High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure

It is quite challenging for through-the-wall radar imaging (TWRI) to achieve high-resolution ghost-free imaging with limited measurements in an indoor multipath scenario. In this paper, a novel high-resolution TWRI algorithm with the exploitation of the target clustered structure in a hierarchical Bayesian framework is proposed. More specifically, an extended spike-and-slab clustered prior is imposed to statistically encourage the cluster formations in both downrange and crossrange domains of the target region, and a generative model of the proposed approach is provided. Then, a Markov Chain Monte Carol (MCMC) sampler is used to implement the posterior inference. Compared to other state-of-the-art algorithms, the proposed nonparametric Bayesian algorithm can preserve underlying target clustered properties and effectively suppress these isolated spurious scatterers without any prior information on targets themselves, such as sizes, shapes, and numbers

A Nonlinear Data-Driven Towed Array Shape Estimation Method Using Passive Underwater Acoustic Data

Large-aperture towed linear hydrophone array has been widely used for beamforming-based signal enhancement in passive sonar systems; however, its performance can drastically decrease due to the array distortion caused by rapid tactical maneuvers of the towed platform, oceanic currents, hydrodynamic effects, etc. In this paper, an enhanced data-driven shape array estimation scheme is provided in the passive underwater acoustic data, and a novel nonlinear outlier-robust particle filter (ORPF) method is proposed to acquire enhanced estimates of time delays in the presence of distorted hydrophone array. A conventional beamforming technique based on a hypothetical array is first used, and the detection of the narrow-band components is sequentially carried out so that the corresponding amplitudes and phases at these narrow-band components can be acquired. We convert the towed array estimation problem into a nonlinear discrete-time filtering problem with the joint estimates of amplitudes and time-delay differences, and then propose the ORPF method to acquire enhanced estimates of the time delays by exploiting the underlying properties of slowly changing time-delay differences across sensors. The proposed scheme fully exploits directional radiated noise targets as sources of opportunity for online array shape estimation, and thus it requires neither the number nor direction of sources to be known in advance. Both simulations and real experimental data show the effectiveness of the proposed method

High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure

It is quite challenging for through-the-wall radar imaging (TWRI) to achieve high-resolution ghost-free imaging with limited measurements in an indoor multipath scenario. In this paper, a novel high-resolution TWRI algorithm with the exploitation of the target clustered structure in a hierarchical Bayesian framework is proposed. More specifically, an extended spike-and-slab clustered prior is imposed to statistically encourage the cluster formations in both downrange and crossrange domains of the target region, and a generative model of the proposed approach is provided. Then, a Markov Chain Monte Carol (MCMC) sampler is used to implement the posterior inference. Compared to other state-of-the-art algorithms, the proposed nonparametric Bayesian algorithm can preserve underlying target clustered properties and effectively suppress these isolated spurious scatterers without any prior information on targets themselves, such as sizes, shapes, and numbers

Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the F 1 score of 0.90 and area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.99 over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar

Optimization algorithm with Kernel PCA to support vector machines for time series prediction

Abstract—As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel Principal Component Analysis (KPCA) to SVM for feature extraction. Then PSO Algorithm is adopted to optimization of these parameters in SVM. The novel time series analysis model integrates the advantage of wavelet, PSO, KPCA and SVM. Compared with other predictors, this model has greater generality ability and higher accuracy

<i>k</i>-Level Extended Sparse Array Design for Direction-of-Arrival Estimation

Sparse arrays based on the concept of a sum-difference coarray (SDCA) have increased degrees of freedom and enlarged effective array aperture compared to those only considering a difference coarray. Nevertheless, there still exist a number of overlapping virtual sensors between the difference coarray and the sum coarray, yielding high coarray redundancy. In this paper, we propose a k-level extended sparse array configuration consisting of one sparse subarray with k-level expansion and one uniform linear subarray. By systematically analyzing the inherent structure of the k-level extended sparse array, the closed-form expressions for sensor locations, uniform DOF and coarray redundancy ratio (CARR) are derived. Moreover, with the utilization of a k-level extended strategy, the proposed array remains a hole-free property and achieves low coarray redundancy. According to the proposed sparse array, the spatial and temporal information of the incident sources are jointly exploited for underdetermined direction-of-arrival estimation. The theoretical propositions are proven and numerical simulations are performed to demonstrate the superior performance of the proposed array

Catalytic hydrothermal liquefaction of microalgae over metal incorporated mesoporous SBA-15 with high hydrothermal stability

Hydrothermal liquefaction (HTL) is one of the most promising technologies for conversion of microalgae, and catalysts with high hydrothermal stability are required for controllable HTL. In this article, SBA-15 incorporated with transition metals (Ni, Pd, Co and Ru) were synthetized via double-template method for catalytic HTL of microalgae. The results showed that metal incorporated SBA-15 represented high hydrothermal stability at 613 K. The incorporated Ni, Co and Ru was dispersed in SBA-15 enhancing the hydrothermal stability. The catalysts greatly influenced the chemical composition of the obtained bio-oil, which contained a higher percentage of furfural derivatives and a lower content of fatty acids and N-containing compounds, thus bio-oil quality was improved significantly. Higher hydrothermal stability and specific surface areas of Co-SBA-15 contribute to the highest preformation with 78.78% conversion and 24.11 wt% bio-oil yield. Metal incorporated SBA-15 provides a potential application for biomass conversion in high-temperature aqueous phase. Keywords: Hydrothermal stability, Metal incorporation, SBA-15, Catalytic HTL, Microalga

An Integrated Real-Time FMCW Radar Baseband Processor in 40-nm CMOS

In this paper, a pipelined frequency-modulated continuous-wave (FMCW) radar baseband processor applied to real-time applications is proposed and implemented in 40-nm CMOS technology. The FMCW radar signal processing time is analyzed according to the system specifications. On the basis of the theoretical analysis and systematic considerations, a pipelined baseband architecture with internal single-port static random access memory (SRAM) is employed. The baseband processor is mainly composed of two-dimensional fast Fourier transform (2D-FFT), two-dimensional constant false alarm rate (2D-CFAR), digital beam-forming (DBF), and memory control modules. The 2D-FFT module is structured with a pipelined scheme and avoids the waste of data transferring time between modules. The 2D-CFAR module is programmable for different applications. The designed address control is proposed to depose the edge cells. The processor occupies a core chip area of 3.353 mm $\times 3.353$ mm and has been tested on the personal computer (PC) and field programmable gate array (FPGA) platform. The power consumption and processing time are also analyzed and compared with other works. The processor consumes 55.65 mW, including SRAMs. The processing time is 12.67 ms with the maximum window size and 256 targets when operating at 125 MHz. This time is estimated based on the assumption that each chirp lasts for 0.04096 ms, and data input takes 10.48 ms. Within this period, the range FFT is completed. The Doppler FFT, 2D-CFAR with the maximum window size, and DBF with 256 targets require 0.80 ms, 1.16 ms, and 0.23 ms respectively

Structure-Aware Bayesian Compressive Sensing for Near-Field Source Localization Based on Sensor-Angle Distributions

A novel technique for localization of narrowband near-field sources is presented. The technique utilizes the sensor-angle distribution (SAD) that treats the source range and direction-of-arrival (DOA) information as sensor-dependent phase progression. The SAD draws parallel to quadratic time-frequency distributions and, as such, is able to reveal the changes in the spatial frequency over sensor positions. For a moderate source range, the SAD signature is of a polynomial shape, thus simplifying the parameter estimation. Both uniform and sparse linear arrays are considered in this work. To exploit the sparsity and continuity of the SAD signature in the joint space and spatial frequency domain, a modified Bayesian compressive sensing algorithm is exploited to estimate the SAD signature. In this method, a spike-and-slab prior is used to statistically encourage sparsity of the SAD across each segmented SAD region, and a patterned prior is imposed to enforce the continuous structure of the SAD. The results are then mapped back to source range and DOA estimation for source localization. The effectiveness of the proposed technique is verified using simulation results with uniform and sparse linear arrays where the array sensors are located on a grid but with consecutive and missing positions