Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions

Morelli, Carlo F.; Nicoli, Monica; Rampa, Vittorio; Spagnolini, Umberto

doi:10.1109/tsp.2006.889978

Cited by 102 publications

(59 citation statements)

References 45 publications

(75 reference statements)

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“…Unfortunately, to the best of the authors' knowledge, it is not possible to generate random variables with pdf given by (14). For this reason, we propose to use a grid-based approximation known as griddy Gibbs sampling to estimate τ. Griddy Gibbs sampling is an approach to generate random variables from pdfs that cannot be readily expressed in a standard format [33], [34]. In this algorithm, for the problem at hand, a grid of points in the interval [0, T ) is first selected.…”

Section: ) Gaussian Mixture Variancesmentioning

confidence: 99%

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

Amuru

Silva

2014

IEEE Trans. Commun.

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This paper presents a new preprocessing stage that allows for the reliable classification of digital amplitude-phase modulated signals in a practical scenario where: 1) the classifier has no knowledge of the timing (symbol transition epochs) of the received signal; 2) the noise added in the channel is non-Gaussian; and 3) the fading experienced by the signal is frequency selective. The proposed preprocessor, which is based on the Gibbs sampling algorithm, is used to acquire timing information and to estimate the channel state and noise distribution parameters blindly, i.e., without knowledge of the received symbol sequence and the modulation scheme used. With the obtained estimates, in a second processing stage, the signal is then classified by using an appropriate (likelihood-or feature-based) classification algorithm. To quantify the performance of the proposed preprocessor, the probability of correct classification obtained by using the preprocessor with different classification algorithms is presented. It is shown that, by using the proposed preprocessor, modulation classification algorithms can perform well compared with clairvoyant classifiers assumed to be symbol synchronous with the received signal and to have perfect knowledge of the channel state and noise distribution.

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Section: ) Gaussian Mixture Variancesmentioning

confidence: 99%

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

Amuru

Silva

2014

IEEE Trans. Commun.

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“…HMM is one of the probabilistic models. It has been used for localization in [7], [8], but it has never been used in the array sensor system. In this paper, HMM uses the value of cost function , as the observable value and calculates the likelihood at each reference point.…”

Section: B Function Of Fingerprintingmentioning

confidence: 99%

Hidden Markov model based localization using array antenna

Inatomi

Hong

Ohtsuki

2012

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

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We present a hidden Markov model based localization using array sensor. In this method, we use the eigenvector spanning signal subspace as a feature for location. The eigenvector does not depend on received signal strength (RSS) but on direction of arrival (DOA) of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the electric wave. The conventional method based on fingerprinting does not take previous information into account. In this paper, we propose an algorithm that applies HMM to conventional fingerprinting of the eigenvector. This algorithm takes previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference location according to setting in advance. In an indoor environment represented in a quantized grid, we decide the HMM transition probabilities denoting the possible moving range from previous estimation location. The most likely trajectory is calculated by means of the Viterbi algorithm. The results show that the localization accuracy is improved owing to the use of a possible moving range from the previous location.

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“…The first approach requires less calibration effort, but only performs well in open spaces, as obstructions in the environment can cause extra signal attenuation, or non-line-of-sight time of flight measurements (which bias the distance estimates). Algorithms have been proposed to deal with obstructions [9], but generally require a large number of anchor points with line-of-sight to the target. The second approach can work in environments with obstructions and reflectors, but requires an intensive calibration process to exhaustively map radio measurement signatures of the environment in fine granularity [10].…”

Section: Related Workmentioning

confidence: 99%

A calibration free hybrid RF and video surveillance system for reliable tracking and identification

Ganz

2011

2011 IEEE International Conference on Technologies for Homeland Security (HST)

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We introduce a calibration free hybrid RF and video surveillance system for reliable tracking, identification in Mass Casualty Incidents (MCI). The system fuses RF measurements along with information harvested from the video such as target temporal, kinematic, and appearance information. Our hybrid RF and video surveillance system can be deployed rapidly without manual calibration of either the camera or the RF devices, and can work in complex RF environments. This is made possible by the simultaneous identification and mapping algorithm that iteratively refines the mapping of the radio beacon signal strength signature to target positions in video frames. The system is the basis of identity aware video surveillance, i.e. enabling unique identification of targets in the video and applying identity specific analytics roles to individual targets.

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Hidden Markov Models for Radio Localization in Mixed LOS/NLOS Conditions

Cited by 102 publications

References 45 publications

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

A Blind Pre-Processor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

Hidden Markov model based localization using array antenna

A calibration free hybrid RF and video surveillance system for reliable tracking and identification

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