一种在被动导引头中检测及估计两源的方法

研究了被动雷达单脉冲导引头主波束内存在两噪声调频干扰源时，对两辐射源进行检测并作角估计的方法。仿真结果表明：奈曼一皮尔逊检测方法和矩方法可实现导引头主波束内两辐射源的检测及较好的角估计。     

单脉冲导引头对多干扰源的角分辨

当多个噪声调频干扰同时进入单脉信号引头主波束内时，导引头对干扰源的角度分辨是非常困难的。本文主要研究了导引头单脉冲接收机对宽带噪声调频干扰源的响应，分析了当单脉冲主波束内存在多个噪声调频干扰源时，导引头指向角的跟踪规律，并重点讨论了一种用聚类来提取角信息的方法，最后仿真证实了该方法的正确性和有效性。     

被动单脉冲导引头对噪声调频干扰的角跟踪

研究了被动振幅和差单脉冲导引头对宽带噪声调频干扰源的响应和跟踪特性，并重点研究了主波束内存在两个非相干干扰源的情况下导引头的角跟踪规律，最后提出了在存在两个噪声调频干扰时导引头提取角信号的一种方法，计算机仿真验证了该方法的正确性和有效性。     

主被动雷达复合导引头信息融合技术研究

针对主被动雷达复合导引头,研究了基于序贯扩展Kalman滤波的信息融合算法。利用主被动雷达复合导引头对目标角误差进行观测,将匹配后的测量角度进行最优加权,进而以角度信息作为量测,估计目标的运动信息。通过试验验证,基于主被动雷达信息融合状态估计比仅依赖主动雷达观测量的状态估计稳态误差小,且滤波器收敛速度更快。     

相控阵雷达导引头捷联去耦数字平台设计

相控阵雷达导引头是未来导引头发展的一个重要领域,为了消除弹体扰动对导引头测量误差的影响,实现相控阵雷达导引头的捷联去耦,设计了一种相控阵雷达导引头捷联去耦数字平台,采用基于四元数法求取弹体的姿态角,采用相控阵雷达导引头波束扫描稳定算法实现对波束扫描误差的补偿,达到导引头捷联去耦的目的,并在MAT-LAB中对四元数法和波束扫描稳定算法进行了仿真验证,取得了较好的去耦效果,该相控阵雷达导引头捷联去耦数字平台具有原理简单、算法运算量小和去耦效率高的特点。     

半捷联导引头机电平台观测器的角速率估计

为准确获取半捷联图像导引头视线角速率,构建了Kalman观测器对电机平台进行框架角速率估计。首先,根据半捷联导引头稳定跟踪原理,建立了以Kalman观测器为状态反馈的数学模型;其次,根据编码器误差特性,应用最优估计理论计算分析了估计精度与观测器参数之间的关系;再次,在保证稳定平台带宽的前提下,设计了两种不同采样率下的Kalman观测器;最后,进行了数字仿真实验验证。结果表明:在2 000 Hz采样率下,估计算法角速率精度为0.098 9 ()/s,优于200 Hz采样率下的0.301 3 ()/s;两种采样率下导引头带宽均为59.6 rad/s,平台隔离度为1.5%。提高导引头稳定系统采样率并与相应控制参数匹配,能有效提高平台角速率估计精度。     

同时数字多波束对改善相控阵雷达搜索和测角精度的分析

文中基于数字波束合成体制的相控阵雷达,研究改善传统单脉冲体制雷达性能的方法。提出了一种基于一组同时数字多波束处理的新方法。文中证明该方法可以将单脉冲测角方法和极大似然估计测角算法进行性能的平衡。该方法利用了基于同时数字多波束形成技术以及多种处理算法。计算机仿真试验证明该方法可以在提升雷达目标检测和测角性能的同时有效的改善波束形状损失。     

捷联式光学导引头视线角速率解耦与估计

为准确估计捷联导引头视线角速率,建立了全捷联导引头数学模型,根据弹目运动相对关系进行视线角速率解耦与估计算法研究。首先,建立了全捷联导引头数学模型,并利用Taylor 级数对其进行线性化;接着,根据弹目运动几何学与坐标系相对关系推导视线角速率解耦算法;然后,针对捷联导引头无法直接测量体视线角速率的问题,提出微分+稳态Kalman 滤波方法估计体视线角速率;最后,建立视线角速率解耦与估计算法验证系统并进行仿真实验,结果表明:解耦算法绝对误差小于510-5 rad/s,相对误差小于0.3%,验证了解耦算法的正确性;在包含导引头数学模型的条件下,采用角频率为19.2 rad/s 的稳态Kalman 滤波器,视线角速率估计误差小于410-3 rad/s,较直接微分方法的估计误差提高近一个量级。视线角速率解耦与估计算法同时能满足制导系统对精度与动态性能的要求。     

基于改进C2算法的双目标角度估计方法

角度是雷达探测和跟踪目标的重要信息,主波束宽度通常决定了雷达角度分辨率,当雷达主波束范围内包含不止一个目标时,采用传统的和差测角方法只能获得多目标的合成角度位置,不能获得场景中目标的真实角度值,严重影响了雷达对真实目标的截获和跟踪。针对雷达主波束内存在双目标的场景,首先通过理论分析和数值仿真说明双目标对和差测角的影响,再结合传统C²多目标角度估计算法在和差测角中的应用,给出一种改进的C²角度估计算法。最后,通过数值仿真验证了所提出的方法在双目标回波能量相同和不同场景下均适用,且角度估计精度相对传统多目标角度估计方法明显提升。     

宽带被动导引头天线系统性能分析

详细讨论了宽带被动导引头中的关键部件－超宽频带天线的性能，并在此基础上分析了三种具备大视角特性的被动测角方法。由于宽带天线没有确定的相位中心，因此不能采用通常的振幅和差式单脉冲法进行宽频带测角。本文提出的波束运算网络可用于一个宽带螺旋天线同时得到两个交叉波束的振幅－振幅式单脉冲系统中。此外，对比幅相干测向法中的测角模糊问题也进行了研究。     

Radiological Source Detection and Localisation Using Bayesian Techniques

The problem considered in this paper is detection and estimation of multiple radiation sources using a time series of radiation counts from a collection of sensors. A Bayesian framework is adopted. Source detection is approached as a model selection problem in which competing models are compared using partial Bayes factors. Given the number of sources, the posterior mean is the minimum mean square error estimator of the source parameters. Exact calculation of the partial Bayes factors and the posterior mean is not possible due to the presence of intractable integrals. Importance sampling using progressive correction is proposed as a computationally efficient method for approximating these integrals. Previously proposed algorithms have been restricted to one or two sources. A simulation analysis shows that the proposed methods can detect and accurately estimate the parameters of four sources with reasonable computational expense.     

Detection and localization of vapor-emitting sources

We develop methods for detecting and localizing vapor-emitting sources using chemical sensor arrays. Potential applications of these methods include detection of explosives or drugs, sensing leakage of hazardous chemicals or pollution, and studying the environment. We describe the pertinent physical models, develop estimation and detection algorithms, and derive performance bounds. The algorithms are tested numerically and compared with the theoretical bounds     

Ambiguity-function-based techniques to estimate DOA of broadband chirp signals

Various methods are available to perform direction-of-arrival (DOA) estimation for random sources. However, the work on DOA estimation of deterministic sources, such as broadband chirp signals, is quite limited. This paper proposes two novel methods for broadband chirp DOA estimation (BCD), namely the incoherent broadband chirp DOA estimation (BCD-I) and coherent broadband chirp DOA estimation (BCD-C). The proposed methods exploit the time-frequency structure of the chirp signal via the ambiguity function, which converts the absolute time and frequency of the chirp signal into relative time lag and frequency difference. The algorithms can be applied to arrays of any aperture size and arbitrary chirp rate signals. The signal frequency of the source can be higher than the conventional array design frequency, and the number of sources can be greater than the number of sensors as long as the signals are separable in the ambiguity function plane. These methods can be applied to single or multiple chirps with the same or different chirp rates. The performance analysis shows that our algorithms generally provide improvements in the DOA estimation of the broadband chirp sources.     

基于模糊聚类的信源个数检测新算法

信源个数检测方法的研究是目前阵列信号处理领域的一个研究热点和重点,它的测量精度直接影响到许多高分辨测向算法的精确度。然而现有的检测方法对于不同的噪声环境非常敏感;而且随着快拍数的减少性能迅速下降。基于以上的限制与缺陷,该文提出了一种基于模糊c均值聚类的信源个数检测方法。在两种不同噪声环境下的仿真结果表明该方法的有效性和鲁棒性。     

Detection and estimation practices in radio and radar astronomy

A review is given of detection and estimation methods in current use in radio and radar astronomy. Methods of determining position and brightness distribution of radio sources by narrow-beam antennas, interferometry or lunar occultations are discussed. Observational procedures for the estimation of frequency spectra are explained. The fundamental problem in radar astronomy, viz., that of mapping, is reviewed, and several methods for the removal of ambiguities are considered. Schemes for the determination of range and velocity of a planet are also described.     

Characterization of Fault Recovery through Fault Injection on FTMP

The statistical methods used to collect and analyze fault-recovery data affect directly the credibility of reliability estimation. To provide data on which to base the development of sampling methods and parameter estimation techniques, pin-level fault-injection was conducted on the FTMP computer. Detection time was chosen for statistical analysis because it accounted for most of the variation in total recovery time. Stuck-at-zero, stuck-at-one, and inverted faults were injected on each of six pins, yielding 18 data sets. The data sets fell into groups of detection behavior; however, none of the factors that were varied in the experiment?fault type, pin, chip, or board?acounted for the groupings. While no single distribution was shown to be the best fit to all the data sets, of greater importance is that the exponential distribution was a bad fit to all data sets. This refutes a common assumption of reliability modeling that detection times are exponentially distributed. These results suggest that stratified random sampling methods and statistically robust parameter estimation techniques are required to characterize fault detection time. Further experimentation is planned to discover the sources of the variation in detection time.     

Passive localization of mixed sources jointly using MUSIC and sparse signal reconstruction

Source localization for mixed far-field and near-field sources is considered. By constructing the second-order statistics domain data of array which is only related to DOA parameters of mixed sources, we obtain the DOA estimation of all sources using the weighted ℓ₁-norm minimization. And then, we use MUSIC spectral function to distinguish the mixed sources as well as to provide a more accurate DOA estimation of far-field sources. Finally, a mixed overcomplete matrix on the basis of DOA estimation is introduced in the sparse signal representation framework to estimate range parameters. The performance of the proposed method is verified by numerical simulations and is also compared with two existing methods.     

Estimation of frequency-delay of arrival (FDOA) using fourth-orderstatistics in unknown correlated Gaussian noise sources

A new family of nonparametric and parametric methods based on fourth-order statistics for the estimation of the frequency-delay of arrival (FDOA) between two sensor signal measurements, corrupted by correlated Gaussian noise sources in an unknown way, is presented. The new family of FDOA estimation methods utilizes the fourth-order cumulants or 1-D Fourier transforms of sliced fourth-order cumulants of the two signal measurements. It is demonstrated that the new family of FDOA estimation methods suppresses the effect due to correlated Gaussian measurement noises and outperforms existing second-order statistics-based FDOA estimation methods using either cross-ambiguity function or MUSIC algorithm. Various simulation results are presented for different types of signals, different lengths of data, and different signal-to-noise ratios     

Distinguishing between moving and stationary sources using EEG/MEG measurements with an application to epilepsy

Performances of electroencephalography (EEG) and magnetoencephalography (MEG) source estimation methods depend on the validity of the assumed model. In many cases, the model structure is related to physical information. We discuss a number of statistical selection methods to distinguish between two possible models using least-squares estimation and assuming a spherical head model. The first model has a single moving source whereas the second has two stationary sources; these may result in similar EEG/MEG measurements. The need to decide between such models occurs for example in Jacksonian seizures (e.g., epilepsy) or in intralobular activities, where a model with either two stationary dipole sources or a single moving dipole source may be possible. We also show that all of the selection methods discussed choose the correct model with probability one when the number of trials goes to infinity. Finally we present numerical examples and compare the performances of the methods by varying parameters such as the signal-to-noise ratio, source depth, and separation of sources, and also apply the methods to real MEG data for epilepsy.     

The detection and resolution of optical signals

The principles of statistical signal-detection theory are applied to the detection of optical signals by postulating the photoelectric effect as the mechanism for the interaction of the electromagnetic field of the light with matter. The data with which the detection system works are the numbers of photoelectric events in various parts of a receptor on which the light falls after passing through an aperture. The theory prescribes how the data should be processed and measures the detectability of the signals in terms of an SNR. The theory is applied to the detection of a point source and of an array of incoherent point sources, all against a uniform background. The resolution of two weak close point sources of radiation observed against a uniform background is also treated as a problem in the testing of hypotheses, and the reliability of the system derived on this basis is measured by an SNR that is a function of the angular separation of the sources. The estimation of the parameters of an optical signal is discussed from the standpoint of statistical estimation theory. Estimation of the angular position of a point source against a uniform background is treated as an example.