Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks

• URL: http://arxiv.org/abs/2401.14411v2
• Date: Mon, 20 May 2024 21:20:16 GMT
• Title: Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks
• Authors: Felipe Giraldo-Grueso, Andrey A. Popov, Renato Zanetti,
• Abstract summary: This work introduces a new approach to online filtering for Martian entry using a neural network to estimate atmospheric density. The network is trained on an exponential atmospheric density model, and its parameters are dynamically adapted in real time to account for any mismatch between the true and estimated densities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spacecraft entering Mars require precise navigation algorithms capable of accurately estimating the vehicle's position and velocity in dynamic and uncertain atmospheric environments. Discrepancies between the true Martian atmospheric density and the onboard density model can significantly impair the performance of spacecraft entry navigation filters. This work introduces a new approach to online filtering for Martian entry using a neural network to estimate atmospheric density and employing a consider analysis to account for the uncertainty in the estimate. The network is trained on an exponential atmospheric density model, and its parameters are dynamically adapted in real time to account for any mismatch between the true and estimated densities. The adaptation of the network is formulated as a maximum likelihood problem by leveraging the measurement innovations of the filter to identify optimal network parameters. Within the context of the maximum likelihood approach, incorporating a neural network enables the use of stochastic optimizers known for their efficiency in the machine learning domain. Performance comparisons are conducted against two online adaptive approaches, covariance matching and state augmentation and correction, in various realistic Martian entry navigation scenarios. The results show superior estimation accuracy compared to other approaches, and precise alignment of the estimated density with a broad selection of realistic Martian atmospheres sampled from perturbed Mars-GRAM data.

Related papers

• Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net [0.0]
  This research aims to improve the accuracy of deep-learning models for predicting underwater radiated noise in far-field scenarios. We propose a novel range-conditional convolutional neural network that incorporates ocean bathymetry data into the input. Our proposed architecture effectively captures transmission loss over a range-dependent, varying bathymetry profile.
  arXiv  Detail & Related papers  (2024-04-11T19:13:38Z)
• Density Estimation for Entry Guidance Problems using Deep Learning [0.0]
  A long short-term memory neural network is trained to learn the mapping between measurements available onboard an entry vehicle and the density profile through which it is flying. The trained LSTM is capable of both predicting the density profile through which the vehicle will fly and reconstructing the density profile through which it has already flown.
  arXiv  Detail & Related papers  (2023-10-30T16:03:37Z)
• An Autonomous Vision-Based Algorithm for Interplanetary Navigation [0.0]
  Vision-based navigation algorithm is built by combining an orbit determination method with an image processing pipeline. A novel analytical measurement model is developed providing a first-order approximation of the light-aberration and light-time effects. Algorithm performance is tested on a high-fidelity, Earth--Mars interplanetary transfer.
  arXiv  Detail & Related papers  (2023-09-18T08:54:29Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions [0.0]
  This work proposes a robust relative localization system based on a fusion of neuromorphic vision-based measurements (NVBMs) and inertial measurements. The proposed system was tested in a variety of experiments and has outperformed state-of-the-art approaches in accuracy and range.
  arXiv  Detail & Related papers  (2022-06-23T08:39:05Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Ill-posed Surface Emissivity Retrieval from Multi-Geometry HyperspectralImages using a Hybrid Deep Neural Network [0.0]
  Atmospheric correction is a fundamental task in remote sensing because observations are taken either of the atmosphere or looking through it. A geometry-dependent hybrid neural network is proposed for automatic atmospheric correction using multi-scan hyperspectral data. Results show that the proposed network has the capacity to accurately characterize the atmosphere and estimate target emissivity spectra with a Mean Absolute Error (MAE) under 0.02 for 29 different materials.
  arXiv  Detail & Related papers  (2021-07-09T18:59:58Z)
• Featurized Density Ratio Estimation [82.40706152910292]
  In our work, we propose to leverage an invertible generative model to map the two distributions into a common feature space prior to estimation. This featurization brings the densities closer together in latent space, sidestepping pathological scenarios where the learned density ratios in input space can be arbitrarily inaccurate. At the same time, the invertibility of our feature map guarantees that the ratios computed in feature space are equivalent to those in input space.
  arXiv  Detail & Related papers  (2021-07-05T18:30:26Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)
• Variable Skipping for Autoregressive Range Density Estimation [84.60428050170687]
  We show a technique, variable skipping, for accelerating range density estimation over deep autoregressive models. We show that variable skipping provides 10-100$times$ efficiency improvements when targeting challenging high-quantile error metrics.
  arXiv  Detail & Related papers  (2020-07-10T19:01:40Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

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.