While millimeter-wave (mm-wave) communication can provide abundant spectrum and support high data rate for future 5G systems, its coverage capability in urban environments is a critical issue due to the severe penetration loss. In this letter, we propose a simple scheme to enhance the coverage area of a cell centered with an mm-wave base station. This key idea is to employ multiple passive-reflectors to reflect the mm-wave to the non-line-of-sight (NLOS) regions with low received power levels. In order to improve the received power, a design method for the passive-reflector is discussed intensively. Numerical experiment for an outdoor urban environment is presented. The numerical results show that, with the proposed scheme, the coverage radius of an mm-wave cell can be remarkably increased. This indicates that our scheme is an attractive option to alleviate the mm-wave coverage problem for future 5G systems.

An Effective Coverage Scheme With Passive-Reflectors for Urban Millimeter-Wave Communication

Localization technologies play an increasingly important role in pervasive applications of wireless sensor networks. Since the number of targets is usually limited, localization benefits from compressed sensing (CS): measurements number can be greatly reduced. Despite many CS-based localization schemes, existing solutions implicitly assume that all targets fall on a fixed grid exactly. When the assumption is violated, the mismatch between the assumed and actual sparsifying dictionaries can deteriorate the localization performance significantly. To address such a problem, in this paper, we propose a novel and iterative multiple target counting and localization framework. The key idea behind the framework is to dynamically adjust the grid to alleviate or even eliminate dictionary mismatch. The contribution of this paper is twofold. First, we consider the off-grid target issue in CS-based localization and formulate multiple target counting and localization as a joint sparse signal recovery and parameter estimation problem. Second, we solve the joint optimization problem using a variational Bayesian expectation-maximization algorithm where the sparse signal and parameter are iteratively updated in the variational Bayesian expectation-step and variational Bayesian maximization-step, respectively. Extensive simulation results highlight the superior performance of the proposed framework in terms of probability of correct counting and average localization error.

Multiple Target Counting and Localization Using Variational Bayesian EM Algorithm in Wireless Sensor Networks

This paper investigates the utilization of satellite broadcast signals such as DVB-S and DVB-S2 as illuminator of opportunity for passive radar imaging techniques like SAR or ISAR. It includes an analysis of the key elements of the structure of the DVB-S2 signal and their implications for SAR/ISAR signal processing, with particular emphasis on the interference properties due to the presence of multiple illuminating satellites. Moreover, first real world DVB-S based radar images are presented using a newly developed passive radar system.

Passive radar imaging using DVB-S2

Radar coincidence imaging (RCI) is a high-resolution imaging technique without the limitation of relative motion between target and radar. In sparsity-driven RCI, the prior knowledge of imaging model requires to be known accurately. However, the phase error generally exists as a model error, which may cause inaccuracies of the model and defocus the image. The problem is formulated using Bayesian hierarchical prior modeling, and the self-calibration variational message passing (SC-VMP) algorithm is proposed to improve the performance of RCI with phase error. The algorithm determines the phase error as part of the imaging process. The scattering coefficient and phase error are iteratively estimated using VMP and Newton’s method, respectively. Simulation results show that the proposed algorithm can estimate the phase error accurately and improve the imaging quality significantly.

https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-25/issue-1/013018/Radar-coincidence-imaging-with-phase-error-using-Bayesian-hierarchical-prior/10.1117/1.JEI.25.1.013018.pdf

Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively.

/pdf/automatic-extraction-of-water-and-shadow-from-sar-images-3vycbah6s0.pdf

Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network.

In passive radar which exploits opportunity source as the illuminator, the presence of direct signal and clutter echoes in surveillance channel is demonstrated strongly affecting the detection performance. In this paper, based on the well-known CLEAN approach which iteratively removes strong signal and detects targets, an effective CLEAN algorithm is derived for interference cancellation. The new technique adopts the method of orthogonal subspace projection and is more robust even under the following cases: (i) The arrival time of interference signals may be fractional sampling interval after sampling; (ii) The Doppler-delay estimation of interference may be inaccuracy; (iii) The echoes from continuous scattering targets may be Dopplerdelay spread. The approach is shown to be effective through simulation results which considers digital video broadcasting satellites (DVB-S) as the illuminator.

An effective CLEAN algorithm for interference cancellation and weak target detection in passive radar

This paper presents a study to demonstrate the feasibility of using digital video broadcasting-satellite (DVB-S) signal for passive radar The study concentrates on the analysis of DVB-S signal properties, mainly including its spectrum, resolution capability, ambiguity function, and analysis of direct path signal-to-noise ratio (SNR) Since DVB-S signal has a higher range resolution due to its wide bandwidth and wider coverage area, it turns out to be more competitive than other passive radar signals Finally, results based on field measurements provide some useful references about the performance and potential capability of using DVB-S signal for passive radar applications

Analysis of the properties of DVB-S signal for passive radar application

In recent years, various applications regarding sparse continuous signal recovery such as source localization, radar imaging, communication channel estimation, etc., have been addressed from the perspective of compressive sensing (CS) theory. However, there are two major defects that need to be tackled when considering any practical utilization. The first issue is off-grid problem caused by the basis mismatch between arbitrary located unknowns and the pre-specified dictionary, which would make conventional CS reconstruction methods degrade considerably. The second important issue is the urgent demand for low-complexity algorithms, especially when faced with the requirement of real-time implementation. In this paper, to deal with these two problems, we have presented three fast and accurate sparse reconstruction algorithms, termed as HR-DCD, Hlog-DCD and Hlp-DCD, which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularizations, by combining with the grid refinement technique. Experimental results are provided to demonstrate the effectiveness of the proposed algorithms and related analysis.

https://www.mdpi.com/1424-8220/14/4/5929/pdf

A fast and accurate sparse continuous signal reconstruction by homotopy DCD with non-convex regularization.

This paper studies passive radar imaging system based on digital video broadcasting (DVB) satellites. Firstly, the 3-dimensional (3-D) imaging model which consists of multiple DVB satellites and one receiver is established. After analyzing in the wavenumber domain, we consider to exploit the sparsity of the target to realize high-resolution imaging from the undersampled wavenumber domain coverage. However, traditional compressive sensing (CS) radar imaging methods require the imaging scene to be pre-discretized into finite grids and all scatterers to be located exactly on the grid. And they are likely to be severely affected by the off-grid scatterers. Therefore, based on the theories of Xampling and the finite rate of innovation (FRI), we propose the analog sparse imaging (ASI) method to deal with arbitrarily-located scatterers, which utilizes the estimating signal parameters through rotational invariance techniques (ESPRIT) and the plane matching technique (PMT). Simulation results show the effectiveness of the proposed method and the related analysis.

Sparse imaging for passive radar system based on digital video broadcasting satellites

This paper investigates the passive radar imaging system which exploits digital video broadcasting (DVB) satellites as opportunity illuminators. Firstly, we establish the imaging model and then analyze the imaging performance in the wavenumber domain. Because of the practical limitations, we are likely to get an undersampled wavenumber domain coverage so that traditional imaging methods such as matched filter (MF) would fail to provide good imaging performance. Therefore, we consider to use the sparse recovery techniques to achieve good inversion results. However, the compressive sensing (CS) imaging methods would be severely affected when off-grid scatterers exist. According to the theories of the Xampling and the finite rate of innovation (FRI), we propose a high-resolution sparse imaging method to overcome the problems above, which is based on the multiple signal classification (MUSIC) algorithm. Simulation results verify the effectiveness of the proposed method.

https://ieeexplore.ieee.org/iel7/6483173/6491878/06491956.pdf

Tianyun Wang

Papers

An effective CLEAN algorithm for interference cancellation and weak target detection in passive radar

Analysis of the properties of DVB-S signal for passive radar application

A fast and accurate sparse continuous signal reconstruction by homotopy DCD with non-convex regularization.

Sparse imaging for passive radar system based on digital video broadcasting satellites

Sparse passive radar imaging based on digital video broadcasting satellites using the MUSIC algorithm