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） employi...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.展开更多
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 parame...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.展开更多
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 ...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.展开更多
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, th...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...展开更多
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...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.展开更多
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 ...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.展开更多
基金supported by the National Natural Science Foundation of China(6127132761671241)
文摘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.
基金supported by National Natural Science Foundation of China(Grant No.52075397,61905184,61701021)Fundamental Research Funds for the Central Universities(Grant No.FRF-TP-19-006A3).
文摘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.
基金This work was supported by the National Natural Science Foundation of China(62073093)the Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province(LBH-Q19098)+1 种基金the Heilongjiang Provincial Natural Science Foundation of China(LH2020F017)the Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology.
文摘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.
基金Aeronautical Science Foundation of China (2007ZC53030)
文摘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...
基金supported by Meteorological information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes of Chengdu University of Information Technology,China(No.QXXCSYS201702)
文摘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.
基金partially supported by the National Key Research and Development Program of China(No.2016YFE0121800)
文摘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.
