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

Short Range Radar Based on UWB Technology

ISBN 978-953-307-029-

Automatic Modulation Classification Using Cyclic Features via Compressed Sensing

Cognitive Radios (CRs) are designed to operate with minimal interference to the Primary User (PU), the incumbent to a radio spectrum band. To ensure that the interference generated does not exceed a specific level, an estimate of the Signal to Interference plus Noise Ratio (SINR) for the PU’s channel is required. This can be accomplished through determining the modulation scheme in use, as it is directly correlated with the SINR. To this end, an Automatic Modulation Classification (AMC) scheme is developed via cyclic feature detection that is successful even with signal bandwidths that exceed the sampling rate of the CR. In order to accomplish this, Compressed Sensing (CS) is applied, allowing for reconstruction, even with very few samples. The use of CS in spectrum sensing and interpretation is becoming necessary for a growing number of scenarios where the radio spectrum band of interest cannot be fully measured, such as low cost sensor networks, or high bandwidth radio localization services. In order to be able to classify a wide range of modulation types, cumulants were chosen as the feature to use. They are robust to noise and provide adequate discrimination between different types of modulation, even those that are fairly similar, such as 16-QAM and 64-QAM. By fusing cumulants and CS, a novel method of classification was developed which inherited the noise resilience of cumulants, and the low sample requirements of CS. Comparisons are drawn between the proposed method and existing ones, both in terms of accuracy and resource usages. The proposed method is shown to perform similarly when many samples are gathered, and shows improvement over existing methods at lower sample counts. It also uses less resources, and is able to produce an estimate faster than the current systems

Cognitive Radio Communications for Vehicular Technology – Wavelet Applications

Wireless communications are nowadays a dominant part of our lives: from domotics, through industrial applications and up to infomobility services. The key to the co-existence of wireless systems operating in closely located or even overlapping areas, is sharing of the spectral resource. The optimization of this resource is the main driving force behind the emerging changes in the policies for radio resources allocation. The current approach in spectrum usage specifies fixed frequency bands and transmission power limits for each radio transmitting system. This approach leads to a very low medium utilization factor for some frequency bands, caused by inefficient service allocation over vast geographical areas (radiomobile, radio and TV broadcasting, WiMAX) and also by the usage of large guard bands, obsolete now due to technological progress. A more flexible use of the spectral resource implies that the radio transceivers have the ability to monitor their radio environment and to adapt at specific transmission conditions. If this concept is supplemented with learning and decision capabilities, we refer to the Cognitive Radio (CR) paradigm. Some of the characteristics of a CR include localization, monitoring of the spectrum usage, frequency changing, transmission power control and, finally, the capacity of dynamically altering all these parameters (Haykin, 2005). This new cognitive approach is expected to have an important impact on the future regulations and spectrum policies. The dynamic access at the spectral resource is of extreme interest both for the scientific community as, considering the continuous request for wideband services, for the development of wireless technologies. From this point of view, a fundamental role is played by the Institute of Electrical and Electronic Engineers (IEEE) which in 2007 formed the Standards Coordinating Committee (SCC) 41 on Dynamic Spectrum Access Networks (DySPAN) having as main objective a standard for dynamic access wireless networks. Still within the IEEE frame, the 802.22 initiative defines a new WRAN (Wireless Regional Area Network) interface for wideband access based on cognitive radio techniques in the TV guard bands (the so-called “white spaces”). Coupled with the advantages and flexibility of CR systems and technologies, there is an ever-growing interest around the world in exploiting CR-enabled communications in vehicular and transportation environments. The integration of CR devices and cognitive radio networks into vehicles and associated infrastructures can lead to intelligent interactions with the transportation system, among vehicles, and even among radios within vehicles. Thus, improvements can be achieved in radio resource management and energy efficiency, road traffic management, network management, vehicular diagnostics, road traffic awareness for applications such as route planning, mobile commerce, and much more. Still open within the framework of dynamic and distributed access to the radio resource are the methods for monitoring the radio environment (the so-called “spectrum sensing”) and the transceiver technology to be used on the radio channels. A CR system works on a opportunistic basis searching for unused frequency bands called “white spaces” within the radio frequency spectrum with the intent to operate invisibly and without disturbing the primary users (PU) holding a license for one or more frequency bands. Spectrum sensing, that is, the fast and reliable detection of the PU’s even in the presence of in-band noise, is still a very complex problem with a decisive impact on the functionalities and capabilities of the CRs. The spectrum sensing techniques can be classified in two types: local and cooperative (distributed). The local techniques are performed by single devices exploiting the spectrum occupancy information in their spatial neighbourhood and can be divided into three categories (Budiarjo et al., 2008): "matched filter" (detection of pilot signals, preambles, etc.), "energy detection” (signal strength analysis) and “feature detection" (classification of signals according to their characteristics). Also, a combination of local techniques in a multi-stage design can be used to improve the sensing accuracy (Maleki et al., 2010). Nevertheless, the above-mentioned techniques are mostly inefficient for signals with reduced power or affected by phenomena typical for vehicular technology applications, such as shadowing and multi-path fading. To overcome such problems, cooperatives techniques can be used. Cooperative sensing is based on the aggregation of the spectrum data detected by multiple nodes using cognitive convergence algorithms in order to avoid the channel impairment problems that can lead to false detections. (Sanna et al., 2009). Within the energy detection method, a particular attention needs to be paid to the properties of the packets wavelet transformation for subband analysis, which, according to the literature, seems to be a feasible alternative to the classical FFT-based energy detection. Vehicular applications are in most cases characterized by the need of coping with fast changes in the radio environment, which lead, in this specific case of cognitive communication, to constrains in terms of short execution time of the spectrum sensing operations. From this point of view, the computational complexity of the wavelet packets method is of the same order of the state-of-the-art FFT algorithms, but the number of mathematical operations is lower using IIR polyphase filters (Murroni et al., 2010). In our work we are investigating the use of the wavelet packets for energy detection spectrum sensing operations based on the consideration that they have a finite duration and are self- and mutually-orthogonal at integer multiples of dyadic intervals. Hence, they are suitable for subband division and analysis: a generic signal can be then decomposed on the wavelet packet basis and represented as a collection of coefficients belonging to orthogonal subbands. Therefore, the total power of the signal can be evaluated as sum of the contributions of each subband, which can be separately computed in the wavelet domain. Furthermore, the wavelet packets can be used also for the feature detection spectrum sensing, using statistical parameters such as moments and medians. We concentrate in our research on both applications of the wavelet packets to the spectrum sensing operations, investigating their efficiency in terms of reliability and execution time, applied specifically to the needs of vehicular technology and transportation environments. The other key issue for the development of the previously mentioned standard is the choice of an adaptive/multicarrier modulation as basic candidate for data transmission, having as the most known representative the Orthogonal Frequency Division Multiplexing (OFDM) modulation. OFDM-like schemes are mature enough to be chosen as a core technology for dynamic access wireless networks. At the same time, the potentialities in terms of optimization for this specific purpose are not yet thoroughly investigated. Particularly, the Wavelet Packet Division Multiplexing (WPDM) modulation method, already known for about ten years to the scientific community, is a suitable candidate to satisfy the requirements on physical level for a dynamic access network (Wong et al., 1997): WPDM has already proven to be able to overcome some of the OFDM limits (limited spectral efficiency, problems with temporal synchronization especially in channels affected by fading) and is at the same time based on use of the same wavelet packets employed for subband analysis used for spectrum sensing operations . Our research investigates the use of the WPDM for cognitive radio purposes, combined with the wavelet approach for spectrum sensing, for offering a complete, wavelet-based solution for cognitive application focused on the problematic of vehicular communication (channel impairments, high relative velocity of the communication peers etc.)

Multiple moving target detection with ultra wideband radar using super-resolution algorithms

The improvements in microwave electronics opened the way to build microwave components such as low noise amplifiers, samplers and pulse generators that are broadband. As these building blocks are being developed, new applications become subject of research. Ultra wideband radar is one of these subjects. Major applications of ultra wideband radars are behind the wall imaging, biomedical imaging and buried land mine detection. In this study we aimed to locate multiple scatterers that are moving. Even though there are many scatterers in an environment, detection of moving targets is possible using differences of successive radar snapshots. This is generally the case when behind the wall human targets are to be detected. We investigated the effectiveness of various types Multiple Signal Classification (MUSIC) algorithms on the data acquired by our ultra wideband radar prototype. In ideal computer simulations, Time Reversal MUSIC (TRM) algorithm provides successful estimations of both directions and distances of multiple targets. However in practice where non-ideal effects are existent, the performance of TRM algorithm is estimating the target distances degrades. On the other hand, Delay Estimation MUSIC algorithm provides better estimates for the distances of the targets since it is less sensitive to phase noise. Combining the output of TRM algorithm for target directions and the output of Delay Estimation MUSIC method for target distances resulted in successful localization of targets. Experiments are performed using two moving targets in order to test the effectiveness the proposed processing scheme. The problem of detection ambiguities is also considered and several methods to resolve actual targets are presented

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design