14 research outputs found
Belief Condensation Filtering For Rssi-Based State Estimation In Indoor Localization
Recent advancements in signal processing and communication systems
have resulted in evolution of an intriguing concept referred to
as Internet of Things (IoT). By embracing the IoT evolution, there
has been a surge of recent interest in localization/tracking within
indoor environments based on Bluetooth Low Energy (BLE) technology.
The basic motive behind BLE-enabled IoT applications is
to provide advanced residential and enterprise solutions in an energy
efficient and reliable fashion. Although recently different state
estimation (SE) methodologies, ranging from Kalman filters, Particle
filters, to multiple-modal solutions, have been utilized for BLEbased
indoor localization, there is a need for ever more accurate and
real-time algorithms. The main challenge here is that multipath fading
and drastic fluctuations in the indoor environment result in complex
non-linear, non-Gaussian estimation problems. The paper focuses
on an alternative solution to the existing filtering techniques
and introduce/discuss incorporation of the Belief Condensation Filter
(BCF) for localization via BLE-enabled beacons. The BCF is a
member of the universal approximation family of densities with performance
bound achieving accuracy and efficiency in sequential SE
and Bayesian tracking. It is a resilient filter in harsh environments
where nonlinearities and non-Gaussian noise profiles persist, as seen
in such applications as Indoor Localization
A smartphone localization algorithm using RSSI and inertial sensor measurement fusion
Indoor navigation using the existing wireless infrastructure and mobile devices is a very active research area. The major challenge is to leverage the extensive smartphone sensor suite to achieve location tracking with high accuracy. In this paper, we develop a navigation algorithm which fuses the WiFi received signal strength indicator (RSSI) and smartphone inertial sensor measurements. A sequential Monte Carlo filter is developed for inertial sensor based tracking, and a radiolocation algorithm is developed to infer mobile location based on RSSI measurements. The simulation results show that the proposed algorithm significantly outperforms the extended Kalman filter (EKF), and achieves competitive location accuracy compared with the round trip time (RTT) based ultra-wideband (UWB) system.National Science Foundation (U.S.) (Grant ECCS-0901034)United States. Office of Naval Research (Grant N00014-11-1-0397)Defense University Research Instrumentation Program (U.S.) (Grant N00014-08-1-0826)Massachusetts Institute of Technology. Institute for Soldier Nanotechnologie
Probabilistic Load Forecasting Based on Adaptive Online Learning
Load forecasting is crucial for multiple energy management
tasks such as scheduling generation capacity, planning
supply and demand, and minimizing energy trade costs. Such
relevance has increased even more in recent years due to the
integration of renewable energies, electric cars, and microgrids.
Conventional load forecasting techniques obtain singlevalue
load forecasts by exploiting consumption patterns of past
load demand. However, such techniques cannot assess intrinsic
uncertainties in load demand, and cannot capture dynamic
changes in consumption patterns. To address these problems,
this paper presents a method for probabilistic load forecasting
based on the adaptive online learning of hidden Markov models.
We propose learning and forecasting techniques with theoretical
guarantees, and experimentally assess their performance in
multiple scenarios. In particular, we develop adaptive online
learning techniques that update model parameters recursively,
and sequential prediction techniques that obtain probabilistic
forecasts using the most recent parameters. The performance of
the method is evaluated using multiple datasets corresponding
with regions that have different sizes and display assorted
time-varying consumption patterns. The results show that the
proposed method can significantly improve the performance of
existing techniques for a wide range of scenarios.Ramon y Cajal Grant RYC-2016-19383
Basque Government under the grant "Artificial Intelligence in BCAM number EXP. 2019/00432"
Iberdrola
Foundation under the 2019 Research Grant
Vehicular Position Tracking Using LTE Signals
This paper proposes and validates, in the field, an approach for position tracking that is based on Long-Term Evolution (LTE) downlink signal measurements. A setup for real data live gathering is used to collect LTE signals while driving a car in the town of Rapperswil, Switzerland. The collected data are then processed to extract the received LTE cell-specific reference signals (CRSs), which are exploited for estimating pseudoranges. More precisely, the pseudoranges are evaluated by using the \u201cESPRIT and Kalman Filter for Time-of-Arrival Tracking\u201d (EKAT) algorithm and by taking advantage of signal combining in the time, frequency, spatial, and cell ID domains. Finally, the pseudoranges are corrected for base station's clock bias and drift, which are previously estimated, and are used in a positioning filter. The obtained results demonstrate the feasibility of a position tracking system based on the reception of LTE downlink signals
Generalized Maximum Entropy for Supervised Classification
The maximum entropy principle advocates to
evaluate eventsâ probabilities using a distribution that maximizes
entropy among those that satisfy certain expectationsâ constraints. Such principle can be generalized for arbitrary decision
problems where it corresponds to minimax approaches. This
paper establishes a framework for supervised classification based
on the generalized maximum entropy principle that leads to
minimax risk classifiers (MRCs). We develop learning techniques
that determine MRCs for general entropy functions and provide
performance guarantees by means of convex optimization. In
addition, we describe the relationship of the presented techniques
with existing classification methods, and quantify MRCs performance in comparison with the proposed bounds and conventional
methods.RYC-2016-1938
Spatiotemporal information coupling in network navigation
Network navigation, encompassing both spatial and
temporal cooperation to locate mobile agents, is a key enabler
for numerous emerging location-based applications. In such
cooperative networks, the positional information obtained by
each agent is a complex compound due to the interaction among
its neighbors. This information coupling may result in poor
performance: algorithms that discard information coupling are
often inaccurate, and algorithms that keep track of all the
neighborsâ interactions are often inefficient. In this paper, we
develop a principled framework to characterize the information
coupling present in network navigation. Specifically, we derive
the equivalent Fisher information matrix for individual agents
as the sum of effective information from each neighbor and the
coupled information induced by the neighborsâ interaction. We
further characterize how coupled information decays with the
network distance in representative case studies. The results of
this work can offer guidelines for the development of distributed
techniques that adequately account for information coupling, and
hence enable accurate and efficient network navigation.RYC-2016-1938