3D Euler-Angle Orientation Control for Two-Ray Fading Mitigation in Maritime Air-to-Sea Communications

• URL: http://arxiv.org/abs/2609.11476v1
• Date: Thu, 10 Sep 2026 12:42:04 GMT
• Title: 3D Euler-Angle Orientation Control for Two-Ray Fading Mitigation in Maritime Air-to-Sea Communications
• Abstract summary: Maritime Air-to-Sea links are dominated by a line-of-sight ray and a sea-surface reflected ray whose destructive combination produces deep fades.<n>Existing mitigation strategies optimize Unmanned Aerial Vehicle position or trajectory but leave attitude unexploited.<n>This paper treats the full three dimensional attitude as a physical-layer control variable that shapes the two-ray interference through antenna phase-center displacement.
• Score: 6.741942263052468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maritime Air-to-Sea links are dominated by a line-of-sight ray and a sea-surface reflected ray whose destructive combination produces deep fades. Existing mitigation strategies optimize Unmanned Aerial Vehicle position or trajectory but leave attitude unexploited. This paper treats the full three dimensional attitude as a physical-layer control variable that shapes the two-ray interference through antenna phase-center displacement. Under small-angle and far-field assumptions, the constructive-interference condition reduces to an affine constraint in the Euler angles and admits a closed-form family of minimum-norm attitude candidates. A differentiable soft-minimum rule yields a smooth reference tracked by a constrained Nonlinear Model Predictive Control controller on a fully-actuated tilting multirotor. The proposed scheme increases cumulative throughput by 11.4% over a pitch-only benchmark and 22.2% over a zero-orientation baseline, while preserving trajectory tracking and respecting actuator limits.

Related papers

• TRaIL-Odom: Tightly Coupled Continuous Time Radar-IMU-LiDAR Odometry with Adaptive Doppler Weighting [24.013238065030635]
  Existing radar-LiDAR fusion methods rely on fixed residual weights.<n>We propose two degeneracy-aware Doppler reweighting modules within a tightly coupled Radar-IMU-LiDAR odometry framework.<n>Across 13 evaluated sequences, TRaIL-Odom achieves state-of-the-art overall performance.
  arXiv  Detail & Related papers  (2026-09-03T09:00:51Z)
• Stokes-Informed Diffusion for Robust Linear Polarization Estimation [49.459667092743445]
  GenPolar is a Stokes-informed diffusion framework grounded in the Mueller formalism from an intensity observation.<n>It achieves state-of-the-art performance in both DoLP fidelity and AoP stability.<n>These improvements translate to significant and consistent gains in downstream applications, including material detection and de-reflection.
  arXiv  Detail & Related papers  (2026-07-23T11:56:15Z)
• Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems [48.501415601682815]
  Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images.<n>We show that flow map models implicitly define a one- parameter family of denoisers that continuously spans the distortion perception frontier.<n>Within a Plug-and-Play solver, the same mechanism extends to general inverse problems, where it controls a tradeoff between perceptual alignment and data consistency.
  arXiv  Detail & Related papers  (2026-06-18T05:15:43Z)
• SDS-LoRA: Overcoming Anisotropic Gradient Scaling in Low-Rank Adaptation [64.33799467286265]
  Low-Rank Adaptation (LoRA) enables efficient adaptation of large pre-trained models to downstream tasks.<n>We show that when a full fine-tuning gradient is backpropagated to the low-rank matrices, it undergoes anisotropic scaling driven by their singular values.<n>We propose a new low-rank parameterization, SDS-LoRA, which structurally decouples singular values from the backward pass.
  arXiv  Detail & Related papers  (2026-06-15T09:27:50Z)
• Closed-Form Spectral Regularization for Multi-Task Model Merging [96.82449201305234]
  Model merging combines several independently fine-tuned experts into a single multi-task model without any training data.<n>State-of-the-art merging methods formulate merging as a layer-wise interference problem.<n>We propose SWUDI, a closed-form method that combines a soft exponential filter, which matches the gradient-flow trajectory of iterative descent.
  arXiv  Detail & Related papers  (2026-06-05T14:00:47Z)
• Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training [56.323119575322146]
  Extra-Merge consistently outperforms standard merging baselines in experiments across GPT-2 and LLaMA families.<n>It yields consistent zero-shot accuracy gains on Pythia-12B downstream tasks and generalizes effectively to the Muon citepjordan2024muon.
  arXiv  Detail & Related papers  (2026-05-26T02:48:34Z)
• AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust [1.7394635819080972]
  Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority.<n>Prior geometric control approaches extend differential flatness through Hopf fibration-based attitude representations to support bidirectional thrust.<n>We propose a learning-based framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions.
  arXiv  Detail & Related papers  (2026-05-23T00:02:22Z)
• FishBack: Pullback Fisher Geometry for Optimal Activation Steering in Transformers [7.108048394322025]
  Activation steering methods modify intermediate representations of language models to control output behavior, but universally assume the activation space is Euclidean.<n>We show this assumption fails drastically: the local geometry induced by the model's own output behavior deviates from the Euclidean metric by over 97% in relative spectral norm on GPT-2.
  arXiv  Detail & Related papers  (2026-05-17T03:00:16Z)
• Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization [55.338784037660474]
  Multi- curvature reinforcement learning in domains requires balancing complex, non-fragile interference trade-offs between conflicting objectives.<n>In this paper we introduce a novel based real-time based real-time optimization on a robotic visual -- a proxy for monitoring ecosystems.
  arXiv  Detail & Related papers  (2026-05-12T21:32:57Z)
• K-GMRF: Kinetic Gauss-Markov Random Field for First-Principles Covariance Tracking on Lie Groups [8.489406212619164]
  We propose K-GMRF, an online, training-free framework for covariance tracking.<n>Our method interprets observations as torques driving a latent angular velocity, propagated via a structure-preserving symplectic integrator.<n>We theoretically prove that this second-order dynamics achieves zero steady-state error under constant rotation.
  arXiv  Detail & Related papers  (2026-03-20T03:16:36Z)
• Riemannian Flow Matching for Disentangled Graph Domain Adaptation [51.98961391065951]
  Graph Domain Adaptation (GDA) typically uses adversarial learning to align graph embeddings in Euclidean space.<n>DisRFM is a geometry-aware GDA framework that unifies embedding and flow-based transport.
  arXiv  Detail & Related papers  (2026-01-31T11:05:35Z)
• Vertical Planetary Landing on Sloped Terrain Using Optical Flow Divergence Estimates [6.3359275271421565]
  Small, lightweight spacecraft, such as rotorcraft and landers, have limited processing capability and payload capacity.<n>Flying insects, such as bees, achieve remarkable landings with minimal neural and sensory resources.<n>This paper proposes a nonlinear control strategy that leverages two distinct local flow divergence estimates to regulate both thrust and attitude.
  arXiv  Detail & Related papers  (2025-12-04T01:46:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.