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

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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Backstepping Mean-Field Density Control for Large-Scale Heterogeneous Nonlinear Stochastic Systems

Backstepping Mean-Field Density Control for Large-Scale Hete...

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American Control Conference (ACC)

作者： Zheng, Tongjia Han, Qing Lin, Hai Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA Univ Notre Dame Dept Math Notre Dame IN 46556 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

This work studies the problem of controlling the mean-field density of large-scale stochastic systems, which has applications in various fields such as swarm robotics. Recently, there is a growing amount of literature that employs mean-field partial differential equations (PDEs) to model the density evolution and uses density feedback to design control laws which, by acting on individual systems, stabilize their density towards a target profile. In spite of its stability property and computational efficiency, the success of density feedback relies on assuming the systems to he homogeneous first-order integrators (plus white noise) and ignores higher-order dynamics, making it less applicable in practice. In this work, we present a backstepping design algorithm that extends density control to heterogeneous and higher-order stochastic systems in strict-feedback forms. We show that the strict-feedback form in the individual level corresponds to, in the collective level, a PDE (of densities) distributedly driven by a collection of heterogeneous stochastic systems. The presented backstepping design then starts with a density feedback design for the PDE, followed by a sequence of stabilizing design for the remaining stochastic systems. We present a candidate control law with stability proof and apply it to nonholonomic mobile robots. A simulation is included to verify the effectiveness of the algorithm.

关键词： Backstepping Stochastic systems Partial differential equations Heuristic algorithms Swarm robotics White noise filtering algorithms

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Semantic Network Interpretation

Semantic Network Interpretation

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22nd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Guo, Pei Farrell, Ryan Brigham Young Univ Provo UT 84602 USA

ISBN: (纸本)9781665458245

Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpretation at both filter and decision level. For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes. The decision-level interpretation is achieved by textual summarization that generates an explanatory sentence containing clues behind a network's decision. A Bayesian inference algorithm is proposed to automatically associate filters and network decisions with visual attributes. Human study confirms that the semantic interpretation is a beneficial alternative or complement to visualization methods. We demonstrate the crucial role that semantic network interpretation can play in understanding a network's failure patterns. More importantly, semantic network interpretation enables a better understanding of the correlation between a model's performance and its distribution metrics like filter selectivity and concept sparseness.

关键词： Measurement Training Visualization Correlation Computational modeling Semantics filtering algorithms

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Improved Kalman-Particle Kernel Filter on Lie Groups Applied to Angles-Only UAV Navigation 39

Improved Kalman-Particle Kernel Filter on Lie Groups Applied...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chahbazian, Clement Dahia, Karim Merlinge, Nicolas Winter-Bonnet, Benedicte Honore, Kevin Musso, Christian Off Natl Etud & Rech Aerosp French Aerosp Lab Informat & Signals Dept Palaiseau France MBDA France Dept Guidance Control & Nav Le Plessis Robinson France

ISBN: (纸本)9781728196817

Kalman-Particle Kernel Filter (KPKF) is a sub-class of Particle Filter (PF) that uses Gaussian kernels as particles, which enables a local Kalman update for each measurement in addition to the usual weight update. Besides, recent research about filtering on Lie groups brought powerful theoretical results, and showed the superiority of this approach. Hence, this paper extends the Euclidean KPKF to a new formulation on Lie groups and introduces substantial improvements based on Lie groups Kalman filters theory and Laplace Particle Filters on Lie groups (LG-LPF) for improved resampling. The proposed algorithm is tested on an angles-only UAV navigation scenario with challenging initial errors. It shows superior robustness and accuracy compared to Lie group Extended Kalman Filter (LG-EKF), with near-to optimal performance, even with a limited amount of particles.

关键词： Weight measurement Navigation Atmospheric measurements filtering algorithms Particle measurements filtering theory Robustness

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Adaptive algorithm for vessel roll prediction based on the Bayesian approach 30

Adaptive algorithm for vessel roll prediction based on the B...

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30th Mediterranean Conference on Control and Automation (MED)

作者： Stepanov, Oleg A. Litvinenko, Yulia A. Antonov, Danila, V Zaitsev, Oleg, V ITMO Univ JSC Concern CSRI Elektropribor St Petersburg Russia

The problem of vessel roll prediction is considered within the framework of the Bayesian approach;the proposed adaptive algorithm for its solution is described. The advantages of the proposed algorithm are discussed, ... 详细信息

ISBN: (纸本)9781665406734

关键词： Measurement errors Adaptation models Uncertainty Automation Neural networks Predictive models filtering algorithms

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A Distributed Adaptive Algorithm for Non-Smooth Spatial filtering Problems

A Distributed Adaptive Algorithm for Non-Smooth Spatial Filt...

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

作者： Charles Hovine Alexander Bertrand Department of Electrical Engineering (ESAT) KU Leuven Leuven Belgium Stadius Center for Dynamical Systems Signal Processing and Data Analytics Leuven Belgium KU Leuven Institute for Artificial Intelligence (Leuven.AI) Leuven Belgium

Computing the optimal solution to a spatial filtering problems in a Wireless Sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. The so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of the original optimization problem, relying solely on the exchange of compressed views of the sensor data between the nodes. However, the DASF algorithm has only been shown to converge for filtering problems that can be expressed as smooth optimization problems. In this paper, we explore an extension of the DASF algorithm to a family of non-smooth spatial filtering problems, allowing the addition of non-smooth regularizers to the optimization problem, which could for example be used to perform node selection, and eliminate nodes not contributing to the filter objective, therefore further reducing communication costs. We provide a convergence proof of the non-smooth DASF algorithm and validate its convergence via simulations in both a static and adaptive setting.

关键词： Wireless sensor networks Costs filtering Signal processing algorithms Distributed databases Signal processing filtering algorithms

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S-MKI: Incremental Dense Semantic Occupancy Reconstruction Through Multi-Entropy Kernel Inference

S-MKI: Incremental Dense Semantic Occupancy Reconstruction T...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Deng, Yinan Wang, Meiling Wang, Danwei Yue, Yufeng Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Autonomous robots are often required to acquire high-level prior knowledge by continuously reconstructing the semantics and geometry of the surrounding scene, which is the basis of exploration and planning. Most existing continuous semantic mapping algorithms cannot distinguish potential differences in voxels, resulting in an over-inflated map. Furthermore, fixed-size query ranges introduce high computational complexity. Based on the limitation of over-inflation and inefficiency, this paper proposes a novel incremental continuous semantic occupancy mapping algorithm (S-MKI). The key innovation of this work comes from the two models in the preprocessing stage. On the one hand, Redundant Voxel Filter Model utilizes context entropy to filter out redundant voxels to improve the confidence of the final map, where objects have accurate boundaries with sharp edges. On the other hand, Adaptive Kernel Length Model adaptively adjusts the kernel length with class entropy, which reduces the inherent amount of training data. The final multi-entropy kernel inference function is formulated to integrate these two models to infer sparse noisy sensor data into dense accurate 3D maps. Experimental results conducted in both indoors and outdoors datasets validate that S-MKI outperforms existing methods.

关键词： Adaptation models Three-dimensional displays Computational modeling Semantics Training data filtering algorithms Entropy

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Conditional entropy minimization principle for learning domain invariant representation features 26

Conditional entropy minimization principle for learning doma...

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26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Nguyen, Thuan Lyu, Boyang Ishwar, Prakash Scheutz, Matthias Aeron, Shuchin Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Tufts Univ Dept Comp Sci Medford MA 02155 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG). Despite promising theory, such approaches fail in common classification tasks due to mixing of true invariant features and spurious invariant features(1). To address this, we propose a framework based on the conditional entropy minimization (CEM) principle to filter-out the spurious invariant features leading to a new algorithm with a better generalization capability. We show that our proposed approach is closely related to the well-known Information Bottleneck (IB) framework and prove that under certain assumptions, entropy minimization can exactly recover the true invariant features. Our approach provides competitive classification accuracy compared to recent theoretically-principled state-of-the-art alternatives across several DG datasets.

关键词： Mixture models filtering algorithms Minimization Feature extraction Entropy filtering theory Classification algorithms

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Uncertainty Aware EKF: a Tracking Filter Learning LiDAR Measurement Uncertainty 25

Uncertainty Aware EKF: a Tracking Filter Learning LiDAR Meas...

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25th International Conference of Information Fusion (FUSION)

作者： Xu, Liang Niu, Ruixin Blasch, Erik P. Virginia Commonwealth Univ Richmond VA 23284 USA Air Force Off Sci Res Arlington VA 22203 USA

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

In this paper, an extended Kalman filter (EKF) framework, called uncertainty aware EKF (UA-EKF), is developed by utilizing contextual knowledge to improve the vehicle tracking accuracy for autonomous vehicles. The proposed framework can learn and estimate the uncertainty associated with the measurements provided by a LiDAR-based vehicle detection algorithm. The UA-EKF has two major parts: one has the ability to estimate the state-dependent measurement noise's statistics for LiDAR object detections, and the other is to create multiple-hypothesis measurements based on the detected vehicle's heading. The measurement uncertainties are learned based on the EKFNet, which is an algorithm that can learn the system noise covariance from measurement data. Both the learned noise statistics and multiple-hypothesis estimators are used to compensate the physical limitations of the LiDAR measurements. A detailed analysis of the measurement uncertainty and the methods to improve tracking performance during filtering are provided for the UA-EKF. The obtained results by using the nuScenes datasets show that estimating the measurement uncertainty is an efficient solution for tracking the vehicle based on LiDAR detections.

关键词： Laser radar Uncertainty filtering Vehicle detection Measurement uncertainty Object detection filtering algorithms

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