James P. Browning

Wireless tomography, Part I: A novel approach to remote sensing

Wireless tomography, a novel approach to remote sensing, is proposed in Part I of this series. The methodology, literature review, related work, and system engineering are presented. Concrete algorithms and hardware platforms are implemented to demonstrate this concept. Self-cohering tomography is studied in depth. More research will be reported, following this initiative.

Generalized FMD Detection for Spectrum Sensing under Low Signal-to-Noise Ratio

Spectrum sensing is a fundamental problem in cognitive radio. We propose a function of covariance matrix based detection algorithm for spectrum sensing in cognitive radio network. Monotonically increasing property of function of matrix involving trace operation is utilized as the cornerstone for this algorithm. The advantage of proposed algorithm is it works under extremely low signal-to-noise ratio, like lower than -30 dB with limited sample data. Theoretical analysis of threshold setting for the algorithm is discussed. A performance comparison between the proposed algorithm and other state-of-the-art methods is provided, by the simulation on captured digital television (DTV) signal.

Spectrum sensing in cognitive radio with robust principal component analysis

Spectrum sensing is a cornerstone in cognitive radio. Covariance matrix based method has been widely used in spectrum sensing. As is well-known that the covariance matrix of white noise is proportional to the identity matrix which is sparse. On the other hand, the covariance matrix of signal is usually low-rank. Robust principal component analysis (PCA) has been proposed recently to recover the low-rank matrix which is corrupted by a sparse matrix with arbitrarily large magnitude non-zero entries. In this paper, robust PCA for spectrum sensing is proposed based on the sample covariance matrix. The received signal will be divided into two segments. Robust PCA will be applied to extract the low-rank matrices from the sample covariance matrices of both segments. The primary users signal is detected if the discrepancy between the recovered low-rank matrices is smaller than a predefined threshold. The simulations are done both on the simulated and captured DTV signal. Also, the simulations that robust PCA is taken as a de-noising process for sample covariance matrix are also implemented in this paper.

Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis

Spectrum sensing is a fundamental component of cognitive radio (CR). How to promptly sense the presence of primary users (PUs) is a key issue to a CR network. The time requirement is critical in that violating it will cause harmful interference to the PU, leading to a system-wide failure. The motivation of this paper is to provide an effective spectrum sensing method to detect PUs as soon as possible. In the language of streaming-based real-time data processing, short time means small data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on McDiarmids concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV (DTV) signal, show that this proposed method can operate either using smaller data or working under a lower signal-to-noise ratio (SNR) environment.

Demonstration of cognitive radar for target localization under interference

An ultrawideband (UWB) multiple input/multiple output (MIMO) cognitive radar has been developed and demonstrated for the first time. Field-programmable gate array (FPGA) is used for waveform-level computing, while waveform optimization is accomplished in CPU. Working as a closed loop, convex optimization is applied to jointly design (arbitrary) transmitted waveforms and the receiving filters in response to the varying wireless environment. Multiple targets localization in the presence of interference is demonstrated. Shown in the experiment, performance improvement is obvious in all interference patterns.

Software-Defined-Radio-Based Wireless Tomography: Experimental Demonstration and Verification

This letter presents an experimental demonstration of software-defined-radio-based wireless tomography using computer-hosted radio devices called Universal Software Radio Peripheral (USRP). This experimental brief follows our vision and previous theoretical study of wireless tomography that combines wireless communication and RF tomography to provide a novel approach to remote sensing. Automatic data acquisition is performed inside an RF anechoic chamber. Semidefinite relaxation is used for phase retrieval, and the Born iterative method is utilized for imaging the target. Experimental results are presented, validating our vision of wireless tomography.

A Novel Single-Step Approach for Self-Coherent Tomography Using Semidefinite Relaxation

This letter presents a novel single-step approach for self-coherent tomography using semidefinite relaxation. Phase retrieval for scattered fields is not required. The general solver can be used to solve the corresponding convex optimization problem and image the target. Both man-made and experimental data is exploited to demonstrate the performance of the proposed approach. The imaging results illustrate the benefit of bringing the state-of-the-art mathematics to inverse scattering or diffraction tomography.

Cognitive radio network as sensors: Low signal-to-noise ratio collaborative spectrum sensing

This paper propose a function of covariance matrix based spectrum sensing approach for cognitive radio systems. The statistical covariance of signal and noise are usually different, so a binary hypothesis test on covariance matrix is employed to determine the existence of primary user. Collaborative sensing scenario is introduced for the proposed algorithm, in which each sensor only needs limited sample data for calculation and sends mediate result to fusion center. A performance comparison among different rational functions is provided, which shows different functions in this algorithm may have similar or distinct performance. So it is important to choose an appropriate function. The proposed algorithm has a reliable performance in very low signal-to-noise ratio (SNR) condition, and outperforms the Estimator-Correlator (EC) approach.

Optimization and waveforms for compressive sensing applications in the presence of interference

Compressive sensing concepts have potential applications to multiple RADAR problems, which include Moving Target Indication, and RADAR imaging in two and three spatial dimensions. Currently known sufficient conditions for reliable sparse signal reconstruction do not seem to be directly applicable or practical for some traditional RADAR problems. But experiments and mathematical invariance properties of some reconstruction methods indicate that useful products can often be obtained using these methods for circumstances outside the usual conditions.

Software defined, Plug-and-Play (PNP) Radar Transceiver for phased-array applications

Digital Radar Transceivers providing precise digital control over waveform amplitude, frequency and phase is critical for modern Phased Array Radar systems. A Plug-and-Play (PNP) Transceiver was developed to support the Air Forces objective to develop a Hybrid Multiple Input Multiple Output (MIMO) Phased Array Radar (HMPAR) system, where the full array is partitioned into multiple sub-arrays which can be driven by mission specific waveforms. This PNP Radar Transceiver has been integrated with the Lockheed Martins Portable Search and Target Acquisition Radar (PSTAR) antenna array. Advanced Radar capabilities including transmit-receive digital beam-steering, direct digital L-band receiver and real-time digital signal processing with the integrated system have been successfully demonstrated. This paper discusses the overall PNP Radar Transceiver concept and highlights the successful phased-array test results when integrated with the PSTAR array.