957 research outputs found
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or
received signal power across a geographic area of interest using spatially
distributed sensor measurements. Applications of these maps include network
planning, interference coordination, power control, localization, and cognitive
radios to name a few. Since existing spectrum cartography techniques require
accurate estimates of the sensor locations, their performance is drastically
impaired by multipath affecting the positioning pilot signals, as occurs in
indoor or dense urban scenarios. To overcome such a limitation, this paper
introduces a novel paradigm for spectrum cartography, where estimation of
spectral maps relies on features of these positioning signals rather than on
location estimates. Specific learning algorithms are built upon this approach
and offer a markedly improved estimation performance than existing approaches
relying on localization, as demonstrated by simulation studies in indoor
scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on
Signal Processin
Position and Orientation Estimation of a Rigid Body: Rigid Body Localization
Rigid body localization refers to a problem of estimating the position of a
rigid body along with its orientation using anchors. We consider a setup in
which a few sensors are mounted on a rigid body. The absolute position of the
rigid body is not known, but, the relative position of the sensors or the
topology of the sensors on the rigid body is known. We express the absolute
position of the sensors as an affine function of the Stiefel manifold and
propose a simple least-squares (LS) estimator as well as a constrained total
least-squares (CTLS) estimator to jointly estimate the orientation and the
position of the rigid body. To account for the perturbations of the sensors, we
also propose a constrained total least-squares (CTLS) estimator. Analytical
closed-form solutions for the proposed estimators are provided. Simulations are
used to corroborate and analyze the performance of the proposed estimators.Comment: 4 pages and 1 reference page; 3 Figures; In Proc. of ICASSP 201
Secret Key Generation Based on AoA Estimation for Low SNR Conditions
In the context of physical layer security, a physical layer characteristic is
used as a common source of randomness to generate the secret key. Therefore an
accurate estimation of this characteristic is the core for reliable secret key
generation. Estimation of almost all the existing physical layer characteristic
suffer dramatically at low signal to noise (SNR) levels. In this paper, we
propose a novel secret key generation algorithm that is based on the estimated
angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an
outstanding performance at very low SNR levels. Our algorithm can exploit
either the Azimuth AoA to generate the secret key or both the Azimuth and
Elevation angles to generate the secret key. Exploiting a second common source
of randomness adds an extra degree of freedom to the performance of our
algorithm. We compare the performance of our algorithm to the algorithm that
uses the most commonly used characteristics of the physical layer which are
channel amplitude and phase. We show that our algorithm has a very low bit
mismatch rate (BMR) at very low SNR when both channel amplitude and phase based
algorithm fail to achieve an acceptable BMR
Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels
In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.publishedVersio
Soft range information for network localization
The demand for accurate localization in complex
environments continues to increase despite the difficulty in extracting
positional information from measurements. Conventional
range-based localization approaches rely on distance estimates
obtained from measurements (e.g., delay or strength of received
waveforms). This paper goes one step further and develops
localization techniques that rely on all probable range values
rather than on a single estimate of each distance. In particular,
the concept of soft range information (SRI) is introduced,
showing its essential role for network localization. We then
establish a general framework for SRI-based localization and
develop algorithms for obtaining the SRI using machine learning
techniques. The performance of the proposed approach is quantified
via network experimentation in indoor environments. The
results show that SRI-based localization techniques can achieve
performance approaching the Cramer–Rao lower bound and
significantly outperform the conventional techniques especially
in harsh wireless environments.RYC-2016-1938
Device Free Localisation Techniques in Indoor Environments
The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised
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