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.

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。

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.

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.

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.

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...

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.