Performance of Predictors with Non-Gaussian Inputs

The trajectory of a maneuvering aircraft can be described by piecewise-constant-coefficient second-order polynomials which constitute a non-Gaussian process at the input of a linear fire control predictor. Consequently, the ?on- target? probability which is determined by the predictor output cannot be analytically evaluated. Using this a priori assumption for this type of target trajectory, the structure of a finite-time polynomial filter is determined. By using the ?go-no-go? characteristic of the loss function, the smoothing time of the predictor can be optimized to yield the highest possible ?on-target? probability. The results obtained constitute lower bounds whose validity is verified by digital simulation of the system. This shows that, on the average, they are 15 percent below the exact values. The simulation program is also used in a comparative study, with a conventional predictor and a Wiener-type predictor. The results demonstrate that the finite time polynomial predictor is markedly superior to both systems for a wide range of parameters and their combinations.     

Object-based SAR image compression using vector quantization

A simple and elegant algorithm is presented to encode images with rich content, which allows easy access to various objects. An object-plane-based encoding method for compression of synthetic aperture radar (SAR) imagery is developed, with different object planes for target classes and background. A variable-rate residual vector quantization (VQ) algorithm is developed to encode the background information. This algorithm is very powerful as indicated by the experimental results. The proposed coding scheme allows compression matched to the final application of the images, which in this case is target recognition and classification.     

Emulating random process target statistics (using MSF)

The topics of matrix spectral factorization (MSF) in conjunction with results from realization theory are applied here in simulating a stationary multi-input/multi-output (MIMO) linear system from a specified power spectral density matrix. MSF provides the appropriate transfer function matrix and realization theory specifies the corresponding parameters of a linear system having this transfer function This approach can be used to correctly capture the cross correlations that exist in a multichannel vector random process (representing a particular radar target signature). Aspects of the solution to this problem are illustrated using an original representative example problem with a closed-form answer. Existing software programs for accomplishing MSF are identified and one has been successfully validated using the known closed-form solution mentioned above. A streamlined realization algorithm (offered here as the primary theoretical contribution) can be used along with the MSF computer program and can now be applied to actual radar data. Besides multichannel spectral estimation, several other important applications of this same MSF solution methodology are summarized in Appendix B including extending applicability to the complex case (to handle radar polarization issues related to coherent phase processing), and finally in reexpressing second order statistics of a multichannel autoregressive moving average (ARMA) process as those of a simpler but mathematically equivalent autoregressive (AR) process of slightly higher dimensions (as another original application of the same major result offered here)     

FOPEN SAR imaging using UWB step-frequency and random noisewaveforms

The detection and identification of targets obscured by foliage have been topics of great interest. Several synthetic aperture radar (SAR) experiments have demonstrated promising images of terrain and man-made objects obscured by dense foliage, by using either linear frequency modulation (LFM) or step-frequency waveforms. We present here the methodology and results of a comparative study on foliage penetration (FOPEN) SAR imaging using ultrawideband (UWB) step-frequency and random noise waveforms. A statistical-physical foliage transmission model is developed for simulation applications. The foliage obscuring pattern is analyzed by means of the technique of paired echoes. The results of the comparative study demonstrates the ability of a UHF band UWB random noise radar to be used as a FOPEN SAR. Advantages of the random noise radar system include covert detection and immunity to radio frequency interference (RFI)     

DOA and steering vector estimation using a partially calibratedarray

We consider the problem of estimating directions of arrival (DOAs) using an array of sensors, where some of the sensors are perfectly calibrated, while others are uncalibrated. We identify a cost function whose minimizer is a statistically consistent and efficient estimator of the unknown parameters-the DOAs and the gains and phases of the uncalibrated sensors. Next we present an iterative algorithm for finding the minimum of that cost function The proposed algorithm is guaranteed to converge. The performance of the estimation algorithm is compared with the Cramer Rao bound (CRB). The derivation of the bound is also included. It is shown that DOA accuracy can be improved by adding uncalibrated sensors to a precisely calibrated array. Moreover, the number of sources that can be resolved may be larger than the number that can be resolved by the calibrated portion of the array     

Envelope Threshold Detection in a Class of Non-Gaussian Interference

This paper considers the detection of a sinusoidal or chirp signal imbedded in wideband FM interference (as might be generated by some types of active jamming), such that after pulse compression or other integration, the interference can be approximated by a sum of sinusoids of independent phase. The detection probability in such non-Gaussian noise is compared to that for Gaussian noise, with the Gaussian result approached, as required, in the limit that the number of sinusoids in the sum increases without bound. For detection using a comparison of the envelope with a threshold which yields a given false-alarm probability (CFAR detection), the detection probability is improved over the case of Gaussian noise, so that the usual approach basing the design on Gaussian noise would be conservative. Using a threshold determined from the envelope mean, the FM interference yields a lower false-alarm probability than for Gaussian noise, with detection probability only slightly degraded.     

Parametric models for helicopter identification using ANN

An artificial neural network (ANN) based helicopter identification system is proposed. The feature vectors are based on both the tonal and the broadband spectrum of the helicopter signal, ANN pattern classifiers are trained using various parametric spectral representation techniques. Specifically, linear prediction, reflection coefficients, cepstrum, and line spectral frequencies (LSF) are compared in terms of recognition accuracy and robustness against additive noise. Finally, an 8-helicopter ANN classifier is evaluated. It is also shown that the classifier performance is dramatically improved if it is trained using both clean data and data corrupted with additive noise.     

Radar target identification of aircraft using polarization-diversefeatures

The characteristics and target identification potential of a representation of the information from two polarization-diverse measurements of the radar backscatter of an unknown target are considered. The locus of these two polarization-diverse waveforms, termed the transient polarization response (TPR), has been shown to be closely related to the geometry of the scattering centers of the target. The polarization-related components of features derived from the TPR concur well with the shape and orientation of the major scattering centers distributed in the downrange profile of the object. This illustrates the intuitively appealing result that the polarization of the backscatter, as represented by the TPR and mapped onto the modified polarization chart, is determined by target geometry. It is shown that both polarization-related and amplitude-related features derived from the TPR are useful for target identification. By using distance measures that depend on various components, the elliptical parametrization information alone is sufficient to allow satisfactory target identification at SNRs of 0 dB and above. The significance of this result is that the absolute amplitudes of the received horizontally polarized and vertically polarized channels need not be known. However, if amplitude information is known, target identification percentages improve     

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.     

Radar target identification using the bispectrum: a comparativestudy

Radar target identification is performed using time-domain bispectral features. The classification performance is compared with the performance of other classifiers that use either the impulse response or frequency domain response of the unknown target. The classification algorithms developed here are based on the spectral or the bispectral energy of the received backscatter signal. Classification results are obtained using simulated radar returns derived from measured scattering data from real radar targets. The performance of classifiers in the presence of additive Gaussian (colored or white), exponential noise, and Weibull noise are considered, along with cases where the azimuth position of the target is unknown. Finally, the effect on classification performance of responses horn extraneous point scatterers is investigated     

Unsteady aerodynamic modeling at high angles of attack using support vector machines

Accurate aerodynamic models are the basis of flight simulation and control law design.Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism.Support vector machines(SVMs)based on statistical learning theory provide a novel tool for nonlinear system modeling.The work presented here examines the feasibility of applying SVMs to high angle-of-attack unsteady aerodynamic modeling field.Mainly,after a review of SVMs,several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail,such as selection of input variables,selection of output variables and determination of SVM parameters.The least squares SVM(LS-SVM)models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration,and then used to predict the aerodynamic responses in other tests.The predictions are in good agreement with the test data,which indicates the satisfying learning and generalization performance of LS-SVMs.     

Radioisotope detection and identification at high speeds using passive sensors

A network of radiation sensors for the protection of a military base against radiological/nuclear threats has been integrated in a testbed. Two key subsystems of this network are the array of sensitive radiation detectors and the subsystem for notifying the operators of radiation events. The alarm/alert notification subsystem automatically issues notifications to the command post in real-time. The resulting reporting to the command post appears to be adequate for non-technical operational personnel. The CONOPS for the protection system relies on the sensitive detection and identification of all radioactive sources well before the transporting vehicles reach the base gates. This is a unique feature of this protection system. Discriminating sodium-iodide detectors, in conjunction with plastic scintillators and neutron detectors comprise a high-performance detection subsystem. The use of various correlation techniques has permitted the demonstration of the detection of source strengths of tens of /spl mu/Ci moving at highway speeds and at several meters separation. A companion algorithm identifies the detected radioisotope when sufficient counts are available.     

Pursuer identification and time-to-go estimation using passive measurements from an evader

We present an algorithm for identifying the parameters of a proportional navigation guidance missile (pursuer) pursuing an airborne target (evader) using angle-only measurements from the latter. This is done for the purpose of classifying the missile so that appropriate counter-measures can be taken. Mathematical models are constructed for a pursuer with a changing velocity, i.e., a direction change and a speed change. Assuming the pursuer is launched from the ground with fixed thrust, its motion can be described by a four-dimensional parameter vector consisting of its proportional navigation constant and three parameters related to thrusting. Consequently, the problem can be solved as a parameter estimation problem, rather than state estimation and we provide an estimator based on maximum likelihood (ML) to solve it. The parameter estimates obtained can be mapped into the time-to-go until intercept estimation results are presented for different scenarios together with the Cramer-Rao lower bound (CRLB), which quantifies the best achievable estimation accuracy. The accuracy of the time-to-go estimate is also obtained. Simulation results demonstrate that the proposed estimator is efficient by meeting the CRLB.     

Aeroelastic system identification using transonic CFD data for a wing/store configuration

This paper is part of a study investigating the prediction of the aeroelastic behaviour of aircraft subjected to non-linear aerodynamic forces. The main objective of the work is the characterization of the dynamic response of aeroelastic models resulting from coupled Computational Fluid Dynamic and Finite Element calculations. Of interest here is the identification of the flight condition at which the response bifurcates to limited or divergent amplitude self-sustained oscillations without carrying out a comprehensive set of full, computationally expensive, time-marching calculations. The model treated in this work is a three-dimensional wing in a transonic flowfield. Short datasets of pre-bifurcation behaviour are analysed to determine the system's stability and degree of non-linearity. It is found that the calculated responses on the run-up to a transonic Limit Cycle Oscillation show little or no evidence of non-linearity. The non-linearity appears abruptly at the bifurcation flight condition. The variation of the local Mach number over the wing's surface in the steady-state case is used to demonstrate that the non-linearity is due to a shock wave that can move along the surface. At Mach numbers where this is not possible the system behaves in a linear manner and its stability can be analysed using linear methods.     

一种基于神经网络的飞机载荷参数识别方法

提出一种经遗传算法优化的Kalman滤波神经网络(GA-KFNN)方法,对飞机特定机动下的载荷进行参数识别.首先,构建Kalman滤波神经网络(KFNN),设计了相关改进算法抑制滤波发散,提高了网络的预测精度和抗噪能力;其次,利用遗传算法(GA)优化KFNN的相关参数,使网络能迅速收敛,提高了运算效率.载荷识别结果显示,改进和优化后的GA-KFNN运行稳定,收敛迅速,具有良好的识别精度和泛化能力,满足工程实际需求.     

Development of random signal radars

Development of random signal radar (RSR) over the past 30 years is described. Conventional methods of implementing RSR are summarized such as correlation, spectrum analysis, and anticorrelation. Some typical RSR systems are introduced, for example, noise frequency modulation CW radar, random binary phase-coded CW radar, etc., and their merits and demerits are also pointed out. Finally, RSR development trends are analyzed     

多自由度非高斯随机振动控制

在振动试验台上进行多自由度(MDOF)随机振动激励时,传统的控制方法生成的驱动信号及试验台的响应信号都是高斯信号。但真实的振动干扰信号多是超高斯的;而相比于高斯激励,亚高斯激励可降低驱动信号的最大幅值。为实现多自由度亚高斯和超高斯振动控制,提出一种多自由度非高斯随机振动控制方法,该方法采用系统辨识解决系统耦合问题,而后通过选择特殊的相位生成非高斯伪随机驱动信号,再经过时域随机化得到真随机非高斯驱动信号。基于Hexapod平台的多自由度微振动试验台的亚高斯和超高斯实验表明,在试验台的响应功率谱(PSD)满足工程中常用的±3dB精度的同时,亚高斯驱动信号的最大幅值相比于高斯驱动信号的最大幅值降低了20%以上;超高斯响应信号的峭度与参考峭度的误差在0.2之内。实验结果验证了所提方法的有效性。     

A unifying approach in simulating the shot peening process using a 3D random representative volume finite element model

Using a modified 3D random representative volume (RV) finite element model,the effects of model dimensions (impact region and interval between impact and representative regions),model shapes (rectangular,square,and circular),and peening-induced thermal softening on resultant critical quantities (residual stress,Almen intensity,coverage,and arc height) after shot peening are systematically examined.A new quantity,i.e.,the interval between impact and representative regions,is introduced and its optimal value is first determined to eliminate any boundary effect on shot peening results.Then,model dimensions are respectively assessed for all model shapes to reflect the actual shot peening process,based on which shape-independent critical shot peening quantities are obtained.Further,it is found that thermal softening of the target material due to shot peening leads to variances of the surface residual stress and arc height,demonstrating the necessity of considering the thermal effect in a constitutive material model of shot peeing.Our study clarifies some of the finite element modeling aspects and lays the ground for accurate modeling of the SP process.