一种基于非线性高频波形的双通道参数估计器

传统的跟踪雷达中使用多普勒滤波器组结构的距离、速度联合估计器是理论上的最大似然估计器,这种估计器结构复杂开销大,在多目标环境下对小目标检测的损失,在对具有比较宽的多普勒响应范围的非线性调频（NonLinear Frequency Modulation,NLFM)波形优化设计的基础上,文章提出了一种双通道非线性调频脉压结构的参数估计器,并分析了其理论性能,给出了相应的仿真结果。     

高速高机动弱小目标检测方法研究

本文针对高速高机动弱小目标检测中的距离走动和多普勒走动问题,提出一种多普勒频率补偿加尺度变换的目标检测方法.首先通过多普勒频率估计构建多普勒频率补偿函数,消除速度引起距离走动的影响,其次采用尺度变换方法消除多普勒调频率的影响,从而使积累时间不再受目标运动的限制.该方法无需多普勒调频率估计,解决了传统弱小目标检测方法中多普勒调频率估计准确性问题.在进行多普勒频率估计时,提出一种无模糊多普勒频率估计方法,该方法避免了模糊因子估计,大大减小了计算量,同时降低了对输入信噪比的要求,且有利于多目标情况.仿真结果验证了所提方法大大提高了高速高机动弱小目标的检测性能.     

基于时间-调频斜率分布的多普勒调频率估计

该文提出基于时间-调频斜率分布(TCD)的目标回波线性调频(LFM)信号的多普勒调频斜率估计算法。分析目标回波LFM信号的TCD表明自项和交叉项均在多普勒调频斜率处取得极大值,采用垂直调频斜率轴投影积分可以有效抑制TCD的非多普勒调频斜率交叉项及噪声,增强多普勒调频斜率项。同时,采用二次搜索方法搜索TCD投影积分量最大值保证多普勒调频斜率估计精度,有效减少运算量;理论分析表明,通过合理控制搜索步长,可以使得计算耗时在一定条件下最小。仿真实验验证了该方法的有效性,该方法具有较高的估计精度以及抗噪性能。     

激光多普勒测速中连续波调频非线性的分析与校正

基于多普勒效应的线性调频激光测速系统的原理是:激光照射到运动目标,引起光束频率发生改变,返回的光束与本振光进行相干混频后得到多普勒频移,进而可以推算出目标的相对运动速度值,但调频的非线性严重影响了测量结果的准确性。仿真分析了调频非线性对测量的影响,调频非线性会使混频后频移产生误差,造成速度测量不准,并且影响速度极性的判断,验证了激光调频线性度对测速有很大影响的结论,并对校正调频非线性提出了可行的方法,完成了调频线性化的矫正,降低了系统测量误差。     

基于Radon-Wigner变换的双基地SAR多普勒调频率估计

在传统单站机载合成孔径雷达（SAR）中,多普勒调频率的估计精度直接影响着成像的质量。在双基地SAR中该参数同样重要,但由于收发平台的独立性,多普勒调频率估计的难度增加,采用传统的方法效果不明显。该文采用了一种基于Radon-Wigner变换的多普勒调频率估计方法,首先给出了机载双基地SAR的空间几何模型和回波信号模型,然后给出了理论分析和仿真,并用单基地SAR回波实际数据进行了多普勒调频率估计。结果表明：该方法对目标统计特性的变化不敏感,它适合多分量和信噪比不高的情况,且估计值精度较高。     

高速运动目标瞬态极化散射矩阵的校准

在瞬态极化雷达测量体制下,利用正负斜率线性调频脉冲虽然可以在一个脉冲周期内测量得到目标的极化散射矩阵,但对于高速运动目标,如果不对多普勒频移加以补偿,测量结果将产生误差.本文提出了利用正负斜率线性调频脉冲对在一个脉冲周期内估计目标多普勒频移的方法,并分析了其信号处理过程,导出了多普勒频移估计的表达式,可以对匹配滤波器进行多普勒补偿,提高极化散射矩阵的测量精度.仿真结果证明了该方法的可行性和有效性.     

一种FM-CW信号非线性估计方法

线性调频连续波是雷达系统的一种常用信号形式。分析了线性调频连续波合成孔径雷达的调频非线性对距离维成像的影响,在此基础上,针对一种基于＂虚拟目标＂的非线性估计方法进行了分析,给出了基于该方法实用的计算方法,并分析了该计算方法的性能。仿真结果表明,给出的计算方法能较精确地估计出调频信号的非线性。     

一种可使MD自聚焦算法失效的SAR干扰方法研究

在SAR成像中，MD算法是一种对多普勒调频斜率进行估计的自聚焦处理过程。文章提出一种转发式SAR干扰方法，通过在子视图像中产生位置互不重叠的假目标，可以使MD算法对多普勒调频斜率估计失效，从而降低SAR图像质量。最后给出了计算机仿真，验证了此干扰方法的可行性。     

基于最小熵DCFT的双基地SAR多普勒参数估计

多普勒参数估计是SAR成像技术研究中的一项关键技术,多普勒参数估计精度将直接影响机载双基地SAR系统成像质量,为了实现对感兴趣目标区域有效地成像,需要对回波信号的参数进行准确估计。本文提出基于最小熵的离散调频傅里叶法(DCFT),该方法基于线性调频信号,可同时估计多普勒质心和多普勒斜率,并用RD算法对双基地SAR仿真数据成像,通过仿真验证了该方法的性能。     

毫米波LFMCW雷达加速运动目标回波检测与加速度-速度估计

在毫米波线性调频连续波（LFMCW）雷达中,目标加速度存在使回波多普勒信号受到二次项调制,造成多普勒频谱畸变,从而导致目标检测性能下降和参数估计精度损失．采用最大似然模型进行加速运动目标检测和加速度-速度估计,提出了适合在一般高斯噪声环境中（包括色噪声）该模型的速度-加速度联合估计快速算法．另外也推导出了一般高斯环境下Chirp信号参数估计的CRB界,为一般高斯环境下Chirp信号参数的方差提供了实际下界．     

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.     

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。     

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.     

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.     

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.     

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.     

Robust mean-squared error estimation in the presence of model uncertainties

We consider the problem of estimating an unknown parameter vector x in a linear model that may be subject to uncertainties, where the vector x is known to satisfy a weighted norm constraint. We first assume that the model is known exactly and seek the linear estimator that minimizes the worst-case mean-squared error (MSE) across all possible values of x. We show that for an arbitrary choice of weighting, the optimal minimax MSE estimator can be formulated as a solution to a semidefinite programming problem (SDP), which can be solved very efficiently. We then develop a closed form expression for the minimax MSE estimator for a broad class of weighting matrices and show that it coincides with the shrunken estimator of Mayer and Willke, with a specific choice of shrinkage factor that explicitly takes the prior information into account. Next, we consider the case in which the model matrix is subject to uncertainties and seek the robust linear estimator that minimizes the worst-case MSE across all possible values of x and all possible values of the model matrix. As we show, the robust minimax MSE estimator can also be formulated as a solution to an SDP. Finally, we demonstrate through several examples that the minimax MSE estimator can significantly increase the performance over the conventional least-squares estimator, and when the model matrix is subject to uncertainties, the robust minimax MSE estimator can lead to a considerable improvement in performance over the minimax MSE estimator.     

A competitive minimax approach to robust estimation of random parameters

We consider the problem of estimating, in the presence of model uncertainties, a random vector x that is observed through a linear transformation H and corrupted by additive noise. We first assume that both the covariance matrix of x and the transformation H are not completely specified and develop the linear estimator that minimizes the worst-case mean-squared error (MSE) across all possible covariance matrices and transformations H in the region of uncertainty. Although the minimax approach has enjoyed widespread use in the design of robust methods, we show that its performance is often unsatisfactory. To improve the performance over the minimax MSE estimator, we develop a competitive minimax approach for the case where H is known but the covariance of x is subject to uncertainties and seek the linear estimator that minimizes the worst-case regret, namely, the worst-case difference between the MSE attainable using a linear estimator, ignorant of the signal covariance, and the optimal MSE attained using a linear estimator that knows the signal covariance. The linear minimax regret estimator is shown to be equal to a minimum MSE (MMSE) estimator corresponding to a certain choice of signal covariance that depends explicitly on the uncertainty region. We demonstrate, through examples, that the minimax regret approach can improve the performance over both the minimax MSE approach and a "plug in" approach, in which the estimator is chosen to be equal to the MMSE estimator with an estimated covariance matrix replacing the true unknown covariance. We then show that although the optimal minimax regret estimator in the case in which the signal and noise are jointly Gaussian is nonlinear, we often do not lose much by restricting attention to linear estimators.     

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     

Computationally efficient angle estimation for signals with knownwaveforms

This paper presents a large sample decoupled maximum likelihood (DEML) angle estimator for uncorrelated narrowband plane waves with known waveforms and unknown amplitudes arriving at a sensor array in the presence of unknown and arbitrary spatially colored noise. The DEML estimator decouples the multidimensional problem of the exact ML estimator to a set of 1-D problems and, hence, is computationally efficient. We shall derive the asymptotic statistical performance of the DEML estimator and compare the performance with its Cramer-Rao bound (CRB), i.e., the best possible performance for the class of asymptotically unbiased estimators. We will show that the DEML estimator is asymptotically statistically efficient for uncorrelated signals with known waveforms. We will also show that for moderately correlated signals with known waveforms, the DEML estimator is no longer a large sample maximum likelihood (ML) estimator, but the DEML estimator may still be used for angle estimation, and the performance degradation relative to the CRB is small. We shall show that the DEML estimator can also be used to estimate the arrival angles of desired signals with known waveforms in the presence of interfering or jamming signals by modeling the interfering or jamming signals as random processes with an unknown spatial covariance matrix. Finally, several numerical examples showing the performance of the DEML estimator are presented in this paper