A causal high-order liquid evidence framework for GNSS spoofing detection that achieves the highest F1-scores among the evaluated temporal models on Dataset~1 and Dataset~3, and detects both labeled normal-to-attack transitions within four sampling steps.
Abstract
Accurate Global Navigation Satellite System (GNSS)-based localization is essential for safe and reliable autonomous driving. However, spoofing attacks can manipulate vehicle position estimates. Continuous and subtle attacks are particularly difficult to detect because individual GNSS observations may remain plausible while the inconsistency between GNSS-implied displacement and onboard vehicle motion gradually increases. Existing methods often rely on static vehicle-behavior features or a single residual signal and do not explicitly model this evolution. To address this problem, we propose a causal high-order liquid evidence framework for GNSS spoofing detection. The method first constructs a physics-guided GNSS--motion inconsistency residual by comparing GNSS-implied displacement with onboard-motion-derived displacement. It then forms separate evidence streams for the residual level and its first- and second-order discrete variations, with relevant contextual cues selected according to the evidence order. Each stream is processed by a separate adaptive liquid encoder, and the resulting temporal states are hierarchically coupled to predict spoofing at the window endpoint using only current and past observations. Experiments on three subsets of the real-world AV-GPS dataset show that the proposed method achieves the highest F1-scores among the evaluated temporal models on Dataset~1 and Dataset~3, reaching 0.9535 and 0.9777, respectively. On Dataset~3, it detects both labeled normal-to-attack transitions within four sampling steps. Code and datasets are publicly available at: https://github.com/pangjunbiao/GNSS_Spoofing.git.
This paper develops the first Vision-Language Model (VLM)-based framework for GNSS spoofing detection for autonomous vehicles by fusing front-camera visual data with in-vehicle sensor readings against GNSS-derived maneuvers.
Mohammed Aldeen, Muhammad Sami Irfan, Sagar Dasgupta et al.· 0 citations
With the digital transformation of air traffic management (ATM) systems, automatic dependent surveillance–broadcast (ADS-B) has become an important data source for aviation surveillance. However, its open-broadcast nature and reliance on external navigation links make it vulnerable to anomalies caused by data-quality fluctuations, equipment failures, and malicious manipulation, which may degrade surveillance trustworthiness and introduce safety risks. To address the insufficient characterization of tunable time–frequency structures in existing ADS-B anomaly detection methods, as well as the limited capability of sequential models to aggregate global trajectory features, this article proposes a fractional Fourier transform-guided conditional autoencoder (FrFT-CAE). The proposed method employs a stacked long short-term memory sequence autoencoder to learn local temporal dynamics and introduces a multilayer FrFT–Gabor convolutional branch to extract global conditioning representations. The conditioning information is then adaptively injected into the latent space via cross-attention, thereby implicitly modeling the consistency between local temporal dynamics and global time–frequency structures during reconstruction. This improves normal-trajectory reconstruction capability and anomaly separability. Anomalies are scored using reconstruction error, and the detection threshold is determined by the $\mathbf {\mathit {\textit {n}} \sigma }$ rule on a validation set. Experiments are conducted on a self-collected ADS-B dataset (SCD) and a public ADS-B benchmark dataset. The results show that FrFT-CAE achieves stable overall detection performance under different data sources and anomaly definitions, with average $F1$ -scores of 0.9309 and 0.9250 on the self-collected dataset and the public benchmark dataset, respectively. Ablation experiments further demonstrate that the FrFT–Gabor conditioning branch and the cross-attention fusion module effectively enhance the collaborative modeling between global structural representation and local temporal reconstruction, thereby improving the overall performance of ADS-B trajectory anomaly detection.
In the Argoverse 2 experiments reported here, the simplest Constant Turn Rate and Acceleration (CTRA) decoder was stable but missed many interaction-driven turns and merges, while residual decoders without enough control improved early displacement but increased final-horizon error. This paper therefore studies a compact decoder in which each of the six futures is represented as a CTRA anchor plus an autoregressive position residual. The residual gated recurrent unit (GRU) is initialized from fused target-history, top-k neighbor, and lane-polyline context, and its contribution is scaled by a mode-specific gate and learned exponential decay. On the 10k/2k sanity ablations, CTRA-only decoding reached minFDE6=7.189 m, while autoregressive residuals with a larger correction GRU reduced it to 4.157 m; removing the gate increased it again to 4.946 m. On the full Argoverse 2 validation split, the final configuration achieves a minimum average displacement error of minADE6=1.21 m and a minimum final displacement error of minFDE6=2.78 m. The reported diagnostics show that the compact model generates a useful six-mode set, but still needs better probability ranking for top-1 selection.
Umut Özkan, Ibraheem Shayea, L. Rzayeva et al.· Technologies· 1 citation
Positioning jumps are a primary cause of trajectory discontinuities in urban canyon environments, severely hindering the widespread adoption of autonomous vehicles. This paper proposes a CNN-GRU fusion-based method for correcting such positioning jumps. A complete 15-dimensional error-state extended Kalman filter (EKF) framework is established to analyze the jump generation mechanisms from three perspectives—pseudorange distortion, inertial drift, and filter gain divergence—thereby justifying the use of inertial measurement unit (IMU) time-series data for anomaly prediction. An end-to-end mapping model is further constructed, in which a one-dimensional convolutional neural network (CNN) extracts cross-channel spatial features from multi-axis inertial data, while a gated recurrent unit (GRU) captures long-term temporal error evolution. A hysteresis navigation quality factor and a piecewise Huber loss function are incorporated to enable hierarchical adaptive optimization. Experimental results demonstrate that the proposed method reduces the positioning root mean square error (RMSE) from 1.24 m to 0.70 m, achieves a jump suppression rate of 43.5%, and maintains a single-frame inference latency of 11.8 ms, meeting the real-time requirements for future autonomous driving localization.
Ming-Yang Deng, Guang-Jiao Chen· Italian National Conference...· 0 citations
Robust state estimation via multi-sensor fusion is strictly constrained in urban environments, driven primarily by Non-Line-of-Sight (NLOS) and multipath interference acting upon Global Navigation Satellite System (GNSS) signals. Current benchmark datasets systematically omit the raw GNSS observables requisite for formulating tightly-coupled mitigation mechanisms. To resolve this, we release CardiffNav, a multi-modal sensor dataset engineered for degraded-environment localisation. The hardware framework synchronises a 128-channel Light Detection and Ranging (LiDAR) sensor, a visual perception array (RGB, stereo, and event cameras), and a 9-axis Inertial Measurement Unit (IMU). Concurrently, the system logs raw multi-constellation, multi-frequency GNSS measurements alongside Intermediate Frequency (IF) signal samples. Recorded trajectories traverse a continuous gradient of signal availability, explicitly documenting the operational transitions across open-sky segments, structural highways, dense urban canyons, and GNSS-denied tunnels. Baseline evaluations indicate that the unconstrained integration of degraded GNSS measurements directly corrupts the coupled state estimate. This dataset consequently provides a rigorous testbed for validating algorithms designed to identify, decouple, and mitigate signal degradation at the measurement level. The complete dataset and benchmark utilities are available at https://github.com/Erika1kuta/CardiffNav.
Shun Liu, Yixin Chen, R. Pullin et al.· 2026 IEEE International Conf...· 0 citations
Direction-of-arrival (DoA) estimation has benefited substantially from advances in deep learning architectures. Despite the success of deep learning in static DoA estimation, most existing approaches rely on discrete snapshot processing, which complicates multi-target tracking and limits robustness under irregular sampling and signal occlusion. Conventional approaches formulate tracking as a discrete sequence regression task, which can degrade performance during target crossovers and signal occlusions. Neural ordinary differential equations have demonstrated effectiveness in modeling continuous-time dynamics and handling irregularly sampled time series; however, their application to multi-target array processing remains limited. In this paper, we propose the Factorized Physics-Informed Neural ODE (Phy-NODE), an architecture that integrates efficient static deep learning estimators with continuous-time dynamical modeling. The proposed framework factorizes the latent representation into independent state vectors, each governed by a learned differential equation. Training is performed using a tripleloss objective that combines sequence-level permutation-invariant training, motion smoothness regularization, and Bartlett beam-forming power maximization, drawing inspiration from physics-informed learning principles. The proposed method is evaluated on multi-target DoA tracking scenarios, with particular emphasis on robustness under signal occlusion conditions.
Constantinos M. Mylonakis, Nikolaos Evangelidis, Pantelis Velanas et al.· 2026 6th International Confe...· 0 citations