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) eliminat...
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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.
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...
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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.
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-vecto...
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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.
One of the main goals of the STAP-BOY program has been the implementation of a, space-time adaptive processing (STAP) algorithm on graphics processing units (GPUs) with the goal of reducing the processing time. Within...
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ISBN:
(纸本)9780819472946
One of the main goals of the STAP-BOY program has been the implementation of a, space-time adaptive processing (STAP) algorithm on graphics processing units (GPUs) with the goal of reducing the processing time. Within the context of GPU implementation, we have further developed algorithms that exploit data redundancy inherent in particular STAP applications. Integration of these algorithms with GPU architecture is of primary importance for fast algorithmic processing times. STAP algorithms involve solving a linear system in which the transformation matrix is a covariance matrix. A standard method involves estimating a covariance matrix from a data matrix, computing its Cholesky factors by one of several methods. and then solving the system by substitution. Some STAP applications have redundancy in successive data matrices from which the covariance matrices are formed. For STAP applications in which a data matrix is updated with the addition of a new data row at the bottom and the elimination of the oldest data in the top of the matrix, a sequence of data matrices have multiple rows in common. Two methods have been developed for exploiting this type of data redundancy when computing Cholesky factors. These two methods are referred to as 1) Fast QR factorizations of successive data matrices 2) Fast Cholesky factorizations of successive covariance matrices. We have developed GPU implementations of these two methods. We show that these two algorithms exhibit reduced computational complexity when compared to benchmark algorithms that do not exploit data, redundancy. More importantly, we show that when these algorithmic improvements are optimized for the GPU architecture, the processing times of a GPU implementation of these matrix factorization algorithms may be greatly improved.
In this paper, a new wave front sensor design that utilizes the benefits of image projections is described and analyzed. The projection-based wave front sensor is similar to a Shack-Hartman type wave front sensor, but...
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ISBN:
(纸本)0819445584
In this paper, a new wave front sensor design that utilizes the benefits of image projections is described and analyzed. The projection-based wave front sensor is similar to a Shack-Hartman type wave front sensor, but uses a correlation algorithm as opposed to a centroiding algorithm to estimate optical tilt. This allows the projection-based wave front sensor to estimate optical tilt parameters while guiding off of point sources and extended objects at very low signal to noise ratios. The implementation of the projection-based wave front sensor is described in detail showings important signalprocessing steps on and off of the focal plane array of the sensor. In this paper the design is tested in simulation for speed and accuracy by processing simulated astro-nomical data. These simulations demonstrate the accuracy of the projection-based wave front sensor and its superior performance to that of the traditional Shack-Hartman wave front sensor. Timing analysis is presented which shows how the collection and processing of image projections is computationally efficient and lends itself to a wave front sensor design that can produce adaptive optical control signals at speeds of up to 500 hz.
Time-frequency distributions (TFDs) of Cohen's class often dramatically reveal complex structures that are not evident in the raw signal. Standard linear filters are often not able to separate the underlying signa...
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ISBN:
(纸本)0819445584
Time-frequency distributions (TFDs) of Cohen's class often dramatically reveal complex structures that are not evident in the raw signal. Standard linear filters are often not able to separate the underlying signal from background clutter and noise. The essense of the signal can often be extracted from the TFD by evaluating strategic slices through the TFD for a series of frequencies. However, TFDs are often computationally intense compared to other methods. This paper demonstrates that quadratic filters may be designed to capture the same information as is available in the specific slices through the TFD at a considerably lower computational cost. The outputs of these filters can be combined to provide a robust impulse-like response to the chosen signal. This is particularly useful when the exact time series representation of the signal is unknown, due to variations and background clutter and noise. It is also noted that Teager's method is closely related to TFDs and are an example of a quadratic filter. Results using an ideal matched filter and the TFD motivated quadratic filter are compared to give insight into their relative responses.
This paper presents a general FIR filter architecture utilizing truncated tree multipliers for computation. The average error, maximum error, and variance of error due to truncation are derived for the proposed archit...
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ISBN:
(纸本)0819445584
This paper presents a general FIR filter architecture utilizing truncated tree multipliers for computation. The average error, maximum error, and variance of error due to truncation are derived for the proposed architecture. A novel technique that reduces the average error of the filter is presented, along with equations for computing the signal-to-noise ratio of the truncation error. A software tool written in Java is described that automatically generates structural VHDL models for specific filters based on this architecture, given parameters such as the number of taps, operand lengths, number of multipliers, and number of truncated columns. We show that a 22.5 % reduction in area can be achieved for a 24-tap filter with 16-bit operands, 4 parallel multipliers, and 12 truncated columns. For this implementation, the average reduction error is only 9.18 x 10(-5) ulps, and the reduction error SNR is only 2.4 dB less than the roundoff SNR of an equivalent filter without truncation.
We address two classical problems relating to harmonic signals. The first of these is the blind recovery of the carrier of a single sideband-AM communication signal, and the second is the isolation and blind estimatio...
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ISBN:
(纸本)0819441880
We address two classical problems relating to harmonic signals. The first of these is the blind recovery of the carrier of a single sideband-AM communication signal, and the second is the isolation and blind estimation of the fundamental of a time-varying harmonic signal. The methods are based on cross-spectra estimated from the short time Fourier transform, a generalization of the Chinese remainder theorem and joint Fourier and autocorrelation representations of the signal spectrum. These tools are developed, and their utility is demonstrated in the solutions of the the two problems. By an additional application of a frequency-lag autocorrelation function, it is demonstrated that the harmonic fundamental can be recovered, even if it is not present in the original spectrum.
Mainly digital images are under sampled. It is the same for SPOT digital image satellite. The very meaning is that the instrument is too much powerful for this sampling. The worth side effect is that artifacts (aliasi...
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ISBN:
(纸本)0819441880
Mainly digital images are under sampled. It is the same for SPOT digital image satellite. The very meaning is that the instrument is too much powerful for this sampling. The worth side effect is that artifacts (aliasing) are introduced in the image, the good side is that images can be improved if the sampling density is increased. In this paper we use images from the two HRVIR instruments onboard SPOT1-4 satellite to multiply by a factor two the density and the resolution of the image.
We address the problem of efficient resolution, detection and estimation of weak tones in a potentially massive amount of data. Our goal is to produce a relatively small reduced data set characterizing the signals in ...
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ISBN:
(纸本)0819463922
We address the problem of efficient resolution, detection and estimation of weak tones in a potentially massive amount of data. Our goal is to produce a relatively small reduced data set characterizing the signals in the environment in time and frequency. The requirements for this problem are that the process must be computationally efficient, high gain and able to resolve signals and efficiently compress the signal information into a form that may be easily displayed and further processed. We base our process on the cross spectral representation we have previously applied to other problems. In selecting this method, we have considered other representations and estimation methods such as the Wigner distribution and Welch's method. We compare our method to these methods. The spectral estimation method we propose is a variation of Welch's method and the cross-power spectral (CPS) estimator which was first applied to signal estimation and detection in the mid 1980's. The CPS algorithm and the method we present here are based on the principles first described by Kodera et at. now frequently called the reassignment principle.
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