In this paper, we propose a new direction of arrival (DOA) estimator for sensor-array processing. The estimator is based on a linear algebraic connection between the standard subspace model of the array correlation matrix and a special signal-plus-interference model, which we develop in this paper. The estimator we propose is a signal subspace scaled MUSIC algorithm, which we call SSMUSIC. It is not a subspace weighted MUSIC, because the scaling depends on the eigenstructure of the estimated signal subspace. SSMUSIC has the advantage of simultaneously estimating the DOA and the power of each source. We employ a second-order perturbation analysis of the estimator and derive stochastic representations for its bias and squared-error. We compare the new DOA estimator with the MUSIC estimator, based on these representations. Numerical results demonstrate the superior performance of SSMUSIC relative to MUSIC and the validity of the perturbation results.
Abstract-We consider the problem of designing signal constellations for the multiple transmit-multiple receive antenna Rayleigh-fading communication channel, when neither the transmitter nor the receiver know the fading. In particular, by employing the asymptotic union bound (AUB) on the probability of error, we give a new formulation of the problem of signal design for the noncoherent fading channel. Since unitary signals are optimal for this channel (in the limit of large signal-to-noise ratios SNRs), the problem can be posed in terms of packings on the Grassmanian manifold. A key difference in our approach from that of other authors is that we use a notion of distance on this manifold that is suggested by the union bound. As a consequence of our use of this distance measure, we obtain signal designs that are guaranteed to achieve the full diversity order of the channel, a result that does not hold when the chordal distance is used. We introduce a new method of recursively designing signals, termed successive updates, to approximately optimize this performance measure. We then examine the use of our signals with several convolutional codes over the fading channel. An upper bound on the bit error probability of the maximum-likelihood decoder is presented together with an asymptotic analysis of that bound.
This paper presents an architecture that combines VLIW (very long instruction word) processing with the capability to introduce application-specific customized instructions and highly parallel combinational hardware functions for the acceleration of signal processing applications. To support this architecture, a compilation and design automation flow is described for algorithms written in C. The key contributions of this paper are as follows: (1) a 4-way VLIW processor implemented in an FPGA, (2) large speedups through hardware functions, (3) a hardware/software interface with zero overhead, (4) a design methodology for implementing signal processing applications on this architecture, (5) tractable design automation techniques for extracting and synthesizing hardware functions. Several design tradeoffs for the architecture were examined including the number of VLIW functional units and register file size. The architecture was implemented on an Altera Stratix II FPGA. The Stratix II device was selected because it offers a large number of high-speed DSP (digital signal processing) blocks that execute multiply-accumulate operations. Using the MediaBench benchmark suite, we tested our methodology and architecture to accelerate software. Our combined VLIW processor with hardware functions was compared to that of software executing on a RISC processor, specifically the soft core embedded NIOS II processor. For software kernels converted into hardware functions, we show a hardware performance multiplier of up to 230 times that of software with an average 63 times faster. For the entire application in which only a portion of the software is converted to hardware, the performance improvement is as much as 30X times faster than the nonaccelerated application, with a 12X improvement on average.
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