对称三角线性调频连续波信号的检测与参数估计

结合低截获概率雷达中常用的对称三角线性调频连续波信号的特点,提出了基于Radon-Ambiguity变换（RAT）和分数阶傅立叶变换（FRFT：Fractional Fourier Transform）的对称三角线性调频连续波信号的检测与参数估计新算法。该方法不仅给出了对称三角线性调频连续泼信号的调频率和初始频率的参数估计,并且推导出了调制带宽估计的表达式。通过仿真验证了该方法的有效性并具有很好的估计性能。     

Robust parameter estimation of a deterministic signal in impulsivenoise

This paper presents a robust class of estimators for the parameters of a deterministic signal in impulsive noise. The proposed technique has the structure of the maximum likelihood estimator (MLE) but has an extra degree of freedom: the choice of a nonlinear function (which is different from the score function suggested by the MLE) that can be adjusted to improve robustness. The effect of this nonlinear function is studied analytically via an asymptotic performance analysis. We investigate the covariance of the estimates and the loss of efficiency induced by nonoptimal choices of the nonlinear function, giving special attention to the case of α-stable noise. Finally, we apply the theoretical results to the problem of estimating the parameters of a sinusoidal signal in impulsive noise     

Joint signal detection and parameter estimation in multiusercommunications

The problem of simultaneously detecting the information bits and estimating signal amplitudes and phases in a K-user asynchronous direct-sequence spread-spectrum multiple-access communication system is addressed. The joint maximum-likelihood (ML) estimator has a computational complexity that is exponential in the total number of bits transmitted and thus does not represent a practical solution to the problem. An estimator that combines a suboptimum tree-search algorithm with a recursive least-squares estimator of complex signal amplitude is considered. The complexity of this estimator is O(K²) computations per decoded bit, and its performance is very close to that of the joint ML receiver. This receiver has the advantage that the transmitted signal powers and phases are extracted from the received signal in an adaptive fashion without using a test sequence     

On simultaneous signal estimation and parameter identificationusing a generalized likelihood approach

A common approach to blind deconvolution of Bernoulli-Gaussian processes consists of performing both signal restoration and hyperparameter identification through maximization of a single generalized likelihood criterion. It is shown on a simple example that the resulting hyperparameter estimates may not converge toward any meaningful value. Therefore, other more reliable approaches should be adopted whenever possible     

Some lower bounds on signal parameter estimation

New bounds are presented for the maximum accuracy with which parameters of signals imbedded in white noise can be estimated. The bounds are derived by comparing the estimation problem with related optimal detection problems. They are, with few exceptions, independent of the bias and include explicitly the dependence on the a priori interval. The new results are compared with previously known results.     

一种LFM信号最大似然估计模型与参数估计快速算法

从最大似然估计模型入手,提出了一种适合在一般高斯噪声环境（包括色噪声）下LFM信号目标的参数估计模型和基于此估计模型的调频斜率和初始频率估计的快速算法。此方法获得了最大似然方法估计精度高的优点,且运算量比传统的最大似然方法大大降低。另外推导出了一般高斯环境下的LFM参数估计的CRB界,为一般高斯环境下的估计的参数的方差提供实际下界。     

Remarks, mostly historical, on signal detection and signal parameter estimation

Historical remarks outlining some of the evolution of the statistical theory of detecting signals in noise and of estimating signal parameters make up the greater part of the paper. The time period covered is roughly from the years of World War II to the middle 1960s. The remarks are rather severely limited in scope; they do not touch on nonparametric statistical methods, or filtering, or prediction theory, for example. The last part of the paper is a discussion of one aspect of a signal-processing problem area that is of current interest in optics and radar, and can be regarded as one of many descendants of the early parameter estimation theory. The area is object-field reconstruction or restoration, and the aspect of interest is the intrinsic relation between the inherent instability of solutions of many such problems and the precision or resolution of detail that can be attained in the construction.     

Fast detection and parameter estimation for polyphase-code radar signal

The detection and parameter estimation for polyphase-code radar signal are analysed in this article. In view of the fact that traditional algorithms of signal detection and parameter estimation have enormous computational complexity, a joint fast algorithm employed fractional operation is proposed to detect the polyphase-code radar signal and estimate its modulation parameter. The proposed algorithm firstly detects the signal and estimates the sweep rate with the detection statistics derived from fractional autocorrelation. Then proposed algorithm achieves the estimation of other modulation parameters by using fractional Fourier transform (FrFT). Simulation results have verified the effectiveness of the proposed algorithm. Compared with traditional algorithms, proposed algorithm has identical performance of detection and parameter estimation, but can remarkably reduce computational cost. The proposed algorithm is suitable for the application of practical equipment.     

A subspace rotation approach to signal parameter estimation

A new approach to Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) is described in the context of direction-of-arrival estimation, but is also applicable to other problems. The method relies on finding the underlying rotation between the common subspaces associated with an array of pairwise-matched and codirectional sensor doublets. ESPRIT has several remarkable advantages over earlier techniques such as MUSIC, and also provides asymptotically unbiased and efficient estimates.     

New EM parameter estimation algorithm for a type of micro-motion SFM signal

Aiming at the problem that the existing method has high computational complexity and high SNR,a novel method of multi-component SFM signals parameter estimation based on EM algorithm was proposed as to spinning tar-get's narrow band micro-motion echoes.The echo model of spinning target and its micro-doppler measurements based on time-frequency analysis were given.The iterative parameter estimation steps based on Gaussian mixture model and EM algorithm to measurements were established.The simulation results demonstrate that when SNR is greater than -3 dB and the SFM components are equal or greater than 2,the method can estimate the target’s micro-motion parameters e.g.projection size accurately in narrow band condition.     

低信噪比下LFM- PRBC信号参数的快速估计

为了解决目前算法中线性调频-伪码( LFM- PRBC)信号参数估计计算量较大的问题,提出了一种快速估计算法。该算法采用解线调与分数阶傅里叶变换( FRFT)进行参数的估计。首先对信号进行解线调估计出调频斜率的粗略值,然后由调频斜率确定旋转角,通过FRFT估计出码元宽度的粗略值。根据延时再进行解线调估计出调频斜率的精确值,再通过FRFT估计出码元宽度的精确值与起始频率。该算法不仅计算量较低,同时具有很高的估计精度与很强的抗噪性,仿真实验验证了该算法的有效性。     

一种扫频干扰信号参数估计方法

现代的干扰信号越来越多的采用复杂的信号波形和调制样式,为了解决对干扰信号时频分析中出现的时间分辨率和频率分辨率无法兼顾的问题,得到更加清晰明确的时频关系图,本文提出了基于平滑伪 Wigner-Ville分布（SPWVD）的时频分析方法,并通过对干扰信号的分析处理结果验证了该方法的有效性。     

A NEW METHOD OF BISPECTRAL ESTIMATION FOR TRANSIENT SIGNAL

Estimation of model parameter for transient signal is very important in many aspects. This paper presents a new Markov ARMA model Q-slice estimation algorithm for transient signal based on bispectrum. Simulation results show that this new method has some special features, such as higher estimation precision, lower amount of calculation, higher fitting effect even in lower signal-to-noise ratio (SNR) situation.     

一种跳频信号实时跟踪与参数估计方法

现有的跳频信号处理方法往往需要积累足够长的样本数据,缺乏实时快速运算的能力,无法处理高速跳频信号。在小样本条件下提出一种跳频信号实时跟踪和参数估计方法。根据跳频信号的频域稀疏性建立信号模型,引入稀疏贝叶斯学习(SBL)算法解决多观测向量(MMV)信号重构问题。在构建新的判决统计量基础上,推导一种保持恒虚警概率的跳变时刻检测方法,设计滑动策略实现跳频信号的实时跟踪。分别利用几何重心法和最小二乘法估计每跳(hop)的载波频率和来波方向(DOA)。实验证明,新方法在低信噪比(SNR)下具有更低的虚警概率,参数估计精确度得到明显提升。     

Multicarrier signal detection and parameter estimation infrequency-selective Rayleigh fading channels

A new joint signal detection and channel parameter estimation scheme is proposed for multiple subcarrier signaling with pilot symbol-assisted modulation (PSAM) schemes. The proposed scheme estimates a pair of parameters associated with the generation process of the fading frequency selectivity, which is common to all the subcarriers. This parameter estimation can effectively extract information regarding the fading frequency selectivity through the pilot symbols received not only by the subcarrier of interest, but by other ones as well. The fading complex envelope with each subcarrier is derived from the estimates of the parameter pair. With the proposed scheme, performances are evaluated through simulations and are compared with the performance of a subcarrier-by-subcarrier detection scheme     

Some results on the stochastic signal parameter estimation problem

The problem of finding maximum-likelihood estimates of the partially or completely unknown autocorrelation function of a zero-mean Gaussian stochastic signal corrupted by additive, white Gaussian noise is analyzed. It is shown that these estimates can be found by maximizing the output of a Wiener estimator-correlator receiver biased by a smoothed version of the output noise-to-signal ratio of the Wiener estimator over the class of admissible autocorrelation functions. For the case where the autocorrelation function is known except for an amplitude scale parameter, an illuminating expression for the Cramer-Rao minimum estimation variance is derived. Detailed study of this expression yields, among other results, an interpretation of the maximum-likelihood estimator as an adaptive processor.     

线性调频步进信号雷达目标运动参数估计方法

针对目标运动对线性调频步进信号雷达成像的影响,提出一种基于时频分析的目标运动参数估计方法。对差频处理后的各簇回波信号进行快速傅里叶变换,利用变换后信号与子脉冲序列的线性调频关系,通过维格纳分布和Radon变换对目标不同时刻运动参数进行估计。结合二值数学形态学与图像边缘检测方法提高对多散射点目标运动参数估计的精度。在此基础上,针对目标可能出现的变速运动情况,利用最小二乘算法拟合整个成像时间内目标运动速度的变化,较好地实现对机动目标的运动参数估计。仿真实验验证了本文方法的有效性。     

Detection of random transient signals via hyperparameter estimation

Difficulties arise with the generalized likelihood ratio test (GLRT) in situations where one or more of the unknown signal parameters requires an enumeration that is computationally intractable. In the transient signal detection problem, the frequency characteristics of the signal are typically unknown; therefore, even if an aggregate signal bandwidth is assumed, the estimation problem intrinsic to the GLRT requires an enumeration of all possible sets of signal locations within the monitored band. In this paper, a prior distribution is imposed over those portions of the signal parameter space that traditionally require enumeration. By replacing intractable enumeration over possible signal characteristics with an a priori signal distribution and by estimating the “hyperparameters” (of the prior distribution) jointly with other signal parameters, it is possible to obtain a new formulation of the GLRT that avoids enumeration and is computationally feasible. The GLRT philosophy is not changed by this approach-what is different from the original GLRT is the underlying signal model. The performance of this new approach appears to be competitive with that of a scheme of emerging acceptance: the “power-law” detector