This paper addresses the theories, experiments and real data of passive detection, classification and identification of "silent" targets in the illumination of ambient noise, a method known as "Acoustic Daylight." A great deal of work and sonar systems exist on active and passive sonar systems, but the principle of using ambient noise as the sole source of acoustic illumination was explored with limited success. This paper presents some of the successes using broadband signal processing and theory of target resonance as proposed in Uricks' text. In addition, the paper will present some of the results from experiments and simulations and Navy data of opportunities.
It has been reported that underwater target models, spheres and cylinders can be detected and classified in background acoustic noise. In this paper, the author presents his recent finding that underwater target is detectable in acoustic background noise in open waters. Using a resonance detection technique, G-Transform, the noise background of a number of AUTEC sample data files with mammal clicks were analyzed. From the noise backgrounds in these data files, a number of possible target signatures were observed. It suggests that real underwater targets may be detected and classified passively in background noise.
In this paper, the author presents recent findings of applying passive Broadband Bionic Sonar technique to same data files with marine mammal "clicks". Using a resonance detection technique, a number of data files with mammal clicks were analyzed. From these data files, many unique mammal "click" signatures were observed. These results seem to indicate that individual marine mammals can be classified and possibly identified.
In this paper, the author presents the recent results of passive Broadband Bionic Sonar System in the detection and identification of underwater targets in background noise, 'acoustic daylight'. Using a resonance detection technique, various underwater objects, cylinders and spheres of different sizes and different material compositions, were detected in acoustic backgrounds noise in Kaneohe Bay, Hawaii. In addition, bottomed and buried 3-inch diameter, stainless steel sphere targets were detected in background noise. The report presents the result of many passive broadband sonar experiments in acoustic background noise.
In this paper, the author discusses a Broadband Bionic Sonar Sensor System and a signal processing technique for detection and identification of underwater targets. This bionic sonar system with the resonance detection technique for detection and identification of underwater objects appears to mimic a dolphin's sensory system. The dolphin's sonar system transmits a very short broadband pulse. It detects and classifies a target by processing the modulation of the echo's (back scattering) broadband spectrum. This spectral modulation is directly related to the target's natural resonance. Using the G-Transform technique, the author has successfully showed that target resonance exists and it is unique to target size, shape, structure and material composition. Furthermore, this natural resonance exists in both (active sonar) acoustic echoes, back scattering and (passive sonar) acoustic scattering in acoustic noise background. Using trained neural networks, these targets' resonances/signatures can be correctly identified for the respective targets. It is conceivable that a broadband radar system, similar to a dolphin's sonar system, can be developed for targets and subsurface targets.
Supported by ONR for the past decades, researchers have succeeded in 'imaging' underwater with background noise, 'Acoustic Daylight.' In this paper, the authors will discuss their success in detecting underwater objects in background noise. This detection method is not an imaging technique. It is a broadband resonant scattering detection technique based on the theory of resonance and/or resonant scattering of the elastic sphere illuminated by 'Acoustic Daylight.' Using the resonant detection technique, it appears that underwater targets can be detected and identified in background noise, 'Acoustic Daylight.'
Supported by ONR, for the past two decades many researchers have been involved in 'imaging' underwater with background noise, 'Acoustic Daylight'. In the January 1996 issue of Scientific America, Dr. Buckingham and others published an article indicating that it is feasible to image underwater with background noise. Since then, many authors have published their successes in imaging underwater in Acoustic Daylight. In this paper, the author will discus his acoustic noise experiments in Kaneohe Bay with Dr. W. Au. The author presents his findings based on the theory of resonance and/or resonant scattering theory. Based on resonance technique, it appears that underwater targets can be detected and may be identified in background noise, Acoustic Daylight.
This paper presents the resonance or resonant scattering technique for detection and identification of underwater targets. Based on the resonance theory and the resonant scattering theory (RST), all underwater objects resonate at their natural frequencies when impinged by acoustic energy. These resonating natural frequencies appear as modulations on the frequency domain of the target echoes. Since these natural resonances are correlated and quasi-stationary signals originate from these underwater targets, the G-transform can efficiently be used for detecting the presence of a target by detecting the presence of some stationary signal. Furthermore, since these targets resonate at their natural frequencies based on target sizes, shapes and material compositions, the G-transform of these echoes can present these target resonances as unique signatures of each target. These unique signatures can then be used for target identification with trained neural networks.
There is no question to most that Gauss developed the concept of least-square estimation which was stimulated by his astronomical studies. This concept was discribed in Gausss book, Theoria Mows. This contribution and insight provided by Gauss has inspired many researchers in estimation theory over the past 200 years. These developments include the Weiner Filter, Kalman Filter, Stochastic Estimation, Bayesian Estimation, Maximu m Likehood Estimation, Auto-Regression and the Robust Filtering, just to name a few. However, during the recent decades, the need for detection and estimation of unknown signal in unknown noise background necessitated the development of correlation techniques for detection ( many correlation techniques were developed for identification). The problems in detection of unknown signals in unknown noise are common in anti-submarine warfare (ASW), automatic target recognition (ATR) and in Infrared search ands Tracking (IRST) of IR images and ocean environment. Author's research in target detection in JR images and ocean environments let to his development of the "Correlation Filter". Correlation Filter became a part of his doctoral dissertation on a Generalized Filter where he has shown that all filters, Weiner, Kalman and Correlation Filters, are related through a "Constrained Gain Matrix" and that the Correlation Filter is a special case of the Weiner Filter, reference 2. This paper presents the derivation of the Correlation Filter for detection and estimation of unknown signals in unknown noise backgrounds and some applications. Reference 1 included two algorithms of his classified DoD applications.
A variety of experimental results indicate that Dolphins possess a unique and highly sophisticated sonar system. In addition, this sonar system is highly adaptive in detecting, discriminating and recognizing objects in highly reverberating and noisy environments. This paper presents possibly a new technique for target detection and recognition using the G- Transform and a new approach based on Resonance and Resonant Scattering Theory. These results show that this approach and signal processing technique used with neural networks may be useful in detection and identification of buried mine and minelike targets.
This study was motivated by the infrared search and tracking (IRST) project. The investigation seeks to develop a technique that could detect the presence of a moving target in a cloud cluttered environment. Particularly, the signals, noise and clutters are unknown to the system. Thus, the correlation technique for image processing was developed, demonstrating its ability to detect moving targets of one pixel in size such as missiles and planes. A real-time image processor using this correlation technique was implemented. A Panoramic Imaging System, a 512 by 480 image processor at 30 frames per second was demonstrated. The demonstrated imaging system was operating at 120 mops (million operations per second) using an assembly- line processor architecture. The successful investigation of the correlation technique for image processing led to the developments of a correlation filter and the inspiration to develop the generalized filter. From the investigation, the author found that the Kalman filter, the Weiner filter and the correlation filter are special cases of a generalized filter. These filters can be related through a cost function in the constrained gain matrix of a generalized filter. However, in developing the correlation filter and the real-time imager, the correlation filter was observed to be a very effective noise and clutter rejecter and yet a very powerful detector. The filter was successfully applied to detection of pixel sized targets in noisy and cluttered IR images. Also it has been successfully applied to detection of intruders in cluttered, trees and bushes, video and IR images in security systems. This paper presents the derivation of the correlation filter for detection and estimation of unknown signals in unknown noise. Several noise rejection and cluttered rejection examples are presented.
A variety of experimental results indicate that dolphins possess a unique and sophisticated sonar system. In addition, this sonar system is highly adaptive in detecting, discriminating, and recoginizing objects in highly reverberating and noisy environments. In this paper a new approach using resonance scattering theory in target detection and recognition is presented. The results seems to imply that this approach may be useful in minelike target detection and identification.
A variety of experimental results indicate that Dolphins possess a unique and sophisticated sonar system. In addition, this sonar system is highly adaptive in detecting, discriminating and recognizing sonar targets in highly reverberating and noisy environments. In this paper a new approach using Resonance Scattering Theory in target detection and recognition is presented. The results seems to imply that this approach may be useful in shallow water target detection and identification.
There is no question that Gauss developed the concept of least-square estimation which was stimulated by his astronomical studies. This concept was discribed in Gauss's book, Tlieoria Motus This contribution and insight provided by Gauss has inspired many researchers in estimation theory over the past 200 years. These developments include the Weiner Filter, Kalman Filter. Stochastic Estimation, Bayesian Estimation. Maximum Likehood Estimation, Auto-Regression and the Robust Filtering, just to name a few. However. during the recent decades, the need for detection and estimation of unknown signal in unknown noise background necessitated the development of correlation techniques for detection ( many correlation techniques were developed for identification). The problems in detection of unknown signals in unknown noise are common in ASW, ATR and in IRST images and ocean environment. Author's research in target detection in IR images and ocean environments let to his development of the "Correlation Filter". Correlation Filter became a part of his doctoral dissertation on a Generalized Filter where he has shown that all filters, Weiner, Kalman and Correlation Filters, are related through a "Constrained Gain Matrix" and that the Correlation Filter is a special case of the Weiner Filter, reference 2. This paper presents the derivation of the Correlation Filter for detection and estimation of unknown signals in unknown noise backgrounds and some applications. Reference I included two algorithms of his classified DoD applications. Since the paper has been selected for poster presentation, many photographs of the results of applications for this paper will be presented at the poster session.
Frequently, multipoint target tracking is achieved using a Kalman Filter or other means. Numerous papers have been published over the past decades on tracking of dynamic systems such as ships, planes, artillery shells, and control processes with Kalman Filters, particularly, when the mathematical equations of motion describing the dynamic system are available. Then, target tracking is a fairly straight forward procedure. In this paper, a back propagation neural network is successfully `trained' for tracking an artillery shell. It is a predictive neural network because its outputs are the future positions of the artillery shell.
In this paper, two back propagation neural networks were trained to recognize four hollow cylinders. These cylinders are of the same size and thickness, but made of different materials. Two neural networks were used in this experiment. In case one a single hidden layer network was used, while in case two a hidden layer neural network was used. The acoustic data from these experiments was taken from references 3 and 7. The data are acoustic echoes of hollow aluminum, bronze, glass, and steel cylinders. The results seem to indicate that characteristics of the cylinders are more observable in the modulations of the frequency spectrum than in the time and spectral signals. The results show that a two hidden layer neural network can improve identification rate of a target.
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