Hossny El-Sherief

Navigation Satellite Selection Using Neural Networks

Abstract The application of neural networks to optimal satellite subset selection for navigation use is discussed. The methods presented in this paper are general enough to be applicable regardless of how many satellite signals are being processed by the receiver. The optimal satellite subset is chosen by minimizing a quantity known as Geometric Dilution of Precision (GDOP), which is given by the trace of the inverse of the measurement matrix. An artificial neural network learns the functional relationships between the entries of a measurement matrix and the eigenvalues of its inverse, and thus generates GDOP without inverting a matrix. Simulation results are given, and the computational benefit of neural network-based satellite selection is discussed.

Fuzzy logic for digital phase-locked loop filter design

The problem of robust phase-locked loop design has attracted attention for many years, particularly since the advent of the global positioning system. This paper proposes and demonstrates the use of a fuzzy PLL to estimate the time-varying phase of a sinusoidal signal. It is shown via simulation results that fuzzy PLLs offer performance comparable to analytically derived PLLs (e.g. Kalman filters and H/sub /spl infin estimators) when the phase exhibits high dynamics and high noise. The fuzzy PLL rules are optimized using a gradient descent method and a genetic algorithm. >

Hybrid Kalman/minimax filtering in phase-locked loops

Abstract A method of combining Kahnan filtering and minimax filtering is proposed and demonstrated in an application to phase-locked loop design. Kalman filtering suffers from a lack of robustness to departures from the assumed noise statistics. Minimax filtering, however, has the drawback of ignoring the engineers (admittedly incomplete) knowledge of the noise statistics. It is shown in this paper that hybrid Kalman/minimax filtering can provide the “best of both worlds” . Phase-locked loop filter design is used in this paper to demonstrate an application of hybrid estimation.

Fault-tolerant training for optimal interpolative nets

The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault-tolerant OI net is presented in a recursive formal, allowing for relatively short training times. A simulated fault-tolerant OI net is tested on a navigation satellite selection problem.

Design of global positioning system receivers for integrated inertial navigation systems

There is much interest in integrated navigation using the global positioning system and inertial measurement units (accelerometers and gyroscopes). The results reported in this paper quantify integrated navigation performance as a function of GPS receiver design parameters. The specific application considered is missile navigation.<<ETX>>

Fuzzy phase-locked loops

The problem of robust phase-locked loop (PLL) design has attracted attention for many years, particularly since the advent of the Global Positioning System. This paper proposes and demonstrates the use of a fuzzy PLL to estimate the time-varying phase of a sinusoidal signal. It is shown via simulation results that fuzzy PLLs offer performance comparable to analytically derived PLLs (e.g., Kalman filters and H/sub /spl infin estimators) when the phase exhibits high dynamics and high noise. The fuzzy PLL rules are optimized using a gradient descent method and a genetic algorithm.<<ETX>>

A Design Approach for a GPS User Segment for Aerospace Vehicles

As new applications for the use of the Global Positioning System (GPS) on aerospace vehicles emerge, more attention is being paid to the design of the user segment, which comprises the hardware and software employed by the user to obtain navigation information from GPS. The complexity of the design of the user segment, as well as the performance demanded of the components (such as the antenna), depends on user requirements such as total navigation accuracy. Other factors, for instance the expected satellite/vehicle geometry or the accuracy of an accompanying inertial navigation system, can also affect the user segment design. The interaction between these effects, the user requirements, and the user segment design is studied. Design curves are developed which allow quick trade studies to be performed.

Lyapunov stability analyses of digital phase-locked loops

This paper uses Lyapunov stability theory to analyze the stability properties of digital phase lock loops (DPLLs). As is the case with most stability analyses, this paper deals with the noisefree case. While practical analysis and simulation of a real system must include some noise model, a prerequisite for such analysis is the knowledge or assumption of the systems stability. The stability conditions derived in this paper are the same as those obtained elsewhere. The purpose of this paper is to expose Lyapunov theory as a viable option for DPLL design and analysis.<<ETX>>

Real-time navigation using the global positioning system

This paper presents the results of an investigation of the application of the Global Positioning System (GPS) to real-time integrated missile navigation. We present quantifiable measures of navigation accuracy as a function of GPS user segment parameters. These user segment parameters include antenna phase response accuracy, single versus dual frequency, and Kalman filter structure and size. We also formulate some new phase-locked loop (PLL) filter designs for application in GPS receivers, and demonstrate their superiority over more conventional filters. >

A fault tolerant optimal interpolative net

The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI Net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault tolerant OI Net is presented in a recursive format, allowing for relatively short training times. A simulated fault tolerant OI Net is tested on a navigation satellite selective problem.<<ETX>>