A Blind Frequency Offset Estimator for Coherent M-PSK System in Autonomous Radio

This paper presents a novel blind frequency offset estimator for coherent M-PSK systems in an autonomous radio. The proposed estimator is based on the spectrum of the signal’s argument. A data removal block is developed. We derive the distribution of the instantaneous phase, which is applied to indicate that the proposed estimator can be considered as a class of nonlinear least-squares estimator. We provide a method to analyze the asymptotic performance of the proposed estimator. This enable us to predict the mean-square error on frequency offset estimation for all signal-to-noise ratio (SNR) values. Computer simulations indicate that the proposed estimator achieves better performance than the original estimator. The performance of the proposed estimator as a blind estimator is also illustrated.     

Bias compensation for the bearings-only pseudolinear target track estimator

The bearings-only pseudolinear target track estimator is known to suffer from severe bias problems. This paper presents a bias analysis for the pseudolinear estimator and develops a method of bias compensation, resulting in a closed-form reduced-bias pseudolinear estimator. The reduced-bias estimator is then incorporated into an instrumental variable estimator to produce asymptotically unbiased target motion parameter estimates. Unlike batch iterative estimators, the proposed instrumental variable estimator has a closed-from solution and therefore avoids the convergence problems associated with iterative estimators. The performance of the proposed instrumental variable estimator is illustrated by way of simulation examples and is shown to be almost identical to that of the computationally more demanding iterative maximum likelihood estimator.     

Maximum likelihood estimation of local average SNR in Rician fading channels

A maximum likelihood estimator for the local average signal-to-noise ratio in a slowly fading Rician channel is proposed. The probability density function of the estimator is derived and the mean, the variance and the mean square error of the estimator are obtained. The performance of the estimator is examined. Numerical results show the asymptotic optimality of the estimator.     

基于循环互相关的LFM信号时延估计及性能分析

研究了复线性调频信号的循环互相关估计时延问题。利用信号循环平稳特性,分析了存在时延条件下的复线性调频信号的循环统计特性,同时提出了基于循环互相关包络的时延估计算法,然后分析了其估计性能,推导了循环互相关包络时延估计后的输出信噪比表达式。最后采用计算机仿真得到了估计性能的曲线,仿真实验验证了文中的结论。     

OFDM系统基于自适应定阶的MMSE信道估计

MMSE估计是OFDM系统中常用的信道估计算法。针对MMSE信道估计的失配问题,该文提出了一种基于自适应定阶的MMSE估计算法。该算法通过对信道最大多径时延的估计,自适应地调整信道自相关函数的产生,获得接近匹配时的MMSE估计效果。     

Estimation of scale parameters for two exponential distributions[reliability theory]

The scale parameter of the exponential distribution is estimated using conditional specification. When there are two censored samples available for estimating the scale parameter, a preliminary test is usually used to determine whether to pool the samples or to use the individual minimum-variance unbiased estimator. This latter estimator (usual preliminary-test estimator) is studied. The optimum levels of significance and their corresponding critical values for the preliminary test are obtained on the basis of the minimax regret criterion. A preliminary-test shrinkage estimator is proposed, and the optimum values of its shrinkage estimator is proposed, and the optimum values of its shrinkage coefficients are obtained. For a mean-square-error criterion of goodness of estimation, the preliminary-test shrinkage estimator is better than the usual preliminary-test estimator     

A MAP Estimator for the m Parameter in Nakagami Fading Ultra-Wide Bandwidth Indoor Channels

A maximum a posteriori (MAP) estimator for the Nakagami m parameter in an ultra-wide bandwidth (UWB) indoor channel is proposed. Previous work exclusively studies maximum likelihood (ML) estimation and moment method (MM) estimation of the Nakagami m parameter. This letter derives the MAP estimator for the Nakagami m parameter by using the a priori probabilities of the Nakagami fading parameters in an indoor UWB channel. The performance of the MAP estimator is examined and compared with those of the ML estimator and the MM estimator. Numerical results demonstrate that the new MAP estimator is superior to the ML estimator and the MM estimator in an indoor UWB channel, especially when the sample size in the estimation is small     

Estimating the correlation coefficient of highly correlated clutter

A maximum-likelihood estimator for the pulse-to-pulse correlation coefficient r of Rayleigh-distributed clutter components is considered. For r greater than 0.9, an approximate computationally feasible estimator is derived. Results of Monte Carlo simulation indicate that the approximate estimator is only slightly inferior to the maximum-likelihood estimator.     

On Shrinkage Estimation of the Exponential Scale Parameter

This paper shows some simple shrunken estimators for the scale parameter of an exponential distribution and compares them with minimum MSE estimator and the estimator proposed by Pandey We have also obtained a Bayes estimator, which is a shrinkage estimator and has smaller MSE than the estimator (sample mean) n/(n + 1) if sample size, n, is small and other restrictions apply.     

Load sensing bearing based road-tyre friction estimation considering combined tyre slip

This work discusses a road-tyre friction estimator considering combined tyre slip. The friction estimator design is motivated by its importance in vehicle dynamics control as accurate friction estimation can improve performance and safety. The estimator uses tyre force measurements from Load Sensing Bearing (LSB) technology and does not rely on parameterized tyre model. The tyre force measurements benefit the estimator mainly because of the uncertainties and nonlinearities of the tyre force characteristics. The proposed estimator uses tyre slip and tyre force representations where the longitudinal and lateral tyre slips and forces are combined into a single tyre slip and tyre force values. This representation makes the method effective during pure longitudinal dynamics, pure lateral dynamics and for combined slip. In addition, individual tyre-road friction estimation is possible with the proposed estimator and a computationally inexpensive algorithm, suitable for real-time implementation, is used to estimate the friction. The estimator is studied in simulation during pure braking, pure cornering and for combined slip. Further, the estimator is simulated in closed loop with a yaw rate controller to study whether the estimator improves vehicle safety. Finally the estimator is validated using test data from several maneuvers performed on a test vehicle instrumented with LSB technology.     

An alternative blind feedforward symbol timing estimator using two samples per symbol

Recently, S.J. Lee proposed a blind feedforward symbol timing estimator that exhibits low computational complexity and requires only two samples per symbol (see IEEE Commun. Lett., vol.6, p.205-7, 2002). We analyze Lee's estimator rigorously by exploiting efficiently the cyclostationary statistics present in the received oversampled signal; its asymptotic (large sample) bias and mean-square error (MSE) are derived in closed-form expression. A new blind feedforward timing estimator that requires only two samples per symbol and presents the same computational complexity as Lee's estimator is proposed. It is shown that the proposed new estimator is asymptotically unbiased and exhibits smaller MSE than Lee's estimator. Computer simulations are presented to illustrate the performance of the proposed new estimator with respect to Lee's estimator and existing conventional estimators.     

Covariance shaping least-squares estimation

A new linear estimator is proposed, which we refer to as the covariance shaping least-squares (CSLS) estimator, for estimating a set of unknown deterministic parameters, x, observed through a known linear transformation H and corrupted by additive noise. The CSLS estimator is a biased estimator directed at improving the performance of the traditional least-squares (LS) estimator by choosing the estimate of x to minimize the (weighted) total error variance in the observations subject to a constraint on the covariance of the estimation error so that we control the dynamic range and spectral shape of the covariance of the estimation error. The presented CSLS estimator is shown to achieve the Cramer-Rao lower bound for biased estimators. Furthermore, analysis of the mean-squared error (MSE) of both the CSLS estimator and the LS estimator demonstrates that the covariance of the estimation error can be chosen such that there is a threshold SNR below which the CSLS estimator yields a lower MSE than the LS estimator for all values of x. As we show, some of the well-known modifications of the LS estimator can be formulated as CSLS estimators. This allows us to interpret these estimators as the estimators that minimize the total error variance in the observations, among all linear estimators with the same covariance.     

Approximate MLEs for the location and scale parameters of thehalf-logistic distribution with type-II right-censoring

For the half-logistic distribution the maximum likelihood method does not provide an explicit estimator for the scale parameter based on either complete or right-censored samples. The authors provide a simple method of deriving an explicit estimator by approximating the likelihood function. The bias and variance of this estimator are studied, and it is shown that this estimator is as efficient as the best linear unbiased estimator. An example to illustrate the method is presented     

Frequency estimation of a single complex sinusoid using a generalized Kay estimator

This letter introduces a generalized version of Kay's estimator for the frequency of a single complex sinusoid in complex additive white Gaussian noise. The Kay estimator is a maximum-likelihood (ML) estimator at high signal-to-noise ratio (SNR) based on differential phase measurements with a delay of one symbol interval. In this letter, the corresponding ML estimator with an arbitrary delay in the differential phase measurements is derived. The proposed estimator reduces the variance at low SNR, compared with Kay's original estimator. For certain delay values, explicit expressions for the window function and the corresponding high SNR variance of the proposed generalized Kay (GK) estimator are presented. Furthermore, for some delay values, the window function is nearly uniform and the implementation complexity is reduced, compared with the original Kay estimator. For a delay value of two, we show that the variance at asymptotically high SNR approaches the Cramer-Rao bound as the sequence length tends to infinity. We also explore the effect of exchanging the order of summation and phase extraction for reduced-complexity reasons. The resulting generalized weighted linear predictor estimator and the GK estimator are compared with both autocorrelation-based and periodogram-based estimators in terms of computational complexity, estimation range, and performance at both low and high SNRs.     

A new non-data-aided feedforward symbol timing estimator using twosamples per symbol

A new non-data-aided feedforward symbol timing estimator is proposed, which can apply to two samples per symbol. Numerical results show the proposed estimator is approximately unbiased. Computational complexity of the new estimator is even simpler than conventional estimators with two samples per symbol. Estimation accuracy of the new estimator is similar to that of the conventional estimator with four samples per symbol presented by Oerder and Meyr in 1988, which is the simplest among the estimators using four samples per symbol. While some conventional estimators have better estimation accuracy than the proposed, this estimator has an attractive advantage over the formers from an implementation point of view     

Low-complexity feedforward symbol timing estimator using conditional maximum-likelihood principle

A low complexity feedforward symbol-timing estimator based on the conditional maximum-likelihood principle is proposed. An approximation is applied to the Fourier series expansion of the conditional maximum-likelihood function such that implementation complexity is greatly reduced. It is shown that the proposed estimator can be viewed as a generalization of the well-known square nonlinearity estimator proposed by Oerder and Meyr in 1988. Simulation results show that the performance of the proposed estimator is very close to the conditional Cramer-Rao bound and is better than that of the square nonlinearity estimator.     

一种新的K分布形状参数估计器

该文提出了一种将已有的U估计器和X估计器结合起来估计K分布形状参数的新估计器。仿真结果表明,与以前提出的方法相比,新估计器在我们所关心的小v值范围能够提供更加准确的形状参数估计,几乎相当于通过数值方法计算的最大似然估计器的性能。     

Shrinkage estimation of scale parameter of the extreme-valuedistribution

The author studies the usual preliminary test estimator of the scale parameter of the extreme-value distribution in censored samples. The optimum levels of significance and their corresponding critical values for the preliminary test are obtained based on the minimax regret criterion. A preliminary test shrinkage estimator that is smoother than the usual preliminary test estimator is proposed as well. The optimum values of shrinkage coefficients for the preliminary test shrinkage estimator are obtained, and are also based on the minimax regret criterion. Comparison of these two estimators shows that if the mean square error is a criterion of goodness of estimation then the preliminary test shrinkage estimator is better than the usual preliminary test estimator     

Estimating the cumulative downtime distribution of a highlyreliable component

The distribution of the cumulative downtime for a highly reliable component over an interval of time is approximated using a compound Bernoulli process. Given a set of observed cumulative downtimes, the maximum likelihood (ML) and uniformly minimum variance unbiased (UMVU) estimator of the approximate cumulative downtime distribution are derived under the assumption of exponential repair times and are compared to the nonparametric estimator. The ML estimator is more efficient than the UMVU estimator which itself is more efficient than the nonparametric estimator