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14th conference on signal and data processing of small targets

作者： Chen, VC USN Res Lab Div Radar Washington DC 20375 USA

ISBN: (纸本)0819444782

In this paper, we analyze features of radar returns from moving targets, introduce the basic concept of time-frequency-Radon transforms, describe the Radon transform for line feature detection, discuss their applications to detection of multiple moving targets in clutter, and demonstrate two examples of moving target detection using simulated radar data.

关键词： time-frequency transform Radon transform moving targets multiple targets

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Multilinear-CLAFIC methods for tensor data in image recognition

Multilinear-CLAFIC methods for tensor data in image recognit...

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2009 IEEE 17th signal processing and Communications Applications conference, SIU 2009

作者： Yavuz, Hasan Serhan Çevikalp, Hakan Elektrik ve Elektronik Mühendisligi Bölümü Eskisehir Osmangazi Üniversitesi Turkey

ISBN: (纸本)9781424444366

In this paper, we propose M-CLAFIC (Multilinear Class-Featuring Information Compression) and M-CLAFIC-μ methods for image recognition problems in which data samples are represented by high order image tensors. Operating directly on the tensor data preserves the natural data representation form, and it may yield better classification accuracies. Compared to the classical subspace methods, CLAFIC and CLAFIC-μ, the proposed methods are more robust to the small sample size problem which is widely encountered in image recognition applications. Experimental results on the AR and COIL100 databases show that MCLAFIC and M-CLAFIC-μ methods can produce successful classification accuracies. ©2009 IEEE.

关键词： Image recognition

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Borehole Radar data processing Based on Empirical Mode Decomposition 17

Borehole Radar Data Processing Based on Empirical Mode Decom...

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17th International conference on Ground Penetrating Radar, GPR 2018

作者： Ma, Chunguang Zhao, Qing Huo, Jianjian Xie, Longhao University of Electronic Science and Technology of China School of Resource and Environment Chengdu611731 China

ISBN: (纸本)9781538657775

Borehole radar (BHR) is a highly efficient geophysical exploration tool, however, the application is limited due to relatively small signal to noise ratio (SNR) of the testing data. Empirical Mode Decomposition (EMD) is a data processing method, which takes the particular advantage in analyzing the nonlinear and non-stationary data. this paper introduces the fundamentals of EMD method and the complex signal analysis theory, and focuses on the sufficient conditions and specific decomposition steps for signal processing based on the EMD. then an actually radar profile, which was obtained by a BHR system in a limestone fracture zone, is used as an example to be analyzed in the following processes. Firstly, the original radar profile is preprocessed to avoid the mode confusion and noise interference to the radar echo. Secondly, the EMD method is used to process a single-channel radar data, and analyze the high-low frequency components of the radar signal. thirdly, the various intrinsic mode of the preprocessing radar profile is also obtained by the EMD. Finally, we reconstruct the intrinsic mode profile, which contains the effective formation information, calculate the complex signals of the reconstruction radar profile using the Hilbert transform (HT), extract the three characteristic parameters such as instantaneous amplitude, instantaneous phase and instantaneous frequency, and draw the separate instantaneous parameters profiles. the results demonstrate that the EMD method has the strong adaptability in processing the BHR signal under a low SNR and the capability to separate the high-low components of the radar echo effectively. the three-instantaneous information produced by the EMD method and complex signal analysis technique, can highlight the abnormal geological feature, provide the theoretical basis and mutual authentication information for evaluating the original radar profile, and avoid the deviation relying solely on a time-distance profile. © 2018 IEEE.

关键词： Empirical mode decomposition

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Target detection by distributed sensors: distributed sensor concept-DISCO for small target detection

Target detection by distributed sensors: distributed sensor ...

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20th conference on signal and data processing of small targets

作者： Rafailov, Michael K. QinetiQ NA Reston VA 20191 USA

ISBN: (纸本)9780819471604

Distributed Sensor Concept - DISCO was proposed [I] for multiplication of individual sensor capabilities through non-coherent cooperative target engagement. the signal processing technique for DISCO is Recursive Adaptive Frame Integration of Limited data - RAFIL technique [2] that was initially proposed as a way to improve the SNR [3], reduce data rate [4] and mitigate FPA noise for IR sensors [5]. In DISCO, the RAFIL technique is used in a segmented way, when constituencies of the technique are spatially and temporally separated between individual sensors. Each sensor provides to and receives data from other sensors in the network. In this paper efficiency of DISCO is discussed for acquisition, accurate handover and track correlation of small targets.

关键词： multiple sensor system distributed sensors distributed apertures accurate target detection frame integration data rate recursive processing double threshold

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Automatic intensity quantification of fluorescence targets from microscope images with maximum likelihood estimation

Automatic intensity quantification of fluorescence targets f...

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17th European signal processing conference, EUSIPCO 2009

作者： Pölönen, Harri Tohka, Jussi Ruotsalainen, Ulla Department of Signal Processing Tampere University of Technology Korkeakoulunkatu 10 33720 Tampere Finland

We introduce a method to determine the quantitative intensity values and sub-pixel locations of closely located small targets from noisy fluorescence microscope images. We model the microscope image with a mixture of point spread functions and the image noise with a stochastic process containing Poisson distribution. Maximum likelihood estimation is used to find the optimal parameters for the model. Numerical ML estimation is performed with differential evolution optimization algorithm. To evaluate the methods, noisy simulated images were created with closely located targets. Methods were compared to conventional methods based on low-pass filtering and Gaussian mixture fitting, and the simulations show better accuracy for the new method. A real microscope image is also quantified to show that the model is applicable in practice. © EURASIP, 2009.

关键词： Stochastic models

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Perceptual organization in range data: Robust detection of low order surfaces in heavy clutter

Perceptual organization in range data: Robust detection of l...

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17th International conference on Pattern Recognition (ICPR)

作者： Boyer, KL Julka, K Ohio State Univ Dept Elect & Comp Engn Signal Anal & Machine Percept Lab Columbus OH 43210 USA

ISBN: (纸本)0769521282

We consider the problem of detecting manmade objects in range data in the presence of extensive clutter. Such situations arise in, for example, the detection of small structures or vehicles beneath a leaf canopy in range data collected from an airborne platform. this problem calls for an extremely robust detection scheme, and it should be fast. Since most manufactured objects comprise large low-order piecewise smooth surfaces (often planes), we focus on detecting locally planar surfaces. We propose a novel technique we call Distribution Weighted Histograms (DWH), which exploits the inherent geometric distributions of manmade objects versus the random occlusions such as those due to an overhanging leaf canopy. the DWH algorithm performs well under heavy occlusion while being computationally inexpensive (linear complexity). We present extensive experimental results.

关键词： data processing

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Detection of small target in IR image sequences using VSLMS

Detection of small target in IR image sequences using VSLMS

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16th conference on signal and data processing of small targets

作者： Ng, LN Ronda, V Lim, ET Nanyang Technol Univ Sch Elect & Elect Singapore Singapore

ISBN: (纸本)081945351X

A Vertical-Strip Least Mean Squared (VSLMS) algorithm is proposed to enhance the detection of small moving targets in IR image sequences. this algorithm is an improvement over the Two-Dimensional LMS (TDLMS), which is designed to detect small targets within highly correlated background of static images. this paper focuses on processing IR image sequences with different background features with layers of sky, sea and land clutter. the VSLMS uses multiple LMS modules and a different scanning method to process individual lines in the IR image sequences. Simulation results show successful enhancement of very small targets in an IR mage sequence.

关键词： VSLMS IR image sequences TDLMS

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ON thE PERFORMANCE OF A MODIFIED SIGN DETECTOR FOR M-DEPENDENT data.

Proceedings - Annual Allerton Conference on Communication, C...

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Proceedings - Annual Allerton conference on Communication, Control, and Computing 1979年 143-151页

作者： Halverson, D.R. Wise, G.L.

A nonparametric detection scheme, the modified sign detector, may be applied to the discrete time detection of a constant signal in additive m-dependent noise. It is shown how the optimal block size for this detector may be selected for two fidelity criteria, one based on a finite number of samples and the other on the asymptotic limits. these results may then be used to exhibit examples of cases where a small block length is indicated by the finite sample criterion, whereas a large block length is indicated by the asymptotic criterion.

关键词： signal DETECTION

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Tracking with asynchronous multiple sensors

Tracking with asynchronous multiple sensors

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13th conference on signal and data processing of small targets

作者： Gray, JE McCabe, DH USN Ctr Surface Warfare Dahlgren Div Syst Res & Technol Dept Dahlgren VA 22448 USA

ISBN: (纸本)0819441872

Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous (i.e., perform the, same operation at the identical time), take measurements at the same time and have no communication delays between sensor platforms and the central processing center. Such assumptions are not valid in practice. this paper removes these assumptions when dealing with multisensor target tracking. In particular, it assumes that the sensors used can have different data rates and communication delays between local and central platforms. A new tracking algorithm using asynchronous sensors is proposed and derived in this paper.

关键词： Filtering (signal processing) Lithium Sensors Silicon Zinc Electronic filtering Lutetium data communications data processing signal processing

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Fast mutual exclusion

Fast mutual exclusion

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16th conference on signal and data processing of small targets

作者： Maskell, S Briers, M Wright, R QinetiQ Ltd Malvern Technol Ctr Malvern Worcs England

ISBN: (纸本)081945351X

A method is presented that circumvents the combinatorial explosion often assumed to exist when summing probabilities of joint association events in a multiple target tracking context. the approach involves no approximations in the summation and while the number of joint events grows exponentially with the number of targets, the computational complexity of the approach is substantially less than exponential. Multiple target tracking algorithms that use this summation include mutual exclusion(11,21) in a particle filtering context and the Joint Probabilistic data Association Filter,(7) a Kalman Filter based algorithm. the perceived computational expense associated with this combinatorial explosion has meant that such algorithms have been restricted to applications involving only a handful of targets. the approach presented here makes it possible to use such algorithms with a large number of targets.

关键词： Detection and tracking algorithms Filtering (signal processing) Electronic filtering Particle filters Particles

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