https://neuroimage.usc.edu/paperspdf/1993_Mosher_EEGJrnl_CRLBs.pdf

Error bounds for EEG and MEG dipole source localization

Radar high-resolution range profile (HRRP) is very sensitive to time-shift and target-aspect variation; therefore, HRRP-based radar automatic target recognition (RATR) requires efficient time-shift invariant features and robust feature templates. Although higher order spectra are a set of well-known time-shift invariant features, direct use of them (except for power spectrum) is impractical due to their complexity. A method for calculating the Euclidean distance in higher order spectra feature space is proposed in this paper, which avoids calculating the higher order spectra, effectively reducing the computation complexity and storage requirement. Moreover, according to the widely used scattering center model, theoretical analysis and experimental results in this paper show that the feature vector extracted from the average profile in a small target-aspect sector has better generalization performance than the average feature vector in the same sector when both of them are used as feature templates in HRRP-based RATR. The proposed Euclidean distance calculation method and average profile-based template database are applied to two classification algorithms [the template matching method (TMM) and the radial basis function network (RBFN)] to evaluate the recognition performances of higher order spectra features. Experimental results for measured data show that the power spectrum has the best recognition performance among higher order spectra.

Radar HRRP target recognition based on higher order spectra

Computer vision systems today fail frequently. They also fail abruptly without warning or explanation. Alleviating the former has been the primary focus of the community. In this work, we hope to draw the community's attention to the latter, which is arguably equally problematic for real applications. We promote two metrics to evaluate failure prediction. We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems--semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction--on individual input images. We also explore attribute prediction, where classifiers are typically meant to generalize to new unseen categories. We show that ALERT can be useful in predicting failures of this transfer. Finally, we leverage ALERT to improve the performance of a downstream application of attribute prediction: zero-shot learning. We show that ALERT can outperform several strong baselines for zero-shot learning on four datasets.

/pdf/predicting-failures-of-vision-systems-2jquibblsl.pdf

Predicting Failures of Vision Systems

In the statistical target recognition based on radar high-resolution range profile (HRRP), two challenging tasks are how to deal with the target-aspect, time-shift, and amplitude-scale sensitivity of HRRP and how to accurately describe HRRPs statistical characteristics. In this paper, based on the scattering center model, range cells are classified, in accordance with the number of predominant scatterers in each cell, into three statistical types. After resolving the three sensitivity problems, this paper develops a statistical model comprising two distribution forms, i.e., Gamma distribution and Gaussian mixture distribution, to model echoes of different types of range cells as the corresponding distribution forms. Determination of the type of a range cell is achieved by using the rival penalized competitive learning (RPCL) algorithm, while estimation for the parameters of Gamma distribution and Gaussian mixture distribution by the maximum likelihood (ML) method and the expectation-maximization (EM) algorithm, respectively. Experimental results for measured data show that the proposed statistical model not only has better recognition performance but also is more robust to noises than the two existing statistical models, i.e., Gaussian model and Gamma model.

A two-distribution compounded statistical model for Radar HRRP target recognition

A system and method for controlling a damping system. The system has at least two dampers for damping between sprung and unsprung masses in the compression and rebound directions. Sensors generate signals based on position and other parameters of motion representative of the displacement between the sprung and unsprung masses. The process determines the appropriate compression and rebound forces to be applied at the wheels. A regulator responds to at least one of the independent compression and rebound control signals for adjusting, respectively, at least one of compression and rebound resisting forces of the dampers between the masses. Compliance for the dampers is emulated with software to produce the desired compliance forces. The distributed controller includes a processor that is responsive to signals representative of the position signals for forming the compression and rebound control signals for the regulator as a function of motion between the masses or a motion of a vehicle in which the dampers are located. The system has the capability of locking the suspension when parked.

Enhanced computer optimized adaptive suspension system and method

The statistical feature based (StaF) classifier is presented for robust high range resolution (HRR) radar aircraft identification (ID). HRR signature peak features are selected "on the fly" with no a priori assumptions about the number or location of the features. Features extracted depends on the information content of the observed signature making the number, location, and amplitude of features random variables. A primary goal for this research is to increase classifier robustness by maintaining high known target ID while minimizing unknown target errors. Results are presented demonstrating that the StaF classifier can significantly reduce errors associated with unknown targets while maintaining a high probability of correct classification.

Robust statistical feature based aircraft identification

Synthetic aperture radar (SAR) imaging and automatic target recognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has received increased interest. High range resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise) (T/(C+N)) via Doppler filtering and/or clutter cancellation. A goal of HRR ATR transition is the implementation and evaluation of algorithms exhibiting robustness under extended operating conditions (EOC). A subset of the moving and stationary target acquisition and recognition (MSTAR) data set was used to study 1D template-based ATR development and performance. Due to the unavailability of a statistically significant moving ground target data set, this approach was taken as an interim step in assessing the separability of ground targets when using range only discriminants. This report summarizes the data and algorithm methodology, performance results, and recommendations.

Automatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar

Synthetic Aperture Radar (SAR) imaging and Automatic Target Recognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has recently received increased interest. High Range Resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise) (T/(C+N)) via Doppler filtering and/or clutter cancellation. A goal of HRR ATR transition is the implementation and evaluation of algorithms exhibiting robustness under extended operating conditions (EOC). The public domain Moving and Stationary Target Acquisition and Recognition (MSTAR) data set was used to study 1D template-based ATR development and performance. Due to the unavailability of a statistically significant moving ground target data set, this approach was taken as an interim step in assessing the separability of ground targets when using range only discriminants. This report summarizes the data and algorithm methodology, simulated performance results, and recommendations.

This paper discusses the design and performance of an optimum linear multielectrode filter for estimating the evoked potential contained in a single scalp-recorded brain response to a visual stimulus. The filter is derived under the criterion of minimum mean-square error and is time varying to allow for the nonstationarity of the brain response. Single-and two-channel filters are designed for simulated and human visual evoked potential data. Cross-correlation matrices required to design the filters are estimated "a posteriori" from multielectrode recordings of the prestimulus electroencephalogram and are derived from a multielectrode model of the evoked potential. Filter performance is evaluated using the criteria of mean-square error, signal bias, noise reduction, and output signal-to-noise ratio. The filter is shown to be very effective at suppressing the ongoing electroencephalogram and emphasizing the underlying evoked potential.

Optimum Multielectrode A Posteriori Estimates of Single-Response Evoked Potentials

The goal of this paper is to demonstrate the benefits of a tracking and identification algorithm that uses a belief data association filter for target recognition. By associating track and ID information, the belief filter accumulates evidence for classifying High-Range Resolution (HRR) radar signatures from a moving target. A track history can be utilized to reduce the search space of targets for a given pose range. The technique follows the work of Mitchell and Westerkamp by processing HRR amplitude and location feature sets. The new aspect of the work is the identification of multiple moving targets of the same type. The conclusions from the work is that moving ATR from HRR signatures necessitates a track history for robust target ID.

John J. Westerkamp

Papers

Robust statistical feature based aircraft identification

Automatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar

Automatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar

Optimum Multielectrode A Posteriori Estimates of Single-Response Evoked Potentials

Identifying moving HRR signatures with an ATR belief data association filter