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检索条件"任意字段=Conference on Signal Processing, Sensor Fusion, and Target Recognition XV"

共 433 条记录，以下是51-60 订阅

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A distributed implementation of a sequential Monte Carlo Probability Hypothesis Density filter for sensor networks

A distributed implementation of a sequential Monte Carlo Pro...

引用

conference on signal processing, sensor fusion, and target recognition xv

作者： Punithakumar, K. Kirubarajan, T. Sinha, A. McMaster Univ ECE Dept Hamilton ON L8S 4K1 Canada

ISBN: (纸本)0819462918

This paper presents a Sequential Monte Carlo (SMC) Probability Hypothesis Density (PHD) algorithm for decentralized state estimation from multiple platforms. The proposed algorithm addresses the problem of communicating and fusing track information from a team of multiple sensing platforms detecting and tracking multiple targets in the surveillance region. Each sensing platform makes multiple, noisy measurements of an underlying, time-varying state that describes the monitored system. The monitored system involves potentially nonlinear target dynamics described by Markovian state-space model, nonlinear measurements, and non-Gaussian process and measurement noises. Each sensing platform reports measurements to a node in the network, which performs sequential estimation of the current system state using the probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior of the multi-target state. A sequential Monte Carlo method is used to implement the filter. The crucial consideration is what information needs to be transmitted over the network in order to perform online estimation of the current state of the monitored system, whilst attempting to minimize communication overhead. Simulation results demonstrate the efficiency of the proposed algorithm for a team of bearing only sensors.

关键词： sensor networks sequential Monte Carlo methods probability hypothesis density filter multi-target tracking nonlinear filtering distributed tracking and fusion

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CalibDNN: Multimodal sensor Calibration for Perception Using Deep Neural Networks 30

CalibDNN: Multimodal Sensor Calibration for Perception Using...

引用

conference on signal processing, sensor/Information fusion, and target recognition XXX

作者： Zhao, Ganning Hu, Jiesi You, Suya Kuo, C-C Jay Univ Southern Calif 3551 Trousdale Pkwy Los Angeles CA 90089 USA US Army Res Lab 12025 E Waterfront Dr Los Angeles CA 90094 USA

ISBN: (数字)9781510643505

ISBN: (纸本)9781510643505

Current perception systems often carry multimodal imagers and sensors such as 2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream perception tasks, robust and accurate calibration of the multimodal sensor data is essential. We propose a novel deep learning-driven technique (CalibDNN) for accurate calibration among multimodal sensor, specifically LiDAR-Camera pairs. The key innovation of the proposed work is that it does not require any specific calibration targets or hardware assistants, and the entire processing is fully automatic with a single model and single iteration. Results comparison among different methods and extensive experiments on different datasets demonstrates the state-of-the-art performance.

关键词： Multimodal sensor calibration sensor fusion Deep neural network Intelligent perception

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Aspects of Detection and Tracking of Ground targets from an Airborne EO/IR sensor 24

Aspects of Detection and Tracking of Ground Targets from an ...

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conference on signal processing, sensor/Information fusion, and target recognition XXIV

作者： Balaji, Bhashyam Sithiravel, Rajiv Daya, Zahir Kirubarajan, Thiagalingam Def R&D Canada Ottawa ON Canada Natl Res Council Canada Ottawa ON Canada McMaster Univ Hamilton ON Canada

ISBN: (纸本)9781628415902

An airborne EO/IR (electro-optical/infrared) camera system comprises of a suite of sensors, such as a narrow and wide field of view (FOV) EO and mid-wave IR sensors. EO/IR camera systems are regularly employed on military and search and rescue aircrafts. The EO/IR system can be used to detect and identify objects rapidly in daylight and at night, often with superior performance in challenging conditions such as fog. There exist several algorithms for detecting potential targets in the bearing elevation grid. The nonlinear filtering problem is one of estimation of the kinematic parameters from bearing and elevation measurements from a moving platform. In this paper, we developed a complete model for the state of a target as detected by an airborne EO/IR system and simulated a typical scenario with single target with 1 or 2 airborne sensors. We have demonstrated the ability to track the target with 'high precision' and noted the improvement from using two sensors on a single platform or on separate platforms. The performance of the Extended Kalman filter (EKF) is investigated on simulated data. Image/video data collected from an IR sensor on an airborne platform are processed using an image tracking by detection algorithm

关键词： EO/IR sensor based tracking Nonlinear filtering Image tracking sensor fusion

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Adaptive Data Reduction with Improved Information Association

Adaptive Data Reduction with Improved Information Associatio...

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conference on signal processing, sensor fusion, and target recognition XXI

作者： Riasati, Vahid R. Gao, Wenhue MacAulay Brown Engn 4021 Execut Dr Dayton OH 45430 USA Univ Calif Los Angeles Dept Math Elmhurst NY 11373 USA

ISBN: (纸本)9780819490704

A class of adaptive data compression routines is presented based on data dependent transformation. The current class of methods identifies improved information association by utilization of eigen-vectors rather than the eigen-values based prioritization. The Karhounen-Loeve, KL, transform limits the importance of the data by the limitation of the eigen-values associated with covariance of data, this leads to the truncation of the eigenvectors by their energy levels. Therefore, the remaining data in the KL transform method limits the information that can be represented to those data contents that represent the majority of signal energy as identified by the prioritized eigen-values of the data covariance matrix. The method presented in this work retains desired data structure and enables a more exact representation of the information in the data leading to the preservation of data that can contain significantly relevant information even-though its energy contents may be relatively low as compared to other bases. This work presents a description of this idea along with an error analysis relative to the original data. The simulation work applies the technique to data from an image by association of neighboring data samples. A discussion of the simulation results though relevant metrics completes the analysis in this work.

关键词： Adaptive processing Karhounen-Loeve Principle Component Analysis Feature Extraction Pattern recognition Data Compression sensor fusion

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Obstacle Detection for Unmanned Ground Vehicle on Uneven and Dusty Environment 24

Obstacle Detection for Unmanned Ground Vehicle on Uneven and...

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conference on signal processing, sensor/Information fusion, and target recognition XXIV

作者： Choe, Tok Son Park, Jin Bae Joo, Sang Hyun Park, Yong Woon Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea Agcy Def Dev Unmanned Technol Ctr Def R&D Inst 5 Taejon 305600 South Korea

ISBN: (纸本)9781628415902

An obstacle detection method for unmanned ground vehicle in outdoor environment is proposed. The proposed method uses range data acquired by laser range finders (LRFs) and FMCW radars. LRFs and FMCW radars are used for distinguishing ground and obstacles on uneven terrain, and for detecting obstacles in dusty environment. The proposed obstacle detection algorithm is broadly composed of three steps: 1) 1D virtual range data generation which ground information is removed by range data of LRFs, 2) 1D virtual range data generation acquired by fusion of multiple FMCW radars, 3) 1D virtual range data generation which dust information is removed by fusion of step 1) and step 2). The proposed method is verified by real experiments.

关键词： Virtual range sensor FMCW radar Laser range finder sensor fusion uneven environment dusty environment

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Machine learning model cards toward model-based system engineering analysis of resource-limited systems 32

Machine learning model cards toward model-based system engin...

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conference on signal processing, sensor/Information fusion, and target recognition XXXII

作者： Booth, Thomas M. Ghosh, Sudipto USAF 309th SWEG Hill AFB UT 84056 USA Colorado State Univ Ft Collins CO USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

sensor fusion combines data from a suite of sensors into an integrated solution that represents the target environment more accurately than that produced by individual sensors. New developments in Machine Learning (ML) algorithms are leading to increased accuracy, precision, and reliability in sensor fusion performance. However, these increases are accompanied by increases in system costs. Aircraft sensor systems have limited computing, storage, and bandwidth resources, which must balance monetary, computational, and throughput costs, sensor fusion performance, aircraft safety, data security, robustness, and modularity system objectives while meeting strict timing requirements. Performing trade studies of these system objectives should come before incorporating new ML models into the sensor fusion software. A scalable and automated solution is needed to quickly analyze the effects on the system's objectives of providing additional resources to the new inference models. Given that model-based systems engineering (MBSE) is a focus of the majority of the aerospace industry for designing aircraft mission systems, it follows that leveraging these system models can provide scalability to the system analyses needed. This paper proposes adding empirically derived sensor fusion RNN performance and cost measurement data to machine-readable Model Cards. Furthermore, this paper proposes a scalable and automated sensor fusion system analysis process for ingesting SysML system model information and RNN Model Cards for system analyses. The value of this process is the integration of data analysis and system design that enables rapid enhancements of sensor system development.

关键词： MBSE SysML Machine Learning Model Card sensor fusion Resource datasheets factsheets

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A simple algorithm for sensor fusion using spatial voting (unsupervised object grouping)

A simple algorithm for sensor fusion using spatial voting (u...

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conference on signal processing, sensor fusion, and target recognition xvII

作者： Jaenisch, Holger M. Albritton, Nathaniel G. Handley, James W. Burnett, Randel B. Caspers, Robert W. Albritton, William P., Jr. Alabama A&M Univ Dept Phys Nanosci Grp Normal AL 35762 USA Licht Strahl Engn INC Toney AL 35773 USA Amtec Corp Huntsville AL 35816 USA

ISBN: (纸本)9780819471598

We present a simple algorithm for achieving unsupervised spatially distributed object fusion using spatial voting. We achieve spatial fusion of uncertain position estimates of disparate objects. These objects are portions of larger assemblies that cannot be directly observed by available sensors. Only the individual objects are discernable. The question arises how to fuse estimates of position uncertainty of these objects (potential assembly pieces) into an "assembly" whose location and extent can only be inferred. Our algorithm uses spatial correlation and stacking with voting of positional uncertainty ellipses. We present the positional algorithm and compare it to other methods of grouping uncertainties and find encouraging improved performance.

关键词： data model sensor fusion spatial voting data fusion clustering K-Means nearest neighbor MLANS

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Retrospectives on the Applications AI and Deep Learning in Information fusion 27

Retrospectives on the Applications AI and Deep Learning in I...

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conference on signal processing, sensor/Information fusion, and target recognition XxvII

作者： Kadar, Ivan Interlink Syst Sci Inc 1979 Marcus Ave Lake Success NY 11042 USA

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Bayes-invariant transformations of uncertainty representations

Bayes-invariant transformations of uncertainty representatio...

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conference on signal processing, sensor fusion, and target recognition xv

作者： Mahler, Ronald Lockheed Martin MS2 Tact Syst Eagan MN USA

ISBN: (纸本)0819462918

Much effort has been expended on devising "conversions" of one uncertainty representation scheme to another-fuzzy to probabilistic, Dempster-Shafer to probabilistic, to fuzzy, etc. Such efforts have been hindered by the fact that uncertainty representation formalisms vary considerably in the degree of complexity of information which they encode. For example, 2(M) - 1 numbers are required to specify a Dempster-Shafer basic mass assignment (b.m.a.) on a space with M elements;whereas only M - 1 numbers axe required to specify a probability distribution on the same space. Consequently, any conversion of b.m.a.'s to probability distributions will result in a huge loss of information. In addition, conversion from one uncertainty representation formalism to another should be consistent with the data fusion methodologies intrinsic to these formalisms. For b.m.a.'s to be consistently converted to fuzzy membership functions, for example, Dempster's combination should be transformed into fuzzy conjunction in some sense. In this paper we show that a path out of such quandaries is to realize that in many applications all information must ultimately be reduced to state estimates and covariances. Adopting a Bayesian approach, we identify Bayes-invariant conversions between various uncertainty representation formalisms.

关键词： Fuzzy logic Data conversion Data fusion Curium Data compression Image compression Logic Space operations Information fusion sensors

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Information fusion Designed for (Robust) Action

Information Fusion Designed for (Robust) Action

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conference on signal processing, sensor/Information fusion, and target recognition XXIII

作者： Jones, Eric Tierno, Jorge Syst & Technol Res Woburn MA USA Barnstorm Res Corp Malden MA USA

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