Localization performance of arrays subject to phase errors

The passive localisation of radiating sources using an array subject to random perturbations in sensor phases is presented. All source signals as well as additive noises observed at the sensors are assumed to be independent identically distributed zero-mean Gaussian random processes. Cramer-Rao bounds are derived for source bearings and ranges for the phase errors at each sensor. It is shown that accurate phase calibration can be achieved when the number of sources exceeds a certain minimum. The locations of the calibrating sources need not be known a priori and need only satisfy mild regularity conditions. A calibration procedure is proposed which uses maximum-likelihood techniques     

Detecting the Presence of Target Multiplicity

A method is discussed for detecting the presence of multiple targets in the radar antenna beam. It is assumed that the targets are unresolvable in range, Doppler, and angle using conventional monopulse resolution techniques. The basic approach taken is a generalization of the "quadrature" method, with significantly enhanced performance in the case when multiple pulses are integrated into a single solution. Detection and false alarm probabilities are derived analytically and the receiver operating characteristics are graphed. This study was performed for application to angle processing in a frequency agile automatic tracking radar, but the underlying concept is general and has applications outside this area.     

Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments

Space-time adaptive processing (STAP) holds tremendous potential for the new generation airborne surveillance radar, in which the phased array antennas and pulse Doppler processing mode are adopted. A new STAP approach using the multiple-beam and multiple Doppler channels is presented here for airborne phased array radar. The approach with space-time multiple-beam (STMB) architecture is robust to array errors and has very low system degrees of freedom (DOFs). Hence, it has low sample support requirement and it is very suitable for the practical planar phased array radar under nonhomogeneous clutter environments. Meanwhile, a new nonhomogeneous detector (NHD) based on the correlation dimension (CD) is also proposed here, which is used as an effective method to screen tracing data prior to detection processing. It can further improve the performance of the STAP approach in the severely nonhomogeneous clutter environments. Therefore, a scheme that incorporates the correlation dimension nonhomogeneity detector (CD-NHD) with the STMB is recommended, which we term CD-NHD-STMB. The experimental simulation results indicate that: 1) the STMB processor is robust to array element error and has high performance under nonhomogeneous clutter environments; 2) the CD-NHD is also effective on the nonhomogeneous clutter. As a result, the CD-NHD-STMB scheme is robust to array element error and nonhomogeneous clutter, and therefore available for airborne phased array radar applications.     

L型构件R区的超声相控阵检测方法

 针对常规超声检测难以胜任L型构件R区检测的问题,开展L型构件的超声相控阵检测方法研究。利用超声相控阵声束灵活可控的优势,制定了线阵换能器检测L型构件R区的检测方案;基于Fermat原理,提出了相控阵检测多层介质的延迟时间计算方法;通过有限差分法数值仿真和检测试验,验证了相控阵检测方案和延迟时间计算方法的正确性;最后,对复合材料L型试样R区进行检测试验,检测结果与实际试样信息基本一致。研究结果表明,所提出的采用线阵换能器检测L型构件R区的检测方法是可行有效的。     

Finite data performance of MUSIC and minimum norm methods

In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient knowledge of array characterization. It is therefore important to study how subspace-based methods perform in such conditions. We analyze the finite data performance of the multiple signal classification (MUSIC) and minimum norm (min. norm) methods in the presence of sensor gain and phase errors, and derive expressions for the mean square error (MSE) in the DOA estimates. These expressions are first derived assuming an arbitrary array and then simplified for the special case of an uniform linear array with isotropic sensors. When they are further simplified for the case of finite data only and sensor errors only, they reduce to the recent results given previously (1989, 1991). Computer simulations are used to verify the closeness between the predicted and simulated values of the MSE     

Performance improvement for constellation SAR using signal processing techniques

A new concept of spaceborne synthetic aperture radar (SAR) implementation has recently been proposed - the constellation of small spaceborne SAR systems. In this implementation, several formation-flying small satellites cooperate to perform multiple space missions. We investigate the possibility to produce high-resolution wide-area SAR images and fine ground moving-target indicator (GMTI) performance with constellation of small spaceborne SAR systems. In particular, we focus on the problems introduced by this particular SAR system, such as Doppler ambiguities, high sparseness of the satellite array, and array element errors. A space-time adaptive processing (STAP) approach combined with conventional SAR imaging algorithms is proposed which can solve these problems to some extent. The main idea of the approach is to use a STAP-based method to properly overcome the aliasing effect caused by the lower pulse-repetition frequency (PRF) and thereby retrieve the unambiguous azimuth wide (full) spectrum signals from the received echoes. Following this operation, conventional SAR data processing tools can be applied to focus the SAR images fully. The proposed approach can simultaneously achieve both high-resolution SAR mapping of wide ground scenes and GMTI with high efficiency. To obtain array element errors, an array auto-calibration technique is proposed to estimate them based on the angular and Doppler ambiguity analysis of the clutter echo. The optimizing of satellite formations is also analyzed, and a platform velocity/PRF criterion for array configurations is presented. An approach is given to make it possible that almost any given sparse array configuration can satisfy the criterion by slightly adjusting the PRF. Simulated results are presented to verify the effectiveness of the proposed approaches.     

New Autocorrelation and Prediction Techniques for the Demodulation of Binary Signals

The classic problem of detecting a narrowband signal received in a wideband noise is treated via new techniques, namely, autocorrelation computation and linear adaptive filtering. It is shown by analysis and simulation that the proposed techniques yield probility of bit errors cimparable to those provided by coding techniques. Expressions are derived for the detectability parameter of the new test statistics, and it is shown that these statistics are very close to the Gaussian approximation. The improvements obtained in the signal-to-noise ratio and the Gaussian nature of the new statistic implies an improvement in the capacity of the channel by the nonlinear and storage processes involved in the new techniques, something not achievable by linear filtering and/or memoryless detection techniques.     

Superimposed HMM transient detection via target tracking ideas

The quickest detection of superimposed hidden Markov model (HMM) transient signals is addressed. It is assumed that a known HMM is always extant but at an unknown time a second known HMM may also be present, and overlapped with the previous. Two approaches are proposed. The first treats the superimposed HMMs as a unit with an expanded state space, thus converting the problem of detecting superimposed HMMs into detection of a change in HMM, this being readily solved using a previously proposed procedure. Such an approach, though excellent in terms of performance, is not suitable for the superposition of multiple HMMs with large state dimensions due to computational complexity. A second detection scheme (based on multiple target tracking ideas) with much lower computational needs but little loss in terms of performance, is therefore developed     

The Effect of Random Steering Vector Errors in the Applebaum Adaptive Array

The effect of random errors in the steering vector of an Applebaum adaptive array is examined. Each component of the steering vector is assumed to have a random error component uncorrelated between elements. The array output signal-to-interferenceplus-noise ratio (SINR) is computed as a function of the error variance. It is shown that the array output SINR becomes more sensitive to steering vector errors as more elements are added to the array and as the received desired signal power becomes larger. The variance of the steering vector error that may be tolerated depends on the required desired signal dynamic range. The larger the dynamic range that must be accommodated, the smaller the error variance must be.     

确定相干机器多噪声源位置的方法研究

提出利用均匀线阵接收的数据构造一种矩阵进行奇异值（SVD）分解,以实现对相干噪声源的方位估计。利用这种方法,通过改变阵列中心位置便可确定机器多噪声源位置。计算机模拟和声学实验证实了这种方法的可行性。     

基于VLBI的上行组阵标校技术研究

天线组阵能否完全替代大口径天线有一个关键性难题,就是天线阵是否支持上行链路组阵。深空航天器无法将不同地面天线的上行信号对齐,所以上行链路信号的调整必须在地面完成。针对上行组阵发射机相位调整问题,提出一种基于VLBI(Very Long Baseline Interferometry,甚长基线干涉测量)技术的接收模式天线上行组阵标校方案,并对标校精度进行了简要分析。将上行链路时延分解为几何时延和发射系统时延,建立了几何时延模型,通过标定接收时延和发射时延,便可以得到天线阵元间的相位标校值。理论分析结果表明,该方案具有一定的可行性,对上行组阵相位标校的研究具有一定的借鉴意义。     

基于离散等价方程的非结构网格有限差分法

激波装配法把解析关系式嵌入流场避免了间断引起的理论问题，使全场一致高精度的实现成为可能，有望解决超声速流动转捩研究中的感受性模拟难题。但装配复杂激波结构以后，被分割的流场空间常出现不规则的几何形状，这给常规的基于结构网格的有限差分法应用带来困难。基于离散等价方程理论，提出了一种新的基于非结构网格的有限差分法，在空间二维离散点附近仅用3条网格线就可以构造出一阶迎风格式。数值算例表明收敛过程对网格质量不敏感，解决了激波装配法模拟激波相交出现小夹角后使用结构网格进行计算存在的难题。根据这种方法的特点展望了未来的应用前景。     

Direction of arrival tracking below the ambiguity threshold

We present an algorithm for direction-of-arrival (DOA) tracking that allows operation below the ambiguity threshold of the direction-finding (DF) system. Using multiple target tracking techniques, the algorithm turns the most likely DOAs of each measurement into multiple potential tracks and then selects the true track as that with the maximum cumulative likelihood. The improvement offered by the algorithm, namely the extension of the ambiguity-free signal-to-noise ratio (SNR) domain, is demonstrated in several simulated experiments using several array structures, including a sparse array and a uniform linear array     

Instrumental variable adaptive array processing

An instrumental variable (IV) approach is presented for estimating the weights of an adaptive antenna array. Theoretical analysis of the IV method shows that the antenna gain weights are independent of finitely correlated noise, so that unbiased estimation of signal arrival angles is possible. Only matrix inversions are required to compute the weight estimates. In this sense, the IV method provides performance comparable with eigenvector techniques but with lower computational burden. Both minimal and overdetermined IV estimators are derived. The overdetermined estimators give the same theoretical array weights as minimal estimators, but yield more accurate weight estimates in real data situations. Simulation results are presented to compare these IV methods with one another and with conventional matrix inversion weight estimators. In these examples it is seen that IV methods are able to resolve closely spaced interference sources when conventional matrix inversion techniques cannot. It is also shown that overdetermined methods are capable of providing weight estimates with lower variances than those of minimal methods     

Analysis of Modeling and Bias Errors in Discrete-Time State Estimation

This paper concerns the effects of modeling and bias errors in discrete-time state estimation. The newly derived algorithms include the effect of correlation between plant and measurement noise in the system. The effects of nonzero mean noise terms and bias errors are considered. With plant or measurement matrix errors, divergence can occur. The local or linear sensitivity approach to error analysis, where the sensitivity is defined as a partial derivative with respect to a variable parameter taken about the modeled value, will not show this divergence due to neglect of higher order terms. Approximate algorithms are presented which circumvent the problem inherent in the local sensitivity approach. These make use of a "conditional bias" concept which views system error as a bias, conditioned on knowledge of the state estimates. It is shown that the actual error in optimum estimation is orthogonal to the residue error for suboptimum estimation where the residue error is defined as the difference between the actual estimation error and the optimum estimation error. Two examples, one concerning an integrated navigation system, demonstrate the theoretical results.     

Efficient fault tolerant estimation using the IMM methodology

Space systems are characterized by a low-intensity process noise resulting from uncertain forces and moments. In many cases, their scalar measurement channels can be assumed to be independent, with one-dimensional internal dynamics. The nominal operation of these systems can be severely damaged by faults in the sensors. A natural method that can be used to yield fault tolerant estimates of such systems is the interacting multiple model (IMM) filtering algorithm, which is known to provide very accurate results. However, having been derived for a general class of systems with switching parameters, the IMM filter does not utilize the independence of the measurement errors in different channels, nor does it exploit the fact that the process noise is of low intensity. Thus, the implementation of the IMM in this case is computationally expensive. A new estimation technique is proposed herein, that explicitly utilizes the aforementioned properties. In the resulting estimation scheme separate measurement channels are handled separately, thus reducing the computational complexity. It is shown that, whereas the IMM complexity is exponential in the number of fault-prone measurements, the complexity of the proposed technique is polynomial. A simulation study involving spacecraft attitude estimation is carried out. This study shows that the proposed technique closely approximates the full-blown IMM algorithm, while requiring only a modest fraction of the computational cost.     

Analysis of array errors and a short-time processor in airbornephased array radars

Array errors are inherent in a realistic phased array radar system. The influence of array errors on the clutter degrees of freedom and the clutter subspace in an airborne phased array radar is analyzed. Based on the presented theoretic results, a method of short-time processing followed by coherent integration is proposed for clutter suppression in airborne phased array radars. It can approximate the two-dimensional optimal processor well even in the presence of array errors, clutter fluctuations and aircraft drift, with a considerable saving in computations     

Design and application of quadratic correlation filters for target detection

We introduce a method for designing and implementing quadratic correlation filters (QCFs) for shift-invariant target detection in imagery. The QCFs are a quadratic classifier that operates directly on the image data without feature extraction or segmentation. In this sense, the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects. Not only is more processing required to detect peaks in the outputs of multiple linear filters, but choosing a winner among them is an error prone task. On the other hand, all channels in a QCF work together to optimize the same performance metric and produce a combined output that leads to considerable simplification of the postprocessing scheme. In addition, QCFs also yield better performance than their linear counterparts for comparable throughput requirements. Two different methods for designing basis functions that optimize the QCF performance criterion are presented. An efficient architecture for implementing QCFs is discussed along with a case study of the proposed approach for detecting targets in LADAR imagery.     

一种MIMO雷达幅相误差估计方法

 针对单基地相关多输入多输出(MIMO)雷达中存在的阵列幅相误差问题进行了研究。给出了单基地相关MIMO雷达的阵列模型,并提出了一种MIMO雷达幅相误差估计方法。利用发射正交信号对阵列接收信号进行匹配滤波,可分离得到类似传统阵列的"虚拟阵列",利用分时信源数据将该阵列中真实导向矢量中信源波达方向(DOA)引起的相位与幅相误差分离开,通过构造代价函数得到波达方向估计值,进而分别得到发射阵与接收阵的幅相误差的估计值,同时给出了误差引入量分析。最后通过仿真验证了该方法的有效性。本文介绍的方法简单可行,适用于任意构型MIMO雷达的幅相误差估计。     

Joint calibration algorithm for gain-phase and mutual coupling errors in uniform linear array

The effect of gain-phase perturbations and mutual coupling significantly degrades the performance of digital array radar (DAR). This paper investigates array calibration problems in the scenario where the true locations of auxiliary sources deviate from nominal values but the angle intervals are known. A practical algorithm is proposed to jointly calibrate gain-phase errors and mutual coupling errors. Firstly, a simplified model of the distortion matrix is developed based on its special structure in uniform linear array (ULA). Then the model is employed to derive the precise locations of the auxiliary sources by one-dimension search. Finally, the least-squares estimation of the distortion matrix is obtained. The algorithm has the potential of achieving considerable improvement in calibration accuracy due to the reduction of unknown parameters. In addition, the algorithm is feasible for practical applications, since it requires only one auxiliary source with the help of rotation platforms. Simulation results demonstrate the validity, robustness and high per-formance of the proposed algorithm. Experiments were carried out using an S-band DAR test-bed. The results of measured data show that the proposed algorithm is practical and effective in appli-cation.