Dictionary-Based Contrastive Learning for GNSS Jamming Detection

Global Navigation Satellite System (GNSS) signals are fundamental in applications across navigation, transportation, and industrial networks. However, their extremely low received power makes them highly vulnerable to radio-frequency interference (RFI) and intentional jamming. Modern data-driven methods offer powerful representational power for such applications, however real-time and reliable jamming detection on resource-limited embedded receivers remains a key challenge due to the high computational and memory demands of the conventional learning paradigm. To address these challenges, this work presents a dictionary-based contrastive learning (DBCL) framework for GNSS jamming detection that integrates transfer learning, contrastive representation learning, and model compression techniques. The framework combines tuned contrastive and dictionary-based loss functions to enhance feature separability under low-data conditions and applies structured pruning and knowledge distillation to reduce model complexity while maintaining high accuracy. Extensive evaluation across varying data regimes demonstrate that the proposed algorithm consistently outperforms modern CNN, MobileViT, and ResNet-18 architectures. The framework achieves a substantial reduction in memory footprint and inference latency, confirming its suitability for real-time, low-power GNSS interference detection on embedded platforms.

EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise

We present a learning-based outlier-robust filter for a general setup where the measurement noise can be correlated. Since it is an enhanced version of EM-based outlier robust filter (EMORF), we call it as EMORF-II. As it is equipped with an additional powerful feature to learn the outlier characteristics during inference along with outlier-detection, EMORF-II has improved outlier-mitigation capability. Numerical experiments confirm performance gains as compared to the state-of-the-art methods in terms of accuracy with an increased computational overhead. However, thankfully the computational complexity order remains at par with other practical methods making it a useful choice for diverse applications.

Trajectory Reconstruction through a Gaussian Adaptive Selective Outlier Rejecting Smoother

Trajectory Reconstruction (TR) is vital for accurately mapping movement patterns and validating analyses, especially in fields like robotics, biomechanics, and environmental tracking, where data might be missing or affected by outliers. Improving Trajectory estimation by employing Gaussian smoothing techniques in the presence of non-Gaussian noise is the subject of this work. We consider the case where data is collected from independent sensors. A variational Bayesian (VB) based Unscented Raunch-Tung-Striebel smoothing (URTSS) scheme is proposed which adopts a vectorized weighing mechanism for the measurement covariance matrix to selectively remove contaminated measurements at each time step. To improve our outlier mitigation, we model our outlier characteristics as a Gamma distribution and dynamically learn the parameters of this distribution from data. We verify the performance of our proposed smoother by a range of simulations and experimental data. We also propose a robustness criterion for smoothers based on the Kullback-Leibler (KL) divergence and show that our proposed method complies with this criterion.