宽带被动雷达目标的小波检测及参数估计

在小波变换的基础上 ,提出了一种新的检测宽带被动雷达目标的方法。详细阐述了此方法的操作步骤 ,并对其在白噪声环境中的应用进行了模拟。之后进一步针对目标与信号在尺度上的差别对目标信号进行了恢复 ,在恢复的基础上利用互小波变换对目标的参数进行了估计。     

基于信号互相关的无源时差定位技术仿真分析

研究了互相关法在无源时差定位系统中的应用。对于单站侦收的微弱副瓣信号,不需要测量其参数,利用互相关法即可提取时差信息;对于受强噪声和干扰信号污染的目标信号,利用互相关法将其分选出来并测量时差。仿真研究了频率、重频、脉宽对两个信号的互相关输出增益的影响。     

基于四阶互模糊函数的TDOA/FDOA参数估计研究

针对二阶互模糊函数在相干高斯噪声环境下会出现较大的噪声相关峰值,影响TDOA/FDOA参数正确估计的问题,利用高阶积累量对高斯噪声的不敏感特性,研究基于四阶互模糊函数的TDOA/FDOA参数估计方法,对该方法的参数估计过程进行了详细的理论分析,并针对二阶与四阶互模糊函数两种参数估计方法进行了对比仿真。仿真结果表明,基于四阶互模糊函数的TDOA/FDOA参数估计方法能有效克服噪声的相关性,但对噪声敏感,存在一定的理论研究价值。     

高精度测向辅助的三星时差定位算法

针对传统的三星时差无源定位算法不能求解辐射源高程这一问题,提出了以高精度测向作为辅助对辐射源进行定位的算法。从该算法的定位原理入手,建立了其定位模型并分析了其定位误差;同时提出采用UKF算法对多次测量的结果进行处理以提高定位的精度;最后对该算法进行了计算机仿真。研究表明,该算法不需要进行高程假设即可实现对辐射源的快速高精度无源定位。     

基于四阶积累量的卫星TDOA定位参数估计算法研究

针对传统的TDOA参数估计方法,在相关噪声环境下无法对TDOA参数进行有效的估计,考虑高阶积累量对相关高斯噪声的不敏感特性,本文推导了基于四阶积累量的卫星TDOA参数估计过程,并将广义互相关法与基于四阶积累量的参数估计方法进行了仿真对比,分别比较了二者在非相关和相关高斯白噪声情况下的参数估计性能。仿真结果表明,基于四阶累积量的TDOA参数估计方法,能有效的克服噪声的相关性,对TDOA参数做出有效估计。     

宽带无源时差尺度差联合估计算法研究

针对宽带无源定位系统中运算复杂度与估计精度之间的矛盾,提出一种频域尺度相关粗测与非线性最小二乘精测相结合的时差频差联合估计算法。该算法采用经过频域重采样改进的频域相关算法,可以在极低运算量的情况下获得时差频差的估计初值,进一步采用平均方差函数替代宽带互模糊函数,从而通过非线性最小二乘算法迭代获得时差频差的精确值。仿真结果验证了提出的算法具有估计精度高、运算复杂度低的特点。     

Underwater Target Tracking Using Bearing-Only Measurements

The purpose of this work is to estimate target coordinates and velocity. Target tracking is performed by an autonomous underwater vehicle (AUV). The observer is equipped with the direction finder that measures the azimuth and elevation angles of the target and the target range. Such problems are normally solved with the aid of non-linear filters due to nonlinearity of measurements. However, most filters produce estimations with unknown bias in the absence of estimations of accuracy. An important problem is related to unobservability of the target. A pseudomeasurement method is proposed to transform the problem of estimation into a linear problem. Such an approach makes possible to estimate coordinates and velocity of a moving target and solve the problem of unobservability. Results of computer simulation prove efficiency of the proposed algorithm in the problem of passive target tracking when only angles are measured.     

Impact of Multiresolution Active and Passive Microwave Measurements on Soil Moisture Estimation Using the Ensemble Kalman Smoother

An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated     

时间差无源定位的工程应用

时间差无源定位技术(TDOA)是目前无线电定位技术中切实可行且精度较高的定位体制,得到了广泛关注与研究.而定位算法的选择决定了TDOA技术是否能顺利应用于实际工程并满足其需求.通过对常见的定位算法进行比较,选择概率定位算法应用于实际工程,设计开发了一个具有完整定位功能的测试平台,并已成功应用于无线电网络监测系统.     

Self-Localization of IoT Devices Using Noisy Anchor Positions and RSSI Measurements

Wireless Personal Communications - Location-enabled Internet of things (IoT) has attracted much attention from the scientific and industrial communities given its high relevance in application...     

Moving‐Target Tracking Based on Particle Filter with TDOA/FDOA Measurements

In this letter, we propose a moving‐target tracking algorithm based on a particle filter that uses the time difference of arrival (TDOA)/frequency difference of arrival (FDOA) measurements acquired by distributed sensors. It is shown that the performance of the proposed algorithm, based on the particle filter, outperforms the one based on the extended Kalman filter. The use of both the TDOA and FDOA measurements is shown to be effective in the moving‐target tracking. It is proven that the particle filter deals with the nonlinear nature of the moving‐target tracking problem successfully.     

针对小型无线电装置的运动分析算法

小型无线电测量装置能够方便地同时测量功率及多普勒频率信息。通过研究基于功率和多普勒频率的目标运动分析算法,运用迭代最小二乘方法给出数值解。仿真实验显示,仅依靠功率信息,在低信噪比的情况下无法正确估计运动参数。而通过结合功率及多普勒频率信息能得到更准确的运动参数估计,适合于实际应用。     

Performance Enhancement of Hybrid TDOA/AOA Using Multipath Delay Estimation

Wireless Personal Communications - Wireless solar blind ultraviolet (UV) scattering communication is a new type of atmosphere optics communication technology with the important and potential...     

基于角度和频率信息的机动目标无源自适应跟踪算法

在进行可观测性分析的基础上,综合利用方位角和频率观测信息,提出了一种利用单个模型对机动目标进行无源跟踪的UKF算法,该算法通过对目标的运动状态和机动参数进行联合估计自适应地逼近真实的运动模型,从而进行准确跟踪。与现有算法相比,本文的算法不仅运算量小,而且降低了对观测站自身的运动要求,但却保持了良好的性能。仿真结果表明了算法的有效性和正确性。     

An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions

This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations.      

基于数字干涉仪的无源测向技术研究

提出了基于数字干涉仪测向技术的无源测向定位系统,简述了其工作原理和工程实现方法,对高精度数字鉴相和宽带数字干涉仪基线配置及解相位模糊等关键技术进行了阐述,并给出了系统性能测试结果及与国外相关系统的情况对比;     

单站被动目标的多假设距离粒子滤波跟踪

针对单站被动目标跟踪的无距离信息,多假设距离粒子滤波方法利用多个粒子滤波的并行运算,对不同初始距离的粒子滤波器进行计算,利用各子滤波的残差概率对并行处理的粒子滤波运算进行自适应采样实现单站纯方位目标跟踪,并与粒子滤波、多假设距离滤波方法进行比较,仿真结果表明该算法收敛速度快,跟踪精度高。     

基于对流层散射传播的超视距时差定位

针对远距离地面目标的平面时差定位误差大的问题,将对流层散射信号的传播路径经过一定的近似之后,提出了一种基于对流层散射传播的超视距时差定位技术。仿真结果表明,与平面内的时差定位相比,目标距离越远,超视距时差定位的优势越明显。     

杂波环境下多被动传感器单目标跟踪算法

由于被动传感器只能获得目标的角度量测,因此杂波环境下基于被动传感器的关联问题较主动传感器更为困难.针对杂波环境下纯方位多被动传感器系统的单目标跟踪问题,提出了一种基于扩展卡尔曼滤波的模糊综合贴近度关联跟踪方法.该方法采用直角坐标系下多被动传感器系统的扩展卡尔曼滤波对目标进行跟踪.首先利用目标航速的预测信息,针对每个传感器建立确认跟踪门;在获得候选关联组合后,直接利用角度信息建立各候选关联组合与角度预测值间的模糊综合贴近度,通过在所获得的全部模糊综合贴近度中寻求最优解完成量测到航进的关联.仿真实验表明,该方法可以有效地解决杂波环境下多被动传感器系统的单目标跟踪问题.