In this work, several mixtures of self-fluxing NiCrBSi alloy powder and a nickel-clad WC powder (10 wt.% Ni and balance WC) were laser cladded on stainless steel substrates of austenitic type (AISI 304). The aim of the study was to determine the influence of the volume fraction of the reinforced WC particles on the formation and performance of the composite layer. The effect of other parameters of the treatment, such as the laser energy, beam profile, traverse speed and the mass rate of the feed powder was also investigated. Clad layers of 0.5–1.5 mm height were obtained, its microhardness measured and the microscopic morphology and distribution of tungsten carbide particles within the layer characterized by scanning electron microscopy (SEM). It was found that most clad layer properties such as its porosity, microhardness and homogeneity are determined by the percentage of WC particles in the mixture. Pores were observed for volume fractions roughly above 50%. Below this limit, homogeneous, dense and crack free clad layers were obtained, with measured hardness ranging between 600 and 1000 HV depending on the WC content.

Morphology and characterization of laser clad composite NiCrBSi–WC coatings on stainless steel

To realize real-time imaging for the spaceborne synthetic aperture radar (SAR) using the chirp scaling (CS) algorithm, high generation rates of phase factors and heavy computation loads are unavoidable. For example, updating phase factors continuously in the 2-D time or frequency space is difficult for generating phase factors for real-time imaging. To solve this problem, a modified CS algorithm based on updating phase factors regionally is proposed. Moreover, conventional floating-point arithmetic is unaffordable for real-time imaging onboard. Therefore, fixed-point processing is adopted. In the proposed imaging algorithm, the phase factors are updated only at some specified times and frequencies, and fixed-point operation is adopted. Furthermore, based on the paired echo theory and the finite word length error mathematical model, the effect of the proposed imaging algorithm on the SAR image quality is studied. Moreover, based on the above analysis and the experiments of Chinese HJ-1C and GF-3 spaceborne SAR practical measured data, a hardware system including digital signal processor and field-programmable gate array boards is constructed.

A Modified Fixed-Point Chirp Scaling Algorithm Based on Updating Phase Factors Regionally for Spaceborne SAR Real-Time Imaging

360-degree video synthetic aperture radar (VideoSAR) presents a powerful capability for the information perception of dynamic scenes. In comparison to time-consuming airborne SAR, miniature SAR (MiniSAR) mounted on an unmanned aerial vehicle platform is a cost-effective configuration that provides a higher operation flexibility for video acquisition. Nevertheless, MiniSAR system development and signal processing limited by multiple factors are still challenging issues, including restricted weight, volume, power consumption, complicated trajectory imaging, and high-efficient video processing. In this article, we develop a multirotors VideoSAR system at X-band in Nanjing University of Aeronautics and Astronautics, concerning hardware architecture, field experiment, and video signal processing. It has the capability of ultrahigh resolution imaging with three available channels up to 1.8-GHz transmission bandwidth. Field programmable gate array-based unified signal processing architecture can further accelerate the generation of massive VideoSAR sequences in terms of both circular spotlight and stripmap modes. Several experimental results have been presented to demonstrate the validity of our developed system.

Multirotors Video Synthetic Aperture Radar: System Development and Signal Processing

Video synthetic aperture radar (ViSAR) is a novel imaging mode where the radar system forms a sequence of SAR images while the radar platform is flying. To achieve a high frame rate, ViSAR system typically operates in the terahertz (THz) band. In this paper, the unified coordinate system algorithm (UCSA) improving on the polar format algorithm (PFA) is proposed for THz ViSAR images formation, which is based on the more accurate wavenumber domain signal model derived by second-order Taylor expansion. Considering the small synthetic angle feature of THz ViSAR, the UCSA divides two-dimensional resampling into range and azimuth resampling using the chirp z-transform to reduce the processing complexity in ViSAR imaging. Through the analysis in this paper, it can be known that THz PFA images are free from the quadratic phase errors. However, the PFA images are oriented to different directions and exist image distortion caused by linear phase errors (LPE). Through an image resampling, the UCSA corrects the LPE and rotates the images to the unified coordinate system simultaneously. The effectiveness of UCSA is validated by a ground-based experiment performed by a 0.3 THz radar system.

Unified Coordinate System Algorithm for Terahertz Video-SAR Image Formation

Video synthetic aperture radar (ViSAR) is a novel imaging model to persistently surveil the region of interest by a series of consecutive images in real time. The formation time of one frame image, which can influence the image latency, is one of the key factors to realize ViSAR system. In general, the polar format algorithm (PFA) is preferred for ViSAR imaging. However, the 2-D resampling in the PFA has a large computation burden and is complex to realize in the real-time processing system. It has become the main obstacle to reduce image latency. Therefore, we derive four judgment criteria in this letter. According to these criteria, we propose the improved method of the PFA. Based on this method, the modified PFA can reduce the image formation time while guaranteeing the image quality. Simulation results are presented to validate the effectiveness of the improved method proposed in this letter.

Improved Method of Video Synthetic Aperture Radar Imaging Algorithm

A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.

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Deep-Learning for Radar: A Survey

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

Video Synthetic Aperture Radar (ViSAR) is the new application of SAR technique. Through reasonable system parameter design, ViSAR system can obtain the SAR image at a faster image rates (also referred to herein as frame rate) which permit the detection of dynamic changes that are occurring in the target scene. Therefore, frame rate as one of the core parameters of ViSAR system should be carefully studied.

Frame rate analysis of video synthetic aperture radar (ViSAR)

Implementation of a miniaturized SAR processor using Virtex-7 FPGA is described in this paper. The polar format algorithm (PFA) is accelerated by utilizing the chirp scaling principle to avoid the two dimensional interpolation. Moreover, an efficient two dimensional autofocus scheme is applied as an effective complementarity to the low cost motion sensor. The FPGA design and resource utilization are investigated, and the accuracy of the processing results are compared with those obtained on a personal computer. The system is now able to process 8K by 8K complex-image in a single precision within 15 seconds when the FPGA work at 200MHZ clock.

Jindong Zhang

Papers

Deep-Learning for Radar: A Survey

Multirotors Video Synthetic Aperture Radar: System Development and Signal Processing

Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

Frame rate analysis of video synthetic aperture radar (ViSAR)

A Miniaturized High Resolution SAR Processor Using FPGA