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Complex forward scattering coefficient for signal characterisation

Complex forward scattering coefficient for signal characteri...

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IET International Radar Conference 2018, IRC 2018

作者： Chen, Jing Liu, Xiangrong Liu, Changjiang Dong, Xichao School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics 100076 China

In this study, modelling and characteristics analysis of forward scattering coefficients and echoes are investigated. Traditional ground-target forward scattering signal model has not considered the impact of scattering phase;thus it is not accurate enough for describing air-target echoes. To solve this problem, a novel echo model was proposed based on the analytical deduction of complex forward scattering coefficient. The novel model separately expresses the target scattering characteristics and Doppler, greatly facilitating the theoretical analyses of echo characteristics. Then, theoretical and simulation investigations on the amplitude and phase characteristics of forward scattering echoes were made based on the complex forward scattering coefficient model. Theoretical explanations for the periodical fluctuation of far-field echo amplitude, periodical saltation of far-field echo phase, shadow blocking of near-field echo amplitude, and slow variation of near-field echo phase were given, respectively. Meanwhile, the boundary between near-field forward scattering and far-field forward scattering was quantitatively defined, which laid the theoretical foundation for the corresponding signal processing algorithms. Finally, with CST simulation results the accuracy of the echo model is verified and impacts of objects' real shapes are analysed. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Forward scattering

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Target recognition in synthetic aperture radar image based on PCANet

Target recognition in synthetic aperture radar image based o...

引用

IET International Radar Conference 2018, IRC 2018

作者： Qi, Baogui Jing, Haitao Chen, He Zhuang, Yin Yue, Zhuo Wang, Chonglei Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai200240 China

Automatic targets recognition (ATR) for synthetic aperture radar (SAR) image is very important. ATR can be used in traffic management, national frontier safety, and so on. Traditional algorithms for SAR ATR is composed of extraction features and training classifier. The features are essential for the classification accuracy. However, choosing good features by hand is a hard task. The deep convolutional neural networks (CNNs) which can learn features automatically have got a great performance in natural images. However, the CNNs have many parameters and need a lot of data to train such networks. The remote-sensing data of SAR is limited. Then, the authors need a simple network which needs not much data and easy to train. The principal component analysis network (PCANet) is a shallow network that performs well in the recognition task and needs no hand features choosing. Though this network has produced a wide application in the natural images, it is rarely used in the SAR images. The experimental result of the moving and stationary target acquisition and recognition (MSTAR) dataset shows that the PCANet can achieve over 99% accuracy on ten-class targets. This result is better than traditional algorithms and is very close to the results of deep-learning methods. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Principal component analysis

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FPGA-based fault injection design for 16K-point FFT processor

FPGA-based fault injection design for 16K-point FFT processo...

引用

IET International Radar Conference 2018, IRC 2018

作者： Mao, Chuang-An Xie, Yu Wei, Xin Xie, Yi-Zhuang Chen, He Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

There are a number of satellites working in the harsh space environment. The charged particles in space may strike the electron devices causing the undesired influences, such as soft errors in memory devices or permanent damage in hardware circuits. Aiming at reliability evaluation of very-large-scale integration circuits implemented in SRAM-based field programmable gate arrays, a fault injection platform is constructed based on the soft error mitigation controller in this study. The authors adopt a 16K-point fast Fourier transformation processor as the design under test (DUT) and inject errors into different positions. The effectiveness of this platform is varied by comparing the results of DUT with Golden data. Compared with the traditional reliability testing techniques, the fault injection method proposed in this study has the advantages of low cost, short test period and low resource consumption. Hence, the proposed fault injection design is suitable for circuits consuming huge resources and large number of repeating tests. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Static random access storage

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Moving target modelling and indication in MIMO GEO SAR

Moving target modelling and indication in MIMO GEO SAR

引用

IET International Radar Conference 2018, IRC 2018

作者： Cui, Chang Liu, Xiangrong Dong, Xichao Hu, Cheng Li, Yuanhao School of Information and Electronics Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

Multiple-input multiple-output geosynchronous synthetic aperture radar (MIMO GEO SAR), composed by several mini-satellites in GEO orbits, has more spatial degrees of freedom, which helps to suppress clutter and improves the performance of ground moving target indication (GMTI). However, in order to ensure the satellite safety, the satellite spacing in MIMO GEO SAR is very large. It results in the failure of far-field assumption and causes the moving target defocusing because of the mismatch between the steering vector in far-field sources and the moving target echo. Here, a new signal model based on the near-field sources is first given according to the GEO SAR satellite-ground geometry relationship. Then, considering the phase difference between the different channels, the spatial steering vector is derived and the weight vector for adaptive processing is proposed to suppress clutter and indicate the moving target. Finally, computer simulations are carried out for the validation of the proposed method. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Multi-scale object detection by bottom-up feature pyramid network

Multi-scale object detection by bottom-up feature pyramid ne...

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IET International Radar Conference 2018, IRC 2018

作者： Boya, Zhao Baojun, Zhao Linbo, Tang Chen, Wu Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China

The deep neural networks has been developed fast and shown great successes in many significant fields, such as smart surveillance, self-driving and face recognition. The detection of the object with multi-scale and multi-aspect-ratio is still the key problem. In this study, the authors propose a bottom-up feature pyramid network, coordinating with multi-scale feature representation and multi-aspect-ratio anchor generation. Firstly, the multi-scale feature representation is formed by a set of fully convolutional layers which is catenated after the backbone network. Secondly, in order to link the multi-scale feature, the deconvolutional layer is involving after each multi-scale feature map. Thirdly, to tackle the problem of adopting object with different aspect ratios, the anchors on each multi-scale feature map are generated by six shapes. The proposed method is evaluated on PASCAL visual object detection dataset and reach the accuracy of 80.5%. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Aspect ratio

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Radar data simulation using deep generative networks

Radar data simulation using deep generative networks

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IET International Radar Conference 2018, IRC 2018

作者： Song, Yiheng Wang, Yanhua Li, Yang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China

Due to the high cost of real experiments, radar data simulation plays an important role in radar applications. However, the accuracy and the calculation speed of existing simulation methods is limited by the model error and the heavy calculation of electromagnetic simulation. Here, a radar data simulation method based on deep generative network (DGN) is proposed. DGN is generative model involving deep network as the representation tool, in which the model is trained with labelled data. When the training phase is finished, the generative model can generate data samples which are similar to the real samples. The performance of the DGN is evaluated on the ground-based radar dataset, and the results show that the distribution of the generated radar data is similar to the training radar data. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Electromagnetic simulation

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Parallel processing of sliding spotlight mode SAR imaging based on GPU

Parallel processing of sliding spotlight mode SAR imaging ba...

引用

IET International Radar Conference 2018, IRC 2018

作者： Gao, Zixin Wei, Chunpeng Yang, Chen Xie, Yizhuang Chen, He Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

Sliding spotlight synthetic aperture radar (SAR) is a hot imaging mode in space-borne SAR. It can acquire high-resolution and large azimuth scene size simultaneously. The main challenges for sliding spotlight SAR processing in real time are the huge volume of raw data and complex imaging algorithm. The rapid development of the graphics processing units (GPUs) provides a new computing platform for high-performance SAR processing. This work presents a parallel processing of sliding spotlight SAR imaging algorithm on GPU via compute unified device architecture and tests the speedup of each module of the algorithm on the K20c GPU platform from the aspect of a small granularity and a large granularity of point target images. Compared with traditional CPU-based SAR imaging technology, the proposed GPU-based sliding spotlight SAR imaging obtains a speedup of 186.80 when processing data with a granularity of 16,384 × 8192 and gets a speedup of 49.72 when processing data with a granularity of 65,536 × 32,768, thereby greatly improving the real-time imaging processing. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Graphics processing unit

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Research on the method of fast inverse realisation of Vandermonde matrix based on FPGA

Research on the method of fast inverse realisation of Vander...

引用

IET International Radar Conference 2018, IRC 2018

作者： Chen, Lei Chen, Liang Li, Bing Y. Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

With the rapid development of science and technology, the operation and calculation in synthetic aperture radar (SAR) imaging systems require high throughput, and the system has high requirement for real-time performance. At the same time, in the SAR imaging technology, many Vandermonde matrix inversion operations are used in places where the weight coefficients are calculated. Especially when multi-channel inverse filtering is performed, the use of the Vandermonde matrix inversion operation is a crucial step. In this study, the authors worked hard to find a high speed and accurate Vandermonde matrix inversion algorithm for complex SAR imaging systems. Considering the field-programmable gate array (FPGA) can be parallel computing and its computing speed is high, the authors chose FPGA to achieve a large number of Vandermonde matrix inversion calculation, and it can meet the requirements of high speed and real time. So, first the realisation method of the fast inversion of the Vandermonde matrix based on Lagrange interpolation polynomial is discussed in this study, which includes pre-processing module, u matrix calculation module, v matrix calculation module, π′ vector calculation module and post-processing module. Then, the simulation results of the design are compared and analysed, which proved the correctness and feasibility of the rapid inversion method of Vandermonde matrix. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Imaging systems

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Robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression

Robust knowledge-aided sparse recovery STAP method for non-h...

引用

IET International Radar Conference 2018, IRC 2018

作者： Peng, Hao Sun, Yuze Yang, Xiaopeng Yang, Jian Beijing China Department of Electronic Engineering Tsinghua University Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronic Beijing Institute of Technology Beijing China

Conventional space-time adaptive processing (STAP) methods would suffer severely performance loss in the complex clutter environment of an airborne-phased array radar, especially when the estimated clutter covariance matrix (CCM) is corrupted by the interference targets (outliers). In order to improve the clutter suppression in a practical complex clutter, a robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression is proposed. In the proposed method, the spectral profiles of the clutter and outliers are firstly estimated by sparse recovery processing. Then based on the system prior parameters, the clutter mask is constructed to select the space-time steering vectors corresponding to the clutter components. Afterwards the clutter suppression is achieved based on the clutter subspace obtained from the selected space-time steering vectors. Since the clutter and outlier profiles are effectively estimated and distinguished by the knowledge-aided sparse recovery processing, robust clutter subspace estimation can be achieved for clutter suppression. Through the simulated and actual airborne-phased array radar data, it is verified that the proposed method can effectively improve the STAP performance in a non-homogeneous environment. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Space time adaptive processing

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High-efficiency scene classification based on deep compressed-domain feature

High-efficiency scene classification based on deep compresse...

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IET International Radar Conference 2018, IRC 2018

作者： Li, Cheng Zhao, Baojun Zhao, Boya Wang, Wenzheng Duan, Chenhui Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key laboratory of Embedded Real-time Information Processing Technology Beijing Institute of technology Beijing100081 China

Remote sensing image (RSI) scene classification has become a more and more fundamental issue in satellite and UAV time-sensitive applications. However, as the volume and velocity of RSIs are undergoing an explosive growth, traditional effective technologies claim a huge amount of computing resources, on a scale that already goes beyond in-orbit processing capacity. Here, we attempt to design a deep feature representation framework based on onboard compressed data to solve the aforementioned problem. Firstly, we extract header and body information in raw JPEG2000 codestream for representing geometrical and geospatial property of RSIs. Moreover, a novel compressed-domain convolutional neural network (CNN) is proposed to obtain high-level representation for effective classification. Extensive experiments demonstrate that, in comparison with the existing relevant state-of-the-art approaches, the proposed method achieves high classification accuracy with faster computation and lower consumption. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Orbits

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