DANAE: a denoising autoencoder for underwater attitude estimation

• URL: http://arxiv.org/abs/2011.06853v1
• Date: Fri, 13 Nov 2020 10:53:01 GMT
• Title: DANAE: a denoising autoencoder for underwater attitude estimation
• Authors: Paolo Russo, Fabiana Di Ciaccio, Salvatore Troisi
• Abstract summary: DANAE is a deep Denoising AutoeNcoder for Attitude Estimation which works on Kalman filter IMU/AHRS data integration. This paper shows that DANAE is robust and reliable, significantly improving the Kalman filter results.
• Score: 1.0312968200748116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the main issues for underwater robots navigation is their accurate positioning, which heavily depends on the orientation estimation phase. The systems employed to this scope are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. In this paper we propose DANAE, a deep Denoising AutoeNcoder for Attitude Estimation which works on Kalman filter IMU/AHRS data integration with the aim of reducing any kind of noise, independently of its nature. This deep learning-based architecture showed to be robust and reliable, significantly improving the Kalman filter results. Further tests could make this method suitable for real-time applications on navigation tasks.

Related papers

• State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements [0.8528368686417979]
  We propose an integrated framework that unifies particle filtering with Q-learning and NEAT to explicitly address the challenge of noisy measurements. Experiments on grid-based navigation and a simulated car environment highlight consistent gains in training stability, final performance, and success rates over baselines lacking advanced filtering.
  arXiv  Detail & Related papers  (2025-04-10T02:20:45Z)
• Gaussian Process Regression for Improved Underwater Navigation [13.221163846643607]
  Doppler velocity logs (DVLs) are typically used to mitigate this drift through velocity measurements. This paper proposes a data-driven alternative based on multi-output Gaussian process regression (MOGPR) to improve DVL velocity estimation. We evaluate our proposed approach using real-world AUV data and compare it against LS and a state-of-the-art deep learning model, BeamsNet.
  arXiv  Detail & Related papers  (2025-02-23T09:13:41Z)
• Performance Evaluation of Deep Learning-Based State Estimation: A Comparative Study of KalmanNet [0.26665679188139185]
  Kalman Filters (KF) are fundamental to real-time state estimation applications. In this work, one prevalent model, KalmanNet, was selected and evaluated on automotive radar data to assess its performance under real-world conditions. Results demonstrate that KalmanNet is outperformed by the IMM filter and indicate that while data-driven methods such as KalmanNet show promise, their current lack of reliability and robustness makes them unsuited for safety-critical applications.
  arXiv  Detail & Related papers  (2024-11-25T20:59:14Z)
• AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks [15.322411959318929]
  This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System (AIDPS) for Underwater Acoustic Sensor Networks (UW-ASNs) The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks.
  arXiv  Detail & Related papers  (2023-09-14T14:07:11Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Histogram Layer Time Delay Neural Networks for Passive Sonar Classification [58.720142291102135]
  A novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition.
  arXiv  Detail & Related papers  (2023-07-25T19:47:26Z)
• Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform [25.887932248706218]
  This work proposes an adaptive generalized recognition system - AGNet. By converting fixed wavelet parameters into fine-grained learnable parameters, AGNet learns the characteristics of underwater sound at different frequencies. Experiments reveal that our AGNet outperforms all baseline methods on several underwater acoustic datasets.
  arXiv  Detail & Related papers  (2023-05-31T06:56:01Z)
• A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion [0.0]
  Inertial sensors are used in a nonlinear filter to estimate an AUV navigation solution. A common practice is to assume that this matrix is fixed during the AUV operation. We propose a learning-based adaptive velocity-aided navigation filter.
  arXiv  Detail & Related papers  (2022-10-08T06:59:50Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• A Hybrid Model and Learning-Based Adaptive Navigation Filter [0.0]
  We propose a hybrid model and learning-based adaptive navigation filter. We show that the proposed method obtained an improvement of 25% in the position error.
  arXiv  Detail & Related papers  (2022-06-14T17:10:47Z)
• Partial Identification with Noisy Covariates: A Robust Optimization Approach [94.10051154390237]
  Causal inference from observational datasets often relies on measuring and adjusting for covariates. We show that this robust optimization approach can extend a wide range of causal adjustment methods to perform partial identification. Across synthetic and real datasets, we find that this approach provides ATE bounds with a higher coverage probability than existing methods.
  arXiv  Detail & Related papers  (2022-02-22T04:24:26Z)
• Neural optimal feedback control with local learning rules [67.5926699124528]
  A major problem in motor control is understanding how the brain plans and executes proper movements in the face of delayed and noisy stimuli. We introduce a novel online algorithm which combines adaptive Kalman filtering with a model free control approach.
  arXiv  Detail & Related papers  (2021-11-12T20:02:00Z)
• Neural Kalman Filtering [62.997667081978825]
  We show that a gradient-descent approximation to the Kalman filter requires only local computations with variance weighted prediction errors. We also show that it is possible under the same scheme to adaptively learn the dynamics model with a learning rule that corresponds directly to Hebbian plasticity.
  arXiv  Detail & Related papers  (2021-02-19T16:43:15Z)

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.