Patrick C. Yip

On information theoretic criteria for determining the number of signals in high resolution array processing

An important problem in high-resolution array processing is the determination of the number of signals arriving at the array. Information theoretic criteria provide a means to achieve this. Two commonly used criteria are the Akaike information criterion (AIC) and minimum descriptive length (MDL) criterion. While the AIC tends to overestimate even at a high signal-to-noise ratio (SNR), the MDL criterion tends to underestimate at low or moderate SNR. By excluding irrelevant parameters, a new log likelihood function has been chosen. Utilizing this new log likelihood function gives a set of more accurate estimates of the eigenvalues and in the establishment of modified information theoretic criteria which moderate the performance of the AIC and the MDL criterion. Computer simulations confirm that the modified criteria have superior performance. >

An exact maximum likelihood registration algorithm for data fusion

Data fusion is a process dealing with the association, correlation, and combination of data and information from multiple sources to achieve refined position and identity estimates. We consider the registration problem, which is a prerequisite process of a data fusion system to accurately estimate and correct systematic errors. An exact maximum likelihood (EML) algorithm for registration is presented. The algorithm is implemented using a recursive two-step optimization that involves a modified Gauss-Newton procedure to ensure fast convergence. Statistical performance of the algorithm is also investigated, including its consistency and efficiency discussions. In particular, the explicit formulas for both the asymptotic covariance and the Cramer-Rao bound (CRB) are derived. Finally, simulated and real-life multiple radar data are used to evaluate the performance of the proposed algorithm.

Statistical analysis of the performance of information theoretic criteria in the detection of the number of signals in array processing

The performances of the Akaike (1974) information criterion and the minimum descriptive length criterion methods are examined. The events which lead to erroneous decisions are considered, and, on the basis of these events, the probabilities of error for the two criteria are derived. The probabilities of the first two events are derived based on the asymptotic distribution of the sample eigenvalues of an estimated Hermitian matrix. It is further shown that the probabilities of missing and false alarm for these two criteria can be evaluated to a close approximation. Although the derivation of the probabilities of error is based on an asymptotic analysis, the results are confirmed to be in very close agreement with computer simulation results. >

A Fast Computational Algorithm for the Discrete Sine Transform

A sparse-matrix factorization is developed for the discrete sine transform (DST). This factorization has a recursive structure and leads directly to an efficient algorithm for implementing the DST, a feature most desirable and very similar ot that of the DCT. This algorithm requires fewer arithmetic operations compared to that for the discrete cosine transform (DCT).

Neural network forecasting of quarterly accounting earnings

Abstract This study uses an artificial neural network model to forecast quarterly accounting earnings for a sample of 296 corporations trading on the New York stock exchange. The resulting forecast errors are shown to be significantly larger (smaller) than those generated by the parsimonious Brown-Rozeff and Griffin-Watts (Foster) linear time series models, bringing into question the potential usefulness of neural network models in forecasting quarterly accounting earnings. This study confirms the conjecture by Chatfield and Hill et al. that neural network models are context sensitive. In particular, this study shows that neural network models are not necessarily superior to linear time series models even when the data are financial, seasonal and non-linear.

On the shift property of DCT's and DST's

The relationship between the family of DCTs and DSTs of original sequence and shifted sequences is developed. While these properties are not as simple as the case for the DFT, they are still useful for processing long streams of data sequences where time-varying filtering is required.

Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm

We propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model is assumed for the target motion, and the multiple target states (MTSs) are defined to describe the states of the target motion, The locally linear model is shown to be strongly locally observable almost everywhere. The approach is to estimate the initial MTS by maximizing the likelihood function of the array data. The tracking is implemented by prediction through the target motion dynamics using the initial MTS estimate. By incorporating the target motion dynamics, the algorithm is able to eliminate the spread spectrum effects due to target motion. A modified Newton-type algorithm is also presented, which ensures fast convergence of the algorithm. Finally, numerical simulations are included to show the effectiveness of the proposed algorithm.

Fast decimation-in-time algorithms for a family of discrete sine and cosine transforms

Fast decimation-in-time (DIT) algorithms for the various discrete cosine transforms (DCT) and discrete sine transforms (DST) are systematically developed, based on a radix-2 factorization of the transformation matrix. The results indicate these to be attractive alternatives to existing algorithms in terms of computational complexity and structural simplicity.

CHAPTER 1 – Discrete Cosine and Sine Transforms

This chapter provides an overview of this book. The book presents the complete set of discrete cosine transforms (DCTs) and discrete sine transforms (DSTs) constituting the entire class of discrete sinusoidal unitary transforms, including their definitions, general mathematical properties, relations to the Karhunen-Loeve transform (KLT), with the emphasis on fast algorithms and integer approximations for their efficient implementations in the integer domain. The book covers various latest developments in DCTs and DSTs in a unified way, and it is essentially a detailed excursion on orthogonal/orthonormal DCT and DST matrices, their matrix factorizations, and integer approximations. For the DCT and DST to be viable, feasible, and practical, the fast algorithms are essential for their efficient implementation in terms of reduced memory, implementation complexity, and recursivity. Extensive definitions, principles, properties, signal flow graphs, derivations, proofs, and examples are provided throughout the book for proper understanding of the strengths and shortcomings of the spectrum of cosine/sine transforms and their application in diverse disciplines.

The decimation-in-frequency algorithms for a family of discrete sine and cosine transforms

In this paper we present results in the development of decimation-in-frequency algorithms for a family of discrete sine and cosine transforms. They are closely related to the decimation-in-time algorithms developed by Yip and Rao [1]. The complexity of the algorithms was examined through the number of multiplications and additions as well as the number of different constants required in the transforms. It was found that the decimation-in-frequency approach provides a viable alternative to other fast algorithms for the discrete sine and cosine transforms. In particular, the recursive and modular structure of the algorithms lends itself readily to possible hardware realization.