A Radial Basis Function Neural Network Approach to Two-Color Infrared Missile Detection

Multicolor infrared imaging missile-warning systems require real-time detection techniques that can process the wide instantaneous field of regard of focal plane array sensors with a low false alarm rate. Current technology applies classical statistical methods to this problem and ignores neural network techniques. Thus the research reported here is novel in that it investigates the use of radial basis function (RBF) neural networks to detect sub-pixel missile signatures. An RBF neural network is designed and trained to detect targets in two-color infrared imagery using a recently developed regression tree algorithm. Features are calculated for 3 by 3 pixel sub-images in each color band and concatenated into a vector as input to the network. The RBF network responds with a value of unity to feature vectors representing missiles and with zero to vectors representing background. Images are thresholded prior to application to the trained RBF network to narrow the field of interest of the RBF network and increase missile detection speed. The RBF network-based technique then generates potential target locations and probabilities that the locations correspond to missiles. Results show that the RBF network-based technique operates in near teal-time and detects 100% of the missiles in data that was not used in training Receiver operating characteristic (ROC) curves show that overly high classification thresholds can exceed the RBF network response for a true missile and result in non-detection. However, these ROC curves also show that adaptive control of the classification threshold on the RBF network output can reduce the number of false alarms to zero

Experimental studies on shock wave interactions with flexible surfaces and development of flow diagnostic tools

Nowadays, light-weight composite materials have increasingly used for high-speed flight vehicles to improve their performance and efficiency. At supersonic speed, sonic fatigue, panel flutter, severe instabilities, and even catastrophic structural failure would occur due to the shock wave impingement on several flexible components of a given structural system either internally or externally. Therefore, investigation on shock wave interaction with flexible surfaces is crucial for the safety and performance of high-speed flight vehicles. This work aims to investigate the mechanism of shock wave interaction with flexible surfaces with and without the presence of the boundary layer. The first part involves the shock wave generated by supersonic starting jets interaction with flexible surfaces and the other one focuses on shock wave and boundary layer interaction (SBLI) over flexible surfaces. A novel miniature and cost-effective shock tube driven by detonation transmission tubing was designed and manufactured to simulate the supersonic starting jet and investigate the interaction of a supersonic starting jet with flexible surfaces. To investigate the characterization of this novel type shock tube, the pressure-time measurement in the driven section and the time-resolved shadowgraph were performed. The result shows that the flow structure from the open end of the shock tube driven by detonation transmission tubing agrees with that of conventional compressed-gas driven shock tubes. Moreover, this novel type of shock tube has good repeatability of less than 3% with a Mach number range of 1.29-1.58 when the weight of the NONEL explosive mixture varies from 3.6mg to 12.6mg. An unsteady background oriented schlieren (BOS) measurement system and a sprayable Polymer-Ceramic unsteady pressure sensitive paint (PC-PSP) system were developed. The preliminary BOS result in a supersonic wind tunnel shows that the sensitivity of the BOS system is good enough to visualize weak density variations caused by expansion waves, boundary layer, and weak oblique shocks. Additionally, compared with the commercial PC-PSP from Innovative Scientific Solutions Incorporated (ISSI), the in-house developed unsteady PSP system has higher pressure sensitivity, lower temperature sensitivity, and photo-degradation rate. To identify the shock movement, distortion and unsteadiness during the processes of the supersonic starting jet impingement and shock wave boundary layer interaction (SBLI) over flexible surfaces, an image processing scheme involving background subtraction in the frequency domain, filtering, resampling, edge detection, adaptive threshold, contour detection, feature extraction, and fitting was proposed and applied to process shadowgraph and schlieren sequences automatically. A large shadowgraph data set characterized by low signal to noise ratio (SNR) and small spatial resolution (312×260-pixel), was used to validate the proposed scheme. The result proves that the aforementioned image processing scheme can detect, track, localize, and fit shock waves in a subpixel accuracy. The mechanism of the interaction between the initial shock wave from a supersonic starting jet and flexible surfaces was investigated based on a square shock tube driven by detonation transmitting tube. Compared with that of the solid plate case, flexible surfaces can delay the shock reflection process because of the flexible panel deformation generated by the pressure difference between the top and the bottom. The delay time is around 8µs in the case of 0.1mm thick flexible surface, whereas it declines to around 4µs in the case of 0.3mm thick flexible surface because of the lower flexibility and deformation magnitude. However, interestingly, the propagation velocity of the reflected shock wave is basically the same for the solid plate and flexible panels, which means the flexible surface doesn’t reduce the strength of the reflection wave, although it delays its propagation. Also, there is not an apparent difference in the velocity of the reflected shock wave in the case of different incident shock Mach numbers when Ms varying from 1.22 to 1.54. These experimental results from this study are useful for validating numerical codes that are used for understanding fluid-structure interaction processes

An evaluation of pixel-based methods for the detection of floating objects on the sea surface

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperformthe more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene

Analysis of Infrared Signature Variation and Robust Filter-Based Supersonic Target Detection

The difficulty of small infrared target detection originates from the variations of infrared signatures. This paper presents the fundamental physics of infrared target variations and reports the results of variation analysis of infrared images acquired using a long wave infrared camera over a 24-hour period for different types of backgrounds. The detection parameters, such as signal-to-clutter ratio were compared according to the recording time, temperature and humidity. Through variation analysis, robust target detection methodologies are derived by controlling thresholds and designing a temporal contrast filter to achieve high detection rate and low false alarm rate. Experimental results validate the robustness of the proposed scheme by applying it to the synthetic and real infrared sequences

Automatic solar feature detection using image processing and pattern recognition techniques

The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system. For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In the applications of the solar filament detection, the Stabilized Inverse Diffusion Equation (SIDE) is used to enhance and sharpen filaments; a new method for automatic threshold selection is proposed to extract filaments from background; an SVM classifier with nine input features is used to differentiate between sunspots and filaments. Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are applied to determine filament properties. Furthermore, a filament matching method is proposed to detect filament disappearance. The automatic detection and characterization of flares and filaments have been successfully applied on Hα full-disk images that are continuously obtained at Big Bear Solar Observatory (BBSO). For automatically detecting and classifying CMEs, the image enhancement, segmentation, and pattern recognition techniques are applied to Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The processed LASCO and BBSO images are saved to file archive, and the physical properties of detected solar features such as intensity and speed are recorded in our database. Researchers are able to access the solar feature database and analyze the solar data efficiently and effectively. The detection and characterization system greatly improves the ability to monitor the evolution of solar events and has potential to be used to predict the space weather

Infrared Image Enhancement Using Wavelet Transform

In Infrared Image Enhancement using Wavelet Transform, two enhancement algorithms namely spatial and spatiotemporal homomorphic filtering (SHF and STHF) have been given for enhancement of the far infrared images based upon a far infrared imaging model. Although spatiotemporal homomorphic filtering may reduce the number of iterations greatly in comparison to spatial one for a similar degree of convergence by making explicit use of the additional information provided temporally, the enhanced results from SHF are in general better than those from STHF. In this dissertation work an additive wavelet transform will be proposed for enhancement and filtration of homomorphic infrared images. Keywords: Infrard Images, Additive Wavelet transform, Homomorphic Image Enhancement

Integrated Approach to Airborne Laser Communication

Lasers offer tremendous advantages over RF communication systems in bandwidth and security, due to their ultra-high frequency and narrow spatial beamwidth. Atmospheric turbulence causes severe received power variations and high bit error rates (BERs) in airborne laser communication. Airborne optical communication systems require special considerations in size, complexity, power, and weight. Conventional adaptive optics systems correct for the phase only and cannot correct for strong scintillation, but here the two transmission paths are separated sufficiently so that the strong scintillation is \averaged out by incoherently summing up the two beams in the receiver. This requisite separation distance is derived for multiple geometries, turbulence conditions, and turbulence effects. Integrating multiple techniques into a system alleviates the deleterious effects of turbulence without bulky adaptive optics systems. Wave optics simulations show multiple transmitters, receiver and transmitter trackers, and adaptive thresholding significantly reduce the BER (by over 10,000 times)

Adaptive detection and tracking using multimodal information

This thesis describes work on fusing data from multiple sources of information, and focuses on two main areas: adaptive detection and adaptive object tracking in automated vision scenarios. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources. Object tracking, a complementary technique to object detection, is also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, is proposed as a means to overcome the difficulties of traditional histogram tracking. As well as performing theoretical analysis of the proposed methods, specific example applications are given for both the detection and the tracking aspects, using thermal infrared and visible spectrum video data, as well as other multi-modal information sources

NASA Automated Rendezvous and Capture Review. A compilation of the abstracts

This document presents a compilation of abstracts of papers solicited for presentation at the NASA Automated Rendezvous and Capture Review held in Williamsburg, VA on November 19-21, 1991. Due to limitations on time and other considerations, not all abstracts could be presented during the review. The organizing committee determined however, that all abstracts merited availability to all participants and represented data and information reflecting state-of-the-art of this technology which should be captured in one document for future use and reference. The organizing committee appreciates the interest shown in the review and the response by the authors in submitting these abstracts