John A. Malas

F-22 radar development

The USAF F-22 Engineering, Manufacturing and Development (EMD) program has pushed the state of airborne fire control radar technology well beyond that found in todays fielded systems. Advancements in performance, reliability, and low observability have been realized in the design of the F-22s new APG-77 Radar through the implementation of active array technology, low noise receiver components, high density packaging, and advanced mode development. This paper will explore these advanced features from a systems engineering perspective by first introducing the F-22 Avionics System concept and then summarizing the hardware and software architecture which comprises the F-22 radar system. Unique F-22 advancements in survivability, lethality, reliability, and supportability are outlined briefly. Aircraft trade considerations that are unique to the implementation of an active array into a low radar cross section fighter application are discussed. Lessons learned in design trade areas such as power, cooling, packaging, weight, low radar cross section considerations, receiver design, antenna design, reliability, supportability, maintainability, and waveform design are reviewed. Implementation of this new capability would not be possible without the incorporation of new development processes and the transition of critical technology made available through the benefit of several long term joint government-industry technology base initiatives. Related details regarding solid state transmit/receive modules, electronically scanning arrays, and advanced radomes extending back to the Advanced Tactical Fighter Demonstration/Validation phase of the F-22 program are reviewed.

Guided execution of hybrid similarity-measures for registration of partially overlapped aerial imagery

This work presents a two-phase image registration technique utilizing a hybrid feature-based and an area-based similarity measure of partially overlapped aerial imagery in presence of affine translation and rotation transformations. The resulting selectively guided execution of similarity measures provides a reduction in search space, reducing the computational cost of the proposed algorithm. This multi-stage approach enhances the capability to perform image registration of low resolution imagery where scenes may have many structures but lack well defined structures for conventional feature extraction or lack to have enough variations in the intensity values to diminish statistical dependencies. The inherent statistical attributes of area-based methods are exploited through the sequential use of complex correlation and mutual information on physics-based features.

Radar signature analysis using information theory

The ability to make radar signature databases portable for use within similar sensor systems is critical to the affordability of airborne signature exploitation systems. The capability to hybridize measured and synthetic signature database components will maximize the impact of the investment required to build complex radar signature databases. Modal mutual information is developed as a measure of similarity to compare measured field data to modeled synthetic data. The approach is demonstrated using synthetic signature sets comprised of both ldquosimilar targetsrdquo and ldquodissimilar targetsrdquo.

Automatic target classification of slow moving ground targets in clutter

A new approach is proposed which will allow air-to-ground target classification of slow moving vehicles in clutter. A wideband space-time adaptive (STAP) filter architecture, based on subbanding, is developed and coupled with a one dimensional template-based minimum mean squared error (MMSE) classifier. The performance of this STAP/ATC (automatic target classification) algorithm is quantified using an extensive simulation. The level of residual clutter afforded by various filter configurations and the associated incremental improvement in ATC performance is quantified, revealing the potential for realizable hardware and software implementations to achieve acceptable ATC performance.

A robust multi-stage information-theoretic approach for registration of partially overlapped hyperspectral aerial imagery and evaluation in the presence of system noise

Image registration is employed in a wide spectrum of fields ranging from medical imaging to remote sensing. This work presents a multi-stage hyper spectral image registration technique utilizing a hybrid feature-based and an area-based similarity measure for partially overlapped aerial imagery. The resulting selectively guided execution of similarity measures provides a reduction in search space for area based methods, reducing the computational cost of the proposed algorithm. The inherent statistical attributes of area-based methods are exploited through the sequential use of correlation and mutual information on physics-based features. The algorithm is evaluated in the presence of sensor uncertainty and system noise. This is critical as data acquired from different sensors may have varying level of sensor uncertainty that may reduce the performance of the post processing techniques. The registration parameters are assessed in the presence of sensor noise, quantization noise, and impulse noise. The three noise sources are modeled and injected into the images to determine the algorithmic performance as a function of signal to noise ratio (SNR). A method to enhance aerial image registration with no well-defined feature structures.Information theoretic and correlation methodology for image low spatial resolution.A derived multi-stage approach resulting in search space and computational reduction.Analysis and modeling of the effect of sensor, quantization and impulsive noise.The method is also applied to heterogeneous hyper spectral bands registration.

An information theoretic metric for identifying optimum solution for normalized cross correlation based similarity measures

Similarity measures such as normalized cross correlation (NCC) are widely employed for applications such as pattern recognition and/or template matching which are commonly used in image registration. This approach, however, is not immune to noise variations present in the images especially in case where multiple bands of interest are dominated by both system and external noise present in the sensors field of view. Thus noise can influence the calculation of correlation coefficients and produce erroneous results during template matching. This work proposes a metric which identifies the best NCC coefficient value or values in case of a spectral data cube, for optimized application of similarity measures for template matching.

Information theoretic approach for template matching in registration of partially overlapped aerial imagery

Image registration is used in computer vision, medical imaging and remote sensing providing the ability to perform 3-D Reconstruction, Autonomous Navigation and Target Detection and Recognition Systems. Two of the more commonly used intensity based similarity measures in template matching for image registration are normalized cross correlation and mutual information. This works presents a novel information theoretic technique as a similarity measure for registration of partially overlapped aerial imagery. Furthermore, system level noise such as sensor noise, quantization noise, and impulse noise is modelled and injected into both the reference and unregistered images to evaluate the algorithmic performance in determining image orientation as a function of signal to noise ratio (SNR).

Establishing a common phase reference for comparing synthetic data to RF range measurements

Discrepancies can result when creating common data sets consisting of comparable synthetic and measured range complex scattered field samples when the phase references of each do not coincide. This can be especially true when using signal processing techniques to produce one dimensional (range profiles) or two dimensional (Synthetic Aperture Radar or SAR images) representations of the target scattered field where range bins and cross-range bins are formed. Range profiles and SAR images can be misaligned or have different bin amplitudes due to target scatterers in synthetic and measured scenarios shifted with respect to one another. Obtaining equivalent data samples requires attention to the measured data calibration process and phase reference location. This paper will address the common phase reference problem by an analysis of experimental data for specific targets and rotation system. Suggestions are provided for possible solutions to current challenges. The data analysis will include synthetic and measured range data comparisons, range calibration, and target position and range alignment processes using Theodolite laser measurements.

Optimized NCC-information theoretic metric for noisy wavelength band specific similarity measures

Image registration, in general, offers an increased field of view from an airborne sensor which provides an advantage in intelligence, surveillance and reconnaissance (ISR) applications for border security scenarios. Normalized Cross Correlation (NCC) is one of the most precise area based template matching technique employed for image registration. In an operational sensor the image acquired will generally contain both system and external noise present in the field of view of the sensor. This, in turn, implies that the NCC may not provide the optimum match for registration. In this paper we present a technique that identifies an optimized correlation coefficient value or a set of coefficient values in the multi-spectral and hyper-spectral cases. In this paper NCC is used in conjunction with information theoretic measures to determine an optimized match, when band varying noise is also present. In particular, the proposed approach is based on a hybrid and combined NCC and information theoretic measures, and does not necessarily use a threshold value to identify potential matches based on the similarity measures. This work also analyzes the effects of varying the template size and the template scene dynamics on template matching accuracy. Finally, the technique is applied to hyper-spectral images, and the band sensitive response for the NCC performance is also determined.

Uncertainty propagation and the Fano based information theoretic method

The Fano equality is joined with the data-processing inequality to develop a theory model for component level trade studies within radar signature exploitation systems. Entropy is used to represent propagating uncertainty within an information channel. Measures are developed to identify information flow bottlenecks within an information loss budget. The propagating effects of various sources of uncertainty on system performance are characterized.