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Innovative implementation and simulation of signal processing algorithms for the ASTP & DITP programs.

Innovative implementation and simulation of signal processin...

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conference on signal and Data processing of Small Targets

作者： DeChaine, MD Kinashi, Y Nichols Res Corp Arlington VA 22202 USA

ISBN: (纸本)0819428221

An innovative approach is being used to implement and simulate the infrared (IR) and laser radar signal processing algorithms for the advanced Sensor Technology Program (ASTP) and the Discrimination Interceptor Technology Program (DITP). Although the algorithms will run on four different computer architectures, they will use the same source code for all implementations. The initial development and testing will occur in Mathcad on a Windows 95/NT personal computer, then move to simulation on a Silicon Graphics (SGI) workstation, then to scaled real-time simulation on a parallel high performance computer (HPC), and finally to the actual flight processor, the miniaturized parallel Wafer Scale signal Processor (WSSP) with a MIMD (multiple-instruction and multiple data) architecture. This flexibility is accomplished with code wrappers that implement interchangeable interface layers for the code modules, one wrapper for Mathcad matrices, one for C++ objects on the workstation, one for message passing with static routing on the HPC, and one for dynamically routed message passing on the WSSP. With this approach, developers can move modules back and forth from the workstation simulation environment to the implementation hardware. This will eliminate the need to maintain different versions of the same algorithm. The signal processing algorithms will be modified to work in a massively parallel architecture, with a message passing interface, which is simulated on the Silicon Graphics workstation, emulated on the HPC, and implemented on the WSSP. This approach will allow for pipeline processing as well as multiple, concurrently running instances of modules. In addition, innovative algorithms will fuse active laser radar detections and passive multicolor IR sensor measurements to improve target state estimation.

关键词： signal processing algorithms parallel processing simulation infrared sensors laser radar ballistic missile defense

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Energy-Efficient Synthetic-Aperture Radar processing on a Manycore Architecture

Energy-Efficient Synthetic-Aperture Radar Processing on a Ma...

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42nd Annual International conference on Parallel processing (ICPP)

作者： Zain-ul-Abdin Ahlander, Anders Svensson, Bertil Halmstad Univ Ctr Res Embedded Syst CERES Halmstad Sweden Saab AB Gothenburg Sweden

ISBN: (纸本)9780769551173

The next generation radar systems have high performance demands on the signal processing chain. Examples include the advanced image creating sensor systems in which complex calculations are to be performed on huge sets of data in real time. Manycore architectures are gaining attention as a means to overcome the computational requirements of the complex radar signal processing by exploiting massive parallelism inherent in the algorithms in an energy efficient manner. In this paper, we evaluate a manycore architecture, namely a 16-core Epiphany processor, by implementing two significantly large case studies, viz. an autofocus criterion calculation and the fast factorized back-projection algorithm, both key components in modern synthetic aperture radar systems. The implementation results from the two case studies are compared on the basis of achieved performance and programmability. One of the Epiphany implementations demonstrates the usefulness of the architecture for the streaming based algorithm (the autofocus criterion calculation) by achieving a speedup of 8.9x over a sequential implementation on a state-of-the-art general-purpose processor of a later silicon technology generation and operating at a 2.7x higher clock speed. On the other case study, a highly memory-intensive algorithm (fast factorized backprojection), the Epiphany architecture shows a speedup of 4.25x. For embedded signal processing, low power dissipation is equally important as computational performance. In our case studies, the Epiphany implementations of the two algorithms are, respectively, 78x and 38x more energy efficient.

关键词： Manycore architecture Parallel programming Radar signal processing

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Proceedings of the 35th Asilomar conference on signals, Systems & Computers

Proceedings of the 35th Asilomar Conference on Signals, Syst...

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35th Asilomar conference on signals, Systems and Computers

This Volume 2 of 2 of the conference proceedings contains 160 papers. Topics discussed include wireless communication and signal processing, algorithms for MIMO links, modulation and detection techniques, signal detection and classification, adaptive communications and arrays, image segmentation and frequency domain processing, multiple user/multiple access techniques, digital signal processing architectures, hyperspectral processing and multisignals or data fusion, computer arithmetic implementations and FPGA designs, radar and sonar processing, equalization and synchronization techniques, higher order statistical signal processing for communications, adaptive signal processing in communication, speech coding and processing, wireless systems, special arithmetic techniques, biomedical imaging and advanced modulation and channel estimation.

关键词： signal processing

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Time-frequency analysis using peak-value sampling of spatial-temporal image data from streak camera

Time-frequency analysis using peak-value sampling of spatial...

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conference on advanced signal processing algorithms, architectures, and implementations XIV

作者： Sun, KX Marlett, K Bechtel Nevada Livermore Operat Livermore CA 94551 USA

ISBN: (纸本)0819454974

A streak camera is a recording instrument in which spatial image is swept in time. creating a spatial-temporal image on a charge-coupled device (CCD). Traditional analysis for captured image data has been using uniform and as sampling points, in which a block of CCD pixel readouts are summed to give one reading. Equivalently simple area moving averages are applied concurrently while sampling, and high frequency content is reduced. To solve this problem. we use peak-value sampling procedure, based on the view from photoelectron statistics. After background correction. maximum values in spatial dimensions are selected to obtain time series data. A DSP filter is then applied and optimized for this time series. A Welch algorithm fast Fourier transform is applied to obtain power spectra. Segmented cumulative spectra is then calculated for global statistics and related to time domain fluctuations. Self similarity at different sweeping time-scales is used to recognize CCD pattern noise. Sinusoidal pattern noise is automatically corrected by peak-value sampling. Computational results show that time-frequency analysis using peak-value sampling algorithm and similar variants is far more effective in discovering high frequency oscillatory noise than traditional uniform binned sampling. We have applied this algorithm to analyze data produced by a 4096x4096 CCD streak camera illuminated with a macro pulse laser. High frequency oscillations in 6similar to10 GHz region were found in laser spectra. Spatial-temporal oscillations of this range are difficult to diagnose with conventional optoelectronic detectors on a per-shot basis. This work has led to improvement of laser design.

关键词： streak camera laser maximum value sampling image processing high frequency spectral analysis

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Estimating the frequency interval of a regularly spaced multi-component harmonic line signal in coloured noise

Estimating the frequency interval of a regularly spaced mult...

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conference on advanced signal processing - algorithms, architectures, and implementations VII

作者： Frazer, GJ Anderson, SJ Defence Science and Technology Organisation (Australia)

ISBN: (纸本)0819425842

The radar returns from some classes of time-varying point targets can be represented by the discrete-time signal plus noise model: x(t) = s(t) + [nu(t) + eta(t)] = (P-1)Sigma(i=0) A(i)e(j2 pi fi/fst) + nu(t) + eta(t), t is an element of 0,...,.N - 1, f(i) = kf(I) + f(o) where the received signal x(t) corresponds to the radar return from the target of interest from one azimuth-range cell. The signal has an unknown number of components, P, unknown complex amplitudes A(i) and frequencies f(i). The frequency parameters f(o) and f(I) are unknown, although constrained such that f(o) < fI/2 and parameter k is an element of {-u,...,-2,-1,0,1,2,...,upsilon) is constrained such that the component frequencies f(i) are bound by [-fs/2,fs/2). The noise term nu(t), is typically coloured, and represents clutter, interference and various noise sources. It is unknown, except that Sigma(t)\nu(t)\(2) < infinity;in general, nu(t) is not well modelled as an auto-regressive process of known order. The additional noise term eta(t) represents time-invariant point targets in the same azimuth-range cell. An important characteristic of the target is the unknown parameter, f(I), representing the frequency interval between harmonic lines. It is desired to determine an estimate of f(I) from N samples of x(t). We propose an algorithm to estimate f(I) based on Thomson's harmonic line F-Test, which is part of the multi-window spectrum estimation method and demonstrate the proposed estimator applied to target echo time series collected using an experimental HF skywave radar.

关键词： HF radar multiple-window methods harmonic line detection frequency interval estimation

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Separating Cognitive Processes with Principal Components Analysis of EEG Time-Frequency Distributions

Separating Cognitive Processes with Principal Components Ana...

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conference on advanced signal processing algorithms, architectures, and Implementantions XVIII

作者： Bernat, Edward M. Nelson, Lindsay D. Holroyd, Clay B. Gehring, William J. Patrick, Christopher J. Univ Minnesota Dept Psychol Minneapolis MN 55455 USA Univ Victoria Dept Psychol Victoria BC Canada Univ Michigan Dept Psychol Ann Arbor MI 48109 USA

ISBN: (纸本)9780819472946

Measurement of EEG event-related potential (ERP) data has been most commonly undertaken in the time-domain, which can be complicated to interpret when separable activity overlaps in time. When the overlapping activity has distinct frequency characteristics, however, time-frequency (TF) signal processing techniques can be useful. The current report utilized ERP data from a cognitive task producing typical feedback-related negativity (FRN) and P300 ERP components which overlap in time. TF transforms were computed using the binomial reduced interference distribution (RID), and the resulting TF activity was then characterized using principal components analysis (PCA). Consistent with previous work, results indicate that the FRN was more related to theta activity (3-7 Hz) and P300 more to delta activity (below 3 Hz). At the same time, both time-domain measures were shown to be mixtures of TF theta and delta activity, highlighting the difficulties with overlapping activity. The TF theta and delta measures, on the other hand, were largely independent from each other, but also independently indexed the feedback stimulus parameters investigated. Results support the view that TF decomposition can greatly improve separation of overlapping EEG/ERP activity relevant to cognitive models of performance monitoring.

关键词： Time-frequency analysis Principal component analysis Electroencephalography signal processing Brain Electrodes Psychology signal attenuation Time metrology Transform theory

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Tuning Time-Frequency methods for the detection of metered HF speech

Tuning Time-Frequency methods for the detection of metered H...

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conference on advanced signal processing algorithms, architectures and implementations XII

作者： Nelson, DJ Smith, LH US Dept Def Ft George G Meade MD 20755 USA

ISBN: (纸本)0819445584

Speech is metered if the stresses occur at a nearly regular rate. Metered speech is common in poetry, and it can occur naturally in speech, if the speaker is spelling a word or reciting words or numbers from a list. In radio communications, the CQ request, call sign and other codes are frequently metered. In tactical communications and air traffic control, location, heading and identification codes may be metered. Moreover metering may be expected to survive even in HF communications, which are corrupted by noise, interference and mistuning. For this environment, speech recognition and conventional machine-based methods are not effective. We describe Time-Frequency methods which have been adapted successfully to the problem of mitigation of HF signal conditions and detection of metered speech. These methods are based on modeled time and frequency correlation properties of nearly harmonic functions. We derive these properties and demonstrate a performance gain over conventional correlation and spectral methods. Finally, in addressing the problem of HF single sideband (SSB) communications, the problems of carrier mistuning, interfering signals, such as manual Morse, and fast automatic gain control (AGC) must be addressed. We demonstrate simple methods which may be used to blindly mitigate mistuning and narrowband interference, and effectively invert the fast automatic gain function.

关键词： signal to noise ratio signal processing signal detection Optical filters Time-frequency analysis Interference (communication) Automatic control Electronic filtering Receivers RF communications

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Adaptive array beamforming with fixed-point arithmetic matrix inversion using Givens rotations

Adaptive array beamforming with fixed-point arithmetic matri...

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conference on advanced signal processing algorithms, architectures, and implementations XI

作者： Rabinkin, DV Song, W Vai, MM Nguyen, H MIT Lincoln Lab Lexington MA 02420 USA

ISBN: (纸本)0819441880

Adaptive array systems require the periodic solution of the well-known w = (R) over tilde (-1)v equation in order to compute optimum adaptive array weights. The covariance matrix (R) over tilde is estimated by forming a product of noise sample matrices X : (R) over tilde = (XX)-X-H. The operations-count cost of performing the required matrix inversion in real time can be prohibitively high for a high bandwidth system with a large number of sensors. Specialized hardware may be required to execute the requisite computations in real time. The choice of algorithm to perform these computations must be considered in conjunction with the hardware technology used to implement the computation engine. A systolic architecture implementation of the Givens rotation method for matrix inversion was selected to perform adaptive weight computation. The bit-level systolic approach enables a simple ASIC design and a very low power implementation. The bit-level systolic architecture must be implemented with fixed-point arithmetic to simplify the propagation of data through the computation cells. The Givens rotation approach has a highly parallel implementation and is ideally suited for a systolic implementation. Additionally, the adaptive weights are computed directly from the sample matrix X in the voltage domain, thus reducing the required dynamic range needed in carrying out the computations. An analysis was performed to determine the required fixed-point precision needed to compute the weights for an adaptive array system operating in the presence of interference. Based on the analysis results. it was determined that the precision of a floating-point computation can be well approximated with a 13-bit to 19-bit word length fixed point computation for typical system jammer-to-noise levels. This property has produced an order-of-magnitude reduction in required hardware complexity. A synthesis-based ASIC design process was used to generate preliminary layouts. These layouts were used t

关键词： Very large scale integration Chemical elements Computing systems Sensors Statistical analysis Computer architecture Matrices Radar Electroluminescence signal processing

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Reconfigurable video tracker

Reconfigurable video tracker

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Acquisition, Tracking, and Pointing xiii conference

作者： Groves, GK White, SW Vahey, MD Harding, JA Raytheon Syst Co El Segundo CA 90245 USA

ISBN: (纸本)0819431664

Reconfigurable computing using SRAM-based field programmable gate arrays (FPGAs) can achieve significant (10X-100X) computational performance advantages over conventional programmable processors. Since FPGAs can be customized, reconfigurable computers can provide optimal logic-circuitry for distinct phases of an application resulting in superior performance compared to generic multi-purpose hardware implementations. This performance improvement can be accomplished by reallocating logic resources to address the critical task at-hand. Consequently, not only can reconfigurable processors provide higher performance than programmable processors;they also enable common module architectures useful for multiple applications or programs. In this paper, we will describe a fielded, ruggedized, fully programmable, single card, image-based tracking system using a reconfigurable computing module. The reconfigurable computing board contains multiple FPGAs, which can be customized at-request by loading configuration data from the host processor to the module over the Peripheral Component Interface (PCI) bus. Configurations can be selectively loaded to a specific FPGA or multiple configurations can be loaded simultaneously to different devices. This system provides multiple video tracking algorithms, automatic and manual target acquisition, RS-170 video input/output, and command/data I/O on a single 6U VME format card. While the initial application for this reconfigurable system was image-based target tracking, its hardware reconfigurability allows it to be applied to a wide variety of image and signal processing applications, such as automatic target recognition, infrared search and track, and image enhancement.

关键词： reconfigurable computing image-based target tracking image processing

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Superresolution imaging: a survey of current techniques

Superresolution imaging: a survey of current techniques

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conference on advanced signal processing algorithms, architectures, and Implementantions XVIII

作者： Cristobal, G. Gil, E. Sroubek, F. Flusser, J. Miravet, C. Rodriguez, F. B. CSIC Inst Opt Serrano 121 E-28006 Madrid Spain ASCR Inst Informat Theory & Automat Prague Czech Republic Univ Autonoma Madrid Escuela Politecn Super Madrid Spain

ISBN: (纸本)9780819472946

Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may, exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR, techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.

关键词： superresolution deconvolution energy minimization probabilistic neural network sequence processing

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