UKF在MEMS陀螺随机噪声补偿的应用

论述了MEMS陀螺静态随机噪声与动态随机噪声的特点;系统分析了无迹卡尔曼滤波(UKF)的特点,将UKF用于MEMS陀螺随机噪声估计;针对MEMS陀螺静态噪声与动态噪声补偿用同一方法处理达不到一定性能的难题,提出了一种由阈值决策静态和动态滤波的工程方法;指出将时间序列用于建立随机噪声的数学模型的缺陷,指出欧美等使用的MEMS陀螺随机噪声补偿的优点与缺点.     

方位多相位中心SAR信号重建误差分析

就方位多相位中心(Azimuth Multiple-Phase-Center,AMPC)合成孔径雷达(Synthetic Aperture Radar,SAR)系统的阵列误差对信号重建性能的影响进行分析。将阵列误差建模为随机过程,结合最小二乘(Least-Square,LS)算法,推导了AMPC SAR误差功率谱的解析表达式,进而得到了AMPC SAR的信噪比与方位模糊比的解析表达式。仿真实验验证了理论分析的正确性。分析指出,随着系统脉冲重复频率的升高,有必要通过减小重建系数以实现重建性能的提升。分析方法与结果对AMPC SAR系统设计以及图像质量预估提供有效支撑。     

非高斯噪声下信号盲检测算法*

针对实际甚低频和超低频接收机不仅受非高斯噪声的影响,同时受到接收机内部和外部环境中高斯噪声影响的问题,对噪声采用高斯尺度混合分布和高斯分布的混合模型建模,根据混合模型的性质,设计了一种基于马尔可夫链蒙特卡罗方法的信号盲检测算法。盲检测算法在贝叶斯层次模型下,采用Gibbs抽样和M-H抽样更新参数,同步检测信道衰落系数、噪声模型参数和信号。算法迭代效率快、精度高。通过与最优检测性能比较,盲检测算法性能优异,对甚低频和超低频信号接收具有重要的现实意义。     

机械设备非平稳噪声成像研究

针对实际机械设备噪声的非平稳特征带来的成像问题,采用总体经验模态分解(EEMD)方法对噪声信号进行分离,从而获取噪声的非平稳特点;利用高密度传感器阵列,采用近场声全息(NAH)与波束形成(Beamforming)相结合的方法实现瞬态成像。结果表明:使用该方法能够获得非平稳声场空间分布的动态特征,实现对实际机械设备噪声的有效监测。     

潜艇对空投鱼雷的防御模型研究

由于空投鱼雷入水点距离潜艇较近,潜艇单靠机动规避摆脱鱼雷跟踪比较困难。针对空投鱼雷的特点,提出了使用噪声干扰器和自航式声诱饵规避敌方鱼雷的对抗方案,分析了噪声干扰器和自航式声诱饵的使用方法并建立了噪声干扰器干扰模型和自航式声诱饵发射扇面模型。通过仿真计算表明:采用本模型确定噪声干扰器和自航式声诱饵的使用方法是可行的。     

基于SNR的烟幕干扰效果仿真

烟幕对红外等电磁波具有吸收、反射和散射特性,可导致导引头上接收的能量大大衰减,从而降低导弹的命中概率.针对外场烟幕性能测试受气象条件和环境因素的影响较大,对准确评估抗红外烟幕遮蔽性能存在较大困难的情况,结合气象条件从烟幕透过率的变化引起导引头信噪比变化的角度,建立了烟幕干扰效果的评估模型,通过计算机仿真,得出了烟幕对红外制导导弹的干扰效果.通过试验对其中的一些结论进行了实验验证,结果表明计算结果能够与实验数据较好吻合.     

不同腐蚀状态不锈钢管焊缝区 碱性腐蚀的电位噪声特征

利用电化学电位噪声方法检测了不同腐蚀状态304与316两种不镑钢管焊缝区试样在室温碱液中的腐蚀行为,并采用电化学噪声时域谱、频域谱进行了分析,结果表明:不同腐蚀状态焊缝试样的腐蚀电位及噪声时城谱特征不同,可利用电位噪声频城谱的高频斜率K值定性判定是否发生局部腐蚀,同时可根据频城谱中发生事件特殊频率fn区分材料的腐蚀状态...     

水下目标辐射噪声高精度仿真方法

在对水下高速运动目标实测辐射噪声数据频谱特征充分分析的基础上,基于能量聚点理论建立了一种“平台型λ水平能量聚点”辐射噪声连续谱模型.利用该模型实现了对水下目标辐射噪声的仿真.首先,对离散化的λ取值分别计算其能量聚点频率区间,然后用谐波叠加法生成频带内的时域信号,将所有频带的信号进行叠加就得到辐射噪声的时域信号,与传统的...     

非平稳噪声背景下DOA分步估计方法研究

针对相干和非相干信源同时存在的情况,结合斜投影理论和互相关矢量Toeplitz矩阵重构(CVTR)的方法,提出一种新的信源DOA分步估计方法。该方法把相干信源和非相干信源分开分辨,对相干信源用CVTR方法来恢复为满秩,避免了常规平滑算法阵列孔径损失大、运算量大的缺点;用斜投影算子而非差分方法对信源进行分离,不受相关矩阵须为Toeplitz结构的限制,使得算法可适用于任意阵列结构形式。这种分步分辨思路可有效增强信源过载能力,同时在互相关矢量Toeplitz矩阵重构过程中,可把非平稳噪声协方差矩阵转换成白噪声结构,使得算法对非平稳噪声有较好的适应能力。     

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