In order to estimate the signal parameters accurately for mobile systems, it is necessary to estimate a system's propagation characteristics through a medium. Propagation analysis provides a good initial estimate of the signal characteristics. The ability to accurately predict radio-propagation behavior for wireless personal communication systems, such as cellular mobile radio, is becoming crucial to system design. Since site measurements are costly, propagation models have been developed as a suitable, low-cost, and convenient alternative. Channel modeling is required to predict path loss and to characterize the impulse response of the propagating channel. The path loss is associated with the design of base stations, as this tells us how much a transmitter needs to radiate to service a given region. Channel characterization, on the other hand, deals with the fidelity of the received signals, and has to do with the nature of the waveform received at a receiver. The objective here is to design a suitable receiver that will receive the transmitted signal, distorted.due to the multipath and dispersion effects of the channel, and that will decode the transmitted signal. An understanding of the various propagation models can. actually address both problems. This paper begins with a review of the information available on the various propagation models for both indoor and outdoor environments. .The existing models can be classified into two major classes: statistical models and site-specific models. The main characteristics of the radio channelsuch as path loss, fading, and time-delay spreadare discussed. Currently, a third alternative, which includes many new numerical methods, is being introduced to propagation prediction. The advantages and disadvantages of some of these methods are summarized. In'addition, an impulse-response characterization for the propagation path is also presented, including models for small-scale fading. Finally, it is shown that when two-way communication ports can be defined for a mobile system, it is possible to use reciprocity to focus the energy along the direction of an intended user without any explicit knowledge of the electromagnetic environment in which the system is operating, or knowledge of the spatial locations of the transmitter and the receiver.
A direct data domain (D 3) least-squares space-time adaptive processing (STAP) approach is presented for adaptively enhancing signals in a nonhomogeneous environment. The nonhomogeneous environment may consist of nonstationary clutter and could include blinking jammers. The D 3 approach is applied to data collected by an antenna array utilizing space and in time (Doppler) diversity. Conventional STAP generally utilizes statistical methodologies based on estimating a covariance matrix of the interference using data from secondary range cells. As the results are derived from ensemble averages, one filter (optimum in a probabilistic sense) is obtained for the operational environment, assumed to be wide sense stationary. However, for highly transient and inhomogeneous environments the conventional statistical methodology is difficult to apply. Hence, the D 3 method is presented as it analyzes the data in space and time over each range cell separately. The D 3 method is deterministic in approach. From an operational standpoint, an optimum method could be a combination of these two diverse methodologies. This paper represents several new D 3 approaches. One is based on the computation of a generalized eigenvalue for the signal strength and the others are based on the solution of a set of block Hankel matrix equations. Since the matrix of the system of equations to be solved has a block Hankel structure, the conjugate gradient method and the fast Fourier transform (FFT) can be utilized for efficient solution of the adaptive problem. Illustrative examples presented in this paper use measured data from the multichannel airborne radar measurements (MCARM) database to detect a Sabreliner in the presence of urban, land, and sea clutter. An added advantage for the D 3 method in solving real-life problems is that simultaneously many realizations can be obtained for the same solution for the signal of interest (SOI). The degree of variability amongst the different results can provide a confidence level of the processed results. The D 3 method may also be used for mobile communications.
In this paper, we propose a numerical method to obtain a solution for the time domain electric field integral equation (TD-EFIE) for arbitrary shaped conducting structures. This method does not utilize the customary marching-on in time (MOT) solution method often used to solve a hyperbolic partial differential equation. Instead we solve the wave equation by expressing the transient behaviors in terms of Laguerre polynomials. By using these causal orthonormal basis functions for the temporal variation, the time derivatives can be handled analytically. In order to solve the wave equation, we introduce two separate testing procedures, a spatial and temporal testing. By introducing first the Galerkin temporal testing procedure, the MOT procedure is replaced by a recursive relation between the different orders of the weighted Laguerre polynomials. The other novelty of this approach is that through the use of the entire domain Laguerre polynomials for the expansion of the temporal variation of the current, the spatial and the temporal variables can be separated and the temporal variables can be integrated out. For convenience, we use the Hertz vector as the unknown variable instead of the electric current density. To verify our method, we compare the results of a TD-EFIE and inverse Fourier transform of a frequency domain EFIE.Index Terms-Electric field integral equation (EFIE), Laguerre, time domain.
This paper investigates an electromagnetic preprocessing technique that transforms the voltages that are induced in a nonuniformly spaced array containing real antenna elements to a set of voltages that will be produced in a uniform linear virtual array (ULVA) containing omnidirectional isotropic point radiators. The objective here is that, in the processing methodology, we would like to include various electromagnetic effects like mutual coupling between the antenna elements, the presence of near‐field scatterers, and the platform effects on which the antenna array is mounted. The preprocessing is carried out using a least squares method, which generates a transformation matrix for the set of induced voltages in the real array. This transformation matrix, when applied to the actual measured voltages, yields an equivalent set of voltages that will be induced in the ULVA. Then, a direct data domain superresolution technique like the matrix pencil method is applied to the processed voltages to yield the DOA for the various signals of interest. Numerical results are presented to illustrate the efficiency and accuracy of this method. © 2002 John Wiley & Sons, Inc. Microwave Opt Technol Lett 32: 335–340, 2002.
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