526,044 research outputs found
An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters
A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values
Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Localization is a key requirement for mobile robot autonomy and human-robot
interaction. Vision-based localization is accurate and flexible, however, it
incurs a high computational burden which limits its application on many
resource-constrained platforms. In this paper, we address the problem of
performing real-time localization in large-scale 3D point cloud maps of
ever-growing size. While most systems using multi-modal information reduce
localization time by employing side-channel information in a coarse manner (eg.
WiFi for a rough prior position estimate), we propose to inter-weave the map
with rich sensory data. This multi-modal approach achieves two key goals
simultaneously. First, it enables us to harness additional sensory data to
localise against a map covering a vast area in real-time; and secondly, it also
allows us to roughly localise devices which are not equipped with a camera. The
key to our approach is a localization policy based on a sequential Monte Carlo
estimator. The localiser uses this policy to attempt point-matching only in
nodes where it is likely to succeed, significantly increasing the efficiency of
the localization process. The proposed multi-modal localization system is
evaluated extensively in a large museum building. The results show that our
multi-modal approach not only increases the localization accuracy but
significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization
Indoor localization in multi-floor buildings is an important research
problem. Finding the correct floor, in a fast and efficient manner, in a
shopping mall or an unknown university building can save the users' search time
and can enable a myriad of Location Based Services in the future. One of the
most widely spread techniques for floor estimation in multi-floor buildings is
the fingerprinting-based localization using Received Signal Strength (RSS)
measurements coming from indoor networks, such as WLAN and BLE. The clear
advantage of RSS-based floor estimation is its ease of implementation on a
multitude of mobile devices at the Application Programming Interface (API)
level, because RSS values are directly accessible through API interface.
However, the downside of a fingerprinting approach, especially for large-scale
floor estimation and positioning solutions, is their need to store and transmit
a huge amount of fingerprinting data. The problem becomes more severe when the
localization is intended to be done on mobile devices which have limited
memory, power, and computational resources. An alternative floor estimation
method, which has lower complexity and is faster than the fingerprinting is the
Weighted Centroid Localization (WCL) method. The trade-off is however paid in
terms of a lower accuracy than the one obtained with traditional fingerprinting
with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based
method for floor estimation via fingerprint clustering of WiFi and various
other positioning sensor outputs is introduced. Our method achieves a floor
estimation accuracy close to the one with NN fingerprinting, while
significantly improves the complexity and the speed of the floor detection
algorithm. The decrease in the database size is achieved through storing and
transmitting only the cluster heads (CH's) and their corresponding floor
labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and
Tracking: Indoors, Outdoors and Emerging Network
Attention explores space periodically at the theta frequency
Voluntary attention is at the core of a wide variety of cognitive functions. Attention can be oriented to and sustained at a location or reoriented in space to allow processing at other locations—critical in an ever-changing environment. Numerous studies have investigated attentional orienting in time and space, but little is known about the spatiotemporal dynamics of attentional reorienting. Here we explicitly manipulated attentional reorienting using a cuing procedure in a two- alternative forced-choice orientation-discrimination task. We interrogated attentional distribution by flashing two probe stimuli with various delays between the precue and target stimuli. Then we used the probabilities that both probes and neither probe were correctly reported to solve a second-degree equation, which estimates the report probability at each probe location. We demonstrated that attention reorients periodically at ~4 Hz (theta) between the two stimulus locations. We further characterized the processing dynamics at each stimulus location, and demonstrated that attention samples each location periodically at ;11 Hz (alpha). Finally, simulations support our findings and show that this method is sufficiently powered, making it a valuable tool for studying the spatiotemporal dynamics of attention
PowerSpy: Location Tracking using Mobile Device Power Analysis
Modern mobile platforms like Android enable applications to read aggregate
power usage on the phone. This information is considered harmless and reading
it requires no user permission or notification. We show that by simply reading
the phone's aggregate power consumption over a period of a few minutes an
application can learn information about the user's location. Aggregate phone
power consumption data is extremely noisy due to the multitude of components
and applications that simultaneously consume power. Nevertheless, by using
machine learning algorithms we are able to successfully infer the phone's
location. We discuss several ways in which this privacy leak can be remedied.Comment: Usenix Security 201
- …