With the plethora of information, there are many aspects to contested environments such as the protection of information, network privacy, and restricted observational and entry access. In this paper, we review and contrast the perspectives of challenges and opportunities for future developments in contested environments. The ability to operate in a contested environment would aid societal operations for highly congested areas with limited bandwidth such as transportation, the lack of communication and observations after a natural disaster, or planning for situations in which freedom of movement is restricted. Different perspectives were presented, but common themes included (1) Domain: targets and sensors, (2) network: communications, control, and social networks, and (3) user: human interaction and analytics. The paper serves as a summary and organization of the panel discussion as towards future concerns for research needs in contested environments.
Recent technology developments in digital radio, low-cost inertial navigation systems and unmanned air vehicle design are converging to enable and make practical several new radar sensing modes such as simultaneous SAR/GMTI from persistent staring-mode radar, 3D SAR from a single-pass, single phase center radar and wide-angle radar tracking of dismounts. One of the challenges for algorithm developers is a lack of high-quality target and clutter signature data from the new radar modes. AFRL's Sensor Directorate and SET Corporation are developing a compact, low-cost wide-angle radar test bed capable of simulating a variety of radar modes, including 3D SAR, SAR/GMTI from staring-mode radar and ultra-fine resolution range-Doppler. We provide an overview of the wide-angle radar test bed architecture, its modular design and our implementation approach. We then describe several non-conventional wide-angle radar sensor modes and outline a corresponding series of test bed data collection experiments that could be used to support the development of new tracking and recognition algorithms.
A feature based approach is taken to reduce the occurrence of false alarms in foliage penetrating, ultra-wideband, synthetic aperture radar data. A set of 'generic' features is defined based on target size, shape, and pixel intensity. A second set of features is defined that contains generic features combined with features based on scattering phenomenology. Each set is combined using a quadratic polynomial discriminant (QPD), and performance is characterized by generating a receiver operating characteristic (ROC) curve. Results show that the feature set containing phenomenological features improves performance against both broadside and end-on targets. Performance against end-on targets, however, is especially pronounced.
We present a novel content-adaptive multiresolution SAR image formation processing algorithm that incorporates dynamic, on-line detection algorithms into the image formation process. The idea is to vary image resolution locally depending on scene content, focusing the SAR imagery to fine resolution only in regions where the scene reflectivity varies rapidly, while forming the rest of the image at coarser resolution or with reduced fidelity. Our `decision-directed' SAR image formation algorithm may have applications in systems where on-board processing or datalink constraints limits the area coverage rates or resolution. We present examples of this multiresolution SAR processing on SAR imagery and show that compression rates on the order of 70:1 or more (i.e., 0.45 bits/pixel starting from 32 bits/complex sample, 16 bits/I, 16 bits/Q), can be obtained while still preserving coherent target signatures and with minor degradation in perceptual image quality.
We present a low-complexity model-based FOPEN target detection algorithm and discuss its potential application as a target screener within an end-to-end FOPEN SAR automatic target detection system. The algorithm uses multiple discriminants extracted over a local sliding window followed by a multivariate discrimination rule to perform target screening at the pixel level. We present detection performance results obtained against FOPEN SAR imagery and show that the multidiscriminant approach achieves better detection performance than a model-template matched-filter detection algorithm.
We describe a band selection process based on wavelet analysis of hyperspectral data which naturally decomposes the data into sub-bands. Wavelet analysis allows the control of the position, resolution, and envelope of the specific spectral sub-bands which will be selected. The sub-band sets are selected to maximize the Kullback-Liebler distance between specific classes of materials for a specific dimensionality contraint or discrimination performance goal. A sequential construction of the sub-band sets is used as an approximation to the global maximization operation over all possible sub-band sets. A max/min strategy is also introduced to provide a robust framework for sub-band selection when faced with multiple materials. We show band selection and material classification results of this technique applied to Fourier transform spectrometer data.
We present a detection concept for initial target screening based on features that are derived from a multiresolution decomposition of synthetic aperture radar (SAR) data. The physical motivation of the multiresolution feature-based approach is the exploitation of signature oscillations produced by the interference between prominant scatterers in cultural objects when resolution is varied. We develop a generalized likelihood ratio test detector which differentiates between first order autoregressive multiresolution processes attributed to different spatial areas. We then derive two special cases of this detector motivated by arguments regarding the clutter statistics. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution only.
Many synthetic aperture radar (SAR) image formation algorithms require the computation of a multidimensional Fourier transform of irregularly sampled or unequally spaced data samples. We apply a recently developed algorithm, the unequally spaced FFT (USFFT), to SAR image formation and compare its accuracy and complexity to a conventional algorithm. We find that the USFFT algorithm allows comparable accuracy to traditional approaches at a slightly reduced computational cost. We briefly discuss extensions of the USFFT algorithm to multiresolution SAR imaging.