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29th IEEE International Conference on Electronics, Circuits and Systems (IEEE ICECS)

作者： Leger, G. Gines, A. Gutierrez, V Barragan, M. J. Univ Seville Inst Microlectron Sevilla CSIC Seville 41092 Spain Univ Grenoble Alpes TIMA Grenoble INP CNRS F-38000 Grenoble France

ISBN: (数字)9781665488235

ISBN: (纸本)9781665488235

The cost of assuring test quality significantly increases when dealing with complex systems with tightly integrated AMS-RF building blocks. Machine learning-based test may be a promising solution to this issue. These tests rely on regression models trained to replace costly performance measurements by simpler test signatures. However, these regression models are targeted only at parametric performance variations in defect-free circuits. The presence of spot defects may be undetected by these tests and lead to test quality degradation and reliability issues. In this work we propose a methodology based on causal discovery algorithms to screen out these spot defects.

关键词： Measurement Degradation Design methodology filtering algorithms Feature extraction Inference algorithms Reliability

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A Compensated Shrinkage Affine Projection Algorithm for Debiased Sparse Adaptive filtering

A Compensated Shrinkage Affine Projection Algorithm for Debi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yi Zhang Isao Yamada Department of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan

In this paper, we propose a novel sparse adaptive filtering algorithm termed compensated shrinkage affine projection algorithm (CS-APA). Our cost function is the sum of a time-varying data fidelity term and a difference-of-convex (DC) type nonconvex sparse regularizer. The regularizer includes the well known MC and SCAD penalty as special instances, thus leading to sparse estimation with small bias. Leveraging the DC structure of the regularizer, the nonconvex forward-backward splitting algorithm can be applied to the cost function, whereby the proposed CS-APA is derived. We present several favourable properties of CS-APA, including its mean stability analysis. Numerical examples demonstrate the superiority of CS-APA with comparisons to existing methods.

关键词： filtering Signal processing algorithms Estimation Adaptive filters filtering algorithms Signal processing Cost function

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Point Data Registration With the Multi-Object, Cardinalized Optimal Linear Assignment Metric

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IEEE ACCESS 2024年 12卷 170459-170477页

作者： Barrios, Pablo A. Guzman, Vicente Adams, Martin D. Perez, Claudio A. Univ Chile AMTC Santiago 8370451 Chile Univ Chile Dept Elect Engn Santiago 8370451 Chile

Point cloud registration is a critical component of many tasks including the estimation of sensor motion and 3D reconstruction. The Iterative Closest Point (ICP) algorithm and its variants were initially used to solve such problems and since then, various global methods have been proposed to improve registration when data outliers exist. Realistic point cloud datasets contain detection errors (outliers) as well as spatial errors, making correct registration fragile when the outlier rejection and point data association methods fail. Therefore, in this article, a registration technique based on the multi-object Cardinalized Optimal Linear Assignment (COLA) metric is presented, which jointly penalizes both detection and spatial errors in a meaningful manner. This allows robust scan registration to take place in the presence of unknown point correspondences and inter-scan translation and orientation as well as point cloud detection and spatial errors. The resulting Particle Swarm Optimization (PSO)-COLA registration algorithm is capable of determining inter-scan point correspondences, but can also run based on point correspondences determined by other algorithms, such as the application of Fast Point Feature Histograms (FPFH) descriptors. In both cases, the PSO-COLA algorithm is shown to outperform current local and global point cloud registration algorithms in the presence of data outliers and spatial uncertainty. Multiple experiments and comparisons are based on the Stanford Bunny and Dragon, ModelNet40 and ETH Apartment datasets.

关键词： Point cloud compression Measurement Computational complexity Uncertainty filtering algorithms Cost function Convergence Approximation algorithms Vectors Three-dimensional displays PSO-COLA registration COLA metric multi-object metric point-cloud registration

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Equalization of Frequency Domain Adaptive Filter (FDAF) Using Signal Prediction Aided Reference Spectrum Model (SPRSM)

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Ding, Mengyu Mi, Xiaolong Chen, Wu Weng, Duojie Ding, Weihao Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Artificial Intelligence Things Hong Kong Peoples R China

filtering jamming signals from intermediate frequency (IF) data is a practical anti-jamming solution for low-cost global navigation satellite system (GNSS) receivers, as it does not change antennas or baseband structures. Frequency domain adaptive filter (FDAF) is a commonly used IF anti-jamming filter, which removes jamming components in the frequency domain. However, this filter also filters out normal satellite signals within the jamming bands, resulting in significant signal distortions. These distortions can negatively impact signal quality and positioning accuracy. To address this issue, this study focuses on improving the performance of FDAF by incorporating a signal-prediction-aided reference spectrum model (SPRSM) equalizer. Initially, the signal loss caused by FDAF is analyzed. Then, the SPRSM equalizer is developed, which differs from classical equalizers that rely on an empirical normal spectrum. Instead, the SPRSM equalizer utilizes GNSS predictions to compensate for distortions. The effectiveness of the SPRSM equalizer is then theoretically compared to that of the classical equalizer, specifically in terms of carrier-to-noise-density ratio (C/N-0) improvement. Real-data experiments are conducted to further evaluate the superiority of the SPRSM equalizer over FDAF and FDAF with the classical equalizer. Specifically, the experimental results show that the SPRSM equalizer can handle narrowband sweep continuous wave interference (SCWI) intensities up to 10 and 6 dB higher than standalone FDAF and the classical equalizer, respectively, while still maintaining a C/N-0 over 27 dB center dot Hz of tracking threshold. Under bandlimited noise (BN) scenarios, the intensities that SPRSM equalizer can handle are up to 6 and 4 dB higher than that of standalone FDAF and the classical equalizer, respectively. Moreover, when subjected to the same jamming attacks, the SPRSM equalizer demonstrates a 11%-60% improvement in horizontal positioning accuracy compared to the c

关键词： Jamming Global navigation satellite system Equalizers Adaptive filters Distortion Frequency-domain analysis filtering algorithms Equalization frequency-domain adaptive filter (FDAF) global navigation satellite system (GNSS) jamming signals signal distortion

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DynaTrack: Low-Power Channel-Aware Dynamic Smartphone Tracking Using UWB DL-TDOA

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IEEE ACCESS 2024年 12卷 181728-181740页

作者： Choi, Junyoung Bhattacharya, Sagnik Lee, Joohyun Samsung Elect Samsung Res Seoul 16677 South Korea

Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. However, temporary or permanent obstacles in the deployment region often lead to non-line-of-sight (NLOS) channel path and signal outage effects, which result in localization errors. Prior research has addressed this problem by increasing the ranging frequency, which leads to a heavy increase in the user device power consumption. It also does not contribute to any increase in localization accuracy under line-of-sight (LOS) conditions. In this paper, we propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm, termed DynaTrack. It comprises a NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. DynaTrack is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and real-time applicability. Based on the conducted experiments, we show that DynaTrack achieves 41% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to existing methods from prior research.

关键词： Location awareness Distance measurement Accuracy Convolutional neural networks Kalman filters filtering algorithms Data mining Accelerometers Wireless communication Gyroscopes Ultra-wideband localization time difference of arrival recurrent neural network Kalman filter

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TRAFFIC STATE ESTIMATION WITH LOSS CONSTRAINT 25

TRAFFIC STATE ESTIMATION WITH LOSS CONSTRAINT

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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Dahmen, Victoria Loder, Allister Tilg, Gabriel Kutsch, Alexander Bogenberger, Klaus Tech Univ Munich Dept Informat Garching Germany Tech Univ Munich Sch Engn & Design Dept Mobil Syst Engn Chair Traff Engn & Control Munich Germany

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Traffic state estimation is relevant for real-time traffic control, providing travel information as well as for expost analysis of traffic patterns. While the output is usually the average speed and vehicle flow along street segments, the type of input data and the existing methods to obtain the output are diverse. Recently, physics-informed data-driven approaches started to emerge that enrich the estimation process with information taken from physical models. In traffic, so far, these have been the continuity equation and the fundamental diagram, designed to describe fully the traffic dynamics along links and corridors. In this paper, we propose a simpler and practiceready physics-informed machine learning approach that informs the estimation through the well-established fundamental diagram in a loss constraint. It is designed for a link-level analysis where traffic homogeneity along the considered link is assumed. We apply the proposed method to full-trajectory drone data from Athens, Greece, demonstrate the applicability of our proposed approach, and point out its potential to future applications, e.g., a filter for control algorithms.

关键词： Sensitivity Partial differential equations Machine learning Traffic control filtering algorithms Prediction algorithms Real-time systems

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From the Modified Smooth Sliding Control to the Super-Twisting Algorithm: UAV Trajectory Tracking Experimental Results 16

From the Modified Smooth Sliding Control to the Super-Twisti...

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16th International Workshop on Variable Structure Systems (VSS)

作者： Serrantola, Wenderson Gustavo Lizarralde, Fernando Peixoto, Alessandro Jacoud COPPE Fed Univ Rio De Janeiro Dept Elect Engn Rio De Janeiro Brazil

ISBN: (数字)9781665463591

ISBN: (纸本)9781665463591

This paper provides a perspective on the problem of trajectory tracking of a hexacopter in the presence of wind disturbances. Aiming to generate a smooth and robust control law, a Modified Smooth Sliding Control (MSSC) is introduced, which is derived from the earlier Smooth Sliding Control (SSC) designed for linear time-invariant uncertain plants and based on an appropriate predictor. This modification is obtained by allowing the smoothing filter and the predictor to be time varying. Generally speaking, robustness with respect to wind disturbances in closed-loop trajectory tracking can be observed. In addition, a clear connection between the MSSC and the Super-Twisting Algorithm (STA) is provided. Experimental results with the MSSC and STA control are obtained for trajectory tracking of a commercial hexacopter.

关键词： Variable structure systems Robust control Smoothing methods Trajectory tracking Conferences filtering algorithms Prediction algorithms

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Online Event Selection for Mu3e using GPUs 21

Online Event Selection for Mu3e using GPUs

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21st IEEE International Symposium on Parallel and Distributed Computing (ISPDC)

作者： Henkys, Valentin Schmidt, Bertil Berger, Niklaus Johannes Gutenberg Univ Mainz Inst Comp Sci Mu3e Collaborat Mainz Germany Johannes Gutenberg Univ Mainz Inst Nucl Phys Cluster Excellence PRISMA Mainz Germany

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

In the search for physics beyond the Standard Model the Mu3e experiment tries to observe the lepton flavor violating decay mu(+) -> e(+) e(-) e(+) By observing the decay products of 1 . 10(s) mu/s it aims to either observe the process, or set a new upper limit on its estimated branching ratio. The high muon rates result in high data rates of 80 Gbps, dominated by data produced through background processes. We present the Online Event Selection, a three step algorithm running on the graphics processing units (GPU) of the 12 Mu3e filter farm computers. By using simple and fast geometric selection criteria, the algorithm first reduces the amount of possible event candidates to below 5% of the initial set. These candidates are then used to reconstruct full particle tracks, correctly reconstructing over 97% of signal tracks. Finally a possible decay vertex is reconstructed using simple geometric considerations instead of a full reconstruction, correctly identifying over 94% of signal events. We also present a full implementation of the algorithm, fulfilling all performance requirements at the targeted muon rate and successfully reducing the data rate by a factor of 200.

关键词： Performance evaluation Graphics Target tracking Mesons Graphics processing units filtering algorithms Distributed computing

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Ground Moving Target Indication via Coprime Array MIMO 2-D-VSAR Joint EGO-DPCA Filter

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Najian, Negar Sebt, Mohammad Ali Hosein Oveis, Amir K N Toosi Univ Technol Dept Elect Engn Tehran *** Iran Univ Pisa Radar & Surveillance Syst RaSS Lab Natl Interuniv Consortium Telecommun CNIT I-56124 Pisa Italy Univ Pisa Dept Informat Engn I-56124 Pisa Italy

The velocity synthetic aperture radar (VSAR) algorithm is a method used for slow ground-moving target indication (GMTI) and parameter estimation based on array processing. However, this method is limited by an expensive computational burden and is disabled for detecting fast-moving targets. This article uses a coprime array configuration with smaller element spacing to improve the unambiguous range velocity estimation. This improvement results in the detection of fast-moving targets. Additionally, we propose an improved coprime VSAR algorithm that uses azimuth time shift instead of two-step phase compensation in every pixel of channel images to decrease the computational burden. However, the weak and slow-moving targets are not detected using the coprime VSAR method. Thus, we introduce a filter called the extended greatest of displacement phase center antenna (EGO-DPCA) to detect weak and slow-moving targets. Furthermore, the proposed algorithm is extended to the coprime 2-D VSAR to estimate the azimuth direction velocity of moving targets neglected in the conventional VSAR algorithm. Therefore, the azimuth resolution of moving target imaging significantly increases by estimating the azimuth direction velocity. Since all processes are performed in the detected pixel of moving targets, the complexity and computational burden are significantly reduced.

关键词： Clutter Azimuth Estimation filtering algorithms Accuracy Object detection Information filters Coprime extended greatest of displacement phase center antenna (EGO-DPCA) ground-moving target indication (GMTI) synthetic aperture radar (SAR) velocity SAR (VSAR)

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A Linear Comb Filter for Event Flicker Removal 39

A Linear Comb Filter for Event Flicker Removal

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wang, Ziwei Yuan, Dingran Ng, Yonhon Mahony, Robert Australian Natl Univ Coll Engn & Comp Sci Syst Theory & Robot Grp Canberra ACT Australia

ISBN: (纸本)9781728196817

Event cameras are bio-inspired sensors that capture per-pixel asynchronous intensity change rather than the synchronous absolute intensity frames captured by a classical camera sensor. Such cameras are ideal for robotics applications since they have high temporal resolution, high dynamic range and low latency. However, due to their high temporal resolution, event cameras are particularly sensitive to flicker such as from fluorescent or LED lights. During every cycle from bright to dark, pixels that image a flickering light source generate many events that provide little or no useful information for a robot, swamping the useful data in the scene. In this paper, we propose a novel linear filter to preprocess event data to remove unwanted flicker events from an event stream. The proposed algorithm achieves over 4.6 times relative improvement in the signal-to-noise ratio when compared to the raw event stream due to the effective removal of flicker from fluorescent lighting. Thus, it is ideally suited to robotics applications that operate in indoor settings or scenes illuminated by flickering light sources.

关键词： Maximum likelihood detection Robot vision systems Nonlinear filters filtering algorithms Fluorescence Cameras Light emitting diodes

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