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5th International Conference on signal processing and Computer Science, SPCS 2024

作者： Li, Hancheng Qu, Jingyi Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In order to enhance the interpretability of deep learning models and improve the credibility of model predictions, we propose an interpretability analysis method for flight delay prediction based on KernelSHAP. Delay prediction uses flight and weather data, and the ATMAP algorithm is used to generate weather condition scores that are strongly correlated with flight delay conditions to enrich features. The deep learning model NR-DenseNet is selected for delay prediction. KernelSHAP is combined to analyze the input features and the decision-making process of the model from two perspectives: feature analysis of overall samples and feature analysis of single sample. The results show that the addition of KernelSHAP enhances the interpretability of the model, effectively breaks the black box characteristics of the model, and can provide professionals with more reliable decision-making guidance. © 2025 SPIE.

关键词： Prediction models

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INS/GNSS integrated navigation algorithm based on CNN-LSTM-Attention 4

INS/GNSS integrated navigation algorithm based on CNN-LSTM-A...

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4th International Conference on Detection Technology and Intelligence System, DTIS 2024

作者： Zhong, Lunlong Li, Xiaodong Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China

GNSS signals are very fragile. When the receiver is located in a tunnel or under a tall building in a city, GNSS signals are easily blocked, resulting in GNSS loss of lock. Therefore, an integrated navigation algorithm based on CNN-LSTM-Attention is proposed. First, when the GNSS signal is intact, important time series features are captured using CNN and attention mechanism, which improves the performance of traditional LSTM networks in processing strongly coupled navigation data. Then the CNN-LSTM-Attention prediction model is trained with the combined navigation data, and the inertial navigation error model is obtained. When the GNSS signal is interrupted, the trained prediction model is used to predict the inertial navigation error information, thereby suppressing the inertial navigation error. Finally, simulation comparison experiments are carried out. The conclusion shows that this method can solve the combined navigation problem when GNSS loses lock. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Global positioning system

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Large-Scale Flight Delay Propagation Prediction Based on Spatio-Temporal Dynamics Network 24

Large-Scale Flight Delay Propagation Prediction Based on Spa...

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3rd International Conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Qu, Jingyi Wang, Junbo Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9798400707308

Capturing the delay propagation mechanism between multiple airports is fundamental to delay prediction, which is crucial for aviation professionals to make correct decisions. The essence of this problem is how to effectively utilize the temporal and spatial characteristics of flight data and other influencing factors. However, previous research has only considered fragmented spatio-temporal features and other influencing factors, or used dynamic models to simulate the delay propagation mechanism. Spatio-Temporal Dynamics Network (STDN) is proposed in this paper, which combines propagation dynamics models with deep learning methods for the prediction of large-scale flight delays. The model adaptively improves the parameter learning method of the Graph Attention Network (GAT), and combines the propagation dynamics method for knowledge representation and reasoning. It predicts the number of flight delays by modeling the flight delay propagation process. Using real historical flight data for prediction, the results show that STDN effectively learns the flight delay propagation pattern. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge representation

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Design of Dual-mode DPE Receiver Based on GPS L1/BDS B1C signals

Design of Dual-mode DPE Receiver Based on GPS L1/BDS B1C Sig...

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2025 International Technical Meeting of The Institute of Navigation, ITM 2025

作者： Jia, Qiongqiong Guo, Qiqi Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300000 China

ISBN: (纸本)9780936406404

With the expanding applications of the Global Navigation Satellite System (GNSS), its operating environment has become increasingly complex, with challenges such as multipath effects, non-line-of-sight (NLOS) propagation, and signal attenuation caused by tall urban buildings and trees. This complexity poses additional challenges for traditional GNSS receivers. Direct Position Estimation (DPE) addresses some of the limitations of traditional receivers by accumulating signals from all visible satellites directly in the navigation domain. However, when severe obstructions result in a reduced number of visible satellites, DPE may fail to function properly. The introduction of multiconstellation satellite signals can help mitigate the issue of insufficient visible satellites in complex environments. Based on this, this paper designs a dual-mode DPE receiver utilizing Global Positioning System (GPS) L1 signals and BeiDou Navigation Satellite System (BDS) B1C signals. © 2025 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM. All rights reserved.

关键词： Global positioning system

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A High-Efficiency Doppler Frequency Parallel Acquisition Method Based on A-GPS

A High-Efficiency Doppler Frequency Parallel Acquisition Met...

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2025 International Technical Meeting of The Institute of Navigation, ITM 2025

作者： Jia, Qiongqiong Zhao, Yuchen China Civil Aviation University Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin300000 China

ISBN: (纸本)9780936406404

Acquisition is a key step for Global Navigation Satellite System (GNSS) receivers to achieve positioning and other functions, and the acquisition speed directly affects the Time To First Fix (TTFF) of the receiver. The acquisition process typically involves generating local reference signals for all satellite signals and matching them with the received signals. For each satellite signal, this matching involves searching in the unknown Doppler frequency and code phase space. Traditional parallel acquisition methods can only perform parallel processing in either the Doppler frequency dimension or the code phase dimension, and still require linear search in the other dimension. To reduce the computational load and improve the acquisition efficiency, this paper proposes a high-efficiency parallel acquisition method based on A-GPS. This method utilizes the auxiliary information provided by the A-GPS base station to convert the linear search of visible star signals in Doppler frequency into a single parallel search in the Doppler search space, thereby reducing the computational load required for acquisition to 1/N of the conventional method, where N is the total number of frequency bands searched. The paper verifies through theoretical analysis and experimental results that the proposed method can significantly improve acquisition efficiency while ensuring acquisition performance. © 2025 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM. All rights reserved.

关键词： Global positioning system

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Vertical Protection Level Optimization Method and Performance Analysis of ARAIM Based on PSA Algorithm 24

Vertical Protection Level Optimization Method and Performanc...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Li, Mengjiao Han, Dan Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China Tianjin Air Traffic Control Sub-Branch North China Regional Air Traffic Management Bureau CAAC Tianjin China

ISBN: (纸本)9798400710865

advanced Receiver Autonomous Integrity Monitoring (ARAIM) algorithm is one of the satellite navigation integrity enhancement technologies to assist civil aviation approach. The basic ARAIM algorithm distributes the integrity risk (IR) and continuity risk (CR) equally, leading to a conservative vertical protection level (VPL) and limiting the availability of ARAM. In this paper, a dynamic risk optimization allocation method PID-based search algorithm (PSA) is proposed. Using the minimum VPL as the optimization objective, iteratively search for the best risk allocation method assigned to each subset of faults while satisfying the constraints. And different optimization variables and objective functions are constructed to analyze the extent to which optimizing the allocation of both IR and CR risks versus optimizing the allocation of a single risk affects the level of protection. Experiments are conducted with static data from multi-GNSS experiment (MGEX) stations and vehicle-mounted dynamic data, and the experimental results show that under GPS and BDS dual-constellation observation, the ARAIM risk optimization allocation method based on the PSA algorithm can effectively reduce the VPL. The smallest VPL is obtained by optimizing the allocation of two risks at the same time, with an average optimization rate of up to 16.1%. And optimizing IR plays a more critical role in the overall protection level optimization, the magnitude of optimizing IR accounts for 68% of the complete optimization. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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GNSS Interference Detection and Affected Area Assessment Based on ADS-B 5

GNSS Interference Detection and Affected Area Assessment Bas...

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5th International Conference on Advances in Electrical, Electronics and Computing Technology, EECT 2025

作者： Chen, Min Li, Haoyu He, Weikun Wu, Renbiao Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9798331541545

Global Navigation Satellite Systems (GNSS) are fundamental to modern aviation infrastructure but remain vulnerable to radio frequency interference (RFI), which may cause flight delays, operational inefficiency, and critical safety risks. The Automatic Dependent Surveillance-Broadcast (ADS-B) system, reliant on GNSS for precise positional data, also suffers performance degradation under interference. This study proposes a two-stage framework for GNSS interference detection using ADS-B data. First, an enhanced feature engineering pipeline integrates Akima interpolation to reconstruct incomplete trajectories and extracts spatiotemporal patterns through sliding-window statistical features. Second, an unsupervised Long Short-Term Memory (LSTM) autoencoder addresses data incompleteness and label scarcity by learning latent representations of normal operations. Finally, grid-based spatial feature extraction combined with a Gradient Boosting Decision Tree (GBDT) model enables spatial interpolation to generate interference risk maps across target airspace. The proposed framework provides significant support for interference source localization and enhances airspace risk management, thereby helping to reduce safety risks in civil aviation under GNSS-compromised environments. © 2025 IEEE.

关键词： Risk management

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GNSS Interference Detection and Affected Area Assessment Based on ADS-B

GNSS Interference Detection and Affected Area Assessment Bas...

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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Min Chen Haoyu Li Weikun He Renbiao Wu Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

关键词： Global navigation satellite system Interpolation Atmospheric modeling Interference Predictive models Feature extraction Trajectory Numerical models Safety Long short term memory

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VnDark: iterative enhanced feature learning network for nighttime airfield object detection

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signal, image and Video processing 2025年第7期19卷 1-14页

作者： Luo, Qijun Zhang, Xuerui Fang, Xigao Zheng, Zhiming Zhang, Tao Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China Institute of Shanghai Aircraft Design and Research Commercial Aircraft Corporation of China Shanghai China

Using onboard vision collection and recognition technologies, the visual assistance driving system for civil aviation aircraft captures information about the surrounding threat scenario to assist pilots in making driving judgments. However, nighttime scene images deteriorate, leading to low visual clarity and decreased scene object detection accuracy. Current object detection algorithms are unable to simultaneously enhance nighttime visual clarity and accurately detect scene objects. To address this problem, a convolutional neural network named VnDark based on iterative feature enhancement learning is proposed. A feature enhancement sub-network is designed based on an iterative light enhancement curve mechanism, which employs a U-shaped encoder-decoder structure to produce sharp edge features from nighttime images, enhancing scene visibility. A multi-task feature collaborative optimization strategy is designed, which shares the enhanced features generated by the feature enhancement sub-network with the object detection sub-network and employs a multi-task weight learning algorithm to optimize the weights for nighttime image enhancement, object classification, and localization tasks. This strategy improves the accuracy of scene object detection. A nighttime scene object dataset containing real-world and simulation data is established to validate VnDark’s performance. Experimental results indicate that the network can accurately detect scene objects while simultaneously enhancing the visibility of dark images. In the enhanced images, the original dark area objects are clearly visible, making them easier for human observation. The average precision of object detection reached 78.5%. Additionally, the proposed algorithm achieved an average precision of 69.3% on the public dataset Exclusively Dark, surpassing typical general object detectors such as Faster R-CNN, SSD, YOLOv7, and YOLOv5s. Both precision and speed of the proposed algorithm surpass that of typical nighttim

关键词： Multi-task learning

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Autonomous Path Planning via Sand Cat Swarm Optimization With Multi-Strategy Mechanism for Unmanned Aerial Vehicles in Dynamic Environment

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IEEE Internet of Things Journal 2025年

作者： Deng, Wu Feng, Jiayi Zhao, Huimin Civil Aviation University of China College of Electronic Information and Automation Tianjin300300 China Civil Aviation University of China Tianjin Key Lab Intelligent Signal and Image Processing Tianjin300300 China State Key Laboratory of Rail Transit Vehicle System of Southwest Jiaotong University Chengdu610031 China

Unmanned Aerial Vehicles (UAVs) face critical challenges in path planning in dynamic environment. Sand Cat Swarm Optimization (SCSO) algorithm has demonstrated promise in addressing complex optimization challenges. However, SCSO is limited by slow convergence, susceptibility to local optima, and insufficient adaptability. To overcome these shortcomings, an enhanced SCSO with the spiral search, Livy flight, tent chaotic mapping, and adaptive sparrow alert mechanism, namely TSLS-SCSO is developed to propose an autonomous path planning method for UAVs in dynamic environment. In TSLS-SCSO, a new population initialization strategy with tent chaotic mapping is designed to achieve a large dynamic range and coverage capability. Then a new spiral search strategy is designed to broaden the search range in the search phase. And a new Livy flight strategy is employed to enrich the diversity of population in the attacking prey phase. A new sparrow alert mechanism with integrating the sand cat group with the sparrow alert is designed to obtain faster convergence speed and accuracy. The experiment results on CEC 2017 and CEC 2022 show that the TSLS-SCSO obtains higher accuracy and more stable solutions and exhibits better competitiveness. Furthermore, the proposed UAV path planning method successfully found effective paths with an obstacle avoidance effectiveness of 95.5%. The obtained results validate the effectiveness and competitiveness of TSLS-SCSO in UAV path planning in the dynamic environment. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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