Track Initiation of Occasionally Unresolved Radar Targets

In a dense detection environment track-while-scan algorithms will introduce many false tracks when processing is performed on a scan-by-scan basis. The maximum likelihood solution involving all detections on all scans is formulated and evaluated for the initiation problem consisting of false alarms, missed detections, and unresolved detections. A comparison is made between the maximum likelihood solutions including and not including the resolution likelihood and it is shown that the resolution likelihood must be included if the data contain unresolved detections. While the maximum likelihood method (with unresolved detections) does not appear to be implementable in real time without very high speed integrated circuit (VHSIC) technology, it can be used as a standard to which more implementable methods can be compared.     

宽带信号近似最大似然方位估计快速算法

 针对短采样宽带信号近似最大似然方位估计（AML）计算量大的问题,将马尔可夫蒙特卡罗(MCMC)方法与近似最大似然方位估计相结合,提出一种基于完美抽样的近似最大似然方位估计快速算法（PAML）。该算法将AML算法的空间谱函数作为信号的概率分布函数,并利用完美抽样方法从该概率分布函数中抽样。与AML和遗传算法的对比实验研究表明,两目标情况下PAML算法在中低信噪比条件下的估计性能与AML和遗传算法性能相当,而计算量分别是二者的1/24和1/3。随着目标个数的增加,PAML算法的计算量优势将更加明显。     

基于最大似然估计的TDOA/FDOA无源定位偏差补偿算法

在到达时差/到达频差(TDOA/FDOA)无源定位系统中,定位问题的非线性使得定位的结果存在偏差,特别是在噪声较大或者接收站布站不合理的情况下,定位的偏差尤其显著。针对这一问题,提出了一种基于最大似然估计的偏差补偿算法。该方法分为3步:首先,利用最大似然估计器对目标的位置和速度进行求解;其次,通过利用目标定位的估计值和含噪的测量值,对目标的位置和速度偏差值进行理论分析和推导;最后,将最大似然估计解减去理论偏差值,得到经过偏差补偿的新的目标定位解。理论分析和实验仿真证明,在一定噪声的情况下,所推导的目标位置和速度的理论偏差值与实际偏差值相符,并且经过偏差补偿后的定位算法,在保持目标定位的均方根误差(RMSE)与原最大似然算法一致的情况下,目标的位置和速度偏差值远远小于原最大似然算法的偏差值,目标定位精度得到了有效的提高。     

非线性最优解在气动参数辨识中的应用

由于采用最大似然算法进行了扭动参数辨识通常存在着局极值问题，为了寻求非线性全局最优解，提高估计精度，介绍了以混沌优化算法作为搜索工具。与最大似然处算法相结合，求解非凸优化问题全局最优解的方法，算例表明，该方法是有效可行的，并且估计精度也较高。     

Bounds on Elevation Error Estimates of a Target in Multipath

The Cramer-Rao bound for an unbiased estimate of the elevation angle of a target in the presence of multipath is calculated for the symmetric (target and image symmetric about the elevation symmetry plane of antenna) and nonsymmetric cases for an antenna consisting of 21 elements. These bounds are compared to the maximum likelihood estimates and it is found that the rms error of the maximum likelihood estimate (which has a bias) is below the Cramer-Rao bound for unbiased estimates.     

极大似然法在飞机起飞性能参数辨识中的应用

以Y12飞机起飞性能飞行试验实测数据为例,研究了利用极大似然法进行飞机起飞性能参数辨识的问题。首先通过理论分析建立了Y12飞机双发起飞性能数学模型;然后从简化灵敏度导数计算角度提出了以飞机在起飞滑跑过程中速度增量为观测量的观测方程,分析给出了待辨参数和利用极大似然法进行参数识别的方法;最后针对极大似然法的几个要素,如迭代初值、代价函数、迭代次数、样本长度、试验数据段的选取等对辨识结果的影响进行了讨论和分析。结果表明,利用极大似然法可以快速、准确地辨识出起飞性能所需的参数,并具有较高的精度,在今后的性能飞行试验数据分析中应注重推广和应用。     

一种基于遗传算法的动力学系统辨识方法

把遗传算法与最大似然法相结合，形成了一种基于遗传算法的动力学系统辨识方法，并以某轴对称战术导弹的非线性气动力参数仿真辨识为例，利用仿真技术，检验了该方法的实用性和有效性。结果表明，该方法是十分有效的。     

Outlier resistant adaptive matched filtering

Robust adaptive matched filtering (AMF) whereby outlier data vectors are censored from the covariance matrix estimate is considered in a maximum likelihood estimation (MLE) setting. It is known that outlier data vectors whose steering vector is highly correlated with the desired steering vector, can significantly degrade the performance of AMF algorithms such as sample matrix inversion (SMI) or fast maximum likelihood (FML). Four new algorithms that censor outliers are presented which are derived via approximation to the MLE solution. Two algorithms each are related to using the SMI or the FML to estimate the unknown underlying covariance matrix. Results are presented using computer simulations which demonstrate the relative effectiveness of the four algorithms versus each other and also versus the SMI and FML algorithms in the presence of outliers and no outliers. It is shown that one of the censoring algorithms, called the reiterative censored fast maximum likelihood (CFML) technique is significantly superior to the other three censoring methods in stressful outlier scenarios.     

一种估计飞行器运动特性的实用极大似然法

飞行器运动特性受非线性因素和随机噪声的影响,给气动系数,飞行稳定性与操纵性指标的结构模型辨识带来许多困难。为了解这个问题,首先提出了使用极大似然估计法,以提高辨识效率。接着建立了基于飞行器运动固定型数学模型下的极大似然函数,并给出了一种实用极大似然算法及其实现。     

由飞行试验数据求取直升机气动导数的最佳方法研究

本文主要介绍利用频率法、卡尔曼滤波和最大似然法求取气动导数的方法。方法的特点是,通过频率法使旋翼高频成份的影响减至最小,同时求取试验数据的测量噪声和过程噪声,然后通过卡尔曼滤波使试验数据包含的随机噪声减小,最后使用最大似然法使试验数据包含的随机噪声进一步减小,并求取最终的气动导数。计算结果表明,该方法可使试验数据中包含的噪声减至最小,复合相关系数提高到0.95以上,特征根更接近真值,其准确度优于最小二乘法、频率法、卡尔曼滤波方法和最大似然法,它保留了使用低通滤波数据的卡尔曼滤波方法的优点,克服了原方法振荡模态频率偏低的缺点,适合于各种直升机的导数识别。     

P-S-N曲线测定的最大似然法

 本文提出了一种测定P-S-N曲线的最大似然法。这种方法利用了三参数S-N曲线方程,并用最大似然法和矩法求参数的估计值。通过对光滑试件和缺口试件在轴向加载下疲劳试验数据的处理,说明了该方法的应用,并分析了该方法的精度。     

Passive target tracking using maximum likelihood estimation

Estimation of target trajectory from passive sonar bearings and frequency measurements in the presence of multivariate normally distributed noise, with unknown inhomogeneous general covariance, is modeled as a nonlinear multiresponse parameter estimation problem. It is shown that maximum likelihood estimation in this case is identical to optimizing a determinant criterion which has a concise form and contains no elements of unknown covariance matrix. A Gauss-Newton type algorithm using only the first-order derivatives of the model function and a new convergence criterion, is presented to implement such estimation. The simulation results demonstrate that performance of the maximum likelihood estimation method with the above noise model is superior to that with the traditional noise assumption     

Statistically Optimum Optical Data Processing with Automatic Focus Estimation

The problem of optimally processing data with unknown focus is investigated. Optimum data processors are found by the method of maximum likelihood under a variety of assumptions that apply to most of the situations arising in practice. The unknown focus may be either an unknown parameter or an unknown random variable; the signal may be of known form or a random function; it is further assumed that the signal is received in additive, white, Gaussian noise. The problems of jointly estimating other unknown parameters and, in the case of a random signal, jointly estimating the signal, are also treated. The asymptotic variance and correlation of the estimators is discussed. Electrooptical realizations of the maximum likelihood computers are given. An iterative method of solution of the likelihood equation is also discussed. The discussion and results are directly applicable to the processing of synthetic aperture radar data.     

一种全速率协作通信系统传输方案

针对协作通信带来链路传输效率降低的问题,在译码转发中继方案的基础上,提出一种新的全速率协作通信传输方案。首先给出全速率协作通信系统的接收信号模型,并利用矩函数方法理论推导给出全速率协作通信系统最大似然接收机的链路成对差错概率及链路分集增益,理论分析表明：全速率协作通信传输方案可获得1符号/时隙的传输效率,且最大似然接收机可获取4重分集增益。计算机仿真结果证明理论分析的正确性。     

The Digital-Data-Transition Tracking Loop as a Practical Implementation of an Optimum Self-Bit Synchronizer

It is shown that the digital-data-transition tracking loop, previouslydevised using physical reasonings, is actually a practical implementation of an optimum (maximum likelihood) self-bit synchronizer.     

基于AMSAA模型的系统可靠性评估方法研究

针对航空机载设备可靠性增长试验数据不规则的特点,在AMSAA模型的基础上提出了识别异常点的AMSAA模型与处理截断数据的AMSAA模型,并给出了模型拟合优度检验方法。经实例验证表明,识别异常点的AMSAA模型可以在给定置信度下识别出异常点并排除异常点对瞬时MTBF（平均故障间隔时间）极大似然估计值的干扰;截断数据的AMSAA模型能够利用被截取的部分数据,得到准确的瞬时极大似然估计值。     

PAM approach to weak CPM and its application to flight termination receivers

A method for computing the PAM representation for weak continuous phase modulation (CPM) is presented and applied to the modulation defined in the enhanced flight termination system (EFTS) standard. The pulse-amplitude modulation (PAM) representation was used to formulate a reduced-complexity detector whose performance is within 0.7 dB of maximum likelihood detection and 5.6 dB better than limiter-discriminator detection in the additive white Gaussian noise (AWGN) environment. The complexity of the reduced-complexity detector is less than 25% that of the maximum likelihood detector but, unlike the limiter-discriminator detector, requires a carrier phase PLL. In the presence of phase noise, the reduced-complexity detector outperforms limiter-discriminator detection when the RMS frequency deviation due to phase noise is less than 10% of the bit rate.     

计及随机噪声的频域多输入多输出模态参数识别

提出了一种计及随机噪声的多输入多输出频域模态参数识别方法。该方法基于连续时间域的频率响应函数有理分式模型,利用Forsythe正交多项式改善求解性态,属频域的极大似然类估计方法。在最小二乘估计的基础上,利用"先验"的随机噪声信息进行优化迭代的极大似然估计,得到更优的模态参数识别结果。根据Cramer Rao下界不等式,在几乎不增加计算量的情况下获得所识别结果的不确定性信息,提高了模态参数识别的可靠性。采用GARTEUR飞机模型作为仿真算例,对本算法进行了仿真验证。     

Markov随机跳变系统的传感器系统误差估计

 针对Markov随机跳变系统的系统误差估计问题,提出一种基于马尔可夫链蒙特卡罗(MCMC)和最大似然估计相结合的在线系统误差估计方法。利用最大似然估计给出系统误差等效后验概率分布函数,采用Metropolis-Hastings抽样方法从该概率分布函数中进行抽样;利用系统误差估计和状态估计互为因果的关系,采用期望极大化(EM)方法迭代估计出最优的系统误差;分别对时变和时不变系统误差场景进行仿真分析,结果表明,在考虑系统误差统计特性的同时,所提方法对解决目标运动模型难以建立情况下的系统误差估计问题具有可行性和有效性。     

基于自适应步长选择的周期格型线搜索估计

稀疏、含噪观测条件下周期点过程的周期估计是一个经典的信号处理问题。针对该问题,提出了一种格型线搜索(LLS)算法,该算法通过数值方式搜索似然函数的最大值,但其性能取决于人为预先选取的搜索步长。推导了一个步长计算公式,并利用该公式改进了LLS算法。改进的LLS算法能够自适应选择搜索步长,其达到的克拉美-罗界(CRLB)的信噪比(SNR)门限与最大似然估计(MLE)算法一致,但计算复杂度比后者低一个多的数量级。性能分析与仿真实验表明,所提算法比已有算法能更好地实现估计精度与复杂度的折中。