FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram

• URL: http://arxiv.org/abs/2107.07312v1
• Date: Fri, 9 Jul 2021 19:20:41 GMT
• Title: FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram
• Authors: Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier, Kevin Chetty
• Abstract summary: noisy surroundings cause uninterpretable motion patterns on the micro-Doppler spectrogram. radar returns often suffer from multipath, clutter and interference. We propose a latent feature-wise mapping strategy, called Feature Mapping Network (FMNet), to transform measured spectrograms.
• Score: 2.9849405664643585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Micro-Doppler signatures contain considerable information about target dynamics. However, the radar sensing systems are easily affected by noisy surroundings, resulting in uninterpretable motion patterns on the micro-Doppler spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and interference. These issues lead to difficulty in, for example motion feature extraction, activity classification using micro Doppler signatures ($\mu$-DS), etc. In this paper, we propose a latent feature-wise mapping strategy, called Feature Mapping Network (FMNet), to transform measured spectrograms so that they more closely resemble the output from a simulation under the same conditions. Based on measured spectrogram and the matched simulated data, our framework contains three parts: an Encoder which is used to extract latent representations/features, a Decoder outputs reconstructed spectrogram according to the latent features, and a Discriminator minimizes the distance of latent features of measured and simulated data. We demonstrate the FMNet with six activities data and two experimental scenarios, and final results show strong enhanced patterns and can keep actual motion information to the greatest extent. On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet. From final classification results, we can see significant improvements.

Related papers

• Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation [53.91958614666386]
  Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs) We propose a novel UGRL method based on Multi-hop feature Quality Estimation (MQE)
  arXiv  Detail & Related papers  (2024-07-29T12:24:28Z)
• Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging [22.365437882740657]
  This paper aims to facilitate more practical NLOS imaging by reducing the number of samplings and scan areas. We leverage a denoising autoencoder scheme to acquire rich and noise-robust representations in the measurement space. We introduce a phasor-based pipeline designed to limit the spectrum of our network to the frequency range of interests.
  arXiv  Detail & Related papers  (2024-07-26T07:57:07Z)
• Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection [53.842568573251214]
  Experimental results on three SAR datasets demonstrate that our WBANet significantly outperforms contemporary state-of-the-art methods. Our WBANet achieves 98.33%, 96.65%, and 96.62% of percentage of correct classification (PCC) on the respective datasets.
  arXiv  Detail & Related papers  (2024-07-18T04:36:10Z)
• Novelty Detection on Radio Astronomy Data using Signatures [5.304803553490439]
  We introduce SigNova, a new semi-supervised framework for detecting anomalies in streamed data. We use the signature transform to extract a canonical collection of statistics from observational sequences. Each feature vector is assigned a novelty score, calculated as the Mahalanobis distance to its nearest neighbor in an RFI-free training set.
  arXiv  Detail & Related papers  (2024-02-22T14:13:44Z)
• Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement [11.793803540713695]
  Inertial sensors are widely used in various portable devices. Wavelet dynamic selection network (WDSNet) intelligently selects appropriate wavelet basis for variable inertial signals. WDSNet, as a weakly-supervised method, achieves the state-of-the-art performance of all the compared fully-supervised methods.
  arXiv  Detail & Related papers  (2023-12-29T07:44:06Z)
• Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing [12.135800589264532]
  We address the challenge of tracking an unknown number of targets in strong clutter environments using measurements from a radar sensor. We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information. We propose a classification-aided robust multiple target tracking algorithm, employing the neural enhanced message passing technique.
  arXiv  Detail & Related papers  (2023-10-19T01:41:11Z)
• Dynamic Spectrum Mixer for Visual Recognition [17.180863898764194]
  We propose a content-adaptive yet computationally efficient structure, dubbed Dynamic Spectrum Mixer (DSM) DSM represents token interactions in the frequency domain by employing the Cosine Transform. It can learn long-term spatial dependencies with log-linear complexity.
  arXiv  Detail & Related papers  (2023-09-13T04:51:15Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• Wavelet-Based Network For High Dynamic Range Imaging [64.66969585951207]
  Existing methods, such as optical flow based and end-to-end deep learning based solutions, are error-prone either in detail restoration or ghosting artifacts removal. In this work, we propose a novel frequency-guided end-to-end deep neural network (FNet) to conduct HDR fusion in the frequency domain, and Wavelet Transform (DWT) is used to decompose inputs into different frequency bands. The low-frequency signals are used to avoid specific ghosting artifacts, while the high-frequency signals are used for preserving details.
  arXiv  Detail & Related papers  (2021-08-03T12:26:33Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• Conditioning Trick for Training Stable GANs [70.15099665710336]
  We propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training. We force the generator to get closer to the departure from normality function of real samples computed in the spectral domain of Schur decomposition.
  arXiv  Detail & Related papers  (2020-10-12T16:50:22Z)

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.