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Factoring in a priori classifier performance into decision fusion

Factoring in a priori classifier performance into decision f...

引用

conference on sensor fusion - architectures, algorithms, and applications VI

作者： Goebel, K Mysore, SP GE Co Corp Res & Dev Niskayuna NY USA

ISBN: (纸本)0819444812

In this paper we present methods to enhance the classification rate in decision fusion with partially redundant information by manipulating the input to the fusion scheme using a priori performance information. Intuitively, it seems to make sense to trust a more reliable tool more than a less reliable one without discounting the less reliable one completely. For a multi-class classifier, the reliability per class must be considered. In addition, complete ignorance for any given class must also be factored into the fusion process to ensure that all faults are equally well represented. However, overly trusting the best classifier will not permit the fusion tool to achieve results that rate beyond the best classifiers performance. We assume that the performance of classifiers to be fused is known, and show how to take advantage of this information. In particular, we glean pertinent performance information from the classifier confusion matrices and their cousin, the relevance matrix. We further demonstrate how to integrate a priori performance information within an hierarchical fusion architecture. We investigate several schemes for these operations and discuss the advantages and disadvantages of each. We then apply the concepts introduced to the diagnostic realm where we aggregate the output of several different diagnostic tools. We present results motivated from diagnosing on-board faults in aircraft engines.

关键词： classification diagnostics information fusion decision fusion a priori information confusion matrix

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fusion of Hyperspectral and LADAR Data for Autonomous Target Detection

Fusion of Hyperspectral and LADAR Data for Autonomous Target...

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Kanaev, A. V. Walls, T. J. USN Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819486387

Robust fusion of data from disparate sensor modalities can provide improved target detection performance over those attainable with the individual sensors. In particular, detection of low-radiance manmade objects or objects under shadow obscuration in hyperspectral imagery (HSI) with acceptable false alarm rates has proven especially challenging. We have developed a fusion algorithm for the enhanced detection of difficult targets when the HSI data is simultaneously collected with LADAR data. Initial detections are obtained by applying a sub-space RX (SSRX) algorithm to the HSI data. In parallel, LADAR-derived digital elevation map (DEM) is segmented and coordinates of objects within a specific elevation range and size are returned to the HSI processor for their spectral signature extraction. Each extracted signature that has not been already detected by SSRX is used in secondary HSI detection employing the adaptive cosine estimator (ACE) algorithm. We show that spatial distribution of ACE score allows for confident discrimination between background elevations and manmade objects. Key to cross-characterization of the data is the accurate co-alignment of the image data. We have also developed an algorithm for automatic co-registration of ladar and HSI imagery, based on the maximization of mutual information, which can provide accurate, sub-pixel registration even if the case when the imaging geometries for the two sensors differ. Details of both algorithms will be presented and results from application to field data will be discussed.

关键词： data fusion hyperspectral imagery LADAR registration maximization of mutual information

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The challenge of scalable and distributed fusion of disparate sources of information

The challenge of scalable and distributed fusion of disparat...

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conference on Multisensor, Multisource Information fusion

作者： Julier, Simon J. Uhlmann, Jeffrey K. Walters, Joshua Mittu, Ranjeev Palaniappan, Kannappan ITT AES NRL 4555 Overlook Ave Washington DC 20375 USA Univ Missouri Dept Comp Sci Columbia MO 65211 USA Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)0819462985

A key enabler for Network Centric Warfare (NCW) is a sensor network that can collect and fuse vast amounts of disparate and complementary information from sensors that are geographically dispersed throughout the battlespace. This information will lead to better situation awareness so that commanders will be able to act faster and more effectively. However, these benefits are possible only if the sensor data can be fused and synthesized for distribution to the right user in the right form at the right time within the constraints of available bandwidth. In this paper we consider the problem of developing Level 1 data fusion algorithms for disparate fusion in NCW. These algorithms must be capable of operating in a fully distributed (or decentralized) manner;must be able to scale to extremely large numbers of entities;and must be able to combine many disparate types of data. To meet these needs we propose a framework that consists of three main components: an attribute-based state representation that treats an entity state as a collection of attributes, new methods or interpretations of uncertainty, and robust algorithms for distributed data fusion. We illustrate the discussion in the context of maritime domain awareness, mobile adhoc networks, and multispectral image fusion.

关键词： distributed data fusion disparate data fusion generalized Covariance Intersection correlated data MANET maritime domain awareness

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Gyroscopic data fusion via a quaternion based complementary filter

Gyroscopic data fusion via a quaternion based complementary ...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Smith, R Frost, A Probert, P Oxford Univ. (UK) and Silsoe Research Institute (United Kingdom) Silsoe Research Institute (United Kingdom) Oxford Univ. (United Kingdom)

ISBN: (纸本)081942482X

We present an application of a complementary filter system to the attitude determination of a remotely operated underwater vehicle (ROV). The main contribution of this paper is to combine existing complementary filter theory with quaternion attitude representation. The combination allows accurate attitude determination by a real-time system using low cost sensors. We fuse the estimate from an Extended Kalman Filter (EKF) with the output from a set of vibrating structure rate gyroscopes. The EKF supplies high-quality low frequency information, the gyroscopes supply corresponding high frequency information. The attitude is described via a quaternion representation. We discuss how the use of quaternions is beneficial for estimator design due to the low computational burden, and lack of discontinuities and singularities. The EKF combines the output from two inclinometers and a magnetometer with a vehicle process model. The EKF assumes that sensor and process noise is broadband and that the process model captures all the important dynamics. An underwater vehicle is capable of rapid rotations, which are difficult to model, and would require computationally unattainable update rates to track effectively. We develop a filter, which uses the difference between the EKF and gyroscopic attitude estimates (an indirect filter) to correct for drift in the gyroscopic attitude estimate. We develop first a feedforward and then a feedback filter. The simplicity of the indirect filter permits very fast update rates, so the system may follow rapid vehicle rotations. We discuss the real-time implementation of the estimator on a Transputer based system mounted within a small ROV. We present experimental results showing the system performance of the combined filter system.

关键词： gyroscopes indirect filter data fusion quaternion estimation

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Optimizing Long-Term Robot Tracking with Multi-Platform sensor fusion

Optimizing Long-Term Robot Tracking with Multi-Platform Sens...

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IEEE/CVF Winter conference on applications of Computer Vision (WACV)

作者： Albanese, Giuliano Mitra, Arka Zaech, Jan-Nico Zhao, Yupeng Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium INSAIT Sofia Bulgaria

ISBN: (纸本)9798350318920;9798350318937

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification pipeline. It fuses external non-calibrated camera data with the robots' internal states using quadratic optimization for tracklet matching. The approach is validated using game recordings from previous RoboCup World Cup tournaments.

关键词： algorithms algorithms and algorithms applications formulations Machine learning architectures Robotics Video recognition and understanding

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Cluster-Based Time Synchronization Protocol for Wireless sensor Networks

Cluster-Based Time Synchronization Protocol for Wireless Sen...

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14th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Zhang, Jian Lin, Shiping Liu, Dandan Wuhan Univ Comp Sch Wuhan 430072 Peoples R China

ISBN: (纸本)9783319111971;9783319111964

Time synchronization is one of the most important premises for many applications in wireless sensor networks, including data fusion, target tracking, military surveillance, etc. Existing protocols generally suffered from higher energy consumption, worse accuracy or applicable only in small wireless sensor networks. In this paper, we propose a novel cluster based time synchronization method for large-scale wireless sensor networks. First, a cluster tree is constructed as the backbone. Then, after network clustering is completed, different time synchronization mechanisms are applied within cluster head nodes and cluster members, respectively. The theoretical analysis and simulation results show that the proposed algorithm can effectively reduce energy consumption comparing to existing protocols, while it also achieves the high accuracy of time synchronization.

关键词： Time synchronization clustering energy efficient wireless sensor networks

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Estimation of subpixel-resolution motion fields from segmented image sequences

Estimation of subpixel-resolution motion fields from segment...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Schultz, RR Stevenson, RL Univ N Dakota Dept Elect Engn Grand Forks ND 58202 USA

ISBN: (纸本)0819428256

Super-resolution enhancement algorithms are used to estimate a high-resolution video still (HRVS) from several low-resolution frames, provided that objects within the image sequence move with subpixel increments. However, estimating an accurate subpixel-resolution motion field between two low-resolution, noisy video frames has proven to be a formidable challenge. Up-sampling the image sequence frames followed by the application of block matching, optical flow estimation, or Bayesian motion estimation results in relatively poor subpixel-resolution motion fields, and consequently inaccurate regions within the super-resolution enhanced video still. This is particularly true for large interpolation factors (greater than or equal to 4). To improve the quality of the subpixel motion fields and the corresponding HRVS, motion can be estimated for each object within a segmented image sequence. First, a reference video frame is segmented into its constituent objects, and a mask is generated for each object which describes its spatial location. As described previously, subpixel-resolution motion estimation is then conducted by video frame up-sampling followed by the application of a motion estimation algorithm. Finally, the motion vectors are averaged over the region of each mask by applying an a-trimmed mean filter to the horizontal and vertical components separately. Since each object moves as a single entity, averaging eliminates many of the motion estimation errors and results in much more consistent subpixel motion fields. A substantial improvement is also visible within particular regions of the HRVS estimates. Subpixel-resolution motion fields and HRVS estimates are computed for interpolation factors of 2, 4, 8, and 16, to examine the benefits of object segmentation and motion field averaging.

关键词： interpolation motion estimation segmentation sensor fusion super-resolution enhancement

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Considerations for Multiple Hypothesis Correlation on Tactical Platforms

Considerations for Multiple Hypothesis Correlation on Tactic...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Thomas, Alan M. Turpen, James E. Georgia Inst Technol Georgia Tech Res Inst Atlanta GA 30318 USA

ISBN: (纸本)9780819495471

Tactical platforms benefit greatly from the fusion of tracks from multiple sources in terms of increased situation awareness. As a necessary precursor to this track fusion, track-to-track association, or correlation, must first be performed. The related measurement-to-track fusion problem has been well studied with multiple hypothesis tracking and multiple frame assignment methods showing the most success. The track-to-track problem differs from this one in that measurements themselves are not available but rather track state update reports from the measuring sensors. Multiple hypothesis, multiple frame correlation systems have previously been considered;however, their practical implementation under the constraints imposed by tactical platforms is daunting. The situation is further exacerbated by the inconvenient nature of reports from legacy sensor systems on bandwidth-limited communications networks. In this paper, consideration is given to the special difficulties encountered when attempting the correlation of tracks from legacy sensors on tactical aircraft. Those difficulties include the following: covariance information from reporting sensors is frequently absent or incomplete;system latencies can create temporal uncertainty in data;and computational processing is severely limited by hardware and architecture. Moreover, consideration is given to practical solutions for dealing with these problems in a multiple hypothesis correlator.

关键词： Data Association Multiple Hypothesis Correlation Multiple Hypothesis Tracking Avionics Information fusion sensors

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fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories

Fusion of ESM reports through Dempster-Shafer and Dezert-Sma...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Valin, Pierre Djiknavorian, Pascal Bosse, Eloi Grenier, Dominic R&D Canada Valcartier Decis Support Syst Sect C2 2459 Blvd Pie XI Nord Quebec City PQ G3J 1X5 Canada Univ Laval Dept Genie Electr & Genie Informat Laval G1V 0A6 PQ Canada

ISBN: (纸本)9780819481740

We address the problem of fusing ESM reports by two evidential reasoning schemes, namely Dempster-Shafer theory and Dezert-Smarandache theory. These schemes provide results in different frames of discernment, but are able to fuse realistic ESM data. We discuss their advantages and disadvantages under varying conditions of sensor data certainty and fusion reliability, the latter coming from errors in the association process. A thresholded version of Dempster-Shafer theory is fine-tuned for performance across a wide range of values for certainty and reliability. The results are presented first for typical scenarios, and secondly for Monte-Carlo studies of scenarios under varying sensor certainty and fusion reliability. The results exhibit complex non-linear functions, but for which clear trends can nevertheless be extracted. A compromise has to be achieved between stability under occasional miss-associations, and reaction time latency under a real change of allegiance. The alternative way of reporting results through Dezert-Smarandache theory is studied under similar conditions, and shown to provide good results, which are however more dependent on the unreliability, and slightly less stable. In this case however, the frame of discernment is larger, and permits additional interpretations, which are outside the scope of Dempster-Shafer.

关键词： Evidential reasoning realistic data Dempster-Shafer Dezert-Smarandache

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TDA2X, A SOC OPTIMIZED FOR ADVANCED DRivER ASSISTANCE SYSTEMS

TDA2X, A SOC OPTIMIZED FOR ADVANCED DRIVER ASSISTANCE SYSTEM...

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IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Sankaran, Jagadeesh Zoran, Nikolic Texas Instruments Inc Dallas TX 13532 USA

ISBN: (纸本)9781479928934

TDA2X is an optimized scalable system on chip (SoC) solution from Texas Instruments that spans various application areas of ADAS such as front-camera, surround-view and the emerging area of sensor fusion. It accomplishes this through a focused set of heterogeneous processors, brought together in a scalable architecture with a rich set of integrated peripherals, providing an optimal mix of performance in a low power footprint for Advanced Driver Assistance Systems (ADAS) vision analytics. Computer vision algorithms across the various ADAS application systems have a rich variation and diversity in processing requirements along with the need to run them concurrently within challenging thermal budgets. A heterogeneous architecture with various programmable elements allows system developers to map various portions of the algorithms to the architectures that are best suited for the underlying task allowing maximizing system performance and reducing development time and effort in developing these complex systems. Scalability of the architecture by varying the number of cores and clock speeds of these heterogeneous architectures, allows for scalability in performance and power across low, mid and high end products with one software investment. A critical focus on functional safety across the cores and various memories is particularly essential given the mission critical nature of ADAS applications.

关键词： ADAS SoC Front Camera Surround View sensor fusion and Programmable Vision Accelerators

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