In this paper we will present determination of refractive index profile of an ion exchanged planar waveguide using wedge technique. The sample preparation, data analysis and experimental results will be presented.
It is shown that when a part of a wave-front bears a sharp change in its phase, the Fresnel diffraction becomes noticeable. To change the phase sharply, one can reflect the wave-front from a step or transmit it through a transparent medium having a sharp change in its thickness or refractive index. The visibility of the corresponding diffraction fringes depends on the amount of phase change and can be varied from zero to one. Since the phase change can be accomplished by various means, the effect renders to measure phase change, refractive index change, displacement, and so on. Here, the change of visibility is the measurement criterion, therefore the fluctuations of the source intensity do not affect the measurement precision. In this paper Fresnel diffraction from one dimensional step, circular step, and single strip are studied, and some of its applications are briefly discussed.
While the laser radar systems have high performance at short ranges and low altitudes, the atmospheric effects have been the major constraints of detection and parameter estimation of laser pulses at long ranges and high altitudes. The turbulence which depends on different atmospheric states is hard to quantify due to the wavelength dependent effects of various conditions at different layers of the atmosphere. The turbulence may also be caused by interaction of the atmosphere with other objects, such as the vortex flow due to the aerodynamics of the air targets, or the nonlinear propagation characteristic of the high energy laser pulses. These adverse effects of the atmosphere have been limiting the usefulness of the laser radar systems for a wide range of applications. If the atmosphere is considered as a nonlinear media with nonuniform index of refraction, then it can be thought of as a nonlinear distributed lens under diffraction limited conditions. In this paper, a neural network modeling of the ionosphere layer is presented and the laser pulse is characterized by a set of input features. The transient CO2 laser pulses is simulated to transmit through the atmosphere to a satellite-borne receiver. The satellite receiver model is composed of three stages, i.e., the filtering and processing of the ionospheric propagated waveform, the envelope extraction and channel simulation, and the detection and parameter estimation. The received signal is then evaluated against the background noise through Monte Carlo simulations.
KEYWORDS: Neural networks, Radar, Sensors, Target detection, Signal to noise ratio, Signal processing, Statistical analysis, Signal detection, Polarimetry, Polarization
In this paper, we introduce a neural network (NN) architecture that utilizes nonparametric as well as the conventional parametric statistics. Use of the Wilcoxon two-sample test along with the classical model (e.g. Gaussian) parameters provide a qualitative as well as a quantitative representation of the target and the background. On an ordinal scale the radar returns from the target background are ranked according to a specified order and the neural network is trained with a qualitative factor for deviation from the normal distribution. In addition, the actual background distribution also depends on the type of the sensor as well as the wavelength of operation. Accordingly, the independence of the neural network training from the background noise and the clutter distribution provides a unified design approach for the microwave and the laser radar detection systems.
In recent years, parallel distributed processing has provided a new paradigm for algorithms, such as in missile guidance, which requires a high degree of computational efficiency as well as reliability and smaller size hardware. A problem of particular interest to the guidance literature is the closed-loop optical solutions that can be achieved on-board the missile. Furthermore, a desirable guidance scheme should be robust to low signal-to-noise conditions that generally arise in long-range applications. In this paper we shall present a neural network- based guidance scheme which provides a real-time optimal control on-board the missile with the inclusion of noise in the LOS angular rate data. The neural network is trained in an off-line session using optimal solutions obtained from an optimal control software resulting in a real- time closed-loop guidance method. The performance of the proposed scheme is then evaluated for different levels of SNR of the Line-Of-Sight (LOS) angular rate in a tail-chase engagement. In doing so, similar tests were conducted for the currently used closed-loop proportional navigation method and the potentially available technique of iterative optimal open-loop control with and without the presence of noise in the LOS angular rate. Although we did not include the noise in the missile/target dynamical model, the results indicate that the neural network-based scheme shows more robustness to low signal-to-noise situations as compared with traditional proportional navigation methods. This superiority is due, among other things, to the elimination of some of the restrictive, and in many cases unrealistic assumptions made in the derivation of most current guidance laws in use such as, for instance, unbounded control, simplified dynamics and/or aerodynamics, and non-maneuvering targets, to name a few.
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