Automatic target recognition for hyperspectral imagery using high-order statistics

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective     

Automatic spectral target recognition in hyperspectral imagery

Automatic target recognition (ATR) in hyperspectral imagery is a challenging problem due to recent advances of remote sensing instruments which have significantly improved sensor's spectral resolution. As a result, small and subtle targets can be uncovered and extracted from image scenes, which may not be identified by prior knowledge. In particular, when target size is smaller than pixel resolution, target recognition must be carried out at subpixel level. Under such circumstance, traditional spatial-based image processing techniques are generally not applicable and may not perform well if they are applied. The work presented here investigates this issue and develops spectral-based algorithms for automatic spectral target recognition (ASTR) in hyperspectral imagery with no required a priori knowledge, specifically, in reconnaissance and surveillance applications. The proposed ASTR consists of two stage processes, automatic target generation process (ATGP) followed by target classification process (TCP). The ATGP generates a set of targets from image data in an unsupervised manner which will subsequently be classified by the TCP. Depending upon how an initial target is selected in ATGP, two versions of the ASTR can be implemented, referred to as desired target detection and classification algorithm (DTDCA) and automatic target detection and classification algorithm (ATDCA). The former can be used to search for a specific target in unknown scenes while the latter can be used to detect anomalies in blind environments. In order to evaluate their performance, a comparative and quantitative study using real hyperspectral images is conducted for analysis.     

Importance sampling for characterizing STAP detectors

This paper describes the development of adaptive importance sampling (IS) techniques for estimating false alarm probabilities of detectors that use space-time adaptive processing (STAP) algorithms. Fast simulation using IS methods has been notably successful in the study of conventional constant false alarm rate (CFAR) radar detectors, and in several other applications. The principal objectives here are to examine the viability of using these methods for STAP detectors, develop them into powerful analysis and design algorithms and, in the long term, use them for synthesizing novel detection structures. The adaptive matched filter (AMF) detector has been analyzed successfully using fast simulation. Of two biasing methods considered, one is implemented and shown to yield good results. The important problem of detector threshold determination is also addressed, with matching outcome. As an illustration of the power of these methods, two variants of the square-law AMF detector that are thought to be robust under heterogeneous clutter conditions have also been successfully investigated. These are the envelope-law and geometric-mean STAP detectors. Their CFAR property is established and performance evaluated. It turns out the variants have detection performances better than those of the AMF detector for training data contaminated by interferers. In summary, the work reported here paves the way for development of advanced estimation techniques that can facilitate design of powerful and robust detection algorithms     

Photoelectric array detectors for use at XUV wavelengths

The characteristics of photoelectric detector systems for use at visible-light, ultraviolet, and X-ray wavelengths are briefly reviewed in the context of the needs of the Spacelab solar-physics facilities. Photoelectric array detectors for use at XUV wavelengths between 90 and 1500 Å are described, and their use in the ESA Grazing-Incidence Solar Telescope (GRIST) facility is discussed.Proceedings of the Conference Solar Physics from Space, held at the Swiss Federal Institute of Technology Zurich (ETHZ), 11–14 November 1980.     

Adaptive variable structure tracking control for constrained robots

The problem of controller design for constrained robots with the consideration of computational efficiency is addressed. An efficient adaptive variable structure control algorithm based on a reduced dynamics formulation, is presented for trajectory tracking of an end-effector on a constrained surface with specified constraint forces. It is shown that the objective can be achieved without exact knowledge of robot dynamics and on-line calculation of nonlinear dynamic functions. The control algorithm is constructed with at most three control parameters to be adjusted adaptively, and that number is determined independently of the number of degrees of freedom of the robotic manipulators. A numerical example is presented to illustrate the developed method     

Mutual and conditional mutual informations for optimizingdistributed Bayes detectors

This paper considers optimization of distributed detectors under the Bayes criterion. A distributed detector consists of multiple local detectors and a fusion center that combines the local decision results to obtain a final decision. Introduced first are distributional distance measures, the mutual information (MI) and the conditional mutual information (CMI), that are obtained by applying information theoretic concepts to detection problems. Error bound analyses show that these distance measures approximate the Bayesian probability of error better than the conventional ones regardless of the operational environments. Then, a new optimization technique is proposed for distributed Bayes detectors. The method uses the distributional distances instead of the original Bayes criterion to avoid the complexity barrier of the optimization problem. Numerical examples show that the proposed distance measures yield solutions far better than the existing ones     

Highly constrained cooperative guidance for flexible landing on asteroids

Flexible lander, composed of multiple nodes connected by flexible material, can reducethe bouncing and overturning during the asteroid landing. To satisfy the complex constraints inthe node cooperation of the flexible landing, an intelligent cooperative guidance method is pro-posed. The method consists of a double-layer cooperative guidance structure, a guidance parameterdetermination approach, and an action priority strategy. The double-layer contains a basic guid-ance used to satisfy the terminal state constraints, and a compensatory guidance used to satisfythe lander's attitude constraint. For the compensatory guidance, the parameters are determinedby multi-agent system, which are trained according to the performance index of flexible landing tra-jectories. The action priority strategy is used to reduce the detrimental effect of parameter inconsis-tency on the node cooperation. The simulation of flexible landing shows that the cooperativeguidance method is effective in improving the landing accuracy while satisfying the constraints.Meanwhile, the method is robust to the disturbance in the navigation and control.     

Performance of receivers with linear detectors

The false-alarm and detection probabilities of a receiver summing M independent outputs of a linear detector are calculated by numerical saddlepoint integration. The saddlepoint approximation is also considered. Both constant-amplitude and Rayleigh-fading signals are treated, and the relative efficiency of the quadratic and the linear detectors for these is calculated for a broad range of values of M . The numerical integration method is the more efficient, the smaller the false-alarm probability or the false-dismissal probability, that is, under just those conditions for which the terms in the Gram-Charlier series oscillate most violently and the series becomes least reliable. The simpler saddlepoint approximation yields values that in those same regions have been found close enough to the exact probabilities to be adequate for most engineering purposes. The larger the number M of samples, the more efficient methods are     

Estimation techniques for GMSK using linear detectors in satellite communications

This paper describes data-aided signal level and noise variance estimators for Gaussian minimum shift keying (GMSK) when the observations are limited to the output of a filter matched to the first pulse-amplitude modulation (PAM) pulse in the equivalent PAM representation. The estimators are based on the maximum likelihood (ML) principle and assume burst-mode transmission with known timing and a block of L₀ known bits. While it is well known that ML estimators are asymptotically unbiased and efficient, the analysis quantifies the rate at which the estimators approach these asymptotic properties. It is shown that the carrier phase, amplitude, and noise variance estimators are unbiased and can achieve their corresponding Cramer-Rao bounds with modest combinations of signal-to-noise ratio and observation length. The estimates are used to estimate the signal-to-noise ratio. It is shown that the mean squared error performance of the ratio increases with signal-to-noise ratio while the mean squared error performance of the ratio in decibels decreases with signal-to-noise ratio. Simulation results are provided to confirm the accuracy of the analytic results.     

Automatic generation of binary feature detectors

The authors discuss the automatic generation of feature detectors, which is the major task in the design of classical pattern recognition systems. They present a software environment that, in place of human intuition, utilizes learning strategies and stochastic search procedures to guide the generation process. The environment allows the exploration of evolutionary learning processes and adaptive control mechanisms. A preliminary experiment with a two-class recognition system is described, and initial observations are discussed. The recognition task requires the classification of upper case English letters into two categories: target and nontarget     

Performance of MAX family of order-statistic CFAR detectors

The MAX family of constant-false-alarm-rate (CFAR) detectors is introduced as a generalization of the greatest of CFAR (GO-CFAR) or MX mean-level detector (MX-MLD). Members of the MAX family use local estimators based on order statistics and generate both a near-range and a far-range noise-level estimate. Local estimates are always combined through a maximum operation; this insures false-alarm control at clutter edges. At the same time, order-statistic-based estimators result in a high-resolution detector. A complete detection analysis is provided for SWII targets and a reference channel contaminated by large outliers. Results are presented for the MX censored MLD (MX-CMLD) operating in clutter. The MX order statistic detector (MX-OSD) based on only a single-order statistic per window, is analyzed, and curves showing the required threshold, CFAR loss, optimum censoring point, and signal-to-noise ratio (SNR) loss in the presence of outliers are given. Simulations are used to compare the dynamic responses of various MX-OSD detectors in a clutter and a multiple-target environment     

Impact speed and angle constrained guidance law for unpowered gliding vehicle

In this paper,a novel guidance law is proposed which can achieve the desired impact speed and angle simultaneously for unpowered gliding vehicles.A guidance law with only impact angle constraint is used to produce the guidance profile,and its convergence in the varying speed scenario is proved.A relationship between flight states,guidance input and impact speed is established.By applying the fixed-time convergence control theory of error dynamics,an impact speed corrector is built with the above guidance profile,which can implement impact speed correction without affecting the impact angle constraint.Numerical simulations with various impact speed and angle constraints are conducted to demonstrate the performance of the proposed guidance law,and the robustness is also verified by Monte Carlo tests.     

GNSS receiver for GLONASS signal reception

This paper deals with the latest version of Experimental GNSS receiver built at the Czech Technical University and describes integration of GLONASS signal processing to the receiver. The new FPGA platform Virtex-D Pro by Xilinx is used and enables integration of whole digital signal processing of GNSS receiver into the single chip. The RE unit of the receiver is capable of processing all GLONASS frequency of the Li and L2 bands in two independent RE channels; each channel can process one band. The frequency selection of the appropriate satellite is accomplished in a digital correlator. The development flow of the GLONASS correlator is discussed herein. The complexity of the GLONASS correlator with complexity of GPS correlator is compared. The developed GLONASS correlator was tested in Simuelink tool during development. The next test was carried out using GLONASS simulator and real GLONASS satellite signal.     

Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.     

Calculating the performance of linear and square-law detectors

A method is presented for calculating the performance of linear and square-law detectors in detection schemes that employ noncoherent integration. The method consists of transforming the coherent characteristic function, which is usually easy to obtain to a noncoherent moment generating function describing the test statistic of a linear or square-law detector. The method provides a single mathematical framework for many signal models (both classical and new) and can be implemented using standard numerical routines. Although the method is not always optimum in terms of computing speed for specific classical models, its common approach for all signal models makes it very efficient in term of learning and implementation times. Classical results as well as results for an extended set of target models consisting of an arbitrary number of constant amplitude random phase returns are presented to demonstrate the technique. It is shown for the signal parameters considered that the performance difference between the linear and square-law detectors is relatively insignificant     

Foliage attenuation and backscatter analysis of SAR imagery

The ability of a synthetic aperture radar (SAR) to detect targets under foliage is in part determined by the attenuation suffered by radiation propagating through the foliage and the backscatter from the foliage. MIT Lincoln Laboratory made measurements of foliage attenuation and backscatter using the NASA/JPL-UHF, L-, C-band fully-polarimetric SAR in July 1990. In this experiment, a 48 km² forested area near Portage, Maine was imaged. Twenty-seven 8 ft trihedral corner reflectors were arrayed throughout the imaged area in order to measure foliage attenuation. Ground truth was recorded at the time of the experiment in order to correlate the attenuation and backscatter results with foliage biophysical properties. The probability densities for foliage attenuation and for backscatter are determined as functions of frequency, polarization, and depression angle     

Automatic stereo processing of high resolution radar imagery

Previously reported experiments with stereoscopic viewing of high resolution radar pictures demonstrated improvements in detection sensitivity up to 7 dB. This paper presents continued research into automatic algorithmic techniques for employing stereo information. A simple two-stage model for a human observer is proposed, and here we investigate the utility of an existing automatic stereo processor technique in application to radar imagery. It is shown that results obtained with the automatic processor very closely resemble those obtained through actual stereo viewing of radar pictures.     

Model-based multifrequency array signal processing for low-angletracking

Tracking low-altitude targets over the sea is problematic because of interference between the direct and reflected signal. Standard monopulse trackers can experience large errors because of multipath maximum likelihood estimation (MLE) has been used to more accurately estimate the target height in the presence of multipath MLE is a model-fitting technique where the model parameters are chosen to maximize the likelihood function. It is shown that the type of observation model has a large effect on performance. Tracking performance is compared using three different observation models employing varying amounts of a priori information. Results are presented for different array sizes: eight and 32-element arrays and two-element subarrays typical of phase monopulse. Performance is compared with that of standard techniques such as Fourier beamforming and phase monopulse     

406 MHz ELT signal spectra for SARSAT

The signal format and spectral properties of the 406-MHz emergency locator transmitter (ELT) used in the search and rescue satellite aided tracking (SARSAT) system are examined. The ELT improves location estimate accuracies and can relay information about the particular aircraft and its problem, by means of the digitally modulated message fields. It is shown that due to the RF signal frequency characteristics and the Doppler shift, processing must be performed over a frequency band of approximately 25 kHz. Through the use of the fast Fourier transformation (FFT), the frequency spectrum of the ELT is analyzed, taking account of effects due to noise, multiple simultaneously received signals, and Doppler shift. It is demonstrated that the FFT provides an effective means for detecting and recognizing the presence of one or more ELT signals over this 25-kHz frequency band. Some recommendations are made to improve the spectral characteristics and the performance of the ELT