Eva Lagunas

Joint Wall Mitigation and Compressive Sensing for Indoor Image Reconstruction

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach.

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Compressive sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the under-utilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency, and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CS-related works applied to different categories such as wideband sensing, signal parameter estimation and radio environment map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.

Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks

The lack of available unlicensed spectrum together with the increasing spectrum demand by multimedia applications has resulted in a spectrum scarcity problem, which affects satellite communications (SatCom) as well as terrestrial systems. The goal of this paper is to propose resource allocation (RA) techniques, i.e., carrier, power, and bandwidth allocation, for a cognitive spectrum utilization scenario where the satellite system aims at exploiting the spectrum allocated to terrestrial networks as the incumbent users without imposing harmful interference to them. In particular, we focus on the microwave frequency bands 17.7-19.7 GHz for the cognitive satellite downlink and 27.5-29.5 GHz for the cognitive satellite uplink, although the proposed techniques can be easily extended to other bands. In the first case, assuming that the satellite terminals are equipped with multiple low block noise converters (LNB), we propose a joint beamforming and carrier allocation scheme to enable cognitive space-to-Earth communications in the shared spectrum where fixed service (FS) microwave links have priority of operation. In the second case, however, the cognitive satellite uplink should not cause harmful interference to the incumbent FS system. For the latter, we propose a joint power and carrier allocation (JPCA) strategy followed by a bandwidth allocation scheme, which guarantees protection of the terrestrial FS system while maximizing the satellite total throughput. The proposed cognitive satellite exploitation techniques are validated with numerical simulations considering realistic system parameters. It is shown that the proposed cognitive exploitation framework represents a promising approach for enhancing the throughput of conventional satellite systems.

Determining building interior structures using compressive sensing

Abstract. We consider imaging of the building interior structures using compressive sensing (CS) with applications to through-the-wall imaging and urban sensing. We consider a monostatic synthetic aperture radar imaging system employing stepped frequency waveform. The proposed approach exploits prior information of building construction practices to form an appropriate sparse representation of the building interior layout. We devise a dictionary of possible wall locations, which is consistent with the fact that interior walls are typically parallel or perpendicular to the front wall. The dictionary accounts for the dominant normal angle reflections from exterior and interior walls for the monostatic imaging system. CS is applied to a reduced set of observations to recover the true positions of the walls. Additional information about interior walls can be obtained using a dictionary of possible corner reflectors, which is the response of the junction of two walls. Supporting results based on simulation and laboratory experiments are provided. It is shown that the proposed sparsifying basis outperforms the conventional through-the-wall CS model, the wavelet sparsifying basis, and the block sparse model for building interior layout detection.

Sparse channel estimation based on compressed sensing for ultra wideband systems

Channel estimation for purposes of equalization is a long standing problem in signal processing. Wireless propagation is characterized by sparse channels, that is channels whose time domain impulse response consists of few dominant multipath fingers. This paper examines the use of Compressed Sensing (CS) in the estimation of highly sparse channels. In particular, a new channel sparse model for ultra-wideband (UWB) communication systems based on the frequency domain signal model is presented. A new greedy algorithm named extended OMP (eOMP) is proposed to reduce the false path detection achieved with classical Orthogonal Matching Pursuit (OMP) allowing better time of arrival (TOA) estimation.

UWB joint TOA and DOA estimation

This paper deals with the simultaneous estimation of Time of Arrival (TOA) and Direction of Arrival (DOA) in UWB systems considering multipath propagation. The estimation is performed in the frequency domain by computing a two dimensional power delay-angle spectrum based on the periodogram because of its low computational complexity. High accuracy is obtained in the estimation of both parameters, TOA and DOA, based on the wide bandwidth of the UWB signals.

Compressive sensing for through wall radar imaging of stationary scenes using arbitrary data measurements

In this paper, we deal with removal of wall EM reflections prior to image reconstruction using step-frequency radars. The goal is to enable behind-the-wall target detection and localization from reduced data measurements. In the underlying problem, few frequency observations are available and they differ from one antenna position to another in a SAR imaging system. Because of using a different set of frequencies for different antennas, direct applications of wall clutter mitigation methods, such as subspace and spatial filtering, prove ineffective. To provide these methods with the response measured at the same set of frequencies, a compressive sensing approach is used to reconstruct the range profiles. We use prior knowledge of the wall standoff distance to speed up the convergence of the Orthogonal Matching Pursuit for sparse data reconstruction.

Resource allocation for cognitive satellite uplink and fixed-service terrestrial coexistence in ka-band

This paper addresses the cognitive Geostationary Orbit (GSO) satellite uplink where satellite terminals reuse frequency bands of Fixed-Service (FS) terrestrial microwave links which are the incumbent users in the Ka 27.5-29.5 GHz band. In the scenario considered herein, the transmitted power of the cognitive satellite user has to ensure that the interference impact on potentially present FS links does not exceed the regulatory interference limitations. In order to satisfy the interference constraint and assuming the existence of a complete and reliable FS database, this paper proposes a Joint Power and Carrier Allocation (JPCA) strategy to enable the cognitive uplink access to GSO Fixed Satellite Service (FSS) terminals. The proposed approach identifies the worst FS link per user in terms of interference and divides the amount of tolerable interference among the maximum number of FSS terminal users that can potentially interfere with it. In so doing, the cognitive system is guaranteed to never exceed the prescribed interference threshold. Subsequently, powers and carriers are jointly allocated so as to maximize the throughput of the FSS system. Supporting results based on numerical simulations are provided. It is shown that the proposed cognitive approach represents a promising solution to significantly boost the performance of conventional satellite systems.

Resource allocation for cognitive Satellite Communications in Ka-band (17.7–19.7 GHz)

In this paper, we consider the problem of resource allocation in the context of cognitive Satellite Communications (SatCom). In particular, we focus on the cognitive downlink access by Geostationary (GEO) Fixed Satellite Service (FSS) terminals in the band 17.7-19.7 GHz, where the incumbent users are Fixed-Service (FS) microwave links. Assuming a multiple Low Noise Block Converter (LNB) satellite receiver at the cognitive FSS terminal-side, an efficient receive beamforming technique combined with carrier allocation is proposed in order to maximize the overall downlink throughput as well as to improve the beam availability. The proposed cognitive exploitation framework allows the flexibility of using non-exclusive spectrum for the FSS downlink system, thus improving the overall system throughput. More importantly, the proposed approach is validated with the help of numerical results considering realistic system parameters.

Wall mitigation techniques for indoor sensing within the compressive sensing framework

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. However, the research work in this area thus far has assumed prior effective wall removal, allowing proper detection of indoor targets. In this paper, we show that wall removal techniques, operating with full data volume and applying backprojection imaging methods, can be equally effective under reduced data volume and within the sparse signal reconstruction framework. Specifically, we demonstrate that the spatial filtering and the singular value decomposition based approaches, which, respectively, exploit the spatial invariance and the strength of the EM wall return, for suppression of the wall reflections, can be employed using few measurements, thus allowing CS to be applied to data with higher target-to-wall-clutter ratio.