Ronald D. Chaney

Coherent aspect-dependent SAR image formation

An adaptive image formation algorithm is proposed to exploit the aspect-angle dependence of man-made scatterers in foliage penetrating (FOPEN) synthetic aperture radar (SAR). Man-made scatterers often exhibit a strong dependence on the aspect angle between the orientation of the scatterer and the line of sight of the radar. More specifically, the return from a man-made target is greater when the target is oriented broadside with respect to the radar. Conventional SAR image formation processing assumes that backscatter is independent of the aspect angle; by relaxing this assumption, it is possible to reformulate the image formation process to improve the separability of man-made scatterers vs. natural clutter. We propose an image formation process that adapts the length and position of the aperture used during the cross-range compression stage. The algorithm identifies the locations that are likely to correspond to aspect- dependent scatterers. In the vicinity of such scatterers, the algorithm chooses the aperture to match the expected return from a man-made scatterer. Elsewhere, the algorithm uses the full aperture. The resulting imagery enhances man-made targets relative to the background clutter and facilitates improved detection performance.

On the performance of polarimetric target detection algorithms

The performance of six polarimetric target detection algorithms is analyzed. The detection performance of the optimal polarimetric detector (OPD), the identity-likelihood-ratio-test (ILRT), the polarimetric whitening filter (PWF), the single-polarimetric-channel detector, the span detector, and the power maximization synthesis (PMS) detector is compared. Results for both probabilistic and deterministic targets in the presence of complex-Gaussian clutter are presented. The results of these studies indicate that the PWF and the ILRT typically achieve near optimal performance. Each remaining detection algorithm typically yields performance that is degraded compared to the performance of the OPD, the PWF, and the ILRT.<<ETX>>

Hidden Markov models for threat prediction fusion

This work addresses the often neglected, but important problem of Level 3 fusion or threat refinement. This paper describes algorithms for threat prediction and test results from a prototype threat prediction fusion engine. The threat prediction fusion engine selectively models important aspects of the battlespace state using probability-based methods and information obtained from lower level fusion engines. Our approach uses hidden Markov models of a hierarchical threat state to find the most likely Course of Action (CoA) for the opposing forces. Decision tress use features derived from the CoA probabilities and other information to estimate the level of threat presented by the opposing forces. This approach provides the user with several measures associated with the level of threat, including: probability that the enemy is following a particular CoA, potential threat presented by the opposing forces, and likely time of the threat. The hierarchical approach used for modeling helps us efficiently represent the battlespace with a structure that permits scaling the models to larger scenarios without adding prohibitive computational costs or sacrificing model fidelity.

Multiple-frame motion estimation using simple region features

Simple region features consist of the salient parts of regions bounded by the zero-crossings of the Laplacian of Gaussian operator. Simple region features have two fundamental advantages over existing features, such as edges. First, there is no threshold involved in simple region feature extraction process; thus, the process is not sensitive to the specification of the threshold. Second, the features consist of regions, rather than points. Consequently, they have geometric attributes, such as area, shape, and orientation, that can be exploited by subsequent processes. In this paper, we use simple region features to estimate camera motion and depth over multiple frames. A tracking algorithm computes the correspondence of the features across the image sequence; recursive estimates are obtained for the optical flow of each of the features. The geometric properties of the features are used to determine a measure of the reliability of the correspondence mapping. A weighted least-squares error estimate is obtained for the camera motion and the depth of each feature; the weights for each error term are derived from the reliability measure of the correspondence mappings.

Analytical Representation of Contours

The interpretation and recognition of noisy contours, such as silhouettes, have proven to be difficult. One obstacle to the solution of these problems has been the lack of a robust representation for contours. In this paper, we present an analytical representation for contours. We introduce a smoothing criterion for the contour that optimizes the tradeoff between the complexity of the contour and proximity of the data points. We describe the computation of the contour representation, the computation of relevant properties of the contour, and the potential application of the representation and smoothing paradigm to contour interpretation and recognition.

Optimal polarimetric processing of SAR imagery

The Advanced Detection Technology Program has as one objective the application of fully polarimetric, high-resolution radar data to the detection, discrimination, and classification of stationary targets. In support of this program, the Advanced Detection Technology Sensor (ADTS), a fully polarimetric, 35-GHz SAR with 1 ft by 1 ft resolution was developed. In April of 1989, the ADTS gathered target and clutter data near Stockbridge, NY. Data from this collection is being used to investigate optimal polarimetric processing techniques. This paper summarizes the results of a recent study of an optimal polarimetric method for reducing speckle in SAR imagery.

Reduction of communication requirements for wide-area surveillance systems: multiscale clipping service

Future wide-area surveillance systems mounted on unmanned air vehicles (UAVs), such as Tier II+, will be capable of collecting SAR imagery at prodigious coverage rates (greater than 1.5 km2/sec, 1 m resolution). One important consideration for making such systems economically feasible is squeezing the large amount of SAR imagery through an available communications link. When the sensor platform is beyond the line of sight from the ground processing facility, it is highly desirable to transmit the imagery via a 1.5 megabit per second T1 satellite communications data link; it would be prohibitively expensive to ensure the availability of a wider bandwidth satcom link at any point on the globe. Use of a T1 link creates an onerous burden for SAR image compression algorithms. In the Tier II+) scenario, for example, use of a T1 link implies a compression rate of less than half a bit per pixel. In the longer term, systems will have greater coverage areas and higher resolution capabilities; the compression requirement will be substantially more severe. Conventional image compression algorithms are incapable of attaining the required compression while retaining the image fidelity required for processing at the ground station. Clipping service is a system concept that reduces communication requirements by using automatic target detection and recognition (ATD/R) algorithms onboard the UAV. The ATD/R algorithms identify regions of interest in the collected imagery. In the regions of interest, the imagery is transmitted with highest fidelity. In other areas, the imagery is transmitted with less fidelity, thereby reducing the communication bandwidth required. In this paper, we describe a multiple-resolution clipping service system. In this system, regions of interest are identified by ATD/R algorithms. The regions of interest are transmitted at the finest resolution achievable by the sensor; elsewhere, imagery is transmitted with reduced resolution and reduced data rate. The system utilizes a multiple-resolution image formation algorithm to reduce computational load: ATD/R algorithms are applied to coarse resolution imagery; the imagery is subsequently processed to fine resolution imagery only where targets are likely to be present. This reduces computation because only a fraction of the imagery is processed to fine resolution. In the paper, we determine the communication requirements for the multiscale system assuming Tier II+ parameters. We demonstrate that it is feasible to transmit Tier II+ imagery via a T1 data link using the clipping service concept.

Neuro-Dynamic Programming for adaptive fusion complexity control

The prodigious amount of information provided by surveillance system and other information sources has created unprecedented opportunities for achieving situation awareness. Because the missions users needs are constantly evolving, fusion control strategies must adapt to these changing requirements. However, the optimal control problem with the desired adaptive control capabilities is enormously complex. Therefore, we solve the adaptive fusion control problem approximately using a methodology called Neuro- Dynamic Programming (NDP) that combines elements of dynamic programming, simulation-based reinforcement learning, and statistical inference techniques. This work demonstrates the promise of using NDP for adaptive fusion control by sign it to allocate computational resources to Bayesian belief networks that use a variety of data types to track and identify clusters of vehicles. We have significantly extended previous work by using NDP to adapt the fusion process itself in addition to deciding which clusters should get their inference updated. Fusion within the Bayesian networks was adapted by using NDP to select the subset of available data to be used when updating the inference. We also extended previous work by using a dynamic scenario with moving vehicles for training and testing models.

Communication and sensor capability tradeoffs for wide-area surveillance systems

Future wide-area surveillance systems will collect prodigious amounts of SAR imagery. To make these systems economical, it is necessary to squeeze the large amount of SAR imagery through an available communications link. Even systems with moderate resolution and a single-polarization channel can yield stressing communications bottlenecks. Communication requirements become more acute as sensor capabilities increase; better resolution and additional polarization channels imply larger amounts of data to be transmitted. However, by applying automatic target detection and recognition (ATD/R) techniques to the imagery onboard the sensor platform, it is possible to ameliorate the communication requirements. The ATD/R algorithms identify regions of interest, where the imagery is transmitted at nominal fidelity and bit rate; the background is transmitted at lower fidelity and bit rate, thereby saving communication bandwidth. ATD/R algorithms are more effective with improved sensor capabilities, making it possible to reduce background clutter more effectively. In this paper, we show that when using this concept, sensors with improved capability require only marginally greater communication bandwidth. In some cases, sensors with improved capability require less communication bandwidth.

Multiscale segmentation of SAR imagery

In this paper, we propose an efficient multiscale approach for the segmentation of natural clutter, specifically grass and forest, in synthetic aperture radar (SAR) imagery. This method exploits the coherent nature of SAR sensors. In particular, we exploit the characteristic statistical differences in imagery of different clutter types, as a function of scale, due to radar speckle. We employ a recently introduced class of multiscale stochastic processes that provide a powerful framework for describing random processes and fields that evolve in scale. We build models representative of each category of clutter of interest (i.e., grass and forest), and use these models to segment the imagery into these two clutter classes. The scale- autoregressive nature of the models allows extremely efficient calculation of the relative likelihoods of different clutter classifications for windows of SAR imagery, and we use these likelihoods as the basis for classifying image pixels and for accurately estimating forest-grass boundaries. We evaluate the performance of the technique by testing it on 0.3 meter SAR data gathered with the Lincoln Laboratory millimeter-wave SAR.