Multistatic radar analysis based on ambiguity function and Cramér-Rao lower bounds

The primary goal of this work is to characterize the impact of weighting selection strategy and multistatic geometry on the multistatic radar performance.With the relationship between the multistatic ambiguity function(AF) and the multistatic Cramér-Rao lower bound(CRLB),the problem of calculating the multistatic AF and the multistatic CRLB as a performance metric for multistatic radar system is studied.Exactly,based on the proper selection of the system parameters,the multistatic radar performance can be significantly improved.The simulation results illustrate that the multistatic AF and the multistatic CRLB can serve as guidelines for future multistatic fusion rule development and multistatic radars deployment.

Direction finding of bistatic MIMO radar in strong impulse noise

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

一种阵列互耦影响下的目标直接定位算法及其理论性能分析

相比传统的两步定位方法,以Weiss和Amar等人提出的目标位置直接确定方法具有定位精度高、目标分辨率高、可避免"目标-量测"匹配等优势.本文基于该类定位方法的思想,提出了一种利用单个运动天线阵列对目标辐射源的直接定位算法.与已有直接定位算法不同的是,文中新算法考虑了阵列互耦的影响,并且能够实现阵列互耦参量和目标位置参量的解耦合估计.此外,文中还基于矩阵特征值扰动理论,定量推导了新算法参数估计的理论方差(包括目标位置估计方差和互耦向量估计方差),并且给出了全部参量估计方差的克拉美罗界.最后,该文通过仿真实验验证了新算法的优越性和理论分析的有效性.

无源定位观测方程的两类伪线性化方法及渐近最优闭式解

为避免无源定位中的迭代运算,该文针对两类特殊的无源定位(非线性)观测方程,分别提出将其进行伪线性化处理,从而实现目标位置闭式解算的理论分析框架.首先,在不限定具体物理观测量的前提下,归纳总结出两类将非线性观测方程转化为伪线性观测方程的数学模型,并推导出用于目标定位的加权线性最小二乘闭式解.接着,利用一阶误差分析方法定量分析两类闭式解的理论定位方差,并证明其参数估计性能均能够达到相应的克拉美罗界(在门限效应发生前),从而证明闭式解的渐近最优性.最后,文中以AOA/TOA联合定位和AOA/TDOA/FDOA联合定位为算例,分别阐述两类伪线性化无源定位方法的具体应用,并通过仿真实验验证文中理论分析的有效性.

基于声矢量传感器阵列的空速估计算法

研究了一种新型的空速测量方法。通过引入大气声学中的有效声速概念,建立了稳定气流作用下声矢量传感器阵列的近场输出模型,模型的阵列流形矢量中包含了待估计的空速信息。在此基础上提出了一种基于多重信号分类(multiple signal classification,MUSIC)的空速估计(airspeed estimation,ASE)算法,该算法可用于对空速的高精度估计。为了降低计算复杂度,进一步提出了一种快速的空速估计(fast airspeed estimation,FASE)算法,该算法虽然在ASE的精度上不如MUSIC-ASE算法,但无需谱搜索,具有更强的实时性。最后,对算法的估计性能进行分析,推导了ASE的克拉美-罗界表达式。仿真实验验证了算法的有效性。

L_(CR)波应力检测中互相关时延估计的误差分析

根据声弹性理论,将应力检测转化为超声波的传播时延估计,建立基于互相关时延估计的临界折射纵波(LCR波)应力检测系统。LCR波在发射、传播及接收过程中存在各种影响因素,这些因素导致接收到的LCR波波形及信噪比发生了较大变化。通过克拉美罗下界(CRLB)理论得到这些变化是限制时延估计精度的根本因素。利用MATLAB进行仿真验证,得到了不同的影响因素对时延估计精度的影响不同。

ZP-OFDM系统中基于信号子空间迭代拟合的盲信道估计

介绍了一种用于ZP-OFDM系统的基于子空间拟合的盲信道估计算法,该算法采用信号子空间迭代拟合技术代替了传统的噪声子空间信道估计中奇异值分解获取子空间进行估计信道。仿真表明,在低信噪比下该算法获得了比传统的噪声子空间信道更优的估计性能,更接近ZP-OFDM盲信道估计的CRB(Cramér-Raobound)。

可容忍信标误差的三维传感网节点定位方法

针对信标位置存在误差情况下的三维无线传感器网络节点定位问题,提出一种基于正交回归的多跳定位方法.同时考虑到自变量误差和因变量偏差对节点坐标估计的影响,基于约束加权正交回归参数估计准则,建立可容忍信标位置误差的三维多跳定位模型,解决了信标位置和距离估计两方面的误差并存时的节点自定位问题,并给出求解节点坐标最优值的数值方法;推导出相应的坐标估计精度评估标准3D-MCRB(3D Multi-hop Cramér-RaoBound).仿真结果表明:此方法对信标位置误差和距离估计误差都具有较好的抑制能力,在大多数实验条件下,能将定位精度提高10%以上.

差分混沌键控系统接收信噪比评估

本文研究了在多径瑞利衰落信道下差分混沌键控（DCSK）系统的接收信噪比（SNR）评估问题。通过使用数据信息和导频信息及基于最大似然（ML）准则,推导了数据辅助（DA）和非数据辅助（NDA）场景下DCSK系统的SNR估计器。为了衡量SNR估计器的有效性,推导全数据辅助（FDA）估计器和NDA估计器的克拉美-劳下界（CRLBs）。仿真结果表明,在大的扩频因子下,SNR估计器的性能能够很好地靠近CRLBs。最后,将NDA估计器用于具有信道编码的DCSK系统,设计一种盲迭代信道估计与译码器,结果表明,瑞利多径衰落信道下,盲迭代译码器获得了很好的误码率（BER）性能。

Time resource management of OAR based on fuzzy logic priority for multiple target tracking

For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming（CCP） employing fuzzy logic priority is proposed for opportunistic array radar（OAR）. In this scheme,the total beam illuminating time is minimized by effective time resource allocation so that the desired tracking performance is achieved. Meanwhile, owing to the randomness of radar cross section（RCS）, the CCP is used to balance tracking accuracy and time resource conditioned on the specified confidence level. The adaptive fuzzy logic prioritization, imitating the human decision-making process for ranking radar targets, can realize the full potential of radar. The Bayesian Crame ′r-Rao lower bound（BCRLB） provides us with a low bound of localization estimation root-mean-square error（RMSE）, and equally important, it can be calculated predictively. Consequently, it is employed as an optimization criterion for the time resource allocation scheme. The stochastic simulation is integrated into the genetic algorithm（GA） to compose a hybrid intelligent optimization algorithm to solve the CCP optimization problem. The simulation results show that the time resource is saved strikingly and the radar performance is also improved.

An Informational Approach to the Sinusoidal Law of Photon Polarization

The photon polarization law po ＝ sin2θ is derived from a simple informational consideration by twomethods: The first is via an intuitive principle of mininum Fisher information, the second is via a symmetry andinvariance argument. The results demonstrate that in photon polarization, Nature has a tendency to hide herselfas deepas possible while obeying some regular conditions.

Robust Frequency Estimation Under Additive Mixture Noise

In many applications such as multiuser radar communications and astrophysical imaging processing,the encountered noise is usually described by the finite sum ofα-stable(1≤α<2)variables.In this paper,a new parameter estimator is developed,in the presence of this new heavy-tailed noise.Since the closed-formPDF of theα-stable variable does not exist exceptα=1 andα=2,we take the sum of the Cauchy(α=1)and Gaussian(α=2)noise as an example,namely,additive Cauchy-Gaussian(ACG)noise.The probability density function(PDF)of the mixed random variable,can be calculated by the convolution of the Cauchy’s PDF and Gaussian’s PDF.Because of the complicated integral in the PDF expression of the ACG noise,traditional estimators,e.g.,maximum likelihood,are analytically not tractable.To obtain the optimal estimates,a new robust frequency estimator is devised by employing the Metropolis-Hastings(M-H)algorithm.Meanwhile,to guarantee the fast convergence of the M-H chain,a new proposal covariance criterion is also devised,where the batch of previous samples are utilized to iteratively update the proposal covariance in each sampling process.Computer simulations are carried out to indicate the superiority of the developed scheme,when compared with several conventional estimators and the Cramér-Rao lower bound.

机载单站无源定位系统中相位差变化率提取方法(英文)

As an important parameter in the single airborne passive locating system, the rate of phase difference change contains range information of the radio emitter. Taking single carrier sine pulse signals as an example, this article illustrates the principle of passive location through measurement of rates of phase difference change and analyzes the structure of measurement errors. On the basis of the Cramér-Rao lower bound (CRLB), an algorithm associated with time-chips is proposed to determine the rates of pha...

A two-step weighted least-squares method for joint estimation of source and sensor locations: A general framework

It is well known that the Two-step Weighted Least-Squares(TWLS) is a widely used method for source localization and sensor position refinement. For this reason, we propose a unified framework of the TWLS method for joint estimation of multiple disjoint sources and sensor locations in this paper. Unlike some existing works, the presented method is based on more general measurement model, and therefore it can be applied to many different localization scenarios.Besides, it does not have the initialization and local convergence problem. The closed-form expression for the covariance matrix of the proposed TWLS estimator is also derived by exploiting the first-order perturbation analysis. Moreover, the estimation accuracy of the TWLS method is shown analytically to achieve the Cramér-Rao Bound(CRB) before the threshold effect takes place. The theoretical analysis is also performed in a common mathematical framework, rather than aiming at some specific signal metrics. Finally, two numerical experiments are performed to support the theoretical development in this paper.

Distribution system state estimation: an overview of recent developments

In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of(industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate,fast, and robust power system state estimation(PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states(i.e., nodal voltage phasors).Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed.Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.

A new iterative algorithm for geolocating a known altitude target using TDOA and FDOA measurements in the presence of satellite location uncertainty

This paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements obtained at satellites. The number of satellites available for the geolocation task is more than sufficient and their locations are subject to random errors. This paper derives the constrained Cramor-Rao lower bound （CCRLB） of the target position, and on the basis of the CCRLB analysis, an approximately efficient constrained maximum likelihood estimator （CMLE） for geolocating the target is established. A new iterative algorithm for solving the CMLE is then proposed, where the updated target position estimate is shown to be the globally optimal solution to a generalized trust region sub-problem （GTRS） which can be found via a simple bisection search. First-order mean square error （MSE） analysis is conducted to quantify the performance degradation when the known target altitude is assumed to be precise but indeed has an unknown but deterministic error. Computer simulations are used to compare the performance of the proposed iterative geolocation technique with those of two benchmark algorithms. They verify the approximate efficiency of the proposed algorithm and the validity of the MSE analysis.

Closed form algorithm of double-satellite TDOA+AOA localization based on WGS-84 model

In this paper,we consider the double-satellite localization under the earth ellipsoid model of the Wideband Geodetic System(WGS-84)using the Time Difference of Arrival(TDOA)and the Angle-of-Arrival(AOA).Several closed-form solution algorithms via the pseudolinearization of the measurement equations are presented to efficiently estimate the location.These algorithms include the Weighted Least Squares(WLS),the Constrained Total Least Squares(CTLS),and the Taylor-Series Iteration(TSI).Performance comparison of the proposed methods with the Cramér-Rao Lower Bound(CRLB)in the simulation is shown to demonstrate that the proposed algorithms are feasible and have stable performance.

Error Data Analytics on RSS Range-Based Localization

The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods.Due to the inevitable measurement error,the analytics on the error data is critical to evaluate localization methods and to find the effective ones.For indoor localization,Received Signal Strength(RSS)is a convenient and low-cost measurement that has been adopted in many localization approaches.However,using RSS data for localization needs to solve a fundamental problem,that is,how accurate are these methods?The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data.In this proposed work,we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cram′er-Rao Lower Bound(CRLB).Through mathematical techniques,the key factors that affect the accuracy of RSS-based localization methods are revealed,and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived.The significance of our discovery has two folds:First,we present a general expression for localization error data analytics,which can explain and predict the accuracy of range-based localization algorithms;second,the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.