一种基于多辐射源匹配的未知辐射源的定位方法

无源定位系统利用民用辐射资源,具备静默探测的优势,为目前的研究热点之一。某些情况不能直接获取辐射源位置,这影响了该系统的应用,例如邻国民用调频台。该文提出了利用多辐射源匹配的定位方法,解决了未知辐射源定位的问题。仿真结果表明该方法计算量小,部分定位精度最好可达到1 m,对提高战术应用具有重要参考价值。      

基于卫星外辐射源的高精度单站定位方法

目前基于卫星外辐射源多采用单站二维测向时差、多站时差定位体制,定位系统复杂,实际工程应用场景受限.为此提出了一种限定约束条件的单站测向时差定位方法,该算法利用侦测目标活动区域的先验信息,实现目标二维方向方程的降维处理,解决单站定位受测量条件制约无法建立有效的定位解算方程问题,并通过牛顿迭代处理实现定位方程有效解算,得到...     

脉冲辐射源时差定位的新方法

脉冲辐射源定位系统主要用于对空基、地基和海基目标的时短信号辐射源进行检测和定位。现有的脉冲辐射源定位系统大都属于长基线系统。本文提出在有限空间环境下进行时差定位的新方法,并对系统的定位精度进行了仿真,提出了实现系统所需的关键技术。     

机载塔康无源定位方法研究

针对塔康信号易被截获的问题,研究了机载塔康的无源定位技术。分析了塔康无源定位的可行性以及工程实现问题,提出了在现有系统中,由于信道标准化以及功能软件化,塔康无源定位具备实际应用的条件。给出了存在方位测量误差时,塔康无源定位的误差以及边界。仿真结果表明,机载塔康在无线电静默模式下,结合高度信息,可以实现双站测向交叉定位。     

一种二维无源交叉定位方法

无源雷达具有作用距离远、隐蔽性好等优点。本文阐述二维无源测向交叉定位技术．给出了定位解公式，并用概率理论分析了误差分布，提出了系统组建方案。进行了计算机仿真，给出了仿真结果。     

一种机载塔康终端单站定位技术

根据塔康信号询问脉冲与应答脉冲之间的规律,提出一种针对空中机载塔康终端的单站定位方法。首先采用测向交叉定位法得到地面信标台的位置,然后利用询问应答规律,获得询问信号分别到达侦察站与信标台的时间差,再加上对空中目标的测向信息,即可实现对空中目标的定位。通过MATLAB程序仿真证明了该方法的可行性,对于解决实际工程中的定位问题具有借鉴意义。     

辐射源载频对测向定位的影响

分析了三维测向交叉定位的基本原理,推导了定位误差的定量表达式,分别针对比幅法测向及干涉仪测向,分析了辐射源载频与测角精度的关系,建立了辐射源与测角精度的定量关系式,从而得到了辐射源载频与定位误差之间的定量关系,并进行了仿真分析,得出被侦察方可以提高载频,使侦察方无法精确定位的结论,在实际应用中具有一定的意义。     

基于双基地距离的多站多外辐射源无源定位算法

针对利用多个外辐射源和多个接收站的无源雷达目标定位场景,提出了一种利用三步加权最小二乘的双基地距离（Bistatic Range,BR）定位问题代数解.首先,在第一步加权最小二乘估计中,通过引入多个辅助参数,将BR观测方程线性化,并得到目标位置粗估计;在后两步加权最小二乘估计中利用辅助参数与目标位置参数的约束关系来提高目标位置的估计精度.对算法理论误差分析表明,其定位精度可以达到克拉美罗界.蒙特卡罗仿真结果表明,算法的定位精度优于现有算法.     

固定单站对运动辐射源的无源定位与跟踪

利用测量获得的辐射源方位角和到达时间差信息 ,探讨了通过两种最小二乘估计实现定位的方法。本文对两种定位算法进行了比较 ,并进行了计算机模拟 ,其中第二种方法具有定位精度高、算法稳定的优点。     

雷电辐射源定位的时间反转方法与应用

将频域时间反转（time reversal,TR）聚焦技术应用于雷电辐射源探测定位,介绍了雷电辐射源探测系统的基本组成和TR定位算法,讨论了定位精度的影响因素,给出了典型应用实例.通过与传统的方法进行对比,发现该方法具有较好的鲁棒性,无需提取波形的到达时刻等信息,可直接利用检测波形的反向传播进行自动聚焦定位,并具有多辐射源同时定位的潜力.     

多校准站TDOA/FDOA无源定位方法

针对运动目标到达时差（Time Difference-of-Arrival,TDOA）/到达频差（Frequency Difference-of-Arrival,FDOA）定位中的接收站定位误差问题,提出了基于多校准站的TDOA/FDOA定位方法,有效降低接收站定位误差的影响,并推导了该方法的克拉美罗下限（Cramér-Rao Lower Bound,CRLB）。理论分析表明,采用多校准站法能有效降低CRLB,提高目标定位精度。同时,当校准站自身定位存在误差时,也将影响对接收站的校准和目标的定位精度。通过仿真实验定量分析了采用多校准站法对定位精度的改善程度。     

Near-field source localization method and application using the time reversal mirror technique

In order to develop the acoustic keyboard for Personal Computer (PC), it is necessary to seek high-precision near-field source localization algorithm for identifying the keyboard characters. First of all, the focusing property of Time Reversal Mirror (TRM) is introduced, and then a mathematical model of microphone array receiving typing sound is established according to the realization of acoustic keyboard from which the TRM localization algorithm is carried out. The results through computer simulation show that the localization Root Mean Square Error (RMSE) performance of the algorithm can reach 10^?3, which demonstrates that the algorithm possesses a high accuracy for the actual near-field acoustic source localization, with potential of developing the computer acoustic keyboard. Furthermore, for the purpose of testing its effect on actual near-field source localization, we organize three experiments for acoustic keyboard characters localization. The experiment results show that the positioning error of TRM algorithm is less than 1 cm within a provided acoustic keyboard region. This will provide theoretical guidance for the further research of computer acoustic keyboard.     

Distributed source localization using ESPRIT algorithm

A new algorithm based on ESPRIT is proposed for the estimation of the central angle and angular extension of distributed sources. The central angles are estimated using TLS-ESPRIT for both incoherently distributed (ID) and coherently distributed (CD) sources. For CD sources, the extension width is estimated by constructing a one-dimensional (1-D) distributed source parameter estimator (DSPE) spectrum for each source. For ID sources, the extension widths are estimated using the central moments of the distribution. The algorithm can be used for sources with different angular distributions     

层次-粗集组合赋权法在雷达辐射源识别中的应用

对雷达辐射源识别中的权系数赋值问题进行了研究,提出了层次-粗集组合赋权法。该方法利用粗糙集理论的属性约简和属性重要度原理,删除了冗余属性,构造出客观判断矩阵,然后与层次分析法的主观判断矩阵相结合,得到组合判断矩阵,最后计算出权重,从而实现了主客观方法共同赋值。仿真表明,本方法所得权系数在进行雷达辐射源识别时有较好的结果。     

Rectangular Pisarenko method applied to source localization

Most high-resolution (HR) direction-of-arrival (DOA) estimation schemes require the extraction of a low-dimensional subspace: a task that takes O(N³) flops for an order S matrix. Different techniques have been recently proposed to reduce this computational load. For those working on blocks of data, the number of flops required is generally O(N²P), where P, which is the dimension of the subspace (the number of sources), is often quite small as compared with N, which is the number of sensors. The method we propose is a HR technique that requires O(NP²)+O(P³) flops, i.e., that is linear in the number of sensors. The price to be paid for this drastic computational saving is a reduction in performance. Although the Cramer-Rao lower bound (CRLB) on the variance of the direction estimates is of the order T^-1 N^-3 (with T the number of snapshots), this variance is of order T^-1 N^-2 for the proposed procedure. The idea behind the method is to apply a Pisarenko method to a rectangular matrix extracted from the Toeplerized estimated covariance matrix, and it is this Toeplerization that allows preservation of the O(T^-1 N^-2) level of performance     

基于遗传算法的声源三维定位方法

相位变换加权的可控响应功率（SRP-PHAT）算法是一种基于麦克风阵列的鲁棒声源定位方法,该算法在有混响和噪声的环境下仍有较高的定位精度.但该算法用网格法对整个声源空间进行搜索,逐点计算其目标函数,因而总的计算量非常大,不适用于实时定位系统.针对SRP-PHAT的特点,采用遗传算法进行搜索,使总的计算量大幅度降低.仿真结果表明在混响时间为300ms,信噪比为5dB的条件下,该算法仍可达到较高的定位精度.     

Brain source localization using reduced EEG sensors

Brain source activation is caused due to certain mental or physical task, and such activation is localized by using various optimization techniques. This localization has vital application for diagnoses of various brain disorders such as epilepsy, schizophrenia, Alzheimer, depression, Parkinson and stress. Various neuroimaging techniques (such as EEG, fMRI, MEG) are used to record brain activity for inference and estimation of active source locations. EEG employs set of sensors which are placed on scalp to measure electric potentials. These sensors have significant role in overall system complexity, computational time and system cost. Hence, sensor reduction for EEG source localization has been a topic of interest for researchers to develop a system with improved localization precision, less system complexity and reduced cost. This research work discusses and implements the brain source localization for real-time and synthetically generated EEG dataset with reduced number of sensors. For this, various optimization algorithms are used which include Bayesian framework-based multiple sparse priors (MSP), classical low-resolution brain electromagnetic tomography (LORETA), beamformer and minimum norm estimation (MNE). The results obtained are then compared in terms of negative variational free energy, localization error and computational time measured in seconds. It is observed that multiple sparse priors (MSP) with increased number of patches performed best even with reduced number of sensors, i.e., 7 instead of 74. The results are shown valid for synthetic EEG data at low SNR level, i.e., 5 dB and real-time EEG data, respectively.     

Efficient wideband source localization using beamforming invariancetechnique

A novel scheme for wideband direction-of-arrival (DOA) estimation is proposed. The technique performs coherent signal subspace transformation by a set of judiciously constructed beamforming matrices. The beamformers are chosen to transform each of the narrowband array manifold vectors into the one corresponding to the reference frequency, regardless of the actual spatial distribution of the sources. The focused data correlation matrix can thus be obtained without any preliminary DOA estimation or iteration. A simplified version of the beamspace Root-MUSIC algorithm is developed and used in conjunction with the proposed method to efficiently localize multiple wideband sources with a linear, equally spaced array. Numerical simulations are conducted to demonstrate the efficacy of the new scheme     

Spatio-temporal EEG source localization using simulated annealing

The estimation of multiple dipole parameters in spatio-temporal source modeling (STSM) of electroencephalographic (EEG) data is a difficult nonlinear optimization problem due to multiple local minima in the cost function. A straightforward iterative optimization approach to such a problem is very susceptible to being trapped in a local minimum, thereby resulting in incorrect estimates of the dipole parameters. Here, the authors present and evaluate a more robust optimization approach based on the simulated annealing algorithm. The complexity of this approach for the STSM problem was reduced by separating the dipole parameters into linear (moment) and nonlinear (location) components. The effectiveness of the proposed method and its superiority over the traditional nonlinear simplex technique in escaping local minima were tested and demonstrated through computer simulations. The annealing algorithm and its implementation for multidipole estimation are also discussed. The authors found the simulated annealing approach to be 7-31% more effective than the simplex method at converging to the true global minimum for a number of different kinds of three-dipole problems simulated in this work. In addition, the computational cost of the proposed approach was only marginally higher than its simplex counterpart. The annealing method also yielded similar solutions irrespective of the initial guesses used. The proposed simulated annealing method is an attractive alternative to the simplex method that is currently more common in dipole estimation applications