Xiaorong Wu

Spectrum efficiency of nested sparse sampling and coprime sampling

This article addresses the spectrum efficiency study of nested sparse sampling and coprime sampling in the estimation of power spectral density for QPSK signal. The authors proposed nested sampling and coprime sampling only showed that these new sub-Nyquist sampling algorithm could achieve enhanced degrees of freedom, but did not consider its spectrum efficiency performance. Spectral efficiency describes the ability of a communication system to accommodate data within a limited bandwidth. In this article, we give the procedures of using nested and coprime sampling structure to estimate the QPSK signal’s autocorrelation and power spectral density (PSD) using a set of sparse samples. We also provide detailed theoretical analysis of the PSD of these two sampling algorithms with the increase of sampling intervals. Our results prove that the mainlobe of PSD becomes narrower as the sampling intervals increase for both nested and coprime sampling. Our simulation results also show that by making the sampling intervals, i.e., N1 and N2 for nested sampling, and P and Q for coprime sampling, large enough, the main lobe of PSD obtained from these two sub-Nyquist samplings are much narrower than the original QPSK signal. That is, the bandwidth B occupancy of the sampled signal is smaller, which improves the spectrum efficiency. Besides the smaller average rate, the enhanced spectrum efficiency is a new advantage of both nested sparse sampling and coprime sampling.

Compressive sensing-based signal compression and recovery in UWB wireless communication system

In this paper, we propose a new compressive sensing-based compression and recovery ultra-wideband UWB communication system. Compared with the conventional UWB system, we can jointly estimate the channel and compress the data, which can also simplify the design of hardware. No information about the transmitted signal is required in advance as long as the channel follows autoregressive process. As an application example, real-world UWB signal is collected and processed to evaluate the performance of our proposed system. The compression procedure is so simple that we just multiply random Gaussian or Bernoulli matrix with the original data to capture all the information we want. Simulation results show that the data could be perfectly recovered if the compression ratio does not exceed 2.5:1 when Bernoulli matrix is chosen as the sensing matrix. Copyright

Experimental study of through-wall human being detection using ultra-wideband (UWB) radar

In this paper, experiments on through-wall human being detection using ultra-wideband (UWB) radar PulsOn 220 in monostatic mode are carried out. For periodic respiratory motion of human target, the detection techniques employed are normalized difference square matrix method, and reference moving average method with Discrete Fourier Transform (DFT). The experimental results for human target detection behind gypsum wall and concrete wall have been separately demonstrated.

Compressive sensing-based data encryption system with application to sense-through-wall UWB noise radar

Security of data is an issue that is of significant interest. In this paper, we propose a new compressive sensing-based data encryption system that can represent the original signal with far fewer samples than the conventional Nyquist sampling-based system. Compressive sensing could also be treated as an encryption algorithm with good secrecy. As an application example, we apply it to sense-through-wall ultra-wideband UWB noise radar that requires enormous storage space and high security. Interestingly, a random Gaussian matrix is sufficient to capture the information of UWB noise radar signal; no knowledge of UWB signal is required in advance. Simulation results indicate only one-third of the original samples are needed to perfectly recover UWB noise radar signal, and compressive sensing provides good secrecy as an encryption algorithm. It is impossible to retrieve the original message without the entire sensing matrix. Copyright

Gulf of Mexico oil spill impact on beach soil: UWB radars-based approach

Gulf of Mexico oil spill in 2010 has generated deep impact on the beach soil. Microwave emission and backscattering of the beach soil depend on its dielectric property which exhibits dispersion and absorption and is affected by the oil, salt, and moisture content. In this paper, we apply Ultra-WideBand (UWB) radars sensor network to perform the beach soil dielectric property studies due to the oil spill. Data were collected from different beaches in New Orleans. Some beaches were contaminated by oil spill; and some beaches were clean. Extensive data were collected on different beaches based on UWB radars. We obtained the soil reflectivity kernel based on the sending chirps and received echoes for soil, and observed that oil spill has reduced the amplitude of reflectivity kernel.

Compressive sensing in radar sensor networks for target RCS value estimation

Recently, there are a growing interest in the study of compressive sensing (CS). In this paper, we introduce CS to radar sensor network (RSN) within the pulse compression technique in order to efficiently compress, restore and then reconstruct the radar data. We employ a set of Stepped-Frequency waveforms as pulse compression codes for transmit sensors, and to use the same set of Stepped-Frequency (SF) waveforms as the sparse matrix for each receive sensor. We conclude that the signal samples along the time domain could be largely compressed so that they could be recovered by a small number of measurements which depend on the number of transmit sensors. In addition, we develop a Maximum Likelihood (ML) Algorithm for radio cross section (RCS) parameter estimation and provide the Cramer-Rao lower bound (CRLB) to validate the theoretical result. We also provide simulation results illustrating that the variance of RCS parameter estimation θ satisfies the CRLB and our ML estimator is an accurate estimator on the target RCS parameter.

Security Analysis of Distributed Compressive Sensing-Based Wireless Sensor Networks

Due to limited energy and physical size of the sensor nodes, the conventional security mechanisms with high computation complexity are not feasible for wireless sensor networks (WSNs). In this paper, we propose a compressive sensing-based encryption scheme for WSN, which provides both signal compression and encryption guarantees, without the additional computational cost of a separate encryption protocol. We also show that, for proposed WSN, if only a fraction of randomizer bits is stored by an eavesdropper, then he/she cannot obtain any information about the plaintext. WSNs usually are deployed in a hostile environment and left unattended, which could be compromised by the eavesdropper. Numerical results show that there is a trade-off between the number of sensor nodes required to reconstruct the original data and the approximation error in both normal and attack conditions. The approximation error of data decreases when less sensor nodes are compromised by the eavesdropper.

Information theoretic performance bounds for noisy compressive sensing

Compressive sensing provides a new approach to data acquisition and storage. In this paper, we derive some information theory bounds on the performance of noisy compressive sensing to calculate the data rate with particular distortion, which has significant meaning in data storage technique. We analyze the rate distortion performance of noisy compressive sensing under Mean Squared distortion and Hamming distortion, and give more accurate results. Besides, mathematical lower bounds of rate distortion function and theoretical minimal useful bit rates are provided for these two distortion for the first time. We also give a theoretical upper bound of the Mean Squared distortion of compressive sensing process. The relationships of bit rate per dimension R(D)/N and M, N, and M/N are given and plotted in this paper, and both theoretical analysis and numerical results show that compressive sensing uses less number of bits to represent the same information compared to conventional information acquisition and reconstruction techniques.

Phase Coded Waveform Design for Sonar Sensor Network

Since it is known that interference with each sonar sensor could be effectively reduced when waveforms are appropriately designed for a Sonar Sensor Network (SSN), we provide a set of new ternary codes called optimized punctured Zero Correlation Zone sequence-pair set (ZCZPS) and provide a method to construct such codes. We study the codes’ properties especially using the ambiguity function to analyze the nature of the output of the matched filter. We apply our provided ternary codes to the SSN as pulse compression codes for narrowband pulse signals and simulate the target detection performance of the system. Comparing with the classical periodic Gold sequences, our codes could improve the system detection performance.

On the Security of Wireless Sensor Networks via Compressive Sensing

Due to energy limitation of sensor nodes, the conventional security algorithms with high computation complexity are not suitable for wireless sensor networks (WSNs). We propose a compressive sensing-based encryption for WSNs, which provides both signal compression and encryption guarantees, without introducing additional computational cost of a separate encryption protocol. In this paper, we also discuss the information-theoretical and computational secrecy of compressive sensing algorithm. For proposed WSN, if only a fraction of randomizer bits is stored by an eavesdropper, then the probability that he/she cannot obtain any information about the plaintext approaches zero. Simulation results show a trade-off can be made between the sparsity of a random measurement matrix and the number of sensor nodes used to reconstruct the original signal at the fusion center.