Yanting Dong

Ultrawide-band synthetic aperture radar for detection of unexploded ordnance: modeling and measurements

Electromagnetic (EM) scattering from subsurface unexploded ordnance (UXO) is investigated both theoretically and experimentally. Three EM models are considered: the multilevel fast multipole algorithm (MLFMA), the method of moments (MoM), and physical optics (PO). The relative accuracy of these models is compared for several scattering scenarios. Moreover, the model results are compared to data measured by an experimental synthetic aperture radar (SAR) system, SAR images have been generated for subsurface UXO targets, in particular 155-mm shells. We compare SAR images from the measured data with theoretical images produced by the MoM and PO simulations, using a standard back-projection imaging technique. In addition to such comparisons with measurement, we consider additional buried-UXO scattering scenarios to better understand the underlying wave phenomenology.

Multi-aspect detection of surface and shallow-buried unexploded ordnance via ultra-wideband synthetic aperture radar

An ultra-wideband (UWB) synthetic aperture radar (SAR) system is investigated for the detection of former bombing ranges, littered by unexploded ordnance (UXO). The objective is detection of a high enough percentage of surface and shallow-buried UXO, with a low enough false-alarm rate, such that a former range can be detected. The physics of UWB SAR scattering is exploited in the context of a hidden Markov model (HMM), which explicitly accounts for the multiple aspects at which a SAR system views a given target. The HMM is trained on computed data, using SAR imagery synthesized via a validated physical-optics solution. The performance of the HMM is demonstrated by performing testing on measured UWB SAR data for many surface and shallow UXO buried in soil in the vicinity of naturally occurring clutter.

Wide-area detection of land mines and unexploded ordnance

Advanced electromagnetic modelling tools are discussed, focused on sensing surface and buried land mines and unexploded ordnance, situated in a realistic soil environment. The results from these forward models are used to process scattered-field data, for target detection and identification. We address sensors directed toward the wide-area-search problem, for which one is interested in detecting a former mine field or bombing range. For this problem class we process data measured from an actual airborne radar system. Signal-processing algorithms applied include Bayesian processing and a physics-based hidden Markov model.

Rate-distortion analysis of discrete-HMM pose estimation via multiaspect scattering data

We consider the problem of estimating the pose of a target based on a sequence of scattered waveforms measured at multiple target-sensor orientations. Using a hidden Markov model (HMM) representation of the scattered-waveform sequence, pose estimation reduces to estimating the underlying HMM states from a sequence of observations. It is assumed that each scattered waveform must be quantized via an encoding procedure. A distortion D is defined as the error in estimating the underlying HMM states, and the rate R represents the size of the discrete-HMM codebook. Rate-distortion theory is applied to define the minimum rate required to achieve a desired distortion, denoted as R(D). After deriving the rate-distortion function R(D), we demonstrate that discrete-HMM performance based on Lloyd encoding is far from this bound. Performance is improved via block coding, based on Bayes VQ. Results are presented for a canonical HMM problem, and then for multiaspect acoustic scattering from underwater elastic targets. Although the examples presented here are for multiaspect scattering and pose estimation, the results are of general applicability to discrete-HMM state estimation.

Rate-distortion bound for joint compression and classification with application to multiaspect scattering

Rate-distortion analysis is applied to the problem of joint compression and classification. A Lagrangian distortion measure is used to consider both the Euclidean error in reconstructing the original data as well as the classification performance. The bound is calculated based on an alternating-minimization procedure, representing an extension of the Blahut-Arimoto algorithm. This rate-distortion framework is then applied to a joint compression and target-orientation estimation problem, based on a sequence of scattered waveforms measured at multiple target-sensor orientations. A hidden Markov model-Markov model (HMM-MM) is used as the statistical description for the source, here representative of multiaspect scattering data. Target-orientation estimation reduces to assessing the underlying HMM states from a sequence of observations. After deriving the rate-distortion function, we demonstrate that discrete HMM performance based on Lloyd encoding is far from this bound. Performance is improved via block coding, based on Bayes vector quantization. Results are presented for multiaspect acoustic scattering from an underwater elastic target, using measured and synthesized data.

Quantization of multiaspect scattering data: target classification and pose estimation

In many sensing scenarios, the observed scattered waveforms must be quantized for subsequent transmission over a communication channel. Rate-distortion theory plays an important role in defining the bit rate required to achieve a desired distortion. The distortion is typically defined in the context of signal reconstruction, with the goal of achieving high-fidelity synthesis of the compressed data. For sensing applications, however, the objective is often not simply signal reconstruction but classification performance as well. Other related metrics include target-pose estimation. We consider multiaspect wave scattering, in which classification and pose estimation are performed based on the quantized scattering data. Moreover, rate-distortion theory is employed to place bounds on pose-estimation performance when both the target identity and pose are unknown a priori. It is demonstrated that block-coding with Bayes-VQ may yield performance approaching the bound. Example results are presented for measured acoustic waveforms scattered from underwater elastic targets.

Rate-distortion bound for joint compression and classification

Rate-distortion analysis is applied to the problem of joint compression and classification. A Lagrangian distortion measure is used to consider both the Euclidean error in reconstructing the original data as well as the classification performance. The bound is calculated based on an alternating-minimization procedure, representing an extension of the Blahut-Arimoto algorithm. This rate-distortion framework is then applied to a joint compression and target-orientation estimation problem, based on a sequence of scattered waveforms measured at multiple target-sensor orientations. A hidden Markov model-Markov model (HMM-MM) is used as the statistical description for the source, here representative of multiaspect scattering data. Target-orientation estimation reduces to assessing the underlying HMM states from a sequence of observations. After deriving the rate-distortion function, we demonstrate that discrete HMM performance based on Lloyd encoding is far from this bound. Performance is improved via block coding, based on Bayes vector quantization. Results are presented for multiaspect acoustic scattering from an underwater elastic target, using measured and synthesized data.Summary form only given. Rate-distortion theory is applied to the problem of joint compression and classification. A Lagrangian distortion measure is used to consider both the Euclidean error in reconstructing the original data as well as the classification performance. The bound is calculated based on an alternating-minimization procedure, representing an extension of the Blahut-Arimoto algorithm. A hidden Markov model (HMM) source was considered as an example application and the objective is to quantize the source outputs and estimate the underlying HMM state sequence. Bounds on the minimum rate are required was presented to achieve desired average distortion on signal reconstruction and state-estimation accuracy.

Rate distortion analysis of pose estimation via Hidden Markov Model

A Hidden Markov Model (HMM) provides an efficient way to model multi-aspect measurements, with each state representing a contiguous set of target orientations. The state sequence estimated by the Viterbi algorithm gives a good estimation of the target pose. In this paper, rate distortion theory is used to develop a bound for the state sequence (pose) estimated by a discrete HMM. A model for the classification process is detailed, facilitating the application of rate distortion theory. Here the rate is defined as the number of codes used in a discrete HMM, and the distortion is the probability of error in estimating the state sequence. The rate distortion function is calculated for an underwater acoustic target using the Blahut algorithm. The performance of a discrete HMM with a different number of codes is compared with the bound and found to be far from optimal. Possible approaches to improve the performance are discussed.

Detection of above-ground and subsurface unexploded ordnance using ultrawideband (UWB) synthetic aperture radar (SAR) and electromagnetic modeling tools

Recent development of wideband, high-resolution SAR technology has shown that detecting buried targets over large open areas may be possible. Ground clutter and soil type are tow limiting factor influencing the practicality of using wideband SAR for wide-area target detection. In particular, the presence of strong ground clutter because of the unevenness, roughness or inconsistency of the soil itself may limit the radars capability to resolve the target from the clutter. Likewise, the soil material properties can also play a major tole. The incident wave may experience significant attenuation as the wave penetrates lossy soil. In an attempt to more fully characterize this problem, fully polarimetric ultra-wideband measurements have been taken by the US Army Research Laboratorys SAR at test sites in Yuma, Arizona, and Elgin Air Force Base, Florida. SAR images have been generated for above-ground and subsurface unexploded ordnance targets, including 155-mm shells. Additionally, a full-wave method of moments (MoM) model has been developed for the electromagnetic scattering from these same targets, accounting for the lossy nature and frequency dependency of the various soils. An approximate model based on phys9cal optics (PO) has also been developed. The efficacy of using PO in lieu of the MoM to generate the electromagnetic scattering data is examined. We compare SAR images from the measured data with images produced by the MoM and PO simulations by using a standard back-projection technique.