and two additional measurement models, based on the absolute difference distance and the Pompeiu-Hausdorff distance. We compare these results by using the UJIIndoorLoc database that was also used for the competition.

Biologically inspired SLAM using Wi-Fi

Wi-Fi is a commonly available source of localization information in urban environments but is challenging to integrate into conventional mapping architectures. Current state of the art probabilistic Wi-Fi SLAM algorithms are limited by spatial resolution and an inability to remove the accumulation of rotational error, inherent limitations of the Wi-Fi architecture. In this paper we leverage the low quality sensory requirements and coarse metric properties of RatSLAM to localize using Wi-Fi fingerprints. To further improve performance, we present a novel sensor fusion technique that integrates camera and Wi-Fi to improve localization specificity, and use compass sensor data to remove orientation drift. We evaluate the algorithms in diverse real world indoor and outdoor environments, including an office floor, university campus and a visually aliased circular building loop. The algorithms produce topologically correct maps that are superior to those produced using only a single sensor modality.

Adaptive motion model for a smart phone based opportunistic localization system

Localization systems will evolve towards autonomous system which will use any useful information provided by mobile devices taking the hardware specification and environmental limitations into account. This paper demonstrates the concept of opportunistic localization using a smart phone with the following sensor technologies: Wi-Fi, GSM, GPS and two embedded accelerometers. A particle filter based estimator with an adaptive motion model is used to seamlessly fuse the different sensory readings. Real experiments in multi-floor, indoor-outdoor environments were conducted to analyze the performance of the proposed system. The achieved results using various sensor combinations are presented.

Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting.

Localization systems are increasingly valuable, but their location estimates are only useful when the uncertainty of the estimate is known. This uncertainty is currently calculated as the location error given a ground truth, which is then used as a static measure in sometimes very different environments. In contrast, we propose the use of the conditional entropy of a posterior probability distribution as a complementary measure of uncertainty. This measure has the advantage of being dynamic, i.e., it can be calculated during localization based on individual sensor measurements, does not require a ground truth, and can be applied to discrete localization algorithms. Furthermore, for every consistent location estimation algorithm, both the location error and the conditional entropy measures must be related, i.e., a low entropy should always correspond with a small location error, while a high entropy can correspond with either a small or large location error. We validate this relationship experimentally by calculating both measures of uncertainty in three publicly available datasets using probabilistic Wi-Fi fingerprinting with eight different implementations of the sensor model. We show that the discrepancy between these measures, i.e., many location estimates having a high location error while simultaneously having a low conditional entropy, is largest for the least realistic implementations of the probabilistic sensor model. Based on the results presented in this paper, we conclude that conditional entropy, being dynamic, complementary to location error, and applicable to both continuous and discrete localization, provides an important extra means of characterizing a localization method.

Asynchronous, electromagnetic sensor fusion in RatSLAM

We show the predictive value of the mean mutual information rate about the RatSLAM algorithm for electromagnetic sensors and their combinations. We calculated the mean mutual information between positions in the environment and sensor measurements performed at a specific position in the environment and defined the mean mutual information rate depending on the sensors measurement rate. We compare these results to RatSLAM experience maps generated using these sensors and sensor combinations and define a mean error to quantify the spatial quality of the experience map. We conclude that the mean mutual information rate generally predicts the performance correctly, but also find and explain discrepancies in specific cases.