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14th China Satellite Navigation Conference, CSNC 2024

作者： Jia, Qiongqiong Li, Weipeng Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9789819969319

The Popular of Global Navigation Satellite System (GNSS) is attributed to its good performance. However, the performance of a conventional GNSS receiver is severely degraded or even failed to provide services in complex environments. Direct Position Estimation (DPE) combines all the visible satellite signals for energy accumulation in the navigation domain first, and then the navigation solution is estimated directly from the accumulation results. Hence the navigation performance of GNSS can be effectively improved in complex environments by DPE. However, the signal accumulation of DPE requires calculating the correlation values of all the visible satellite signals at each candidate grid in the navigation domain, the computation load of which limits its application. To alleviate this problem, a GPU-based parallel implementation of the DPE receiver is designed, which decomposes the operations involved in the joint accumulation into three levels of parallel processing. The effectiveness of the designed parallel implementation of the DPE receiver is verified by experiment results. With the available hardware in our laboratory, the designed DPE implementation improves the efficiency to approximately 146 times on average over the conventional DPE implementation. © Aerospace Information Research Institute 2024.

关键词： Graphics processing unit

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The DME Interference Mitigation Based Wavelet Thresholding Algorithm for L-DACS Receiver 9

The DME Interference Mitigation Based Wavelet Thresholding A...

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9th International Conference on Intelligent Computing and signal processing, ICSP 2024

作者： Peng, Xiangqing Wang, Jiani Wang, Lei Liu, Haitao Civil Aviation University of China Tianjin Key Lab for Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350376548

The L-DACS operates as an inlay system between two adjacent channels of the Distance Measuring Equipment (DME) system since the spectrum resources in the L-band are scarce and fragmented. This DME signal severely impacts the performance of L-DACS. This paper proposes an approach for mitigating DME interference by integrating spectrum aggregation technology and a wavelet thresholding algorithm. To enhance spectral efficiency, the method utilizes direct wideband aggregation technology at the transmission end to expand the bandwidth. Then, at the receiving end of Orthogonal Frequency Division Multiplexing (OFDM), the received signal is decomposed layer by layer using the wavelet threshold algorithm. The difference between the spectrum-aggregated signal and the DME signal in the wavelet domain is effectively identified to reconstruct the DME signal, thereby achieving interference elimination. Simulation results demonstrate that this method improves spectrum efficiency and data transmission rate while significantly mitigating DME interference, thereby enhancing the link transmission reliability of the L-DACS system. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Infrared Small Target Detection Algorithm Based on Improved DeeplabV3+ 8

Infrared Small Target Detection Algorithm Based on Improved ...

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8th International Conference on Intelligent Computing and signal processing, ICSP 2023

作者： Qi, Mengfei Jia, Guimin Civil Aviation University of China Tianjin Key Lab for Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350302455

Infrared small target detection aims to segment small targets from the infrared images. Aiming at the problem of infrared small target detection, an improved algorithm is proposed based on DeeplabV3+ network. For the encoding part of DeeplabV3+, considering the small size of infrared small targets, the dilation rate of ASPP module is adjusted to capture the features of small targets more effectively. For the small targets are prone to lose spatial information during feature extraction, a location enhancement module is introduced to supplement accurate location information and to improve the accuracy of detection. In order to preserve detail information, an additional feature map is added from the backbone network to fuse detail information and semantic information for the decoding part of DeeplabV3+. Experimental results have demonstrated that the proposed algorithm is capable of enhancing the detection performance of DeeplabV3+ network in the infrared small target detection task. © 2023 IEEE.

关键词： Feature extraction

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PolSAR Image Classification Based on Deep Features 8th

PolSAR Image Classification Based on Deep Features

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8th China High Resolution Earth Observation Conference, CHREOC 2022

作者： Han, Ping Men, Lunan Chen, Zetao Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9789811982019

The interpretation of polarimetric synthetic aperture radar (PolSAR) images has wide and important applications in target identification, disaster survey, urban planning, road detection and image classification. In PolSAR image classification tasks, some algorithms often obtain different polarimetric scattering features by different target decomposition methods which can generate feature redundant. To solve the problems of complex processing and excessive time consumption caused by multiple dimensional features, a deep feature-based PolSAR image classification algorithm is given in this paper. The algorithm first extracts the original feature set from the PolSAR image which is used to pre-train the Restricted Boltzmann Machine (RBM) model. The redundant features in the original feature set are rejected according to the error which is calculated by the reconstruction error of the input features. The RBM network is initialized by the remaining features and corresponding weights, then train the Deep Boltzmann Machine (DBM). Finally, the softmax classifier is trained with the training samples containing the deep features to achieve image classification. The experimental results show that the proposed algorithm has good feature selection timeliness and the deep features obtained from the algorithm can improve the image classification accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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DACNet: Single Image Dehazing Network Based on Attention Mechanism 3

DACNet: Single Image Dehazing Network Based on Attention Mec...

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3rd International Conference on Intelligent Communications and Computing, ICC 2023

作者： Liu, Yongling Han, Ping Wang, Wanwei Civil Aviation University of China Tianjin Key Lab for Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350308327

Aiming at the problem that atmospheric scattering model-based haze removal methods are prone to degradation of haze removal performance due to inaccurate parameter estimation, a single-image haze removal network based on the attention mechanism is proposed for direct restoration of haze-free images. The network consists of encoder and decoder, where the traditional convolution is replaced by combining dilated convolutions with different dilated rates for acquiring and processing haze-related image features. Due to the different weighted information contained in different channel features, an increased attention mechanism is used to enhance the feature representation of important channels through adaptive learning channel features, thereby enabling the encoder to pay more attention to channel information in the haze region. In the encoding and decoding structure, dilated convolution and attention mechanism are used in combination for image dehazing for the first time. By targeting the network to pay attention to the regions with high fog distribution while increasing the sensory field to obtain global information. The method presented in this paper improves the ability of network to process hazy images and brings a new breakthrough in the field of hazy image processing. Experiments show that the algorithm proposed in this paper achieves significant improvements in both subjective and objective indicators comparing with many state-of-the-art methods. © 2023 IEEE.

关键词： Image enhancement

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ESA-Net:A Network for Enhancing Small Target Detection Performance in SAR Imagery Aircraft

ESA-Net:A Network for Enhancing Small Target Detection Perfo...

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IET International Radar Conference 2023, IRC 2023

作者： Han, Ping Zhao, Han Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9781839539954

In synthetic aperture radar (SAR) image aircraft target detection, aircraft small targets have a lower AP than aircraft targets and large targets due to the small number of pixel points they occupy and background clutter interference. To solve this problem, this paper proposes a network for enhancing the performance of aircraft small target detection for SAR images(ESA-Net). The network is designed based on yolov5s with a backbone network BH-CSPDarknet and a detection head TFA-Head. BH-CSPDarknet allocates more computational resources in the shallow layer of the network to process high-resolution feature maps, which effectively extracts richer feature information of aircraft small targets. TFA-Head provides appropriate feature preferences for classification and localisation tasks to provide TFA-Head provides appropriate feature information for classification and positioning task feature preferences, which can effectively suppress the background clutter interference while enhancing the aircraft small target features. The experimental results using the SAR-ADRD dataset show that the method improves the average accuracy of YOLOv5s by 1.9% and the average accuracy of aircraft small targets by 15.6%. © The Institution of Engineering & Technology 2023.

关键词： Synthetic aperture radar

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A novel strategy for fine-grained semantic verification of civil aviation radiotelephony read-backs

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Chinese Journal of Aeronautics 2022年第12期35卷 266-277页

作者： Guimin JIA Junxian LI Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of ChinaTianjin 300300China College of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China

During the flight of the aircraft,the pilot must repeat the instruction sent by the controller,and the controller must further confirm these read-backs,in this way to further ensure the safety of air ***,fatigue,tension,negligence and other human factors may prevent the controller from realizing read-back errors in time,which is a huge hidden danger for the safety of civil aviation *** paper proposes a novel strategy to implement fine-grained semantic verification of radiotelephony read-backs by introducing interaction layer and attention mechanism at the output of BiLSTM *** with the traditional twochannel verification strategy,the interaction layer is added to obtain fine-grained semantic matching relation representation,rather than connecting the BiLSTM output vectors to obtain the overall semantic representation of the *** by adding attention layer,the new strategy can capture the potential semantic relation between the read-backs and the instructions,which is applicable to non-standard diction and abbreviated read-backs in real radiotelephony *** experiments are conducted and the results show that the proposed new strategy is more effective than the traditional method for read-backs checking,and the average test accuracy of the new strategy based on the Chinese ATC radiotelephony read-backs corpus can reach 93.03%.

关键词： Attention mechanism BiLSTM Interaction layer Radiotelephony read-backs Semantic verification

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A robust vector tracking loop structure based on potential bias analysis

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Chinese Journal of Aeronautics 2024年第4期37卷 405-420页

作者： Qiongqiong JIA Yiran LUO Bing XU Li-Ta HSU Renbiao WU Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of ChinaTianjin 300300China Interdisciplinary Division of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University999077Hong KongChina Department of Geomatics Engineering University of CalgaryCalgary T2N1N4Canada

This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops(VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly,the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis,a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases;meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.

关键词： Global Navigation Satellite System(GNSS) Vector Tracking Loop(VTL) Multipath Non-Line-of-Sight(NLOS) Tracking bias propagation Least Absolute Shrinkage and Selection Operator(LASSO)

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Short-Term 4D Trajectory Prediction Method Based on LSTM-IMM 41

Short-Term 4D Trajectory Prediction Method Based on LSTM-IMM

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41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022

作者： Liu, Baichao Shi, Qingyan Han, Ping Civil Aviation University of China Tianjin Key Lab for Advanced Signal Processing Tianjin China

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

Interacting Multiple Model (IMM) is a popular algorithm in trajectory prediction research field, and it can predict the Three-Dimensional (3D) position information about longitude, latitude and altitude, but it is difficult to accurately predict the time. In order to realize the prediction of time and improve the prediction accuracy of longitude, latitude and altitude, neural network is used to model the implicit relationship from historical data, and combining the Long and Short-Term Memory neural network (LSTM) with IMM algorithm, a short-term 4D trajectory prediction method based on LSTM-IMM is proposed. IMM algorithm is used to predict 3D trajectory information about the longitude, latitude and altitude while the LSTM neural network is used to achieve the prediction of time and compensate for the longitude, latitude, and altitude predicted by the IMM algorithm to improve the prediction accuracy. In terms of time, the time interval between adjacent trajectory points is used as a feature for prediction;in terms of longitude, latitude and altitude, LSTM neural network is used to predict the error values between the trajectory predicted by IMM algorithm and the actual trajectory, and these error values are used to compensate the 3D position information predicted by IMM algorithm. The performance of the algorithm is verified by the actual flight data, the results show that this method can well predict the time of trajectory points, significantly improve the prediction accuracy of all flight stages, and has faster response speed in case of trajectory mutation. © 2022 IEEE.

关键词： Forecasting

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Simulation System of Aircraft Surveillance in Airport Terminal Area

Simulation System of Aircraft Surveillance in Airport Termin...

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2021 International Conference on Mechatronics Technology and Aerospace Engineering, ICMTAE 2021

作者： Shen, Xiaoyun Zhai, Haosong Zhang, Shuo Cui, Peng Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China

Because ordinary users cannot quickly obtain the three-dimensional space position of aircraft in the airport terminal area through a two-dimensional display navigation map, the aircraft surveillance simulation system in the airport terminal area based on virtual reality technology is designed. Through analyzing the Automatic Dependent Surveillance-Broadcast (ADS-B) data, the aircraft motion model is established. The three-dimensional aircraft surveillance simulation system for the airport terminal area is realized by Unreal Engine 4 (UE4). Especially, the changes in flight track and flight attitude about aircraft in the three-dimensional scene are designed. The results show that the aircraft movements can be visualized smoothly and correctly based on ADS-B data. It is helpful for users to observe the aircraft of the airport terminal area and analyze the operation status of the plane. © Published under licence by IOP Publishing Ltd.

关键词： Aircraft

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