High resolution reconstruction of ISAR images

A 2-D high-resolution spectral analysis algorithm with application to an inverse synthetic aperture radar (ISAR) imaging, is presented. This algorithm is based on two-dimensional linear prediction using autoregressive (AR) coefficients. Stability is guaranteed by AR process pole adjustment. An ISAR target is modeled for a complex scatterer geometry. Computer simulation results are provided for the high-resolution reconstruction of ISAR images     

Fast converging adaptive processor or a structured covariance matrix

The use of adaptive linear techniques to solve signal processing problems is needed particularly when the interference environment external to the signal processor (such as for a radar or communication system) is not known a priori. Due to this lack of knowledge of an external environment, adaptive techniques require a certain amount of data to cancel the external interference. The number of statistically independent samples per input sensor required so that the performance of the adaptive processor is close (nominally within 3 dB) to the optimum is called the convergence measure of effectiveness (MOE) of the processor. The minimization of the convergence MOE is important since in many environments the external interference changes rapidly with time. Although there are heuristic techniques in the literature that provide fast convergence for particular problems, there is currently not a general solution for arbitrary interference that is derived via classical theory. A maximum likelihood (ML) solution (under the assumption that the input interference is Gaussian) is derived here for a structured covariance matrix that has the form of the identity matrix plus an unknown positive semi-definite Hermitian (PSDH) matrix. This covariance matrix form is often valid in realistic interference scenarios for radar and communication systems. Using this ML estimate, simulation results are given that show that the convergence is much faster than the often-used sample matrix inversion method. In addition, the ML solution for a structured covariance matrix that has the aforementioned form where the scale factor on the identity matrix is arbitrarily lower-bounded, is derived. Finally, an efficient implementation is presented.     

A non-local vectorial total variational model for multichannel SAR image speckle suppression

This paper aims at the multichannel synthetic aperture radar(SAR)image speckle reduction.This paper proposes a novel energy minimized regularization model for multichannel image denoising,which is an extension of the non-local total variational model for gray-scale image.It contains two terms,namely the vectorial data fidelity term and the non-local vectorial total variation term.The latter is constructed by high-dimensional non-local gradient that contains the structure information of the multichannel image.The existence and the uniqueness of the solution of the model are proved.A fixed point iterative algorithm is designed to acquire the solution of this model.The convergence property of this algorithm is proved as well.This model is applied to the multipolarimetric and multi-temporal RADARSAT-2 images despeckling.The result shows that this model performs better than the original vectorial total variational model on texture preserving.     

Estimating matched filter amplitude probability functions byfailure rate analysis

Performance analysis of echolocation systems requires knowledge of the probability density function (pdf) or cumulative distribution function (cdf) of a matched filter output. A method is presented to estimate these and other probability functions from data by estimating the failure rate function, a function employed in reliability theory. The method can also be used to derive approximations to closed-form probability functions. The method is demonstrated using experimental sonar and radar clutter data and a closed-form radar clutter model     

New correlated MIMO radar covariance matrix design with low side lobe levels and much lower complexity

In this paper, a new correlated covariance matrix for Multi-Input Multi-Output (MIMO) radar is proposed, which has lower SideLobe Levels (SLLs) compared to the new covariance matrix designs and the well-known multi-antenna radar designs including phased-array, MIMO radar and phased-MIMO radar schemes. It is shown that Binary Phased-Shift Keying (BPSK) waveforms that have constant envelope can be used in a closed-form to realize the proposed covariance matrix. Therefore, there is no need to deploy different types of radio amplifiers in the transmitter which will reduce the cost, considerably. The proposed design allows the same transmit power from each antenna in contrast to the phased-MIMO radar. Moreover, the proposed covariance matrix is full-rank and has the same capability as MIMO radar to identify more targets, simultaneously. Performance of the proposed transmit covariance matrix including receive beampattern and output Signal-to-Interference plus Noise Ratio (SINR) is simulated, which validates analytical results.     

A Sequential Approach to Target Discrimination

The Wald sequential probability ratio test is applied to the discrimination of targets observed by a radar or other sensor and a form for the classifier involving linear predictive filtering is developed. In this sequential approach, a target is illuminated with consecutive pulses until a classification of the target can be made to within a prescribed probability of error. Because of the linear-predictive formulation, the computational and storage requirements for the classifier are related only to the number of returns necessary to predict the target signature and not to the length of signature observed; a classifier with modest storage and computational requirements can be employed to process signatures consisting of an arbitrarily large number of returns. The classifier is based on some well-known results in mean-square filtering theory and has a simple intuitive interpretation. The classifier structure can also be related to autoregressive time series analysis and innovations process concepts and has an interpretation in the frequency domain in terms of the maximum entropy and maximum likelihood spectral estimates for the target signatures.     

Radar Detection of Correlated Targets in Clutter

This paper provides general models of radar echoes from a target. The rationale of the approach is to consider the echoes as the output of a linear dynamic system driven by white Gaussian noise (WGN). Two models can be conceived to generate N target returns: samples generated as a batch, or sequentially generated one by one. The models allow the accommodation of any correlation between pulses and nonstationary behavior of the target. The problem of deriving the optimum receiver structure is next considered. The theory of "estimator-correlator" receiver is applied to the case of a Gaussian-distributed time-correlated target embedded in clutter and thermal noise. Two equivalent detection schemes are obtained (i. e., the batch detector and the recursive detector) which are related to the above mentioned procedures of generating radar echoes. A combined analytic-numeric method has been conceived to obtain a set of original detection curves related to operational cases of interest. Finally, an adaptive implementation of the proposed processor is suggested, especially with reference to the problem of on-line estimation of the clutter covariance matrix and of the CFAR threshold. In both cases detection loss due to adaptation has been evaluated by means of a Monte Carlo simulation approach. In summary, the original contributions of the paper lie in the mathematical formulation of a powerful model for radar echoes and in the derivation of a large set of detection curves.     

Analytical solution of discrete colored noise ECA tracking filter

New analytical solutions of steady-state Kalman gains are presented for a discrete-time tracking filter with correlation in both the measurement noise and the target maneuver. The measurement noise model is a first-order discrete Markov process characterized by a correlation coefficient ρ. The target motion is examined for an exponentially correlated acceleration maneuver type in which the vehicle oscillation such as wind-induced-bending is also considered. The present solution method is based on factorizing the observed spectral density matrix Ψ(z) in frequency domain. The algorithm proposed here gives the Kalman gain matrix directly. For a case when the steady-state error covariance matrix is desired, such gains can be incorporated with the algebraic Riccati equation     

On the Accuracy And Resolution of Radar Signals

Information matrices are derived for estimates of the range parameters of moving targets as obtained by combining a priori information (if available) with reflected radar signals observed in the presence of additive white Gaussian noise. The inverse of the information matrix provides a lower bound on the covariance matrix of any unbiased parameter estimates. This bound can be approached with a high signal-to-noise ratio and optimum data processing (matched filters). Arbitrary frequency modulation, amplitude modulation, and target motion as well as various assumptions on processing the RF phase are considered. The multiple-target case makes possible investigation of a signal's resolution ability, as well as its accuracy potentials. Results for a carrier frequency much greater than the effective signal bandwidth are obtained as a special case. A main purpose of the paper is the reduction of the original radar problem to a linear model which is equivalent in the sense of having the same information matrix. These models provide valuable insight into the relative effects of multiple targets, choice of modulation, a priori information, and assumptions regarding RF phase and bandwidth. The linear equivalent model also leads to a valuable computational algorithm for investigations using digital or hybrid computers. The various special cases of interest are obtained by simple modifications of the general case, and thus the algorithm can provide a very versatile tool for evaluating and designing radar signals.     

Spectral Properties of Homogeneous and Nonhomogeneous Radar Images

On the basis of a two-dimensional, nonstationary white noisemodel for the complex radar backscatter, the spectral properties ofa one-look synthetic-aperture radar (SAR) system is derived. It isshown that the power spectrum of the complex SAR image is sceneindependent. It is also shown that the spectrum of the intensityimage is in general related to the radar scene spectrum by a linearintegral equation, a Fredholm's integral equation of the third kind.Under simplifying assumptions, a closed-form equation giving theradar scene spectrum as a function of the SAR image spectrum canbe derived.     

The Proper Computation of the Matrix Pseudoinverse and its Impact in MVRO Filtering

There have been two new algorithms of fairly recent origin offered for the calculation of the matrix pseudoinverse. Unfortunately, nonpathological counterexamples can be constructed, as offered herein, that demonstrate the questionable nature of these two algorithms; however, a resolution is offered here to help prevent possible uncritical propagation of the questionable algorithms. As a rigorous alternative, a well-established technique (endorsed by numerical analysts) is reviewed for calculating the correct matrix pseudoinverse using a computer. Additionally, this technique possesses existent independently verified/validated and assessible software code for a convenient implementation. However, historical loose ends in calculating the associated condition number are singled out here as cause for concern and as a topic for future resolution and refinement. Finally, as the primary motivation for considering these issues, an application example is offered from estimation theory in the implementation and analysis of a minimum variance reduced-order (MVRO) filter having proper performance that critically hinges on the correct computation of the matrix pseudoinverse. While examples of applying MVRO to navigation applications were provided almost a decade ago, a clear indication of the somewhat restrictive conditions of applicability were wanting and so are elucidated here since there appears to be a resurgence of interest in this analytic technique. Another contribution is in providing a tally of the drawbacks to be incurred in using MVRO as well as its previously publicized benefits.     

模态密集柔性空间结构二阶内平衡降阶

 给出一种针对二阶线性系统方程直接进行降阶的二阶系统内平衡降阶方法。大型柔性空间结构动力学方程采用二阶线性微分方程描述,采用传统的一阶内平衡降阶方法降阶后的状态方程是一阶形式,破坏了原系统的二阶结构和物理意义。采用新方法降阶后的系统可以保持原系统二阶结构,同时可以进一步保持原系统的对称和正定特性。柔性空间结构系统级降阶的柔性模态方程通常为对角形式,针对这种特殊形式,系统可控和可观Gramian矩阵存在闭合解析解,给出了闭合解的具体表达形式。数值仿真结果表明,二阶内平衡降阶方法可以达到一阶内平衡方法一样的降阶精度,Gramian矩阵的闭合解析解可以大幅度提高Lyapunov方程求解速度。     

An Exact Trajectory Solution from Doppler Shift Measurements

Three radar Doppler shift measurements are sufficient to determine the range and velocity, and the angle between them, of a target in uniform rectilinear motion. A solution using derivative approximations has recently appeared. An exact solution in terms of the original measurements is presented here.     

Parametric Rao Test for Multichannel Adaptive Signal Detection

The parametric Rao test for a multichannel adaptive signal detection problem is derived by modeling the disturbance signal as a multichannel autoregressive (AR) process. Interestingly, the parametric Rao test takes a form identical to that of the recently introduced parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) in airborne surveillance radar systems and other similar applications. The equivalence offers new insights into the performance and implementation of the PAMF detector. Specifically, the Rao/PAMF detector is asymptotically (for large samples) a parametric generalized likelihood ratio test (GLRT), due to an asymptotic equivalence between the Rao test and the GLRT. The asymptotic distribution of the Rao test statistic is obtained in closed form, which follows an exponential distribution under the null hypothesis H ₀ and, respectively, a noncentral Chi-squared distribution with two degrees of freedom under the alternative hypothesis H ₁. The noncentrality parameter of the noncentral Chi-squared distribution is determined by the output signal-to-interference-plus-noise ratio (SINR) of a temporal whitening filter. Since the asymptotic distribution under H ₀ is independent of the unknown parameters, the Rao/PAMF asymptotically achieves constant false alarm rate (CFAR). Numerical results show that these results are accurate in predicting the performance of the parametric Rao/PAMF detector even with moderate data support.     

一种基于协方差矩阵加权的阵元误差补偿STAP处理

机载多通道阵列雷达天线在工程实践中不可避免地存在各类阵元误差,所产生的通道失配问题会对空时二维自适应处理的性能造成大的影响。对存在阵元误差时的阵列信号模型进行了分析,提出了一种基于协方差矩阵加权（CMT）的阵元误差补偿空时自适应处理（STAP）方法,在工程应用中该加权矩阵可通过地面天线定标及校飞过程确定,通过对总干扰协方差矩阵估计的加权预处理,可将实际阵元误差对STAP性能的影响控制在测量误差的影响范围,最后通过仿真验证了算法的有效性。     

基于稀疏随机阵列配置的CS-MIMO雷达感知矩阵构造

压缩感知(CS)理论中的感知矩阵在观测数据获取和信号重建过程中起关键性作用。目前,大部分研究通过引入高斯随机矩阵作为测量矩阵实现压缩观测,这类测量矩阵对硬件要求很高,工程实现困难。提出了一种基于稀疏随机阵列配置的压缩感知-多输入多输出(CS-MIMO)雷达中的感知矩阵构造方法,当MIMO雷达阵元配置为满足某种概率分布的稀疏随机阵列时,发射与接收导引矢量的Kronecker积能够起到压缩测量的作用。从理论上分析了所构造的感知矩阵的归一化互相关系数、Gram矩阵以及阵列方向图之间的内在联系,并证明了当随机阵元位置满足均匀分布时所构造的感知矩阵满足压缩感知重构条件。在这种稀疏随机阵列配置方式下,既可以避免额外引入随机测量矩阵,又能减少所需的阵元个数,从而大大降低CS-MIMO雷达系统复杂度。仿真实验表明,该方法具有较低的感知矩阵归一化互相关系数,与满阵CS-MIMO雷达相比能够在减少阵元个数的同时获得良好的重构性能,且使重构所需运算量大大降低。     

几何规划在飞机总体设计中的应用

系统地讨论了代数多项式的算术－ 几何均值定理,并对原型几何规划理论作出了简明的推导与分析。提出了具有缩并迭代特性的几何规划求解理论和编程步骤。还用２ 个工程设计的优化求解算例来说明这种缩并迭代几何规划优化求解特点和优点。例１ 显示了几何规划的工程实用性和简易性;例２ 通过轻型飞机总体方案参数优化,说明所提出的优化方法,可随设计人员思路的变动而得到及时地相适应。     

Incoherent radar detection in compound-Gaussian clutter

The detection of incoherent pulse trains in compound-Gaussian disturbance with known spectral density is dealt with here. Two alternative approaches are investigated, The first, assuming perfect knowledge of the signal fluctuation law and implementing the Neyman-Pearson test on the observed waveform, turns out to be not applicable to the radar problem. The second, instead, relying on the generalized likelihood ratio optimization strategy, leads to a canonical detector, whose structure is independent of the clutter amplitude probability density function. Interestingly, this detector turns out to be constant false-alarm rate in the sense that threshold setting does not require any knowledge as to the clutter distribution, Moreover, since such a processor is not implementable in real situations, we also present an FFT-based (fast Fourier transform) suboptimum structure. Finally, we give closed-form formulas for the detection performance of both receivers, showing that both of them largely outperform the square-law detector, especially in the presence of very spiky clutter     

Optimal guidance of extended trajectory shaping

To control missile＇s miss distance as well as terminal impact angle, by involving the timeto-go-nth power in the cost function, an extended optimal guidance law against a constant maneuvering target or a stationary target is proposed using the linear quadratic optimal control theory.An extended trajectory shaping guidance（ETSG） law is then proposed under the assumption that the missile-target relative velocity is constant and the line of sight angle is small. For a lag-free ETSG system, closed-form solutions for the missile＇s acceleration command are derived by the method of Schwartz inequality and linear simulations are performed to verify the closed-form results. Normalized adjoint systems for miss distance and terminal impact angle error are presented independently for stationary targets and constant maneuvering targets, respectively. Detailed discussions about the terminal misses and impact angle errors induced by terminal impact angle constraint, initial heading error, seeker zero position errors and target maneuvering, are performed.