Electro-optics system engineers often require the 2D optical point spread function (PSF) to predict a sensor’s system level performance. The 2D PSF is used to compute metrics like detection range and signal to noise (SNR) at the target, or to develop a matched filter algorithm to improve detection performance. Lens designers generate the 2D optical PSF using optical design tools like CODE-V or Zemax. If the systems engineer needs the PSF data at different field angles, the lens designer has to generate those results again resulting in a cumbersome interaction between these two disciplines. The problem gets exacerbated when going from narrow field of view (NFOV) optics to wide field of view (WFOV) optics where there can be significant differences in the PSF between the on-axis and the off-axis case. This is particularly relevant in the cases of WFOV threat warning and situational awareness infrared sensors where the on-axis Airy-disk model of the PSF is no longer valid for the large off axis angles experienced in those sensors. This paper will present the Zernike Math Model (ZMM) approach to generate 2D optics PSF outside the lens design platform and move the analysis to a more common scientific/engineering tool that systems engineers use. Once an optical design is completed by the lens designer, its performance can be represented by a unique set of Zernike polynomial coefficients. The ZMM approach will only require the lens designer to provide this set of coefficients once, and the system engineer can then use these data to model the optical PSF performance on and off-axis, using standard engineering analysis tools such as MATLAB or Mathcad. Once the optical PSF is known, it can be transformed into the modulation transfer function (MTF) form and used to model sensor performance. Our approach simplifies the interaction between systems engineering and optical engineering disciplines in helping translate optical performance into system level sensor performance modeling.
The need for achieving high areal density in data storage devices that are smaller, cheaper and have faster access time has led to a number of new data storage and read-write technologies. Alternate substrate materials are being evaluated in order to achieve these goals. Plastic substrates offer exciting new possibilities in this area. The present study focuses on the understanding of dynamic performance of disks under loads typically seen in data storage applications. The role of substrate material damping in enhancing the dynamic performance is clearly established both analytically and experimentally. Simple analytical models were developed to predict the frequency and displacement of disks under dynamic loading (including the effects of material damping). The loss modulus of the material was identified as the main parameter that controls the damping behavior of the substrate material. The predicted response of the dynamic performance of disks was verified successfully through experiments. Results indicated that the analytical predictions agree well with experiments. The models developed in this study were used to develop materials with enhanced damping characteristics. These materials showed greatly improved vibration response and were comparable to the current substrate (aluminum) material.
Time critical search & rescue (s&r) operations often requires the detection of small objects in a vast area. While an airborne search can cover the area, no operational instrumental tools currently exist to actually replace the human operator. By producing the spectral signature of each pixel in a spatial image, multi- and hyper-spectral imaging (HSI) sensors provides a powerful capability for automated detection of subpixel size objects that are otherwise unresolved objects in conventional imagery. This property of HSI naturally lends itself to s&r operations. A lost hiker, skier, life raft adrift in the ocean, downed pilot or small aircraft wreckage targets, can be detected from relatively high altitude based on their unique spectral signatures. Moreover, the spectral information obtained allows the search craft to operate at substantially reduced spatial resolution thereby increasing scene coverage without a significant loss in detection sensitivity. The paper demonstrates the detection of objects as small as 1/10 of an image pixel from a sensor flying at over 6 km altitude. A subpixel object detection algorithm using HSI, based on local image statistics without reliance on spectral libraries is presented. The technique is amenable to fast signal processing and the requisite hardware can be built using inexpensive off the shelf technology. This makes HSI a highly attractive tool for real-time, autonomous instrument-based implementation. It can complement current visual-based s&r operations or emerging synthetic aperture radar sensors that are much more expensive.
Neural networks (NN) have been applied to hyperspectral image classification when traditional linear statistical classifiers have proven inadequate. The nonlinear and non- parametric properties of NN have often been cited for their apparent success. It has also been known that data preprocessing techniques such as principal component analysis (PCA) greatly improves classification accuracy. While PCA finds the axes of maximum variance in the data it does not guarantee increased separation between an arbitrary pair of classes. A transformation that is sensitive to class structure is obtained by solving the generalized eigenvalue problem of the amongst and within class covariance matrices of the data. Using this transformation, we demonstrate a case where the performance of linear statistical classifiers is comparable to that of NN classifiers for hyperspectral image classification.
The TIRIS is a pushbroom long wave infrared imaging spectrometer designed to operate in the 7.5 to 14.0 micrometer spectral region from an airborne platform, using uncooled optics. The focal plane array is a 64 by 20 extrinsic Si:As detector operating at 10 K, providing 64 spectral bands with 0.1 micrometer spectral resolution, and 20 spatial pixels with 3.6 milliradians spatial resolution. A custom linear variable filter mounted over the focal plane acts to suppress near field radiation from the uncooled external optics. This dual- use sensor is developed to demonstrate the detection of plumes of toxic gases and pollutants in a downlooking mode.
A novel feed forward neural network is used to classify hyperspectral data from the AVIRIS sensor. The network applies an alternating direction singular value decomposition technique to achieve rapid training times. Very few samples are required for training. 100 percent accurate classification is obtained using test data sets. The methodology combines this rapid training neural network together with data reduction and maximal feature separation techniques such as principal component analysis and simultaneous diagonalization of covariance matrices, for rapid and accurate classification of large hyperspectral images. The results are compared to those of standard statistical classifiers.
A methodology is described for an airborne, downlooking, longwave infrared imaging spectrometer based technique for the detection and tracking of plumes of toxic gases. Plumes can be observed in emission or absorption, depending on the thermal contrast between the vapor and the background terrain. While the sensor is currently undergoing laboratory calibration and characterization, a radiative exchange phenomenology model has been developed to predict sensor response and to facilitate the sensor design. An inverse problem model has also been developed to obtain plume parameters based on sensor measurements. These models, the sensors, and ongoing activities are described.
This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. We discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the intelligent missile seeker (IMS) demonstration project for real-time target/decoy discrimination, and the thermal infrared imaging spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.
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