Adaptive matched field processing in an uncertain propagation environment

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution January 1992Adaptive array processing algorithms have achieved widespread use because they are very effective at rejecting unwanted signals (i.e., controlling sidelobe levels) and in general have very good resolution (i.e., have narrow mainlobes). However, many adaptive high-resolution array processing algorithms suffer a significant degradation in performance in the presence of environmental mismatch. This sensitivity to environmental mismatch is of particular concern in problems such as long-range acoustic array processing in the ocean where the array processor's knowledge of the propagation characteristics of the ocean is imperfect. An Adaptive Minmax Matched Field Processor has been developed which combines adaptive matched field processing and minmax approximation techniques to achieve the effective interference rejection characteristic of adaptive processors while limiting the sensitivity of the processor to environmental mismatch. The derivation of the algorithm is carried out within the framework of minmax signal processing. The optimal array weights are those which minimize the maximum conditional mean squared estimation error at the output of a linear weight-and-sum beamformer. The error is conditioned on the propagation characteristics of the environment and the maximum is evaluated over the range of environmental conditions in which the processor is expected to operate. The theorems developed using this framework characterize the solutions to the minmax array weight problem, and relate the optimal minmax array weights to the solution to a particular type of Wiener filtering problem. This relationship makes possible the development of an efficient algorithm for calculating the optimal minmax array weights and the associated estimate of the signal power emitted by a source at the array focal point. An important feature of this algorithm is that it is guarenteed to converge to an exact solution for the array weights and estimated signal power in a finite number of iterations. The Adaptive Minmax Matched Field Processor can also be interpreted as a two-stage Minimum Variance Distortionless Response (MVDR) Matched Field Processor. The first stage of this processor generates an estimate of the replica vector of the signal emitted by a source at the array focal point, and the second stage is a traditional MVDR Matched Field Processor implemented using the estimate of the signal replica vector. Computer simulations using several environmental models and types of environmental uncertainty have shown that the resolution and interference rejection capability of the Adaptive Minmax Matched Field Processor is close to that of a traditional MVDR Matched Field Processor which has perfect knowledge of the characteristics of the propagation environment and far exceeds that of the Bartlett Matched Field Processor. In addition, the simulations show that the Adaptive Minmax Matched Field Processor is able to maintain it's accuracy, resolution and interference rejection capability when it's knowledge of the environment is only approximate, and is therefore much less sensitive to environmental mismatch than is the traditional MVDR Matched Field Processor.The National Science Foundation, the General Electric Foundation, the Office of Naval Research, the Defense Advanced Research Projects Agency, and the Woods Hole Oceanographic Institution

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research Grant N00014-91-J-1628Vertical Arrays for the Heard Island Experiment Award No. SC 48548Charles S. Draper Laboratories, Inc. Contract DL-H-418472Defense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489Rockwell Corporation Doctoral FellowshipMIT - Woods Hole Oceanographic Institution Joint ProgramDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-90-J-1109Lockheed Sanders, Inc./U.S. Navy - Office of Naval Research Contract N00014-91-C-0125U.S. Air Force - Office of Scientific Research Grant AFOSR-91-0034AT&T Laboratories Doctoral ProgramU.S. Navy - Office of Naval Research Grant N00014-91-J-1628General Electric Foundation Graduate Fellowship in Electrical EngineeringNational Science Foundation Grant MIP 87-14969National Science Foundation Graduate FellowshipCanada Natural Sciences and Engineering Research CouncilLockheed Sanders, Inc

Adaptive matched field processing in an uncertain propagation environment

Also issued as Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineeing and Woods Hole Oceanographic Institute, 1991.Includes bibliographical references (p. 171-173).Supported by DARPA. N00014-89-J-1489 Supported by NSF. MIP 87-14969 Supported by a General Electric Foundation Graduate Fellowship in Electrical Engineering, and a National Science Foudnation Graduate Fellowship.James C. Preisig

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction, reports on sixteen research projects and a list of publications.Bose CorporationMIT-Woods Hole Oceanographic Institution Joint Graduate Program in Oceanographic EngineeringAdvanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-93-1-0686Lockheed Sanders, Inc./U.S. Navy - Office of Naval Research Contract N00014-91-C-0125U.S. Air Force - Office of Scientific Research Grant AFOSR-91-0034AT&T Laboratories Doctoral Support ProgramAdvanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation FellowshipMaryland Procurement Office Contract MDA904-93-C-4180U.S. Navy - Office of Naval Research Grant N00014-91-J-162

A Novel Chirp Slope Keying Modulation Scheme for Underwater Communication

A digital modulation method using Chirp-Slope Keying (CSK) is developed for coherent underwater acoustic communications. Effective signal detection is a critical stage in the implementation of any communications system; we will see that CSK solves some significant challenges to reliable detection. This thesis is primarily based on analyzing the effectiveness of CSK through simulations using Matlab\u27s Simulink for underwater communications. The procedure begins with modulating a chirp\u27s slope by random binary data with a linear-down-slope chirp representing a 0, and a linear-up-slope chirp representing a 1. Each received symbol is demodulated by multiplying it with the exact linear-up-slope chirp and then integrating over a whole period (i.e., integrate and dump). This slope-detection technique reduces the need for the extensive recognition of the magnitude and/or the frequencies of the signal. Simulations demonstrate that CSK offers sturdy performance in the modeled ocean environment, even at very low signal-to-noise ratio (SNR). CSK is first tested using the fundamental communication channel, Additive White Gaussian Noise (AWGN) channel. Simulation results show excellent BER vs. SNR performance, implying CSK is a promising method. Further extensive analysis and simulations are performed to evaluate the quality of CSK in more realistic channels including Rayleigh amplitude fading channel and multipath

Development of Fuzzy Receiver for GSM Application

A channel equalizer is one of the most important subsystems in any cellular communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances maximum-aposteriori probability (MAP) receivers also called Bayesian receivers perform better. This thesis proposes a fuzzy receiver that implements MAP equalizer and provides a performance close to the optimal Bayesian receiver. Here Bit Error Rate (BER) has been used as the performance index. This thesis presents an investigation on design of fuzzy based receivers for GSM application. Extensive simulation studies which shows that the performance of the proposed receiver is close to optimal receiver for variety of channel conditions in different receiver speeds where channel suffers from Rayleigh fading. The proposed receiver also provides near optimal performance when channel suffers from nonlinearities

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters