T. Lo

The use of fractals for modeling EM waves scattering from rough sea surface

A rough surface model based on fractal geometry is presented for the study of surface scattering. In particular, the Pierson-Moskowitz spectrum is incorporated into this model to represent a fully developed sea surface. The Kirchoff approximation is used to evaluate the scattered field from this rough surface. Some interconnection are found between the surface model developed and the statistical characteristics of the scattered field. These include: 1) the relationship between the surface correlation length and the surface fractal dimension; 2) the relationship between the shape parameter of the K-distribution and the surface fractal dimension; 3) the mean value of the scattered amplitude as a function of the surface fractal dimension; and 4) the effect of the incident angle on the scattered field.

Using linear and nonlinear predictors to improve the computational efficiency of the FD-TD algorithm

It is well known that the Finite-Difference Time-Domain (FD-TD) method requires long computation times for solving electromagnetic problems, especially for high-Q structures. The reason for this is because the algorithm is based on the leap-frog formula. In this paper, both linear and nonlinear predictors, which are widely used in signal processing, are introduced to reduce the computation time of the FD-TD algorithm. A short segment of an FD-TD record is used to train the predictor. As long as the predictor is set up properly, an accurate future realization can be obtained. We demonstrate, by means of numerical results, that the efficiency of the FD-TD method can be improved by up to 90%. With this result, the FD-TD algorithm becomes a much more attractive technique for solving electromagnetic problems. >

A new approach for estimating indoor radio propagation characteristics

Presents a new approach for estimating the propagation characteristics of indoor radio channels. The technique is based on the use of principal component analysis and the information theoretic criterion. It is shown, based on the simulation results, that the new technique can be used to overcome difficulties experienced by conventional methods and, as a result, is able to produce greater accuracy in its estimates of the channel parameters. The authors demonstrate the use of this technique by carrying out data analysis using measured indoor radio channel data. >

Radial basis function neural network for direction-of-arrivals estimation

The authors propose the use of a radial basis function (RBF) network for direction-of-arrival (DOA) estimation. The RBF network is used to approximate the functional relationship between sensor outputs and the direction of arrivals. Simulation results show that the new technique has a better performance in terms of estimation errors than the standard MUSIC algorithm.<<ETX>>

A spatial temporal dynamical model for multipath scattering from the sea

new method for modeling sea scattered signals is proposed in this paper. Instead of using a probabilistic model, a spatial temporal dynamical model is employed to model the sea scatter phenomenon. Our approach is empirical in the sense that a model is constructed based on experimental data. We extend the approach of using a temporal predictor for temporal dynamical system reconstruction to a spatial temporal predictor for reconstructing a spatial temporal one. The basic spatial temporal dynamical model used in this study is a couple map lattice (CML) rather than the conventional partial differential equation. The Radial Basis Function (RBF) neural network is incorporated into the CML to enhance the function approximation ability, and the autocorrelation function is used to determine the spatial effect across individual channel. An array antenna was used to collect real spatial temporal sea scattered data for this study. Preliminary results shows that the new model provides an accurate description of the sea scattered signals, and has the potential for signal processing applications.

An efficient decentralized multiradar multitarget tracker for air surveillance

We present an efficient multiradar multitarget tracking (MTT) algorithm for air surveillance. This tracker uses a multisensor track-to-track correlation method called the sequential minimum normalized distance nearest neighbor (SMNDNN) correlation with the majority decision making MDM/OR logic to solve the multisensor assignment problem. A sequential fuser based on the mean square error criterion is then used to fuse the tracks generated by the trackers. Real-life multiradar data collected from an air surveillance radar network located along the coastline of Canada is used to evaluate the effectiveness of this distributed tracker. Analysis shows that this tracker provides a reliable air surveillance picture.

Genetic algorithm for multiple target tracking data association

The heart of any tracking system is its data association algorithm where measurements, received as sensor returns, are assigned to a track, or rejected as clutter. In this paper, we investigate the use of genetic algorithms (GA) for the multiple target tracking data association problem. GA are search methods based on the mechanics of natural selection and genetics. They have been proven theoretically and empirically robust in complex space searches by the founder J. H. Holland. Contrary to most optimization techniques, which seek to improve performance toward the optimum, GA find near-optimal solutions through parallel searches in the solution space. We propose to optimize a simplified version of the neural energy function proposed by Sengupta and Iltis in their network implementation of the joint probability data association. We follow an identical approach by first implementing a GA for the travelling salesperson problem based on Hopfield and Tanks neural network research.

Low-angle tracking using a multifrequency sampled aperture radar

The improvements that can be achieved in low-angle radar by using a sampled aperture radar (SAMPAR) and a maximum likelihood (ML) algorithm are discussed. The SAMPAR system described is unique in that it has a wide-ranging multifrequency capability. The ML technique is also unique because its estimation is based on the use of a highly refined signal model. It is shown, by using both simulated data and real data, that this combination, i.e., a SAMPAR system and the modified ML algorithm, provides a multiple signal resolution that exceeds any reported in the open literature. The measured data used in this study were recorded using a 32-element sampled aperture antenna on an over-water path. >

Separation of a mixture of chaotic signals

A new technique is specifically devised to separate chaotic signals. In this approach, the authors reconstruct the trajectory of the mixture in an embedding space. The correlation characteristics of the trajectory in each dimension of the embedding space are then determined and used to reduce the separation problem into an eigen-problem that can be solved using linear algebra.

Artificial neural network for AOA estimation in a multipath environment over the sea

In this paper, we use a neural network to carry out angle-of-arrival (AOA) estimation in a multipath oceanic environment. In particular, the AOA problem is considered as a mapping from the space of AOA to the space of the sensor output. A neural network is used to determine the inverse mapping from the sensor output space to the space of AOA and this inversion is realized using a radial basis function (RBF) network. We will present the development of the RBF approach for AOA estimation. Simulations are carried out to understand the efficiency and performance of this method. Furthermore, real data are used to evaluate the RBF approach and the results demonstrate the robustness and effectiveness of this neural network method. >