Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 24 references
Abstract
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.
Visual localization is vital for autonomous systems but remains challenging under dynamic conditions. Transformers offer strong temporal modeling at quadratic cost, while CNNs are efficient yet limited in long-range dependencies. Existing methods also lack robustness to illumination, weather, and seasonal changes, constraining real-world applicability. To address this, this paper proposes AdapseqNet, a dual-branch architecture that integrates stabilized state-space modeling with differential temporal enhancement. First, a stabilized state-space formulation featuring Lyapunov-constrained parameterization and adaptive discretization is proposed, ensuring asymptotic stability and linear computational complexity for reliable processing of extended sequences. Second, a selective Mamba architecture is developed to combine temporal-state modeling with content-aware gating, enabling adaptive feature selection that emphasizes discriminative cues while suppressing redundancy. Third, a differential enhancement module is designed to extract motion-invariant representations through symmetric temporal differencing and LSTM-based refinement, enhancing resilience to appearance variations caused by lighting, weather, and seasonal changes. Beyond architectural design, multi-scale feature fusion and output distribution control are incorporated to optimize representation quality and ensure consistency for similarity-based retrieval. Extensive experiments on multiple benchmarks demonstrate that AdapseqNet achieves a better localization accuracy across diverse and challenging conditions. Note to Practitioners—Visual localization is crucial for autonomous robots but often fails under varying lighting, weather, or seasonal conditions. We propose a dual-path approach: one path captures long-term patterns using control-inspired stable modeling, while the other extracts motion cues that remain consistent despite appearance changes. This combination enables accurate place recognition even in extreme environments. Our system operates efficiently on standard hardware and was tested on an indoor robot, achieving centimeter-level accuracy. This approach can enhance existing navigation systems without requiring additional sensors. Future work will focus on real-time optimization for outdoor deployment.
Zhenyu Li, Tian-Yi Shang· IEEE Transactions on Automat...· 0 citations
This work proposes XVINS, a hybrid VIO frontend integrating XFeat—a lightweight deep feature extractor—into the optimization-based VINS-Fusion framework, presenting XVINS as a viable, real-time state estimation solution for agile Micro-Aerial Vehicles (MAVs) and mobile platforms.
Thura Peou, Sarot Srang, Lychek Keo· E3S Web of Conferences· 0 citations
Rapid trajectory shifts, complex kinetics, and strong measurement noise are major difficulties that arise in the tracking of underwater passive maneuvering targets. This study suggests an innovative framework of memory-augmented temporal features learning, based on a Long Short-Term Memory (LSTM) model, to overcome these challenges. The LSTM aims to effectively develop and preserve long-term temporal relationships in the formation of the target's state, leading to reliable prediction of location, velocity, and trajectory even during high maneuverability and extensive observed noise. The designed model is configured through state-space physics and investigated under distinct levels of Gaussian measurement distortion. The evaluation of performance is carried out in the mean squared error (MSE) sense to analyze the degree of accuracy. In comparison, the LSTM-based estimation model offers better results than generalized pseudo-Bayesian estimators, including the Interacting Multiple model Extended Kalman Filter (and the Interacting Multiple model Unscented Kalman Filter. The outcomes reveal that the proposed design significantly lowers state estimation deficiencies and shows significant flexibility for various maneuvering behaviors, proving a feasible option for real-time passive tracking in acoustically challenging underwater situations.
Wasiq Ali, Mahtab Ali, Xiaohua Li et al.· Journal of the Acoustical So...· 0 citations
A training-free method for multi-modal trajectory prediction that achieves comparable accuracy to a 57M-parameter transformer while requiring no GPU and zero learned parameters, which enables deployment in new geographic regions from an order of magnitude less historical data.
Michael Fore, Akshay Jain, J. Downes et al.· 0 citations
Existing unified 4D reconstruction and point tracking approaches typically rely on heuristic interpolations or just predict at integer timestamps, lacking kinematic coherence and failing to model dynamics at any arbitrary timestamp. In this paper, we propose Uni4R, a framework that unifies these tasks by learning continuous velocity fields through the synergy of Optimal Transport (OT) and Ordinary Differential Equation (ODE). Importantly, this continuous velocity field acts as a kinematic prior that mutually benefits both 4D reconstruction and point tracking. Specifically, we propose the Flow Matching Guided Decoder (FMGD). A global velocity branch first extracts anchor features that capture the global dynamic state of the sequence. Then, FMGD leverages Flow Matching (FM) theory to formulate a probability path defined by OT on the anchor feature manifold, instantiating it as FM-guided velocity features for velocity prediction. This establishes a robust kinematic inductive bias. Meanwhile, a point reconstruction branch provides geometric features. The local velocity prediction module then joint above features and time embeddings, to decode velocities at arbitrary timestamps. To overcome the absence of high-quality ground-truth velocities in fractional frames, we propose an integral-consistency training strategy. This strategy uses an ODE solver to integrate velocities to recover target pointmaps, enabling the model to be supervised end-to-end directly from integer timestamps. Experimental results demonstrate that Uni4R achieves SOTA performance in both 4D reconstruction and point tracking, and achieves SOTA in our new kinematics-aware benchmark at continuous time.
Liying Yang, Hao Mo, Jialun Liu et al.· 0 citations
DAR-Track is proposed, a novel framework that harmonizes dynamic computation with generative modeling and outperforms state-of-the-art methods, including MixFormer and SGLATrack, while maintaining superior inference speeds suitable for real-time aerial robotics.
Wenqin Dong· International Conference on...· 0 citations
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