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。     

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。     

利用重要性采样的时差-频差联合估计算法

针对无源定位中参考信号真实值未知的时差(TDOA)-频差(FDOA)联合估计问题,构建了一种新的时差-频差最大似然(ML)估计模型,并采用重要性采样(IS)方法求解似然函数极大值,得到时差-频差联合估计。算法通过生成时差-频差样本,并统计样本加权均值得到估计值,克服了传统互模糊函数(CAF)算法只能得到时域和频域采样间隔整数倍估计值的问题,且不存在期望最大化(EM)等迭代算法的初值依赖和收敛问题。推导了时差-频差联合估计的克拉美罗下界(CRLB),并通过仿真实验表明,算法的计算复杂度适中,估计精度优于CAF算法和EM算法,在不同信噪比条件下估计误差接近CRLB。     

非圆实信号MIMO雷达中基于实值ESPRIT的角度估计

研究单基地多输入多输出(MIMO)雷达中的波达角(DOA)估计问题,提出了一种基于非圆(NC)实信号的实值旋转不变性信号参数估计(ESPRIT)算法.首先对接收信号进行降维变换,降低运算复杂度;之后根据非圆实信号特性构造中心Hermitian对称矩阵,通过酉(Unitary)变换将复数运算转为实数,进一步降低复杂度;最后根据ESPRIT得到角度估计.该算法无需谱峰搜索,运算复杂度较NC ESPRIT和Unitary ESPRIT大大降低,且该算法的角度估计性能优于后两种算法.论文分析了所提算法的复杂度,并推导了克拉美-罗界(CRB).仿真结果验证了该算法的有效性.     

基于两步最小二乘定位的偏差改进算法

针对传统最小二乘(LS)定位算法在噪声较大时会出现有偏估计的问题。首先详细推导了传统两步最小二乘算法在时差角度联合定位场景下的理论偏差,给出了出现偏差的原因;其次对误差均值加入二次约束条件,提出一种基于时差角度联合定位的改进算法,并详细推导新算法的理论偏差以及均方误差。相比于其他加限制条件的方法,新算法能有效降低估计偏差,另外由于其不需要进行特征值分解且能得到闭式解,计算复杂度较小。仿真结果表明,新算法在保持原有均方误差(MSE)的前提下能显著降低估计偏差,其定位偏差与最大似然估计器相当。     

一种低复杂度的极低信噪比高动态信号载波粗捕获算法

 针对传统的时域匹配平均周期图算法计算复杂度高的问题,对极低信噪比高动态信号的载波粗捕获算法进行了研究,提出了一种改进的带有补零的频域移位平均周期图算法。该算法采用多速率频域移位运算简化了多支路多普勒变化率匹配,与原算法相比,其计算复杂度降低倍数为匹配支路数与补零倍数之比,捕获性能几乎不损失。给出了算法中影响捕获性能与计算复杂度的关键参数设计方法。在信噪比(SNR)为－41 dB(载噪比C/N₀=18 dBHz)、载波多普勒频偏为－300～300 kHz、多普勒变化率为－800～800 Hz/s、码速率为20 bps条件下对两种算法进行了仿真,结果表明在基本满足后级载波跟踪需求条件下,即频偏精度均达±12 Hz时,多普勒变化率精度均达±25 Hz/s,捕获概率都在90%以上时,改进算法捕获时间比原算法增加了8%,计算复杂度降低了70%。     

一种改进的雷达低空目标仰角估计算法

由于地(海)面反射的多径镜像信号的存在,使雷达低空目标的仰角估计成了难题。而三子孔径算法是针对该难题而提出的一种有效算法。此算法基于极大似然算法将等距线阵均分为三个子孔径,从而简化了估计函数,也使计算量大大减少。本文在三子孔径算法的基础上,利用目标信号与镜像信号在入射角度上的正弦约束条件,给出了一种改进算法。计算机仿真结果显示改进算法在性能上要优于三子孔径算法。     

基于Viterbi算法的扩频码与信息序列联合估计

针对短码DSSS（Direct Sequence Spread Spectrum,直接序列扩频）信号扩频码MLE（Maximum Likelihood Estimation,最大似然估计）问题,提出了一种基于Viterbi算法的扩频码搜索方法,并将其应用到同步CDMA（Code Division Multiple Access,码分多址）信号的扩频码估计中.该算法利用了扩频码码元为±1的先验知识,以向量的2-范数平方或1-范数作为度量值;每次判决扩频码码元时,计算2条可能路径的度量值,并选择使度量值最大的那条路径作为幸存路径,最终的幸存路径即为估计的扩频码;所提算法不仅计算复杂度低,而且能同时估计扩频信号的扩频码和信息序列.仿真实验表明,本算法在低信噪比时同样具有较好的性能.     

基于改进ICPF的多分量LFM信号参数估计

针对线性调频信号参数估计中搜索匹配过程带来的计算量问题,提出一种基于改进ICPF的多分量线性调频信号参数估计方法。给出了一种基于非均匀快速傅立叶变换的ICPF改进算法,利用改进的ICPF算法估计线性调频信号的调频率,利用解线频调方法估计中心频率和幅度。方法避免了参数估计过程中的搜索匹配过程,计算复杂度低,在低信噪比条件下具有较好的估计性能。仿真结果验证了方法的有效性。     

一种考虑传感器位置误差的改进源定位算法

在无线传感器网络的定位问题中,通过移动目标与传感器之间的到达时差(TDOA)与到达频差(FDOA)测量量可以估计目标的位置和速度。但是,当传感器自身信息存在误差时,传统的定位方法将失去精确的定位效果。针对带有传感器误差的源定位问题,基于极大似然(ML)法获取一个封闭的近似解。提出了一种改进的近似极大似然(AML)算法,更新带有传感器误差的代价函数,不仅能实现实时定位,而且保证全局收敛。仿真结果表明,本文算法在存在传感器误差场景下依然可以达到克拉美-罗下限(CRLB),比已有的改进两步加权最小二乘(2-step WLS)法更有效。     

Fast approximate maximum likelihood period estimation from incomplete timing data

 To estimate the period of a periodic point process from noisy and incomplete observations, the classical periodogram algorithm is modified. The original periodogram algorithm yields an estimate by performing grid search of the peak of a spectrum, which is equivalent to the periodogram of the periodic point process, thus its performance is found to be sensitive to the chosen grid spacing. This paper derives a novel grid spacing formula, after finding a lower bound of the width of the spectral mainlobe. By employing this formula, the proposed new estimator can determine an appropriate grid spacing adaptively, and is able to yield approximate maximum likelihood estimate (MLE) with a computational complexity of O(n²). Experimental results prove that the proposed estimator can achieve better trade-off between statistical accuracy and complexity, as compared to existing methods. Simulations also show that the derived grid spacing formula is also applicable to other estimators that operate similarly by grid search.     

A fast pulse phase estimation method for X-ray pulsar signals based on epoch folding

X-ray pulsar-based navigation (XPNAV) is an attractive method for autonomous deep-space navigation in the future. The pulse phase estimation is a key task in XPNAV and its accuracy directly determines the navigation accuracy. State-of-the-art pulse phase estimation techniques either suffer from poor estimation accuracy, or involve the maximization of generally non-convex object function, thus resulting in a large computational cost. In this paper, a fast pulse phase estimation method based on epoch folding is presented. The statistical properties of the observed profile obtained through epoch folding are developed. Based on this, we recognize the joint prob-ability distribution of the observed profile as the likelihood function and utilize a fast Fourier transform-based procedure to estimate the pulse phase. Computational complexity of the proposed estimator is analyzed as well. Experimental results show that the proposed estimator significantly outperforms the currently used cross-correlation (CC) and nonlinear least squares (NLS) estima-tors, while significantly reduces the computational complexity compared with NLS and maximum likelihood (ML) estimators.     

修正容积卡尔曼滤波数据域直接定位算法

为解决数据域直接定位（DPD）算法面临的计算压力,提高算法效率,提出一种基于修正的容积卡尔曼滤波（MCKF）的DPD算法。首先,融合各观测信号波达方向信息,利用子空间数据融合方法建立一种基于间接观测量的DPD滤波模型;然后,根据模型特点设计MCKF算法进行求解,解决间接观测量带来的噪声累积问题;最后,对算法计算量进行分析和对比,说明计算效率的提升。仿真结果表明,所提算法与基于最大似然遍历搜索和遗传算法的DPD算法相比,在相同的估计性能下,计算量下降明显,时效性显著提升,增加了算法实用价值。     

Maximum Likelihood DOA Estimation in Unknown Colored Noise Fields

Direction-of-arrival (DOA) estimation in unknown noise environments is an important but challenging problem. Several methods based on maximum likelihood (ML) criteria and parameterization of signals or noise covariances have been established. Generally, to obtain the exact ML (EML) solutions, the DOAs must be jointly estimated along with other noise or signal parameters by optimizing a complicated nonlinear function over a high-dimensional problem space. Although the computation complexity can be reduced via derivation of suboptimal approximate ML (AML) functions using large sample assumption or least square criteria, nevertheless the AML estimators still require multi-dimensional search and the accuracy is lost to some extent. A particle swarm optimization (PSO) based solution is proposed here to compute the EML functions and explore the potential superior performances. A key characteristic of PSO is that the algorithm itself is highly robust yet remarkably simple to implement, while processing similar capabilities as other evolutionary algorithms such as the genetic algorithm (GA). Simulation results confirm the advantage of paring PSO with EML, and the PSO-EML estimator is shown to significantly outperform AML-based techniques in various scenarios at less computational costs.     

Approximate Maximum Likelihood Algorithm for Moving Source Localization Using TDOA and FDOA Measurements

 A closed-form approximate maximum likelihood (AML) algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements of a signal received at a number of receivers. The maximum likelihood (ML) technique is a powerful tool to solve this problem. But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space, and it is very computationally expensive, and prohibits real-time processing. On the basis of ML function, a closed-form approximate solution to the ML equations can be obtained, which can allow real-time implementation as well as global convergence. Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares (WLS) approach, which makes it possible to attain the Cram閞-Rao lower bound (CRLB) at a sufficiently high noise level before the threshold effect occurs.     

CFAR detection and estimation for STAP radar

The algorithm presented here provides both a constant false-alarm rate (CFAR) detection and a maximum likelihood (ML) Doppler-bearing estimator of a target in a background of unknown Gaussian noise. A target is detected, and its parameters estimated within each range gate by evaluating a statistical test for each Doppler-angle cell and by selecting the cell with maximum output and finally comparing it with a threshold. Its CFAR performance is analyzed by the use of the sample matrix inversion (SMI) method and is evaluated in the cases of a fully adaptive space-time adaptive processing (STAP) and two partially adaptive STAPs. The performances of these criteria show that the probability of detection is a function only of the sample size K used to estimate the covariance matrix and a generalized signal-to-noise ratio. The choice of the number K is a tradeoff between performance and computational complexity. The performance curves demonstrate that the finer the resolution is, the poorer the detection capability. That means that one can trade off the accuracy of ML estimation with the performance of the CFAR detection criterion     

Pursuer identification and time-to-go estimation using passive measurements from an evader

We present an algorithm for identifying the parameters of a proportional navigation guidance missile (pursuer) pursuing an airborne target (evader) using angle-only measurements from the latter. This is done for the purpose of classifying the missile so that appropriate counter-measures can be taken. Mathematical models are constructed for a pursuer with a changing velocity, i.e., a direction change and a speed change. Assuming the pursuer is launched from the ground with fixed thrust, its motion can be described by a four-dimensional parameter vector consisting of its proportional navigation constant and three parameters related to thrusting. Consequently, the problem can be solved as a parameter estimation problem, rather than state estimation and we provide an estimator based on maximum likelihood (ML) to solve it. The parameter estimates obtained can be mapped into the time-to-go until intercept estimation results are presented for different scenarios together with the Cramer-Rao lower bound (CRLB), which quantifies the best achievable estimation accuracy. The accuracy of the time-to-go estimate is also obtained. Simulation results demonstrate that the proposed estimator is efficient by meeting the CRLB.     

On joint track initiation and parameter estimation under measurement origin uncertainty

The problem of joint detection and estimation for track initiation under measurement origin uncertainty is studied. The two well-known approaches, namely the maximum likelihood estimator with probabilistic data association (ML-PDA) and the multiple hypotheses tracking (MHT) via multiframe assignment, are characterized as special cases of the generalized likelihood ratio test (GLRT) and their performance limits indicated. A new detection scheme based on the optimal gating is proposed and the associated parameter estimation scheme modified within the ML-PDA framework. A simplified example shows the effectiveness of the new algorithm in detection performance under heavy clutter. Extension of the results to state estimation with measurement origin uncertainty is also discussed with emphasis on joint detection and recursive state estimation.     

Small Aperture Angle Measurement for Active Echo Location Systems

The problem of bearing estimation for active systems is examined from the point of view of the generalized wideband ambiguity function (GAF). The maximum likelihood (ML) estimator is derived and its local and global properties are discussed. A structure is proposed which searches the three-dimensional ambiguity surface in two stages first, in range-Doppler, and then, in bearing with the goal of reducing search complexity when utilizing highly resolvent waveforms. Comparisons are made between the ML estimators and structures utilizing phase information which generate closed form estimators. The beneficial results of full bandwidth utilization are discussed in terms of both local and global properties of the GAF.