Pattern-theoretic foundations of automatic target recognition in clutter

Issued as final reportAir Force Office of Scientific Research (U.S.

Investigation of advanced navigation and guidance system concepts for all-weather rotorcraft operations

Results are presented of a survey conducted of active helicopter operators to determine the extent to which they wish to operate in IMC conditions, the visibility limits under which they would operate, the revenue benefits to be gained, and the percent of aircraft cost they would pay for such increased capability. Candidate systems were examined for capability to meet the requirements of a mission model constructed to represent the modes of flight normally encountered in low visibility conditions. Recommendations are made for development of high resolution radar, simulation of the control display system for steep approaches, and for development of an obstacle sensing system for detecting wires. A cost feasibility analysis is included

Analytical evaluation of ILM sensors, volume 1

The functional requirements and operating environment constraints are defined for an independent landing monitor ILM which provides the flight crew with an independent assessment of the operation of the primary automatic landing system. The capabilities of radars, TV, forward looking infrared radiometers, multilateration, microwave radiometers, interferometers, and nuclear sensing concepts to meet the ILM conditions are analyzed. The most critical need for the ILM appears in the landing sequence from 1000 to 2000 meters from threshold through rollout. Of the sensing concepts analyzed, the following show potential of becoming feasible ILM's: redundant microwave landings systems, precision approach radar, airborne triangulation radar, multilateration with radar altimetry, and nuclear sensing

Modeling and performance analysis of a UAV-based sensor network for improved ATR

Automatic Target Recognition (ATR) is computer processing of images or signals acquired by sensors with the purpose to identify objects of interest (targets). This technology is a critical element for surveillance missions. Over the past several years there has been an increasing trend towards fielding swarms of unattended aerial vehicles (UAVs) operating as sensor networks in the air. This trend offers opportunities of integration ATR systems with a UAV-based sensor network to improve the recognition performance. This dissertation addresses some of design issues of ATR systems, explores recognition capabilities of sensor networks in the presence of various distortions and analyzes the limiting recognition performance of sensor networks.;We assume that each UAV is equipped with an optical camera. A model based recognition method for single and multiple frames is introduced. A complete ATR system, including detection, segmentation, recognition and clutter rejection, is designed and tested using synthetic and realistic images. The effects of environmental conditions on target recognition are also investigated.;To analyze and predict ATR performance of a recognition sensor network, a general methodology from information theory view point is used. Given the encoding method, the recognition system is analyzed using a recognition channel. The concepts of recognition capacity, error exponents and probability of outage are defined and derived for a PCA-based ATR system. Both the case of a single encoded image and the case of encoded correlated multiple frames are analyzed. Numerical evaluations are performed. Finally we discuss the joint recognition and communication problems. Three scenarios of a two node recognition sensor network are analyzed. The communication and recognition performances for each scenario are evaluated numerically

Exploitation of infrared polarimetric imagery for passive remote sensing applications

Polarimetric infrared imagery has emerged over the past few decades as a candidate technology to detect manmade objects by taking advantage of the fact that smooth materials emit strong polarized electromagnetic waves, which can be remotely sensed by a specialized camera using a rotating polarizer in front of the focal plate array in order to generate the so-called Stokes parameters: S0, S1, S2, and DoLP. Current research in this area has shown the ability of using such variations of these parameters to detect smooth manmade structures in low contrast contrast scenarios. This dissertation proposes and evaluates novel anomaly detection methods for long-wave infrared polarimetric imagery exploitation suited for surveillance applications requiring automatic target detection capability. The targets considered are manmade structures in natural clutter backgrounds under unknown illumination and atmospheric effects. A method based on mathematical morphology is proposed with the intent to enhance the polarimetric Stokes features of manmade structures found in the scene while minimizing its effects on natural clutter. The method suggests that morphology-based algorithms are capable of enhancing the contrast between manmade objects and natural clutter backgrounds, thus, improving the probability of correct detection of manmade objects in the scene. The second method departs from common practices in the polarimetric research community (i.e., using the Stokes vector parameters as input to algorithms) by using instead the raw polarization component imagery (e.g., 0°, 45°, 90°, and 135°) and employing multivariate mathematical statistics to distinguish the two classes of objects. This dissertation unequivocally shows that algorithms based on this new direction significantly outperform the prior art (algorithms based on Stokes parameters and their variants). To support this claim, this dissertation offers an exhaustive data analysis and quantitative comparative study, among the various competing algorithms, using long-wave infrared polarimetric imagery collected outdoor, over several days, under varying weather conditions, geometry of illumination, and diurnal cycles

An Impulse Detection Methodology and System with Emphasis on Weapon Fire Detection

This dissertation proposes a methodology for detecting impulse signatures. An algorithm with specific emphasis on weapon fire detection is proposed. Multiple systems in which the detection algorithm can operate, are proposed. In order for detection systems to be used in practical application, they must have high detection performance, minimizing false alarms, be cost effective, and utilize available hardware. Most applications require real time processing and increased range performance, and some applications require detection from mobile platforms. This dissertation intends to provide a methodology for impulse detection, demonstrated for the specific application case of weapon fire detection, that is intended for real world application, taking into account acceptable algorithm performance, feasible system design, and practical implementation. The proposed detection algorithm is implemented with multiple sensors, allowing spectral waveband versatility in system design. The proposed algorithm is also shown to operate at a variety of video frame rates, allowing for practical design using available common, commercial off the shelf hardware. Detection, false alarm, and classification performance are provided, given the use of different sensors and associated wavebands. The false alarms are further mitigated through use of an adaptive, multi-layer classification scheme, leading to potential on-the-move application. The algorithm is shown to work in real time. The proposed system, including algorithm and hardware, is provided. Additional systems are proposed which attempt to complement the strengths and alleviate the weaknesses of the hardware and algorithm. Systems are proposed to mitigate saturation clutter signals and increase detection of saturated targets through the use of position, navigation, and timing sensors, acoustic sensors, and imaging sensors. Furthermore, systems are provided which increase target detection and provide increased functionality, improving the cost effectiveness of the system. The resulting algorithm is shown to enable detection of weapon fire targets, while minimizing false alarms, for real-world, fieldable applications. The work presented demonstrates the complexity of detection algorithm and system design for practical applications in complex environments and also emphasizes the complex interactions and considerations when designing a practical system, where system design is the intersection of algorithm performance and design, hardware performance and design, and size, weight, power, cost, and processing

Combat Identification with Sequential Observations, Rejection Option, and Out-of-Library Targets

This research extends a mathematical framework to select the optimal sensor ensemble and fusion method across multiple decision thresholds subject to warfighter constraints for a combat identification (CID) system. The formulation includes treatment of exemplars from target classes on which the CID system classifiers are not trained (out-of-library classes) and enables the warfighter to optimize a CID system without explicit enumeration of classifier error costs. A time-series classifier design methodology is developed and applied, yielding a multi-variate Gaussian hidden Markov model (HMM). The extended CID framework is used to compete the HMM-based CID system against a template-based CID system. The framework evaluates competing classifier systems that have multiple fusion methods, varied prior probabilities of targets and non-targets, varied correlation between multiple sensor looks, and varied levels of target pose estimation error. Assessment using the extended framework reveals larger feasible operating regions for the HMM-based classifier across experimental settings. In some cases the HMM-based classifier yields a feasible region that is 25\% of the threshold operating space versus 1\% for the template-based classifier

Advanced machine learning approaches for target detection, tracking and recognition

This dissertation addresses the key technical components of an Automatic Target Recognition (ATR) system namely: target detection, tracking, learning and recognition. Novel solutions are proposed for each component of the ATR system based on several new advances in the field of computer vision and machine learning. Firstly, we introduce a simple and elegant feature, RelCom, and a boosted feature selection method to achieve a very low computational complexity target detector. Secondly, we present a particle filter based target tracking algorithm that uses a quad histogram based appearance model along with online feature selection. Further, we improve the tracking performance by means of online appearance learning where appearance learning is cast as an Adaptive Kalman filtering (AKF) problem which we formulate using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Then, we introduce an integrated tracking and recognition system that uses two generative models to accommodate the pose variations and maneuverability of different ground targets. Specifically, a tensor-based generative model is used for multi-view target representation that can synthesize unseen poses, and can be trained from a small set of signatures. In addition, a target-dependent kinematic model is invoked to characterize the target dynamics. Both generative models are integrated in a graphical framework for joint estimation of the target's kinematics, pose, and discrete valued identity. Finally, for target recognition we advocate the concept of a continuous identity manifold that captures both inter-class and intra-class shape variability among training targets. A hemispherical view manifold is used for modeling the view-dependent appearance. In addition to being able to deal with arbitrary view variations, this model can determine the target identity at both class and sub-class levels, for targets not present in the training data. The proposed components of the ATR system enable us to perform low computational complexity target detection with low false alarm rates, robust tracking of targets under challenging circumstances and recognition of target identities at both class and sub-class levels. Experiments on real and simulated data confirm the performance of the proposed components with promising results

Adaptive sampling by histogram equalization: theory, algorithms, and applications, 2007

We present the investigation of a novel, progressive, adaptive sampling scheme. This scheme is based on the distribution of already obtained samples. Even spaced sampling of a function with varying slopes or degrees of complexity yields relatively fewer samples from the regions of higher slopes. Hence, a distribution of these samples will exhibit a relatively lower representation of the function values from regions of higher complexity. When compared to even spaced sampling, a scheme that attempts to progressively equalize the histogram of the function values results in a higher concentration of samples in regions of higher complexity. This is a more efficient distri-bution of sample points, hence the term adaptive sampling. This conjecture is confirmed by numerous examples. Compared to existing adaptive sampling schemes, our approach has the unique ability to efficiently obtain expensive samples from a space with no prior knowledge of the relative levels of variation or complexity in the sampled function. This is a requirement in numerous scientific computing applications. Three models are employed to achieve the equalization in the distribution of sampled function values: (1) an active-walker model, containing elements of the random walk theory, and the motion of Brownian particles, (2) an ant model, based on the simulation of the behavior of ants in search of resources, and (3) an evolutionary algorithm model. Their performances are compared on objective basis such as entropy measure of information, and the Nyquist-Shannon minimum sampling rate for band-limited signals. The development of this adaptive sampling scheme was informed by a need to effi-ciently synthesize hyperspectral images used in place of real images. The performance of the adaptive sampling scheme as an aid to the image synthesis process is evaluated. The synthesized images are used in the development of a measure of clutter in hyperspectral images. This process is described, and the results are presented