Paul Runkle

Hidden Markov models for multiaspect target classification

This article presents a new approach for target identification, in which we fuse scattering data from multiple target-sensor orientations. The multiaspect data is processed via hidden Markov model (HMM) classifiers, buttressed by physics-based feature extraction. This approach explicitly accounts for the fact that the target-sensor orientation is generally unknown or hidden. Discrimination results are presented for measured scattering data.

Identification of ground targets from sequential high-range-resolution radar signatures

An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification results are presented for the ten-target MSTAR data set.

Multi-aspect target detection for SAR imagery using hidden Markov models

Radar scattering from an illuminated object is often highly dependent on the target-sensor orientation. In typical synthetic aperture radar (SAR) imagery, the information in the multi-aspect target signatures is diffused in the image-formation process. In an effort to exploit the aspect dependence of the target signature, the authors employ a sequence of directional filters to the SAR imagery, thereby generating a sequence of subaperture images that recover the directional dependence of the target scattering. The scattering statistics are then used to design a hidden Markov model (HMM), wherein the orientation-dependent scattering statistics are exploited explicitly. This approach fuses information embodied in the orientation-dependent target signature under the assumption that. Both the target identity and orientation are unknown. Performance is assessed by considering the detection of tactical targets concealed in foliage, using measured foliage-penetrating (FOPEN) SAR data.

Multiaspect target identification with wave-based matched pursuits and continuous hidden Markov models

Multiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms; these waveforms are characteristic of viewing the target from a sequence of distinct orientations. Classification is performed in the maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). We utilize a continuous-HMM paradigm and compare its performance to its discrete counterpart. The feature parsing is performed via wave-based matched pursuits. Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.

Genetic algorithm wavelet design for signal classification

Biorthogonal wavelets are applied to parse multiaspect transient scattering data in the context of signal classification. A language-based genetic algorithm is used to design wavelet filters that enhance classification performance. The biorthogonal wavelets are implemented via the lifting procedure and the optimization is carried out using a classification-based cost function. Example results are presented for target classification using measured scattering data.

Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data

Angle-dependent scattering (electromagnetic or acoustic) is considered from a general target, for which the scattered signal is a non-stationary function of the target-sensor orientation. A statistical model is presented for the wavelet coefficients of such a signal, in which the angular non-stationarity is characterized by an “outer” hidden Markov model (HMMo). The statistics of the wavelet coefficients, within a state of the outer HMM, are characterized by a second, “inner” HMMi, exploiting the tree structure of the wavelet decomposition. This dual-HMM construct is demonstrated by considering multi-aspect target identification using measured acoustic scattering data.

Target identification with wave-based matched pursuits and hidden Markov models

The method of matched pursuits is an algorithm by which a waveform is parsed into its fundamental constituents here, in the context of short-pulse electromagnetic scattering, wavefronts, and resonances (constituting what we have called wave-based matched pursuits). The wave-based matched-pursuits algorithm is used to develop a codebook of features that are representative of time-domain scattering from a target of interest, accounting for the variability of such as a function of target-sensor orientation. This codebook is subsequently used in the context of a hidden Markov model (HMM) in which the probability of measuring a particular codebook element is quantified as a function of target-sensor orientation. We review the wave-based matched-pursuits algorithm and its use in the context of an HMM (for target identification). Finally, this new wave-based signal processing algorithm is demonstrated with simulated scattering data, with additive noise.

Multiaspect identification of submerged elastic targets via wave-based matching pursuits and hidden Markov models

This paper investigates classification of submerged elastic targets using a sequence of backscattered acoustic signals corresponding to measurements at multiple target-sensor orientations. Wavefront and resonant features are extracted from each of the multiaspect signals using the method of matching pursuits, with a wave-based dictionary. A discrete hidden Markov model (HMM) is designed for each of the target classes under consideration, with identification of an unknown target effected by considering which model has the maximum likelihood of producing the observed sequence of feature vectors. HMMs are stochastic models which are well suited to describing piecewise-stationary processes, and are appropriate for multiaspect classification due to the strong aspect dependence of the scattered fields for most realistic targets. After establishing the physical and geometric correspondence between multiaspect sensing and the HMM parameters, performance is assessed through consideration of measured acoustic data ...

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.

Multiaspect classification of airborne targets via physics-based HMMs and matching pursuits

Wideband electromagnetic fields scattered from N distinct target-sensor orientations are employed for classification of airborne targets. Each of the scattered waveforms is parsed via physics-based matching pursuits, yielding N feature vectors. The feature vectors are submitted to a hidden Markov model (HMM), each state of which is characterized by a set of target-sensor orientations over which the associated feature vectors are relatively stationary. The N feature vectors extracted from the multiaspect scattering data implicitly sample N states of the target (some states may be sampled more than once), with the state sequence modeled statistically as a Markov process, resulting in an HMM due to the hidden or unknown target orientation. In the work presented here, the state-dependent probability of observing a given feature vector is modeled via physics-motivated linear distributions, in lieu of the traditional Gaussian mixtures applied in classical HMMs. Further, we develop a scheme that yields autonomous definitions for the aspect-dependent HMM states. The paradigm is applied to synthetic scattering data for two simple targets.