Signal processing

Synthetic Aperture Sonar (SAS) provides the best opportunity for side-looking sonar mounted on underwater platforms to achieve high-resolution images. However, SAS processing requires strict constraints on resolvable platform motion. The most common approach to estimate this motion is to use the Redundant Phase Center (RPC) technique. Here the ping interval is set, such that a portion of the sonar array overlaps as the sensor moves forward. The time delay between the pings received on these overlapping elements is estimated using cross-correlation. These time delays are then used to infer the pingto-ping vehicle motion. Given the stochastic nature of the operational environment, some level of decorrelation between these two signals is likely.
In this research, two iterative signal decomposition methods well suited for nonlinear and non-stationary signals, are investigated for their potential to improve the Time Delay Estimation (TDE). The first of this type, the Empirical Mode Decomposition (EMD) was introduced by Huang in the seminal paper, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis and is the foundation for the algorithms used in this research. This method decomposes a signal into a finite sequence of simple components termed Intrinsic Mode Functions (IMFs). The Iterative Filter (IF) approach, developed by Lin, Wang and Zhou, builds on the EMD framework. The sonar signals considered in this research are complex baseband signals. Both the IF and EMD algorithms were designed to decompose real signals. However, the IF variant, the Multivariate Fast Iterative Filtering (MFIF) Algorithm, developed by Cicone, and the EMD variant, the Fast and Adaptive Multivariate Empirical Mode Decomposition (FAMVEMD) algorithm, developed by Thirumalaisamy and Ansell, preserve both the magnitude and phase in the decomposition and hence were chosen for this analysis.

In the field of machine prognostics, vibration analysis is a proven method for
detecting and diagnosing bearing faults in rotating machines. One popular method
for interpreting vibration signals is envelope demodulation, which allows a technician
to clearly identify an impulsive fault source and its severity. However incipient faults -faults in early stages - are masked by in-band noise, which can make the associated impulses difficult to detect and interpret. In this thesis, Wavelet De-Noising (WDN) is implemented after envelope-demodulation to improve accuracy of bearing fault diagnostics. This contrasts the typical approach of de-noising as a preprocessing step.
When manually measuring time-domain impulse amplitudes, the algorithm
shows varying improvements in Signal-to-Noise Ratio (SNR) relative to background
vibrational noise. A frequency-domain measure of SNR agrees with this result.

An in situ acoustic measurement system was developed to estimate the compressional wave attenuation of marine sediments. The system uses acoustic probes to measure a wideband acoustic pulse traveling horizontally though various sediments. The system transmits a 20 millisecond frequency-modulated (FM) pulse swept from 3 to 50 kHz and match filters the received signals. A special ratio of data collected at two horizontal ranges from the source is used to estimate attenuation as a function of frequency. Data is collected with the in situ system and a chirp subbottom profiling sonar at two offshore sites to compare the attenuation of horizontally and vertically traveling waves in sediment. The collected data is also used to determine the feasibility of remotely estimating in situ attenuation using a chirp sonar. In situ and chirp sonar estimates agree and fall within the range of attenuation measurements made by other investigators in similar sediments.

Signal processing requires a steady flow of sampled data to be able to properly manipulate the signal to get the desired output. By using Asynchronous Transfer Mode (ATM) networks, it is possible to divide signal processing amongst a number of stations where each station can be specialized to a single function. Unfortunately, most commercially available ATM Network Interface Cards (NIC) only support message mode ATM Adaptation Layer 5 (AAL5) which is unsuitable to signal processing due to the delays of having to wait for an entire message to be formed prior to sending. It is shown that by using an ATM NIC using streaming mode AAL5, where cells are sent as soon as enough data to create an ATM cell of 48 bytes, leads to better quality signal processing. It is also shown that the message latency (time it takes for a message to traverse the network) is reduced by using streaming mode AAL5.

Glottal pulse models provide vocal tract excitation signals which are used in producing high quality speech. Most of the currently used glottal pulse models are obtained by concatenating a small number of parametric functions over the pitch period. In this thesis, a new glottal pulse model is proposed. It is an alternative approach, which is based on the projection of glottal volume velocity over multiresolution subspaces spanned by wavelets and scaling functions. A detailed multiresolution analysis of the glottal models is performed using the compactly supported orthogonal Daubechies wavelets. The wavelet representation has been tested for optimality in terms of the reconstruction error and the energy compactness of the coefficients. It is demonstrated that by choosing proper parameters of the wavelet representation, high compression ratios and low rms error can be achieved.

This thesis is concerned with the use of ultra-wideband radar detection specific to the following target and background considerations. (1) Statistical attributes of the RCS models of stealth-targets illuminated by ultra-wideband radars. (2) Analysis of radar echo signatures of low flying stealth-targets with a background of sea-clutter and illuminated by an ultra-wideband radar. (3) Analysis of impulse echoes from simple (planar) surface(s) coated with a radar absorbing material (RAM). The first problem refers to the elucidation of Swerling-Marcum type classifications of RCS fluctuation(s) to characterize the stochastical aspects of the echoes from stealth-targets illuminated by an impulse from an ultra-wideband radar. In the second analysis, performance of a radar receiver configuration, using the log-likelihood function of the signal received from a stealth target flying at low altitude over the sea-surface is predicted. The third effort addressed provides analytical representations in time-domain of echoes from planar surface(s) coated with RAM's for normal incidence of ultra-wideband short pulse illumination.

Most infrared based target-seeking systems operate as passive detectors of the target with no energy being required to be transmitted from the seeker in order to detect a target. Reticles are used commonly in these passive homing seekers to modulate the incoming radiation from the target. Since signals are invariably corrupted by noise, the analysis of signal-to-noise characteristics of a passive homing system is crucial in elucidating its performance towards successful homing on the target. The objective of this thesis refers to the analysis of a passive homing system which employs a frequency modulated reticle. Studies pertinent to the feasibility aspect of using electrochromic, nonmoving reticles also constitute a part of the present study. In essence, the effort addressed in this work are concerned with the performance analysis and feasibilities considerations in using nonmoving reticles in passive homing systems in lieu of conventional rotating reticles.

Buckingham et al. (Nature Vol. 356, p 327) first introduced the concept of acoustic imaging using ambient noise as a method for passively detecting objects in the ocean. Several analytical studies followed, and it was shown that a two dimensional acoustic image could be obtained based on this approach, and that at least 900 pixels are necessary to restitute the details of spherical objects placed in an underwater sound channel. The alternative approach described in this paper is based on a signal processing which uses the broadband nature of the ambient noise in the ocean, and therefore, optimizes the use of available sound energy scattered by the object. Images with thousands of pixels can be obtained using a relatively small number of transducers. This method has been validated using simple experiments in air, scaled to represent an ocean application, and results showing images of various objects will be presented.

A PC-based Expert System that uses symbolic manipulations and an inference engine rule-based system to solve direct and inverse kinematics of revolute-jointed manipulators of arbitrary configuration is presented and discussed. Similar applications in the areas of Discrete Signal Processing and Optimal Control are analyzed.

This thesis describes a software model of a Linear Predictive Coding (L.P.C.) that is written in the Ada language. The novel feature of this program is that it attempts to execute the maximum possible number of concurrent arithmetic operations in the L.P.C. algorithm. Each arithmetic operation is implemented by an active process which is the "task" construct in the Ada language. The computational part of the algorithm is implemented as a wavefront array of computing tasks. These computational arrays are driven by a driver task which coordinates the flow of data into and out of the computing surfaces. If the inter process communications time between tasks is small, then this model shows a potential for speed-up. If this be the case, one may conclude that this model is an appropriate implementation for a linear predictive coding.