基于熵最小化的LFM信号调频率估计算法

利用LFM信号频谱的熵随着调频率减小而降低的性质,提出了一种基于频谱熵最小化的LFM信号调频率的估计SEM方法。建立参数待估的相位补偿因子,通过搜索得到使得补偿后信号频谱熵全局最小的调频率估值。在搜索过程中,采用两级搜索策略,并引入牛顿迭代算法,有效降低了算法复杂度。理论推导和仿真结果证明,该算法为有偏算法,估计偏差量与初始频率相关,理论估计方差比较CR下界低12d B。对雷达实测回波信号进行验证,与离散多项式变换算法相比发现,提出算法估计的鲁棒性更好,并具有较高的测速精度,具有一定的应用价值。     

基于负熵最大的FastICA语音信号分离算法

语音信号分离是现代信号处理的热点问题,针对未知信号源个数的情况,提出一种基于负熵最大的FastICA(Fast Independent Component Algorithm)语音信号盲分离算法,有效解决了源信号数目估计、语音信号分离及复原等问题。改进的算法增加了源信号数目估计环节,放宽了算法适用条件,即在源信号数目未知的情况下,也能够实现信号盲分离功能。并将其成功应用于运用信号分选过程中,最终复原语音时域波形,完成信号分选任务。仿真实验中,详细讨论了该方法在不同信噪比以及不同源信号数目情况下的分选能力,证明了方法的有效性和优越性。     

INS辅助的GNSS接收机信号捕获性能分析

针对信号捕获过程中Doppler频移造成的影响,采用INS辅助的GNSS信号捕获方法,分析了INS辅助后卫星信号捕获的性能。利用INS速度误差方程与Doppler误差之间的关系,分析INS辅助后的捕获性能,并通过性能参数的比较,理论上验证了INS辅助后的捕获能力的提升。仿真结果表明,INS辅助能够有效提高信号捕获的检测概率和对弱信号的捕获能力,并大幅减小平均捕获时间。同时,考虑到INS器件参数的关键性作用,采用战术级的INS器件完全满足工程需求。     

新的IFF系统及其信号检测方法

介绍了一种新型敌我识别系统Mode 5 IFF,并分析了该系统的特点及信号产生流程。通过分析Mode 5 IFF系统询问及应答信号格式,提出了基于同步脉冲相关的Mode 5 IFF信号检测方法,该方法根据Mode 5 IFF信号同步脉冲数据一致性的特点,利用同步脉冲信号与所接收到的中频信号进行相关,取相关峰值达到Mode 5 IFF信号检测的目的。最后分析了该方法在"混扰"和低信噪比条件下的信号检测效果,验证了该方法的有效性。     

基于Kalman预测的自适应线性化信号的生成方法

自旋弹体的姿态通常采用单通道控制方法进行控制。该方法中采用线性化信号对控制信号进行调制,而线性化信号采用固定频率随机相位的信号发生器产生,这就会对弹体控制产生不良的影响。提出了一种基于Kalman预测的自适应线性化信号的实现方法。该方法通过弹旋频率测定、频率的Kalman预测和信号发生器实现了线性化信号对弹旋信号的同步、自适应倍频、定步长、定幅度的信号发生功能。仿真实验证明了方的正确性,为更加合理的控制自旋导弹的姿态提供了一种有效的方法。     

基于信号仿真支撑的角度欺骗功能仿真方法

根据雷达信号仿真和功能仿真各自的优缺点,提出了一种基于信号仿真支撑的雷达角度欺骗干扰功能仿真方法。在介绍了雷达系统仿真的两种基本形式后,提出了雷达欺骗干扰功能仿真的基本思想。以交叉极化角度欺骗干扰为例,提出了雷达欺骗干扰功能仿真关键模型的建立方法,并详细说明了其实现步骤。最后进行了仿真验证,分析了功能仿真和信号仿真精度和仿真用时的差别,结果表明了所提方法的可行性和有效性。     

基于滤波与BP神经网络的水下探测信号检测算法

针对水下探测系统探测船舶磁场信号时信噪比较低的问题,首先根据磁异常信号的频域特征,设计了约束最小二乘FIR滤波器,通过对含噪信号进行带通滤波,滤除高频噪声;再采用BP神经网络对低频分量进行学习,提取船舶目标特征信号。将该算法应用于船模实测实验,结果表明:该算法可以显著提高信噪比,增强对船舶磁场信号的检测能力。     

基于CZT的频谱细化算法及应用

针对复杂信号频域特征提取问题,利用Delphi语言编写了Zoom-FFT和基于CZT的频谱细化方法应用软件,以某型坦克传动箱振动速度信号为例进行了对比分析,验证了算法的有效性和程序设计的正确性。结果表明：基于CZT的频谱细化方法具有运算效率高、细化效果好等优点,可以在复杂信号分析中推广应用。     

高阶BOC信号电离层色散效应的模拟方法

导航信号模拟器需要模拟电离层引起的延时和色散效应,与传统导航信号相比,新体制高阶BOC信号具有更宽的带宽,而传统导航信号的模拟方法会引入不可忽略的电离层延迟建模误差。通常情况下BOC(14,2)信号上下边带信号的电离层延迟可达到米级的差异。提出运用双边带模型的BOC信号模拟方法并进行了仿真,结果表明改进方法可以准确模拟电离层色散效应的影响。     

Signal‐to‐interference‐plus‐noise ratio estimation for wireless communication systems: Methods and analysis

The Signal‐to‐Interference‐plus‐Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for both a more efficient system and a higher user‐perceived quality of service. In this paper, we develop new SINR estimators and compare their mean squared error (MSE) performance. We show that our new estimators dominate estimators that have previously appeared in the literature with respect to MSE. The sequence of transmitted bits in wireless communication systems consists of both pilot bits (which are known both to the transmitter and receiver) and user bits (which are known only by the transmitter). The SINR estimators we consider alternatively depend exclusively on pilot bits, exclusively on user bits, or simultaneously use both pilot and user bits. In addition, we consider estimators that utilize smoothing and feedback mechanisms. Smoothed estimators are motivated by the fact that the interference component of the SINR changes relatively slowly with time, typically with the addition or departure of a user to the system. Feedback estimators are motivated by the fact that receivers typically decode bits correctly with a very high probability, and therefore user bits can be thought of as quasipilot bits. For each estimator discussed, we derive an exact or approximate formula for its MSE. Satterthwaite approximations, noncentral F distributions (singly and doubly) and distribution theory of quadratic forms are the key statistical tools used in developing the MSE formulas. In the case of approximate MSE formulas, we validate their accuracy using simulation techniques. The approximate MSE formulas, of interest in their own right for comparing the quality of the estimators, are also used for optimally combining estimators. In particular, we derive optimal weights for linearly combining an estimator based on pilot bits with an estimator based on user bits. The optimal weights depend on the MSE of the two estimators being combined, and thus the accurate approximate MSE formulas can conveniently be used. The optimal weights also depend on the unknown SINR, and therefore need to be estimated in order to construct a useable combined estimator. The impact on the MSE of the combined estimator due to estimating the weights is examined. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004