Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method

• URL: http://arxiv.org/abs/2009.03091v1
• Date: Mon, 7 Sep 2020 13:24:47 GMT
• Title: Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
• Authors: Luka Kolar, Rok \v{S}ikonja, Lenart Treven
• Abstract summary: We present a novel method for inferring ground-truth signal from degraded signals. The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals. We include theoretical analysis and prove convergence to the ground-truth signal for the noiseless measurement model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure. The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals solely from the ratio between them. The degradation function d should be continuous, satisfy monotonicity, and d(0) = 1. We use smoothed monotonic regression method, where we easily incorporate the aforementioned criteria to the fitting part. We include theoretical analysis and prove convergence to the ground-truth signal for the noiseless measurement model. Lastly, we present an approach to fuse the noisy corrected signals using Gaussian processes. We use sparse Gaussian processes that can be utilized for a large number of measurements together with a specialized kernel that enables the estimation of noise values of all sensors. The data fusion framework naturally handles data gaps and provides a simple and powerful method for observing the signal trends on multiple timescales(long-term and short-term signal properties). The viability of correction method is evaluated on a synthetic dataset with known ground-truth signal.

Related papers

• Robust semi-parametric signal detection in particle physics with classifiers decorrelated via optimal transport [0.20971479389679337]
  In particle physics, a supervised classifier is used to separate a signal model from the known Standard Model physics. But errors in the background model might adversely affect the signal detection procedure. This paper shows how to use the (possibly misspecified) classifier only to perform a preliminary signal-enrichment step. We then carry out a bump hunt on the signal-rich sample using only the real experimental data.
  arXiv  Detail & Related papers  (2024-09-10T10:32:21Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods [24.06775799553418]
  We study the matrix denoising problem of estimating the singular vectors of a rank-$1$ signal corrupted by noise with both column and row correlations. Our work establishes the information-theoretic and algorithmic limits of matrix denoising with doubly heteroscedastic noise.
  arXiv  Detail & Related papers  (2024-05-22T18:38:10Z)
• Residual ANODE [0.0]
  We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection. The key to R-ANODE is to enhance the inductive bias of the anomaly detection task by fitting a normalizing flow directly to the small and unknown signal component. We show that the method works equally well whether the unknown signal fraction is learned or fixed, and is even robust to signal fraction misspecification.
  arXiv  Detail & Related papers  (2023-12-18T19:00:03Z)
• Learning Radio Environments by Differentiable Ray Tracing [56.40113938833999]
  We introduce a novel gradient-based calibration method, complemented by differentiable parametrizations of material properties, scattering and antenna patterns. We have validated our method using both synthetic data and real-world indoor channel measurements, employing a distributed multiple-input multiple-output (MIMO) channel sounder.
  arXiv  Detail & Related papers  (2023-11-30T13:50:21Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• Off-the-grid prediction and testing for linear combination of translated features [2.774897240515734]
  We consider a model where a signal (discrete or continuous) is observed with an additive Gaussian noise process. We extend previous prediction results for off-the-grid estimators by taking into account that the scale parameter may vary. We propose a procedure to test whether the features of the observed signal belong to a given finite collection.
  arXiv  Detail & Related papers  (2022-12-02T13:48:45Z)
• Iterative Adaptive Spectroscopy of Short Signals [0.1338174941551702]
  We develop an adaptive frequency sensing protocol based on Ramsey interferometry. High precision is achieved by enhancing the Ramsey sequence to prepare with high fidelity both the sensing and readout state.
  arXiv  Detail & Related papers  (2022-04-10T18:07:50Z)
• Graph Signal Restoration Using Nested Deep Algorithm Unrolling [85.53158261016331]
  Graph signal processing is a ubiquitous task in many applications such as sensor, social transportation brain networks, point cloud processing, and graph networks. We propose two restoration methods based on convexindependent deep ADMM (ADMM) parameters in the proposed restoration methods are trainable in an end-to-end manner.
  arXiv  Detail & Related papers  (2021-06-30T08:57:01Z)
• Learning Frequency Domain Approximation for Binary Neural Networks [68.79904499480025]
  We propose to estimate the gradient of sign function in the Fourier frequency domain using the combination of sine functions for training BNNs. The experiments on several benchmark datasets and neural architectures illustrate that the binary network learned using our method achieves the state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2021-03-01T08:25:26Z)
• Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors [52.06292503723978]
  Motivated by advances in compressive sensing with generative models, we study the problem of 1-bit compressive sensing with generative models. We first consider noiseless 1-bit measurements, and provide sample complexity bounds for approximate recovery under i.i.d.Gaussian measurements. We demonstrate that the Binary $epsilon$-Stable Embedding property, which characterizes the robustness of the reconstruction to measurement errors and noise, also holds for 1-bit compressive sensing with Lipschitz continuous generative models.
  arXiv  Detail & Related papers  (2020-02-05T09:44:10Z)

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.