53,531 research outputs found
Selective AP-sequence Based Indoor Localization without Site Survey
In this paper, we propose an indoor localization system employing ordered
sequence of access points (APs) based on received signal strength (RSS). Unlike
existing indoor localization systems, our approach does not require any
time-consuming and laborious site survey phase to characterize the radio
signals in the environment. To be precise, we construct the fingerprint map by
cutting the layouts of the interested area into regions with only the knowledge
of positions of APs. This can be done offline within a second and has a
potential for practical use. The localization is then achieved by matching the
ordered AP-sequence to the ones in the fingerprint map. Different from
traditional fingerprinting that employing all APs information, we use only
selected APs to perform localization, due to the fact that, without site
survey, the possibility in obtaining the correct AP sequence is lower if it
involves more APs. Experimental results show that, the proposed system achieves
localization accuracy < 5m with an accumulative density function (CDF) of 50%
to 60% depending on the density of APs. Furthermore, we observe that, using all
APs for localization might not achieve the best localization accuracy, e.g. in
our case, 4 APs out of total 7 APs achieves the best performance. In practice,
the number of APs used to perform localization should be a design parameter
based on the placement of APs.Comment: VTC2016-Spring, 15-18 May 2016, Nanjing, Chin
Hybrid Radio-map for Noise Tolerant Wireless Indoor Localization
In wireless networks, radio-map based locating techniques are commonly used
to cope the complex fading feature of radio signal, in which a radio-map is
built by calibrating received signal strength (RSS) signatures at training
locations in the offline phase. However, in severe hostile environments, such
as in ship cabins where severe shadowing, blocking and multi-path fading
effects are posed by ubiquitous metallic architecture, even radio-map cannot
capture the dynamics of RSS. In this paper, we introduced multiple feature
radio-map location method for severely noisy environments. We proposed to add
low variance signature into radio map. Since the low variance signatures are
generally expensive to obtain, we focus on the scenario when the low variance
signatures are sparse. We studied efficient construction of multi-feature
radio-map in offline phase, and proposed feasible region narrowing down and
particle based algorithm for online tracking. Simulation results show the
remarkably performance improvement in terms of positioning accuracy and
robustness against RSS noises than the traditional radio-map method.Comment: 6 pages, 11th IEEE International Conference on Networking, Sensing
and Control, April 7-9, 2014, Miami, FL, US
SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization
Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field
An acoustic multi-touch sensing method using amplitude disturbed ultrasonic wave diffraction patterns
This paper proposes an acoustic multi-touch tactile sensing method. The proposed method is based on an amplitude disturbed ultrasonic wave diffraction pattern. An A0 Lamb wave transmitted in a thin finite copper plate is processed to provide tactile information, for one or two fingers. A touch event is localized by identifying the diffraction signals among a database of diffracted Lamb wave references. Statistic models are used to improve the localization reliability. An artificial silicone finger is used in the calibration procedure. This touch interface is evaluated as a 2-touch interface
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
Managing uncertainty in sound based control for an autonomous helicopter
In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper ). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system
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