We develop an algorithm for adaptively estimating the noise subspace of a data matrix, as is required in signalprocessing applications employing the 'signal subspace' approach. The noise subspace is estimated...
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We develop an algorithm for adaptively estimating the noise subspace of a data matrix, as is required in signalprocessing applications employing the 'signal subspace' approach. The noise subspace is estimated using a rank-revealing QR factorization instead of the more expensive singular value or eigenvalue decompositions. Using incremental condition estimation to monitor the smallest singular values of triangular matrices, we can update the rank-revealing triangular factorization inexpensively when new rows are added and old rows are deleted. Experiments demonstrate that the new approach usually requires O(n2) work to update an n × n matrix, and accurately tracks the noise subspace.
We are presenting a new class of transforms which facilitates the processing of signals that are nonlinearly stretched or compressed in time. We refer to nonlinear stretching and compression as warping. While the magn...
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We are presenting a new class of transforms which facilitates the processing of signals that are nonlinearly stretched or compressed in time. We refer to nonlinear stretching and compression as warping. While the magnitude of the Fourier transform is invariant under time shift operations, and the magnitude of the scale transform is invariant under (linear) scaling operations, the new class of transforms is magnitude invariant under warping operations. The new class contains the Fourier transform and the scale transform as special cases. Important theorems, like the convolution theorem for Fourier transforms, are generalized into theorems that apply to arbitrary members of the transform class. Cohen's class of time-frequency distributions is generalized to joint representations in time and arbitrary warping variables. Special attention is payed to a modification of the new class of transforms that maps an arbitrary time-frequency contour into an impulse in the transform domain. A chirp transform is derived as an example.
In this paper we consider cyclostationary signalprocessing techniques implemented via acousto-optics (AO). Cyclic processing methods are reviewed and motivated, including the cyclic correlation and the cyclic spectru...
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ISBN:
(纸本)0819416207
In this paper we consider cyclostationary signalprocessing techniques implemented via acousto-optics (AO). Cyclic processing methods are reviewed and motivated, including the cyclic correlation and the cyclic spectrum. We show how a 1D AO spectrum analyzer can be used to detect the presence, and estimate the value, of cycle frequencies. The cyclic correlation can then be computed at cycle frequencies of interest using a 1D time-integrating correlator. Next we consider the problem of computing the (2D) cyclic correlation for all cycle frequencies and lags simultaneously. This is accomplished via an AO triple-product processor, configured in a manner similar to that used for ambiguity function generation. The cyclic spectrum can be obtained in a post-processing step by Fourier transforming the cyclic correlation in one dimension. We then consider higher order extensions of the cyclic correlation and show how a 2D slice of the 3D cyclic triple-correlation can be computed using an AO four-product processor.
We develop an algorithm for adaptively estimating the noise subspace of a data matrix, as is required in signalprocessing applications employing the 'signal subspace' approach. The noise subspace is estimated...
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This paper describes an efficient implementation of auxiliary constraints for a concurrent block least squares adaptive sidelobe canceller when a single array of sensors is used to form one or more main beams. The app...
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ISBN:
(纸本)0819406945
This paper describes an efficient implementation of auxiliary constraints for a concurrent block least squares adaptive sidelobe canceller when a single array of sensors is used to form one or more main beams. The approach is to compute QR decomposition of the auxiliary data matrix and then send this information to main beam processors, where the constraints are applied using a blocking matrix and the individual residuals are computed. The blocking matrix can be chosen with special structure which is used to derive a new fast algorithm and architecture for constrained main beam processing that reduces the operation count from order n3 to order n2, where n is the number of auxiliary sensors.
advanced error control coding and signalprocessing techniques find wide applications in various communication systems, such as magnetic recording channels, fiber optical channels, wireline and wireless communication ...
advanced error control coding and signalprocessing techniques find wide applications in various communication systems, such as magnetic recording channels, fiber optical channels, wireline and wireless communication systems. Low-density parity-check (LDPC) codes and multiple-multiple-output (MIMO) technology have been receiving a lot of attention, since they greatly increase the capacity and improve the performance of future communication systems. In this dissertation, we focus on designing algorithms that enable efficient hardware implementations of LDPC codes and MIMO detection systems. Quasi-cyclic (QC) LDPC codes are of great interest since their regular code structure leads to efficient hardware implementations. We propose and implement in FPGA two partly parallel decoder architectures for QC LDPC codes to improve the decoding throughput and memory requirement of existing decoders. Our over-lapped message passing (OMP) decoder achieves the maximum throughput gain and hardware utilization efficiency (HUE) due to overlapping, hence has higher throughput and HUE than previously proposed OMP decoders while maintaining the same hardware requirements and the same error performance. We also show that the maximum throughput gain and HUE achieved by our OMP decoder are ultimately determined by the given code. Thus, we propose a coset-based construction method, which results in QC LDPC codes that allow our optimal OMP decoder to achieve higher throughput and HUE. To further reduce the memory requirement of our OMP decoder, we propose the parallel turbo-sum-product (PTSP) decoder architecture. Implementation results show that our PTSP decoder achieves better error performance, faster convergence and hence higher throughput than the OMP decoder with reduced memory requirement. Hardware implementations of tree search based MIMO detection often have limited performance due to large memory requirement or high computational complexity of sophisticated MIMO detection algorithm
This paper presents a new systolic array implementation of the Kalman filter that is not excessive in either hardware or computation steps. For a dynamic system with N states and M observation components, the array us...
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This paper presents a new systolic array implementation of the Kalman filter that is not excessive in either hardware or computation steps. For a dynamic system with N states and M observation components, the array uses N(N+1) processors and about 4N+6M computation steps. In some applications, it is also required that the processing system continue to function even after some of the components of the system fail. The Kalman filter systolic array is extended to one that is tolerant of faults in the processing elements of the array by using techniques of algorithm-based fault tolerance. Overhead for fault tolerance is about 47% additional hardware and 17% additional computational steps in the example of radar tracking.
A new parallel Jacobi-like algorithm for computing the eigenvalues of a general complex matrix is presented. The asymptotic convergence rate of this algorithm is provably quadratic and this is also demonstrated in num...
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A new parallel Jacobi-like algorithm for computing the eigenvalues of a general complex matrix is presented. The asymptotic convergence rate of this algorithm is provably quadratic and this is also demonstrated in numerical experiments. The algorithm promises to be suitable for real-time signalprocessing applications. In particular, the algorithm can be implemented using n2/4 processors, taking O (n log2 n) time for random matrices.
Effective signal detection and feature extraction in noisy environments generally depend on exploiting some knowledge of the signal. When the signal is exactly known, the matched filter is the optimum signal processin...
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Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However, the choice of a fixed kernel limits the class of signals that can be analyzed effectively. This paper pr...
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Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However, the choice of a fixed kernel limits the class of signals that can be analyzed effectively. This paper presents optimality criteria for the design of signal-dependent kernels that suppress cross-components while passing as much auto-component energy as possible, irrespective of the form of the signal. A fast algorithm for the optimal kernel solution makes the procedure competitive computationally with fixed kernel methods. Examples demonstrate the superior performance of the optimal kernel for a frequency modulated signal.
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