相位编码脉冲串的相参性识别算法研究

本文提出了一种相位编码脉冲串的相参性识别算法,通过检测脉冲相位的线性度判别序列的相参性。该算法通过M次方运算将相位编码信号变换成正弦波信号,再通过相参积累,有效地提高了信号的信噪比。文中讨论了算法的信噪比门限,并通过仿真实验加以验证。仿真实验表明,本方法可以在一定信噪比条件下实现对相位编码脉冲串相参性的识别。本文算法有助于实现对脉冲多普勒雷达的识别和告警,对研制数字射频存储器也具有重要意义。     

相参脉冲信号识别方法和性能分析

提出了一种相参脉冲信号识别算法,通过检测脉冲相位的线性度判别序列的相参性.该算法通过相参积累,有效地提高了信号的信噪比.文中讨论了门限的选取对算法性能的影响,给出了相应的数学推导,并通过仿真实验对推导结果加以验证.同时还讨论了算法的信噪比门限.仿真实验表明,本方法可以在较低信噪比情况下实现对脉冲信号相参性的识别.本文算法有助于实现对脉冲多普勒雷达的识别和告警,对研制数字射频存储器也具有重要意义.     

基于Keystone变换的OFDM信号距离走动校正方法

基于Keystone变换技术研究正交频分复用(Orthogonal frequency division multiplexing,OFDM)雷达信号距离单元走动校正方法.在研究OFDM雷达信号数学原理的基础上,本文给出了理想点目标的脉冲串回波信号表达式.针对发射信号内在结构特性,分析了相对运动对目标回波信号的影响.推导了脉冲体制OFDM回波信号的距离频率域模型,讨论了基于Keystone变换的距离走动校正技术.最后给出典型系统参数下的仿真实验结果,验证了方法的有效性.     

重频参差相参脉冲串频率估计算法

研究了重频参差相参脉冲串的频率估计算法。通过脉内相关积累,提高了新序列的信噪比。对新序列的相位差按参差重数抽样平均,减小了等效的相位噪声方差,利用重频参差比解相位模糊,扩大了频偏允许范围,降低了算法的信噪比门限。分析了本算法实现相参频率估计的条件,推导了相应信噪比门限的解析表达式,指出了信噪比门限与信号样本总数、参差重数、参差比之间的关系。仿真结果表明:上述结论是正确的,在满足信噪比门限条件下,频率估计的精度接近相参脉冲串频率估计的克拉美-罗限(CRLB)。     

基于相参脉冲串的多普勒雷达回波仿真

雷达回波是一切雷达信号处理和系统性能评估的基础,由于目标处于复杂的环境背景之下,所以雷达接收机端除了接收到真实目标的有用回波外,还将混入环境杂波以及接收机内部的噪声信号。利用Simulink工具箱丰富的信号发生及处理模块,对包括目标回波、噪声以及环境杂波等信号的脉冲多普勒雷达回波进行了仿真。通过在相参脉冲串雷达信号中加入目标RCS幅度起伏和延迟信息以及多普勒信息,进行仿真了目标的回波信号。同时,在对分布函数分析的基础上,由Simu-link基本模块产生的均匀分布信号作相应变换与处理,分别得到满足各种分布的典型雷达噪声。并对环境杂波和各种回波信号进行仿真。仿真结果与理论分析相一致,证明对雷达回波的有效性为信号的处理和分析提供了保障。     

基于相参处理的宽脉冲信号参数估计快速算法

提出了一种宽脉冲信号参数估计快速算法,利用抽取的方法来降低宽脉冲信号参数估计的计算量。首先,对宽脉冲正弦波信号进行抽取构造相参脉冲串,利用相参脉冲频率估计算法进行频率估计。其次,对于宽脉冲线性调频信号,对其做延时相关之后即可转化为宽脉冲正弦波信号,然后在上述抽取和相参处理方法的基础上即可得到宽脉冲线性调频信号参数估计值。性能分析和仿真结果表明,该算法在保证一定的参数估计精度前提下,大大降低了算法的计算量,有利于宽脉冲信号参数估计的实时处理。     

基于参考站的多传感器时差定位误差校正分析

多传感器时差定位系统中,接收预处理与时间频率基准源会导致系统时差,同时观测站几何位置测量的不准确性也会引入站址误差.针对系统时差与站址误差对定位误差的影响,研究了在已知地理位置引入参考站,通过差分目标信号时差值与参考信号时差值,抑制系统时差与站址误差对定位的影响,实现对系统时差与站址误差的校正.仿真结果表明,在定位盲区之外引入参考站可以提高参考站附近区域的定位性能.     

相参脉冲串多普勒频率变化率估计算法

给出了一种新的利用相参脉冲串进行多普勒频率变化率估计的算法,选取准最佳算法估计频率,减小了频率估计误差.对各脉冲进行脉内相关积累,将相参脉冲串变换成一个新的线性调频信号序列.该序列的样本点数是脉冲个数、采样间隔是脉冲重复周期、调频斜率是多普勒频率变化率、信噪比提高为输入信噪比的N倍(N是脉内采样点数).因此,可以在较低的信噪比条件下精确估计多普勒频率变化率.仿真结果表明,该算法能在比现有算法更低的信噪比条件下精确估计多普勒频率变化率,其性能接近相参脉冲串多普勒频率变化率估计的克拉美-罗限.     

无源相干脉冲雷达时间同步误差影响分析

在无源相干脉冲雷达系统中,由于直达波信号的起伏,难以实现对其脉冲重复频率实时且精确地估计,导致脉冲重复频率与采样时钟之间总是存在时间同步误差,很难满足脉冲间相参采样的条件.文中首先通过对无源相干脉冲雷达系统的中频采样过程进行建模,推导了脉冲间相对采样时刻的变化周期与时间同步误差的关系,然后定义了归一化干扰功率来分析时间同步误差对无源相干脉冲雷达系统相参处理输出的影响.在此基础上,推导了脉冲间相对采样时刻的差服从均匀分布和Gauss分布时的归一化干扰功率,并给出了相应的仿真结果.同时,推导了存在时间同步误差时Doppler频率估计的理论误差,并给出了相对采样时刻变化周期不同时Doppler频率估计的仿真实例,验证了理论分析的正确性.     

基于小波变换的相位编码调制函数估计方法

相位编码调制信号是一种典型的脉冲压缩信号.广泛应用于脉冲压缩体制的低截获概率雷达中.获取相位编码信号的编码规律对单站无源定位与跟踪十分重要.推导了基于连续小渡变换的相位编码信号相位调制函数的估计算法,能得到相位编码信号的相位调制函数的正确估计.计算机仿真结果证明了算法的有效性.     

基于高斯和滤波的高轨双星高超音速目标跟踪

针对基于高轨双星时频差观测的无源跟踪问题,提出一种适用于跟踪高超音速巡航目标GMM-AEKF算法,该算法使用基于Euler采样建立的WGS-84椭球下的目标离散时间运动方程,在已有的GMM-EKF跟踪框架的基础上引入时差均匀高斯混合（GMM）表示、替代扩展卡尔曼滤波（AEKF）和基于Kullback-Leibler散度的高斯分量管理。仿真实验结果表明,AEKF的引入使得跟踪算法的状态更新运算变为线性,其估计精度收敛速度较快,适用于高超音速目标跟踪。     

CDMA信号的时频差高精度估计算法

针对多用户CDMA信号的时频域重叠特征,提出了一种新颖的时频差高精度估计方法。该方法结合扩频信号的捕获和解扩操作,以较短的信号样本和较低的计算量,仅两次时间-频率分维迭代实现了用户信号分离和时频差估计,再通过时域和频域内插进一步提高估计精度。仿真结果表明,与直接互模糊函数相关法相比,该方法能够有效提高CDMA信号时频差估计精度,降低计算量。     

无线传感器网络中基于TDOA/FDOA的增强半正定松弛定位算法研究

本文在无线传感器网络定位问题中,考虑了基于到达时间差（Time-Difference-of-Arrival,TDOA）和到达频率差（Frequency-Difference-of-Arrival,FDOA）的移动未知目标定位问题,TDOA/FDOA联合定位可以有效利用传感器的位置和速度信息,提高了定位精度。本文在现有的半正定松弛（Semidefinite Relaxation, SDR）方法的基础上,提出了一种增强半正定松弛方法。通过挖掘现有半正定规划问题中优化变量之间的内在联系并将这些联系转化为凸约束,有效提高了现有半正定松弛方法的紧度,从而使估计的未知目标的位置和速度精度达到了克拉美-罗下界 (Cramer Rao lower bound,CRLB)。仿真结果表明,该方法的性能在大噪声时优于现有方法。     

Source localization and calibration using TDOA and FDOA measurements in the presence of sensor location uncertainty

Source localization accuracy is very sensitive to sensor location error.This paper performs analysis and develops a solution for locating a moving source using time difference of arrival(TDOA)and frequency difference of arrival(FDOA)measurements with the use of a calibration emitter.Using a Gaussian random signal model,we first derive the Cram′er-Rao lower bound(CRLB)for source location estimate in this scenario.Then we analyze the differential calibration technique which is commonly used in Global Positioning System.It is indicated that the differential calibration cannot attain the CRLB accuracy in most cases.A closed-form solution is then proposed which takes a calibration emitter into account to reduce sensor location error.It is shown analytically that under some mild approximations,our approach is able to reach the CRLB accuracy.Numerical simulations are included to corroborate the theoretical developments.     

Energy-efficient cognitive access approach to convergence communications

This article studies the cognitive access in convergence communications.Convergence communications provide upper-layer applications with uniform communication service,converging different lower-layer networks into a uniform access pattern such as all-IP communications.As an import access in convergence communications,the cognitive access provides users with a flexible and dynamic access to networks.In this article,we do not only take into account the spectrum usage of convergence communication networks,but also consider theirs energy efficiency.An energy-efficient access algorithm is proposed to improve network performance and efficiency.Different from the existing cognitive access,we regard energy efficiency as the optimal objective to turn the energy-efficient cognitive access into an optimal problem.The collision avoidance and sleeping mechanisms are used to reduce energy consumption and raise network throughput.The utility function is proposed to maximize networks’energy efficiency and then achieve the energy-efficient cognitive access.Simulation results show that the proposed approach is effective and feasible,which can significantly improve networks’energy efficiency.     

一种新的星载TDOA/FDOA/AOA联合定位算法

针对双星时频差定位体制在星下轨迹附近区域存在定位盲区的问题,提出一种单星二维干涉仪测向（AOA）与双星到达时差（TDOA）/到达频差（FDOA）联合定位方法。采用更精确的WGS_84地球模型,通过构造子空间基矢量恒等式以及地球表面约束获取联合观测的伪线性定位方程,实现了双星TDOA/FDOA/AOA联合高精度定位。在相同场景下将时频差定位算法和提出的联合定位算法进行比较,仿真结果表明该算法可缓解时频差定位盲区缺陷,星下轨迹附近区域的定位精度可至少提升两倍以上。     

TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors

This paper addresses the target tracking problem using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measured by moving sensor network whose position and velocity are noise contaminated. It is a known fact that the existing approaches to this problem still have two unsolved technical issues; the unsatisfactory convergence behavior of the tracking filter mainly caused by severe nonlinearity of the problem itself and the tracking performance degradation due to the sensor position and velocity errors. In order to resolve these matters radically, the given target tracking problem is formulated as the robust state estimation problem of the linear system with stochastic uncertainties in its measurement matrix and solved by using the robust Kalman filter theory. The proposed scheme enables us to overcome the inherent limitations of the conventional nonlinear filters for its linear filter structure. It can also prevent the performance degradation due to imperfect sensor position and velocity information. Through the simulations, the effectiveness and reliable target tracking performance of the proposed method are demonstrated.     

On the use of calibration sensors in source localization using TDOA and FDOA measurements

The accuracy of a source location estimate is very sensitive to the presence of the random noise in the known sensor positions. This paper investigates the use of calibration sensors, each of which is capable of broadcasting calibration signals to other sensors as well as receiving the signals from the source and other calibration sensors, to reduce the loss in the source localization accuracy due to uncertainties in sensor positions. We begin the study with deriving the Cramer–Rao lower bound (CRLB) for source localization using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements when a single calibration sensor is available. The obtained CRLB result is then extended to the more general case with multiple calibration sensors. The performance improvement due to the use of calibration sensors is established analytically. We then propose a closed-form algorithm that can explore efficiently the calibration sensors to improve the source localization accuracy when the sensor positions are subject to random errors. We prove analytically that the newly developed localization method attains the CRLB accuracy under some mild approximations. Simulations verify the theoretical developments.     

Localization of multiple disjoint sources with prior knowledge on source locations in the presence of sensor location errors

Sensor location errors are known to be able to degrade the source localization accuracy significantly. This paper considers the problem of localizing multiple disjoint sources where prior knowledge on the source locations is available to mitigate the effect of sensor location uncertainty. The error in the priorly known source location is assumed to follow a zero-mean Gaussian distribution. When a source location is completely unknown, the covariance matrix of its prior location would go to infinity. The localization of multiple disjoint sources is achieved through exploring the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) measurements. In this work, we derive the Cramér–Rao lower bound (CRLB) of the source location estimates. The CRLB is shown analytically to be able to unify several CRLBs introduced in literature. We next compare the localization performance when multiple source locations are determined jointly and individually. In the presence of sensor location errors, the superiority of joint localization of multiple sources in terms of greatly improved localization accuracy is established. Two methods for localizing multiple disjoint sources are proposed, one for the case where only some sources have prior location information and the other for the scenario where all sources have prior location information. Both algorithms can reach the CRLB accuracy when sensor location errors are small. Simulations corroborate the theoretical developments.     

基于遗传算法的时频差快速估计

利用互模糊函数进行时频差估计运算量大,现有算法对时频差搜索大都基于遍历思想,实时性较差。针对此问题,提出基于遗传算法的时频差快速估计方法,该方法针对互模糊函数特点对遗传算法做出改进,通过种群收敛程度自适应调整变异概率,并结合多次种群初始化,有效改善种群多样性防止算法陷入局部最优。仿真结果表明,遗传算法相对现有算法,能够快速得到时频差估计结果,计算效率有较大提升。