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signal processing algorithms by permutation test in radar application 6

Signal processing algorithms by permutation test in radar ap...

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

作者： AlvarezVaquero, F SanzGonzalez, JL UNIV POLITECN MADRID ETSI TELECOMUNDEPT SIGNAL SYST & RADIOCOMMUNE-28040 MADRIDSPAIN

ISBN: (纸本)0819422347

A hypothesis H is parametric if every distribution from the process defined by H belongs to a family of distributions characterized by a finite number of parameters; on the other hand, if the distribution can not be defined by a finite number of parameters, the hypothesis is nonparametric. In this paper, we analyze a detector based on the optimum permutation test, applied to nonparametric radar detection which provide good performances without a large computational work, and we compare it with the parametric test and rank test in the Neyman-Pearson sense. The computational complexity of the detector is high and its implementation in real time is difficult, due to the number of operations increase with the factorial of the number of samples. Also, we present an algorithm that reduces the computational work required. We also present the detectability characteristic of the optimum permutation test against rank test and parametric test under Gaussian noise environments and different types of target models (nonfluctuating, Swerling I and Swerling II). The detection probability versus signal-to-noise ratio is estimated by Monte-Carlo simulations for different parameter values (N pulse, M reference samples and false alarm probability P_fa).

关键词： nonparametric permutation test rank test radar detection detectability target models

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An algorithm for weighted least squares positive time-frequency distributions

An algorithm for weighted least squares positive time-freque...

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

作者： Pitton, JW MathSoft Seattle WA 98109 1700 Westlake Ave N United States

ISBN: (纸本)0819425842

An algorithm for computing positive time-frequency distributions (TFDs) for nonstationary signals is presented. This work extends the earlier work of the author and his colleagues in computing positive TFDs [11, 15]. A general approach to the problem of computing these signal-dependent distributions is derived. The method is based on an evolutionary spectrum formulation of positive TFDs. Following earlier work [15], a relationship is derived between positive TFDs and the ambiguity function of the observed signal. In particular, it is shown that the TFD is approximately equal to the two-dimensional Fourier transform of the ambiguity function. A method for computing the positive TFD is then presented based on minimizing the squared error in this approximation subject to the TFD being positive and satisfying the time and frequency marginals. The squared error may be weighted non-uniformly, resulting in a constrained weighted least-squares optimization problem. A solution to this optimization problem based on an alternating projections framework is presented, and an example is provided. The resulting TFD provides excellent time-frequency resolution while maintaining positivity and satisfying the marginals.

关键词： signal processing time-frequency analysis time-varying signals optimization alternating projections

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Time-frequency analysis for the study of phase synchrony in the brain

Time-frequency analysis for the study of phase synchrony in ...

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

作者： Aviyente, Selin Evans, Westley Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9780819472946

The quantification of synchrony is important for the study of large-scale interactions in the brain. Current synchrony measures depend on the energy of the signals rather than the phase, and cannot be reliably used as measures of neural synchrony. Moreover, the current methods are insufficient since they are limited to pairs of signals. These approaches cannot quantify the synchrony across a group of electrodes and over time-varying frequency regions. In this paper, we propose two new measures for quantifying the synchrony between both pairs and groups of electrodes using time-frequency analysis. The proposed measures are applied to electroencephalogram (EEG) data to quantify neural synchrony.

关键词： Phase measurement Time-frequency analysis Brain Electrodes Electroencephalography Mode locking Wavelets Iterated function systems signal analyzers Wavelet transforms

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Wavelet windowed time-frequency distribution decompositions

Wavelet windowed time-frequency distribution decompositions

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

作者： Williams, WJ Sang, TH ONeill, JC Zalubas, EJ Univ. of Michigan (United States)

ISBN: (纸本)0819425842

This paper outlines means of combining and reconciling concepts associated with Cohen's class of distributions and with the wavelet transform. Both have their assets and their liabilities. Previous work has shown that one can decompose any time-frequency distribution (TFD) in Cohen's class into a weighted sum of spectrogram. A set of orthogonal analysis windows which also have the scaling property in common with wavelets is proposed. Successful application of this theory offers very fast computation of TFDs, since very few analysis windows may be needed and fast algorithms can be used. In addition, the decomposition idea offers the possibility of shaping the analysis such that good local and global properties as well as a number of desirable TFD properties are retained. Finally, one may view the result in terms of conventional Cohen's class concepts or, alternatively, in terms of wavelet concepts and potentially combine powerful insights and concepts from both points of view. Preliminary results applied to radar backscatter are provided. Performance curves for several wavelet types are also provided.

关键词： time-frequency wavelets signal decomposition radar fast algorithms

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Design and evaluation of automated checks for signal processing applications 6

Design and evaluation of automated checks for signal process...

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

作者： Nair, VSS Kim, HC Krishnamurthy, N Abraham, JA SO METHODIST UNIV DEPT COMP SCI & ENGNDALLASTX 75275

ISBN: (纸本)0819422347

Most of the signal processing application programs involve computationally intensive iterative steps. In such programs, various failures in the underlying hardware manifest as control-flow errors that affect the reliability of the computed results. Various techniques have been proposed in the past to detect and recover from such control-flow errors. Unfortunately, all these techniques need either additional hardware or modification of the hardware and are not portable across various platforms. To circumvent these limitations, recently we have developed a high-level control-flow checking approach using assertions (CCA). In CCA, branch-free intervals in a given high-level language program are identified and the entry and exit points of the intervals are fortified through pre-inserted assertions. In this paper we describe an implementation of CCA through a pre-processor that will automatically insert the necessary assertions into a high-level language program. Based on the implementation we study the fault detection capabilities of CCA with the help of fault injection experiments using FERRARI.

关键词： Simulation of CCA and DLA aggregates Switches signal processing Reliability Computing systems Computer engineering Data modeling Error analysis Error control coding Computer science

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Very low-noise (switching free) CNN-based adder

Very low-noise (switching free) CNN-based adder

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

作者： Sadeghi-Emamchaie, S Jullien, GA Miller, WC Univ Windsor VLSI Res Grp Windsor ON N9B 3P4 Canada

ISBN: (纸本)0819432938

This paper presents novel methods of designing analog Cellular Nonlinear (Neural) Networks (CNNs) to implement very low-noise binary addition. In these techniques the continuous characteristic of the current that charges (discharges) the load capacitor leads to a virtually switching free addition process that significantly reduces the switching noise. This switching mechanism also leads to higher slew of output voltage during the transitions which in turn reduces the cross talk. Simulation results demonstrate a three orders of magnitude reduction in the noise generated by this structure compared to that generated by a digital adder running at the same speed. This very good noise performance of these new adder structures makes them suitable choices for low to moderate speed high precision mixed signal applications.

关键词： Cellular Nonlinear Networks switching noise mixed-signal IC analog design low-noise digital adder

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Time sequenced adaptive filtering using a modified P-vector algorithm 6

Time sequenced adaptive filtering using a modified P-vector ...

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

作者： Williams, R USAF WRIGHT LABAUTOMAT TARGET RECOGNIT DEV BRANCHWRIGHT PATTERSON AFBOH 45433

ISBN: (纸本)0819422347

An adaptive algorithm and two stage filter structure were developed for adaptive filtering of certain classes of signals that exhibit cyclostationary characteristics. The new modified P-vector algorithm (mPa) eliminates the need for a separate desired signal which is typically required by conventional adaptive algorithms. It is then implemented in a time-sequenced manner to counteract the nonstationary characteristics typically found in certain radar and bioelectromagnetic signals. Initial algorithm testing is performed on evoked responses generated by the visual cortex of the human brain with the objective, ultimately, to transition the results to radar signals. Each sample of the evoked response is modeled as the sum of three uncorrelated signal components, a time-varying mean (M), a noise component (N), and a random jitter component (Q). A two stage single channel time-sequenced adaptive filter structure was developed which improves convergence characteristics by de coupling the time-varying mean component from the `Q' and noise components in the first stage. The EEG statistics must be known a priori and are adaptively estimated from the pre stimulus data. The performance of the two stage mPa time-sequenced adaptive filter approaches the performance for the ideal case of an adaptive filter having a noiseless desired response.

关键词： time sequenced adaptive filter EEG biomedical signal processing radar signal processing

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Fast blind signal separation for multi-user interference rejection in smart antenna arrays

Fast blind signal separation for multi-user interference rej...

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

作者： Sarajedini, A Chau, PM Univ. of California/San Diego (United States)

ISBN: (纸本)0819425842

Spread spectrum modulation techniques such as frequency-hopped code division multiple access (FH-CDMA) are an efficient way to allow multi-user transmission over a limited bandwidth. Recently, there has been a push to increase user capacity over the same bandwidth. In particular, smart antenna arrays have been studied for their capacity gain potential. In this regard, we propose the use of blind signal separation for recovery of signals received through an antenna array. To implement this technique requires the antenna array vectors to be stationary which does not hold in FH-CDMA, so we also propose a new method for frequency compensation. We present the theoretical details of the frequency compensation and compare its performance to no frequency compensation. We then present the blind signal separation algorithm applied to a complex antenna array matrix and complex signals with noise and show in simulation that the blind signal separation works.

关键词： blind signal separation frequency compensation frequency-hopped CDMA spread spectrum

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Computing the signal subspaces of a sparse and/or structured matrix

Computing the signal subspaces of a sparse and/or structured...

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

作者： Fierro, RD California State Univ./San Marcos (United States)

ISBN: (纸本)0819425842

Many techniques involve the computation of singular subspaces associated with an extreme cluster of singular values of an m x n data matrix A. Frequently A is sparse and/or structured, which usually means matrix-vector multiplications involving A and its transpose can be done with much less than O(mn) flops, and A and its transpose can be stored in static data structures with much less than O(mn) storage locations. Standard complete orthogonal decompositions may be unattractive due to the computational and dynamic storage overhead associated with the initial preprocessing of the data. We describe an efficient Matlab implementation of the low-rank ULV algorithm for extracting reliable and accurate approximations to the singular subspaces associated with the cluster of large singular values without altering the matrix. The user can choose any principal singular vector estimator to underwrite the algorithm, may call a specialized routine to compute matrix-vector products involving A and its transpose, and can choose the desired level of accuracy of a residual. The main computational savings stems from preserving A and avoiding the explicit formation of unwanted information.

关键词： singular value decomposition singular subspaces signal subspaces ULV decomposition fast algorithms Householder transformations

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Large dynamic range time-frequency signal analysis with application to helicopter Doppler radar data

Large dynamic range time-frequency signal analysis with appl...

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advanced signal processing algorithms, architectures, and implementations

作者： Marple Jr., S. Lawrence Marino, Claudio Strange, Shawn Oregon State University School of EECS Corvallis OR United States Lockheed Martin Orincon San Diego CA United States

Despite the enhanced time-frequency analysis (TFA) detailing capability of quadratic TFAs like the Wigner and Cohen representations, their performance with signals of large dynamic range (DNR in excess of 40 dB) is not acceptable due to the inability to totally suppress the cross-term artifacts which typically are much stronger than the weakest signal components that they obscure. AMTI and GMTI radar targets exhibit such high dynamic range when microDoppler is present, with the aspects of interest being the weakest components. This paper presents one of two modifications of linear TFA to provide the enhanced detailing behavior of quadratic TFAs without introducing cross terms, making it possible to see the time-frequency detail of extremely weak signal components. The technique described here is based on subspace-enhanced linear predictive extrapolation of the data within each analysis window to create a longer data sequence for conventional STFT TFA. The other technique, based on formation of a special two-dimensional transformed data matrix analyzed by high-definition two-dimensional spectral analysis methods such as 2-D AR or 2-D minimum variance, is compared to the new technique using actual AMTI and GMTI radar data.

关键词： signal processing

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