Tommi Perälä

Bio: Tommi Perälä is an academic researcher from University of Jyväskylä. The author has contributed to research in topics: Kalman filter & Food web. The author has an hindex of 10, co-authored 22 publications receiving 843 citations. Previous affiliations of Tommi Perälä include Nokia & Tampere University of Technology.

A comparative survey of WLAN location fingerprinting methods

Abstract: The term “location fingerprinting” covers a wide variety of methods for determining receiver position using databases of radio signal strength measurements from different sources. In this work we present a survey of location fingerprinting methods, including deterministic and probabilistic methods for static estimation, as well as filtering methods based on Bayesian filter and Kalman filter. We present a unified mathematical formulation of radio map database and location estimation, point out the equivalence of some methods from the literature, and present some new variants. A set of tests in an indoor positioning scenario using WLAN signal strengths is performed to determine the influence of different calibration and location method parameters. In the tests, the probabilistic method with the kernel function approximation of signal strength histograms was the best static positioning method. Moreover, all filters improved the results significantly over the static methods.

Robust Extended Kalman Filtering in Hybrid Positioning Applications

Abstract: The Kalman filter and its extensions has been widely studied and applied in positioning, in part because its low computational complexity is well suited to small mobile devices. While these filters are accurate for problems with small nonlinearities and nearly Gaussian noise statistics, they can perform very badly when these conditions do not prevail. In hybrid positioning, large nonlinearities can be caused by the geometry and large outliers (blunder measurements) can arise due to multipath and non line-of-sight signals. It is therefore of interest to find ways to make positioning algorithms based on Kalman-type filters more robust. In this paper two methods to robustify the Kalman filter are presented. In the first method the variances of the measurements are scaled according to weights that are calculated for each innovation, thus giving less influence to measurements that are regarded as blunder. The second method is a Bayesian filter that approximates the density of the innovation with a non-Gaussian density. Weighting functions and innovation densities are chosen using Hubers min-max approach for the epsilon contaminated normal neighborhood, the p-point family, and a heuristic approach. Six robust extended Kalman filters together with the classical extended Kalman filter (EKF) and the second order extended Kalman filter (EKF2) are tested in numerical simulations. The results show that the proposed methods outperform EKF and EKF2 in cases where there is blunder measurement or considerable linearization errors present.

Fingerprint Kalman Filter in indoor positioning applications

Abstract: In this paper, we present a new filter, the Fingerprint Kalman Filter (FKF), and apply it to indoor positioning. FKF enables sequential position estimation using WLAN RSSI measurements and fingerprint data. Fingerprints that are collected beforehand in a calibration phase contain samples of the radio map in certain points, namely, calibration points. This means that FKF does not need an analytic formula of the measurement equation like conventional Kalman-type filters do (e.g. the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF)). Like EKF and UKF, FKF is based on the recursive computation of the Best Linear Unbiased Estimator (BLUE) and has small computational and memory requirements. An often-used Kalman-type filter for this problem is so-called Position Kalman Filter (PKF) that uses static position solutions as measurements for the conventional Kalman filter. In the test part of the paper, we compare FKF, PKF and different static location estimation methods, namely, the Nearest Neighbor (NN) and the Kernel method. The test data is real WLAN RSSI measurement data. The results indicate that the filters give more accurate position estimates than the static methods. FKF performs better than PKF with NN as the static estimator, and the computational load of FKF is smaller than PKF with the Kernel method.

Indoor positioning using WLAN coverage area estimates

Abstract: This paper introduces a novel method for positioning using coverage area estimates of wireless communication nodes. The coverage areas are estimated in a Bayesian inference framework using location fingerprints that are collected in an offline calibration phase, and the estimated coverage areas are stored in a database. In the online positioning phase the coverage areas of the heard communication nodes are used to infer the position of the mobile terminal. Floor plan information is used to enhance the positioning accuracy. In a field study comparing Kalman Filter, Box Filter and Particle Filter using real WLAN measurement data, it is found that Kalman Filter achieves almost the same accuracy as Box Filter and Particle Filter but with smaller computational load.

Detection of Allee effects in marine fishes: analytical biases generated by data availability and model selection

Abstract: The demographic Allee effect, or depensation, implies positive association between per capita population growth rate and population size at low abundances, thereby lowering growth ability of sparse...

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Abstract: The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

Life History Evolution

Abstract: Life history biology sits on the interface between genetics and ecology, and both have made important theoretical and empirical contributions to our understanding. However, the connections between the disciplines have not always been as close as they might have been and this book takes some useful steps towards remedying this. It gives an excellent review of life history theory, and integrates this well with results from the empirical literature. After an 11-page introduction, Roff sets out ‘a framework for analysis’ in which he covers the necessary elements of quantitative and population genetics. This includes clear definitions of fitness in a range of circumstances, from density independent populations in constant environments through to the more complex situations of density and frequency dependence and environments that are spatially or temporally stochastic. Trade-offs are then examined, including a valuable analysis of potential pitfalls in studying them and ways that these can be avoided. The author then deals in turn with evolution in constant environments; stochastic environments and ‘predictable environments’. The last of these covers situations where there is environmental variation, but at least some information is available to allow individuals to make an adaptive response. The final chapter identifies 20 topics for future study. Some will find the book too dominated by theory. Others (but probably not readers of Heredity!) will find it contains too much genetics. But Roff does an excellent job of making the theory accessible, covering the essential issues and pointing to original sources for the details. Theory is related to a significant number of empirical studies, although there is room for another book reviewing the empirical literature on life histories in detail, and Roff’s book would provide a robust skeleton on which to hang this. To make my own assessment, I examined in detail Roff’s discussion of the question of fitness measures for density dependent populations in stochastic environments – an area in which I have been involved. I could not fault him – all the key references were there and the issues were made very clear without the more esoteric mathematics. I also examined some areas that I was less familiar with, and again the text was clear and easy to read. My only real criticism of the book would be that its very long chapters (more than 130 pages in one case) makes it difficult to find things. It would have been simple to address this by including the section headings on the contents pages. A minor personal quibble would be that the book usually expresses problems in terms of the intrinsic rate of increase, r, and the characteristic (Lotka) equation. A matrix formulation is often more tractable and is easier to generalise to density dependent populations and stochastic environments, so expanding on the relationship between the two would have been useful. But overall this is an excellent book. It brings together the key theory in a single place. It gives an invaluable route into the literature, with a bibliography of 1600 or so items. These features, and its identification of topics that need further study should make an important contribution to moving the field forward.

Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances.

Abstract: In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

Location Fingerprinting With Bluetooth Low Energy Beacons

Abstract: The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work, we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a $\sim\! 600\ \mbox{m}^2$ testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve $30\ \mbox{m}^2$ ), compared to $100\ \mbox{m}^2$ ) and < 8.5 m for an established WiFi network in the same area.