Abstract-High-resolution parameter estimation techniques have recently been applied to jointly estimate multiple signal parameters. In this work, we consider the problem of determining the directions and center frequencies of a number of narrowband sources in a band of interest. We present a joint angle-frequency estimation method, based on the multidimensional ESPRIT algorithm. A perturbation error analysis gives bounds on the parameter estimates and provides optimal values for the temporal and spatial smoothing parameters. The analysis is shown to be consistent with simulation results.

In this paper, we present a unified approach to the (related) problems of recovering signal parameters from noisy observations and the identification of linear system model parameters fiom observed inputloutput signals, both using singular value decomposition (SVD) techniques. Both known and new SVD-based identification methods are classified in a subspace-oriented scheme. The singular value decomposition of a matrix constructedfr-om the observed signal data provides the key step to a robust discrimination between desired signals and disturbing signals in terms of signal and noise subspaces. The methods that are presented are contrasted by the way in which the subspaces are determined and how the signal or system model parameters are extracted fiom these subspaces. Typical examples such as the direction-of-arrival problem and system identification from inputloutput measurements are elaborated upon, and some extensions to time-varying systems are given.

In this paper we present an approach for quantitative analysis of application-specific dataflow architectures. The approach allows the designer to rate design alternatives in a quantitative 1: IntroductionIn the application domain of real-time video, the required processing power is in the order of hundreds of Risc-like operations per pixel, while the data rate of pixel streams is in the range of 10 to 100 Msamples per second. Consequently architectures are needed that perform billions of operations per second and have an internal communication bandwidth of Gbytes per second.In the application domain of real-time video we focus on dedicated architectures that support the concept of streams [17] and achieve the required performance by exploiting the inherent parallelism of the applications on domain-specific, coarse-grain processors, with limited internal flexibility (i.e. weakly programmable). An example of such a domain-specific architecture is given in figure 1. The architecture consists of different dedicated application-specific coarse-grain processors that operate independently of each other on data-streams. These streams are exchanged between the coarse-grain processors via a communication network and is controlled by some global controller. These kinds of architectures are typically embedded in a larger system that also contains memory and a general purpose processor, e.g. a Risc processor.In the design of these architectures, many choices have to be made. In this paper we present a simulation environment that aids the designer in making these choices based on quantitative information. In section 2 we present our problem statement. A solution approach is given in section 3. In section 4 we review related work of quantitative evaluation of design alternatives. The solution approach is further detailed for application-specific dataflow architectures in the following sections. In

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We present a methodology for the exploration of signal processing architectures at the system level. The methodology, named SPADE, provides a means to quickly build models of architectures at an abstract level, to easily map applications, modeled as Kahn Process Networks, onto these architecture models, and to analyze the performance of the resulting system by simulation. The methodology distinguishes between applications and architectures, and uses a trace-driven simulation technique for co-simulation of application models and architecture models. As a consequence, architecture models need not be functionally complete to be used for performance analysis while data dependent behavior is still handled correctly. We have used the methodology for the exploration of architectures and mappings of an MPEG-2 decoder application.

In this paper, we discuss a number of high-resolution direction finding methods for determining the two-dimensional directions of arrival of a number of plane waves, impinging on a sensor array. The array consists of triplets of sensors that are identical, as an extension of the 1D ESPRIT scenario to two dimensions. New algorithms are devised that yield the correct parameter pairs while avoiding an extensive search over the two separate one-dimensional parameter sets. EDICS: 5.2.2., 5.2.4. PRELIMINARIES The data modelConsider m sensor triplets, each composed of three identical sensors with unknown gain and phase patterns, which may vary from triplet to triplet. For every triplet, the displacement vectors d xy and d xz between its components are required to be the same (and, for convenience, are assumed to be orthogonal). This way, assigning the three sensor of each triplet to each of the sensor-arrays X, Y, Z, respectively, three identical although displaced arrays are obtained. This is a direct extension of the 1D ESPRIT scenario to two dimensions (see also [9]). Impinging on every array are d narrowband non-coherent signals s k t, The subspace approachMatrix polynomials of the form E − αF;α ∈ | C ; are called matrix pencils. Forming the pencilsit is seen that, in the noise free case, numbers λ = λ i and µ = µ j , i; j = 1; 2; : : : ; d , that reduce the rank of the pencil by one are equal to φ −1 i and θ −1 j respectively. With square data matrices, these rank reducing numbers are the generalized eigenvalues of the matrix pairs X ; Y and X ; Z.With noise present, however, a large number of samples are taken to improve accuracy. As a result, X, Y and Z will not be square. Noise will also increase the rank of the pencils, and this will introduce new rank reducing numbers. By computing a Total Least Squares projection of the data matrices (see e.g., [10] on

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