805 research outputs found
Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices
Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi
devices has gained attention with recent advances in wireless technology. HGR recognizes the human
activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing
them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction
and transformation to pre-process the raw CSI traces. However, these methods fail to capture
the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal
representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts
higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the
recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order
cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods
derived from information theory construct a robust and highly informative feature subset, fed as
input to the multilevel support vector machine (SVM) classifier in order to measure the performance.
The proposed methodology is validated using a public database SignFi, consisting of 276 gestures
with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home
environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of
97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average
recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was
96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio
Cumulant based identification approaches for nonminimum phase FIR systems
Cataloged from PDF version of article.In this paper, recursive and least squares methods
for identification of nonminimum phase linear time-invariant
(NMP-LTI) FIR systems are developed. The methods utilize the
second- and third-order cumulants of the output of the FIR
system whose input is an independent, identically distributed
(i.i.d.) non-Gaussian process. Since knowledge of the system
order is of utmost importance to many system identification algorithms,
new procedures for determining the order of an FIR
system using only the output cumulants are also presented. To
illustrate the effectiveness of our methods, various simulation
examples are presented
System identification using a linear combination of cumulant slices
In this paper we develop a new linear approach to identify the parameters of a moving average (MA) model from the statistics of the output. First, we show that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. Then, this
result is used to obtain a new well-conditioned linear method
to estimate the MA parameters of a non-Gaussian process. The
proposed method presents several important differences with
existing linear approaches. The linear combination of slices used
to compute the MA parameters can be constructed from dif-
ferent sets of cumulants of different orders, providing a general
framework where all the statistics can be combined. Further-
more, it is not necessary to use second-order statistics (the autocorrelation slice), and therefore the proposed algorithm still
provides consistent estimates in the presence of colored Gaussian noise. Another advantage of the method is that while most
linear methods developed so far give totally erroneous estimates if the order is overestimated, the proposed approach does
not require a previous estimation of the filter order. The simulation results confirm the good numerical conditioning of the
algorithm and the improvement in performance with respect to existing methods.Peer Reviewe
New approaches without postprocessing to FIR system identification using selected order cumulants
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A Survey of Blind Modulation Classification Techniques for OFDM Signals
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed
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