基于FRFT的LFMCW雷达加速动目标检测与参数估计

在线性调频连续波雷达中,目标加速度使回波多普勒信号受到速度和加速度的调制,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.采用分数阶傅立叶变换的快速算法实现了加速运动目标回波检测和加速度-速度估计,并提出了一种自适应多级搜索算法实现了加速度-速度的快速估计,该算法具有运算复杂度低、精度高的特点.另外推导出了一般高斯白噪声环境下Chirp信号参数估计的CRB界,为高斯白噪声环境下参数估计的方差提供了实际下界.     

正弦调制相位信号参数估计的Cramer-Rao下限

正弦凋制相位信号是微动目标雷达回波微多谱勒信号的一般形式,微动特征提取最终可以转化为这类信号的参数估计问题.本文首先给出了目标微动背景下正弦调制相位信号的一般彤式以及各参数对应的物理意义,重点推导了高斯白噪声条件下,正弦调制相位信号参数估计的Cramer-Rao下限,得到了估计相位幅度和频率的Cramer-Rao下限与信噪比及数据长度之间的近似关系,并给出了近似所需满足的条件.最后通过理论推导和仿真计算得到了近似处理的误差曲线,证明了近似的合理性.     

基于极大似然估计的高分辨雷达测角方法

研究了高分辨雷达的测角方法:在介绍单脉冲测角原理的基础上,建立了宽带高分辨雷达角度估计模型,推导了目标偏角与回波信号的联合概率密度函数;提出了基于极大似然估计的宽带雷达测角方法,通过采用迭代算法交替更新目标偏角和回波信号,实现了目标偏角和回波信号的联合估计,并给出了目标偏角估计精度的近似克拉美罗界。仿真比较了该方法与传统窄带雷达测角方法的估计性能,结果表明,本方法的测角精度优于传统窄带测角方法,且在信噪比一定的条件下目标距离单元数越多,测角的均方根误差越小。仿真验证了该角度估计方法是渐近无偏的,估计误差方差小于传统窄带测角方法,且随距离单元数增多而减小。     

基于GNSS的多基地雷达目标定位与运动参数提取精度分析

基于GNSS的外辐射源雷达具备潜在的多基地雷达特性,聚焦基于GNSS的外辐射源雷达目标定位和运动参数提取精度分析,利用克拉美-罗下界分析目标定位和运动参数提取精度与卫星个数及目标回波信噪比之间的关系。仿真结果表明,卫星个数越多,目标定位与运动参数提取精度越高;当卫星数量相同时,不同卫星照射下目标回波信噪比相差越小,目标定位与运动参数提取精度越高。     

激光多普勒测速参数估计的Cramer-Rao下限

针对固体运动目标高速度、高加速度的特点,研究了高斯白噪声背景下激光多普勒测速同波信号的参数估计问题.通过计算参数矢量的费希尔(Fisher)信息矩阵,分析了实同波信号的多普勒频率和频率变化率估计方差的克拉末-雷奥(Cramer-Rao)下限(CRLB),推导了采样点数较大时同波信号参数方差估计的CRLB计算公式,讨论了各参数的最大似然估计(MLE).指出同波信号参数估计方差的CRLB与采样点数、信噪比及初相有关,采样点数较大时,实信号参数估计方差的CRLB为对应复信号的2倍.在不同的采样点数和信噪比下仿真表明,提高回波信号的信噪比和增加采样点数可以减小各参数估计方差的CRLB,结果与理论分析吻合.     

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．     

针对参数估计的组网雷达欺骗干扰策略

针对组网雷达系统对探测目标进行参数联合估计,提出了一种针对该参数估计的组网雷达欺骗干扰策略.首先,分析了电子干扰机编队对组网雷达实施欺骗干扰的原理,并建立了欺骗干扰下的目标回波信号模型.然后,在此基础上给出了欺骗干扰特征矢量参数的费希尔信息矩阵(FIM),并分析了其估计性能.最后,推导了达到待估计参数克拉美罗下界(CR...     

一种新的机动目标速度特征参数测量方法

建立了目标雷达回波模型,分析了高速目标的径向速度、径向加速度与目标速度、初始位置的运动方向与雷达视线的夹角关系;在基于最大似然估计线性调频参数估计的基础上,研究了高速目标径向速度、径向加速度的克劳美-饶界;研究了高速目标的初始速度为v0、初始位置的运动方向与雷达视线的夹角为θ0、目标加速度a等的估计方法,并推导了v0、θ0 、a估计的克劳关-罗界,分析了影响v0、θ0、a估计的克劳关-罗界的因素.     

基于循环平稳特征的正弦调制相位信号参数估计

正弦调制相位信号是微动目标雷达回波微多普勒信号的一般形式。该文利用正弦调制相位信号的循环平稳特性推导了信号的循环谱,并由循环谱特征估计信号的参数。相对于传统的频谱分析方法,基于循环谱的参数估计方法对平稳噪声有较好的抑制作用。仿真实验以及误差分析证明了方法的有效性。     

基于游标测距的微动参数估计

精确地估计微动参数有利于对微动目标进行分类识别。本文根据目标微动在全相参脉冲多普勒雷达体制下的回波特点,提出了一种基于游标测距(Range Vernier)的微动参数估计方法。首先建立微动目标雷达回波模型,主要是进动目标回波模型。以某一回波脉冲为参考,采用游标测距技术测量后续回波脉冲接收时刻目标的距离,该距离与时间的关系反映了目标的运动规律,最后通过正弦基分解(Sin FM Basis Decomposition)的方法从测量结果中估计出微动参数,包括振幅、角频率和初始相位。参数估计过程中峰值搜索的范围由经验知识和雷达测量信息确定。算法性能分析推导了雷达测速误差、测相位误差以及脉冲重复频率(PRF)和载频之间的约束关系,以保证游标测距正常进行。仿真结果验证了在现有雷达体制和测量精度条件下,游标测距可以正常应用,并且微动参数估计的精度非常高。      

Parametric autofocus of SAR imaging - inherent accuracy limitations and realization

In synthetic aperture radar (SAR) imaging, low scene contrast may degrade the performance of most of the existing autofocus methods. In this paper, by dividing a slow-time signal into three isolated components, namely target, clutter, and noise, in SAR imaging, a novel parametric statistical model is proposed during the coherent processing interval. Based on the model, Cramer-Rao bounds (CRBs) of the estimation of unknown parameters are derived. It is shown that the CRBs of the target parameter estimation strongly depend on the background, i.e., clutter and noise, and the CRBs of the background parameter estimation may be obtained regardless of the target component. Motivated from this result and using the estimated background parameters, a novel effective parametric autofocus method is developed, which is applicable to any scene contrast. In addition, a preprojection is also introduced to simplify the subsequent parameter estimation. Finally, the proposed model and the novel method are illustrated by some real SAR data.     

Cramer-Rao bounds and maximum likelihood estimation for randomamplitude phase-modulated signals

The problem of estimating the phase parameters of a phase-modulated signal in the presence of colored multiplicative noise (random amplitude modulation) and additive white noise (both Gaussian) is addressed. Closed-form expressions for the exact and large-sample Cramer-Rao Bounds (CRBs) are derived. It is shown that the CRB is significantly affected by the color of the modulating process when the signal-to-noise ratio (SNR) or the intrinsic SNR is small. Maximum likelihood type estimators that ignore the noise color and optimize a criterion with respect to only the phase parameters are proposed. These estimators are shown to be equivalent to the nonlinear least squares estimators, which consist of matching the squared observations with a constant amplitude phase-modulated signal when the mean of the multiplicative noise is forced to zero. Closed-form expressions are derived for the efficiency of these estimators and are verified via simulations     

A note on the Cramer-Rao bound for 2-D direction finding based on2-D array

The authors present explicit expressions for the Cramer-Rao bound (CRB) for estimating the two-dimensional (2-D) direction of a single source based on 2-D arrays of identical omnidirectional sensors. Two commonly used models, random wave and unknown wave, are compared. It is shown that the CRBs for the two models have the same dependency on the array structure. A specialization of the CRB to two orthogonal uniform linear arrays (ULAs) is discussed. It is found that the joint CRBs of the direction angles based on the two orthogonal ULAs can be as low as one quarter (for a random waveform model with a large number of snapshots and low SNR) or one half (for both models with high SNR) of the CRBs based on each ULA     

The Cramer-Rao bound for pole and amplitude coefficient estimatesof damped exponential signals in noise

A complete Cramer-Rao bound (CRB) derivation is provided for the case in which signals consist of arbitrary exponential terms in noise. Expressions for the CRBs of the parameters of a damped exponential model with one set of poles and multiple sets of amplitude coefficients are derived. CRBs for the poles and amplitude coefficients are derived in terms of rectangular and polar coordinate parameters. For rectangular parameters it is shown that CRBs for the real and imaginary parts of poles and amplitude coefficients are equal and uncorrelated. In polar coordinates, the angle and magnitude CRBs are also uncorrelated. Furthermore, the CRBs of the pole angles and relative magnitudes are equal and are logarithmically symmetric about the unit circle     

Bounds on bearing and symbol estimation with side information

We develop Cramer-Rao bounds (CRBs) for bearing, symbol, and channel estimation of communications signals in flat-fading channels. We do this using the constrained CRB formulation of German and Hero (1990), and Stoica and Ng (see IEEE Signal Processing Lett., vol.5, p.177-79, 1998), with the unknown parameters treated as deterministic constants. The equality constraints may be combined arbitrarily, e.g., we may develop CRBs for bearing estimation of constant modulus (CM) signals where a subset of the symbols are known (semi-blind, CM case). The results establish the value of side information in a large variety of communications scenarios. We focus on the CM and semi-blind properties and develop closed-form CRBs for these cases. Examples are presented indicating the relative value of the training and CIM property. These show the significant amount of signal processing information provided under these two conditions. In addition, we consider the performance of the maximum-likelihood beamformer for the semi-blind case, assuming the bearings are known. This semi-blind beamformer achieves the appropriate (constrained) CRB with finite data at finite SNR. Analysis also reveals that in a semi-blind scenario with two closely spaced sources, ten or more training symbols are sufficient to achieve the asymptotic training regime. Together with previous results on angle estimation for known sources, these results indicate that relatively few training samples enable both angle estimation and closely spaced co-channel source separation that approaches the CRB with finite data and finite SNR     

Robust estimation of fetal heart rate variability using Doppler ultrasound

This paper presents a new measure of heart rate variability (HRV) that can be estimated using Doppler ultrasound techniques and is robust to variations in the angle of incidence of the ultrasound beam and the measurement noise. This measure employs the multiple signal characterization (MUSIC) algorithm which is a high-resolution method for estimating the frequencies of sinusoidal signals embedded in white noise from short-duration measurements. We show that the product of the square-root of the estimated signal-to-noise ratio (SNR) and the mean-square error of the frequency estimates is independent of the noise level in the signal. Since varying angles of incidence effectively changes the input SNR, this measure of HRV is robust to the input noise as well as the angle of incidence. This paper includes the results of analyzing synthetic and real Doppler ultrasound data that demonstrates the usefulness of the new measure in HRV analysis.     

Cramer-Rao bounds of DOA estimates for BPSK and QPSK Modulated signals

This paper focuses on the stochastic Cramer-Rao bound (CRB) of direction of arrival (DOA) estimates for binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) modulated signals corrupted by additive circular complex Gaussian noise. Explicit expressions of the CRB for the DOA parameter alone in the case of a single signal waveform are given. These CRBs are compared, on the one hand, with those obtained with different a priori knowledge and, on the other hand, with CRBs under the noncircular and circular complex Gaussian distribution and with different deterministic CRBs. It is shown in particular that the CRBs under the noncircular [respectively, circular] complex Gaussian distribution are tight upper bounds on the CRBs under the BPSK [respectively, QPSK] distribution at very low and very high signal-to-noise ratios (SNRs) only. Finally, these results and comparisons are extended to the case of two independent BPSK or QPSK distributed sources where an explicit expression of the CRB for the DOA parameters alone is given for large SNR.     

基于MIMO雷达的相干分布式目标 参数估计Cramer-Rao下界

 本文研究了MIMO雷达对相干分布式目标参数估计的Cramer-Rao下界(CRB).首先,给出相干分布式目标的MIMO雷达信号模型,推导出目标参数估计CRB的一般关系式;然后,给出在一个分布式目标、点目标和目标部分信息已知等特殊情况下的CRB;其次,讨论了MIMO雷达CRB的性质;最后,进行计算机仿真试验,研究不同条件下的MIMO雷达性能.结果表明由于具有避免波束形状损失等优点,MIMO雷达对相干分布式目标的参数估计CRB性能优于普通相控阵雷达.本文的研究揭示了MIMO雷达的相干分布式目标参数估计性能.     

基于改进LS-ESPRIT算法的GTD模型参数估计与RCS重构

针对传统LS-ESPRIT算法在估计GTD模型参数时抗噪效果差,估计精度不高这一问题,该文提出了一种改进的LS-ESPRT算法,有效地提高了算法的参数估计性能与抗噪性。首先,根据雷达目标的回波数据构建Hankel矩阵;其次,采用核范数凸优化方法对上述Hankel矩阵进行降噪处理,得到低秩的重构Hankel矩阵;最后,利用传统的LS-ESPRIT算法对降噪后的数据进行处理,估计出GTD模型参数。基于改进算法与传统算法分别得到重构RCS,并针对不同带宽对参数估计精度的影响作以仿真探究。仿真结果表明,与传统LS-ESPRIT算法与传统TLS-ESPRIT算法相比,改进LS-ESPRIT算法的参数估计性能更高,抗噪性更强,且重构RCS的幅值与相角误差更小。对不同带宽下的参数估计精度也进行了探究,并得出:带宽越大,估计精度越高。     

基于MUSIC法的宽带分布式全相参雷达相参参数估计方法

宽带分布式全相参雷达具有高距离分辨力和高角度分辨力,可在实现对目标探测的同时获得更为精细的信息。其中,准确的相参参数是实现宽带全相参的关键。在远距离探测场景下,目标回波SNR较低,导致单部雷达无法探测到目标。如何在此情况下准确地估计相参参数,完成多部雷达相参合成,是分布式全相参雷达实现远距离探测的核心问题。本文首先建立了针对高速运动复杂目标的宽带Chirp去斜回波模型,并建立了相参参数的数学模型。然后,提出了基于MUSIC法的宽带分布式全相参雷达相参参数估计方法,通过估计观测目标的散射点个数、极点以及复幅度,重建宽带回波信号,并基于该无噪声重建信号估计相参参数。最后,利用仿真分析验证了本文提出方法在低SNR下的有效性。