Navigation Using Signals of Opportunity in the AM Transmission Band

Maintaining a precision navigation solution both in a GPS hostile jamming environment and also in a GPS non-compatible terrain area is of great importance. To that end, this thesis evaluates the ability to navigate using signals from the AM band of the electromagnetic spectrum (520 to 1710 kHz). Navigation position estimates are done using multi-lateration techniques similar to GPS. However, pseudoranges are created using Time Difference of Arrival (TDOA) distances between a reference receiver and a mobile receiver, allowing the mobile receiver to obtain absolute position estimates over time. Four methods were developed for estimating the cross-correlation peak within a specified (sampled) portion of the cross-correlation data for use in TDOA measurement generation. To evaluate the performance of each peak locating method, a simulation environment was created to attempt to model real-world Amplitude Modulation (AM) signal characteristics. The model simulates AM transmission sources, signal receivers, propagation effects, inter-receiver frequency errors, noise addition, and multipath. When attempting to develop a data collection system for real-world signals, it became clear that selecting a proper analog front-end prior to digitization is pivotal in the success of the navigation system. Overall, this research shows that the use of AM signals for navigation appears promising. However, the characteristics of AM signal propagation, including multipath, need to be studied in greater detail to ensure the accuracy of the simulation models

Functional quantization

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 119-121).Data is rarely obtained for its own sake; oftentimes, it is a function of the data that we care about. Traditional data compression and quantization techniques, designed to recreate or approximate the data itself, gloss over this point. Are performance gains possible if source coding accounts for the user's function? How about when the encoders cannot themselves compute the function? We introduce the notion of functional quantization and use the tools of high-resolution analysis to get to the bottom of this question. Specifically, we consider real-valued raw data Xn/1 and scalar quantization of each component Xi of this data. First, under the constraints of fixed-rate quantization and variable-rate quantization, we obtain asymptotically optimal quantizer point densities and bit allocations. Introducing the notions of functional typicality and functional entropy, we then obtain asymptotically optimal block quantization schemes for each component. Next, we address the issue of non-monotonic functions by developing a model for high-resolution non-regular quantization. When these results are applied to several examples we observe striking improvements in performance.Finally, we answer three questions by means of the functional quantization framework: (1) Is there any benefit to allowing encoders to communicate with one another? (2) If transform coding is to be performed, how does a functional distortion measure influence the optimal transform? (3) What is the rate loss associated with a suboptimal quantizer design? In the process, we demonstrate how functional quantization can be a useful and intuitive alternative to more general information-theoretic techniques.by Vinith Misra.M.Eng