Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

Sequential Parameter Estimation Using Modal Dispersion Curves in Shallow Water

Existing sequential parameter estimation methods use the acoustic pressure of a line array as observations. The modal dispersion curves are employed to estimate the sound speed profile(SSP) and geoacoustic parameters based on the ensemble Kalman filter. The warping transform is implemented to the signals received by a single hydrophone to obtain the dispersion curves. The experimental data are collected at a range-independent shallow water site in the South China Sea. The results indicate that the SSPs are well estimated and the geoacoustic parameters are also well determined. Comparisons of the observed and estimated modal dispersion curves show good agreement.     

Adaptive autoregressive modeling of non-stationary vibration signals under distinct gear states. Part 1: modeling

Non-parametric time-frequency techniques are increasingly developed and employed to process non-stationary vibration signals of rotating machinery in a great deal of condition monitoring literature. However, their capacity to reveal power variations in the time-frequency space as precisely as possible becomes a hard constraint when the aim is that of monitoring the occurrence of mechanical faults. Therefore, for an early diagnosis, it is imperative to utilize methods with high temporal resolution, aiming at detecting spectral variations occurring in a very short time. This paper proposes three new adaptive parametric models transformed from time-varying vector-autoregressive model with their parameters estimated by means of noise-adaptive Kalman filter, extended Kalman filter and modified extended Kalman filter, respectively, on the basis of different assumptions. The performance analysis of the proposed adaptive parametric models is demonstrated using numerically generated non-stationary test signals. The results suggest that the proposed models possess appealing advantages in processing non-stationary signals and thus are able to provide reliable time-frequency domain information for condition monitoring.     

Particle filtering for passive fathometer tracking

Seabed interface depths and fathometer amplitudes are tracked for an unknown and changing number of sub-bottom reflectors. This is achieved by incorporating conventional and adaptive fathometer processors into sequential Monte Carlo methods for a moving vertical line array. Sediment layering information and time-varying fathometer response amplitudes are tracked by using a multiple model particle filter with an uncertain number of reflectors. Results are compared to a classical particle filter where the number of reflectors is considered to be known. Reflector tracking is demonstrated for both conventional and adaptive processing applied to the drifting array data from the Boundary 2003 experiment. The layering information is successfully tracked by the multiple model particle filter even for noisy fathometer outputs.     

光谱导数Kaiman滤波同时测定苯酚-邻氯苯酚和2，4二氯苯酚

文中提出了一个新的、稳定的光谱导数Kalman滤波紫外分光光度法同时定量分析方法，并且成功地将其应用于极难分辨的苯酚一邻氯苯酚和2，4二氯苯酚三组分混合体系的同时测量。选择分析光谱导数的原因是其不仅包含着吸光度，还包括在指定波长位置变化趋向等更多的信息量，这样更利于在吸收峰重叠的位置捕捉有较大差异的信号。利用Kalman滤波可以解决由光谱导数而引起的噪声放大问题，也可以过滤源于实验的噪声以及来自传递模型误差。在260-290nm区间测量了30组浓度各自在1～10mg·L^-1范围的苯酚一邻氯苯酚和2，4二氯苯酚标准混合溶液紫外吸收光谱图。利用分段延伸高次多项式回归模拟求导准确获得吸光度导数，并且利用偏最小二乘法将其制作成Kalman滤波标准工作系数矩阵。通过随机线性离散系统的Kalman滤波器最优平滑计算，对混合体系中的各组分进行定量分析。回收实验显示出，光谱导数Kalman滤波分析法应用于本实验的极难分辨的三组分体系，得到了非常高的回收率，并且在整个实验范围内光谱导数Kalman滤波分析法具有非常好的稳定性。     

Imaging through an inhomogeneous layer with an acoustic antenna array

A method is proposed for obtaining images through a layer of an inhomogeneous medium by using an antenna array scanning in angle or space. The method is based on the wave front inversion, which allows one to form an undisturbed sound field on the object of location in an inhomogeneous medium. This property makes it possible to suppress the effect of the thin inhomogeneous layer on the signals observed at the array output. The technique consists in a mutual processing of two received signals, one of which is obtained by locating the objects through the inhomogeneous medium and the other is obtained by locating the same objects, in the same medium, by the front-inverted wave. The mutual processing procedure consists in using the first received signal to form a filter for the second signal. The method is tested by a numerical simulation.     

Modeling the recorded spectrum and reconstructing the transfer function of the wideband ionospheric HF radio channel in the case of chirp sounding

We model theoretically the received spectrum in the case of sounding of the ionospheric HF radio channel by a chirp signal. It is shown that the result of processing of an individual time sample of the received signal is equivalent to the sounding of the radio channel by a complex narrow-band pulsed signal such that the group delays of its propagation modes determine the maxima in the received spectrum. We analyze the quadrature components of realizations of the received signal at the intermediate frequency at the bandpass-filter output in the receiving channel of the chirp ionosonde. The results of our analysis show the possibility of reconstructing the transfer function of a HF radio channel in the sounding-frequency band for the delay range determined by the characteristics of the intermediate-frequency bandpass filter. We propose a method for reconstructing the transfer function of the ionospheric radio channel, which involves supplementing the circuit of primary processing of the signal by a corrective digital filter with specified amplitude-frequency and phase-frequency characteristics. The proposed method can be used for all operating regimes of the chirp ionosonde in the case of digital recording and processing of signals. __________ Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 50, No. 5, pp. 387–395, May 2007.     

生物组织成像中用反相位脉冲技术提高二次谐波信噪比的研究

本文基于有限振幅声波在介质中的非线性传播理论,分析了反相位脉冲技术对生物组织中二次谐波增强的原理.实验中利用反相位脉冲激发超声换能器,对生物组织中传播的非线性信号相加分析.结果表明反相位脉冲技术可有效抑制基波及奇次谐波信号,而可增强偶次谐波信号6dB.与滤波器滤波法相比,反相位脉冲技术在抑制基波信号的同时,可有效地提高二次谐波的信噪比,因而在生物组织的二次谐波成像中具有广阔的应用前景.     

一种自适应层进式Savitzky‐Golay光谱滤波算法及其应用

可调谐半导体激光吸收光谱技术（TDLAS）利用半导体激光器的可调谐和窄线宽特性,通过选择特定气体的单条吸收线,排除其余气体的干扰,可以实现高精度、高选择性的气体浓度测量,在气体浓度检测系统中具有广泛的应用前景。在不同的应用条件和环境下,需要解决相应的硬件和数据处理方面的技术问题。主要研究TDLAS技术机动车尾气CO组分浓度遥测系统中的光谱数据处理问题,该系统利用路面漫反射回波信号遥测行驶中的机动车尾气CO组分浓度。由于激光扫描光谱回波信号受到漫反射面情况变化、空气环境变化、尾气湍流影响等因素影响,探测器收集到的信号不仅较弱同时也夹杂着多种噪声, 即测量光路信噪比较差, 故提出一种自适应层进式Savitzky-Golay(S-G)平滑滤波算法,实现了对光谱进行滤波处理从而更加准确地反演CO浓度。S-G滤波算法因其原理简单、功能强大、只需设置两个参数（窗口大小、拟合阶数）等优点,已广泛应用于光谱处理。如何正确设置S-G算法参数使滤波效果在去噪不足和过度滤波之间找到平衡点,是该滤波算法应用的一大难题。设计的检测系统中,测量光路光谱信号为非平稳信号,噪声和有效信号幅度时变,最佳窗口大小和多项式阶数随信号动态而变化,且变化区间较大,使用固定参数的S-G滤波器难以达到最佳效果。提出的自适应层进式S-G平滑滤波算法,通过逐层将测量光路光谱信号经过S-G滤波后,与参考光路的光谱信号设置的参考段比对信号相关系数和信号一阶导相关系数的和,以自适应得到逐层最优参数。通过对信噪比从9.81～29.77的10组不同带噪光谱分析验证了该算法的有效性,自适应层进式S-G算法能较好地去除噪声并还原带噪信号所携带的待测气体浓度信息,与带噪光谱对比,吸收光谱峰值最大误差由25.152%降至5.917%,积分吸光度最大误差由18.1%降至3.9%。在实现的系统中,使用自适应层进式S-G算法对测量光路进行滤波处理,并对不同车型、不同排量、燃烧不同油品的机动车在怠速和缓速通过(5 km·h-1)系统时其排放的CO浓度进行实时在线监测。     

扩展卡尔曼滤波和不敏卡尔曼滤波在实时雷达回波反演大气波导中的应用

为了改善雷达回波反演大气波导(RFC)方面存在的单时次、单方位角反演的问题,提出利用扩展卡尔曼滤波和不敏卡尔曼滤波的反演算法对大气波导结构的多方位角实时跟踪反演. 在卡尔曼滤波方法中分别给出大气波导结构的参数化方程、观测方程、滤波算法的状态转移方程,最后导出滤波反演算法的迭代求解流程. 在大气波导结构不随时间变化和随时间变化的两种条件下,对扩展卡尔曼滤波和不敏卡尔曼滤波算法进行数值实验. 实验结果表明,不敏卡尔曼滤波更适用于RFC这高度非线性反演问题,它可能今后为大气波导结构多方位角实时跟踪反演的业务化运行提供理论基础与技术保证. 关键词： 大气波导 雷达回波 扩展卡尔曼滤波 不敏卡尔曼滤波     

A speech enhancement method based on Kalman filtering

I.IntroductionKa1manfilteringisjustamethodtoestimatestatistica1lythestateoftheobservedsystemfromthecorruptedsigna1s,andthiskindofcstimationisarecurrcneeestimationbasedon1inear,nonbiasandminimumvariance.Moreover,Ka1manfilteringisapplicabletonon-sta-honarysignalsandtime-variantdynamicsystem.Therefore,Kalmanfilteringisveryapplica-bletoenhancingthespeechsigna1sthatarecorruptedbynoise.ThispaperreportStheconcretcmethodofenhanccmentofnoisyspccchanditscxperimentresults.Experimentsindicate:Afterthes…     

爆炸信号中气泡脉动去除方法及其应用

水下爆炸声源激发的声信号包括冲击波和气泡脉动,气泡脉动严重干扰冲击波的传播特性。当气泡脉动的幅度小于冲击波的幅度时,通过对此类混合爆炸信号进行复倒谱分析,提出了基于卷积型的爆炸信号模型的同态解卷积气泡脉动去除方法,其中,针对同态滤波系统中通常采用的“梳状”滤波器会在倒谱的尖峰处产生不连续点的问题,采用了对尖峰附近的采样点进行多项式函数拟合,并根据拟合函数对尖峰采样点插值的改进型滤波方法。数值仿真和实验信号的处理及分析表明,应用该方法可以明显消除爆炸信号自相关曲线中由气泡脉动造成的对称次尖峰,并在信号的时频分布图中再现冲击波的简正波特性。这为进一步利用爆炸声源研究海洋信道的声传递函数和反演海洋环境参数提供了技术途径。      

强非线性时间演化声速剖面的序贯反演

受海面波浪起伏、降雨和内波等海洋动力学过程的影响,浅水声速剖面的时间演化具有高度非线性,针对该问题提出使用改进的粒子滤波方法进行声速剖面序贯反演.该方法通过建立声速剖面的经验正交模型(EOF)以及描述声速剖面时间演化特征的状态空间模型,将声速剖面反演问题建模为状态跟踪问题,利用不敏粒子滤波(UPF:Uncented Particle Filter)算法进行声速剖面序贯反演。仿真试验通过实测声速剖面数据和先验地声参数信息产生接收声场数据,再利用模拟声场数据估计声速剖面的时间变化.结果表明,相比于集合卡尔曼滤波(EnKF:Ensemble Kalman Filter),在计算效率等同的情形下,该方法可以在状态参数的时间跳变点保持良好的跟踪性能,一定程度上克服了现有反演算法在跳变点发散的问题,可以有效提高声速剖面反演精度,尤其在声速剖面时变性较强时具有显著优势.      

一种基于强跟踪滤波的混沌保密通信方法

提出了一种基于强跟踪滤波器的混沌保密通信方法. 在发送端, 混沌映射和信息符号被建模成非线性状态空间模型, 信息符号被加性混沌掩盖或乘性混沌掩盖调制, 然后通过信道输出. 在接收端, 驱动信号被接收, 使用带有贝叶斯分类器(信息符号估计)的强跟踪滤波器算法动态地恢复信息符号. Logistic混沌映射的仿真表明, 当信息符号为二进制编码时, 不管是加性混沌掩盖调制还是乘性混沌掩盖调制, 强跟踪滤波器均能较好地从混沌信号中恢复信息符号. 与扩展卡尔曼滤波器相比, 由于卡尔曼滤波器对于离散的信息符号跟踪能力差, 混沌映射中信息符号难以恢复, 比特误码率高. 因此, 这种基于强跟踪滤波器的混沌保密通信方法是有效的.     

声管中的宽带脉冲法的水声材料吸声系数测量

现有的水声管吸声系数测量的脉冲法,由于水声管高度的限制,存在低频限制。提出了基于"后置""逆滤波的宽带脉冲声测试方法,在测量获得系统的传递函数后,换能器发射宽频短脉冲信号,然后对接收到的标准反射体和待测样本的反射信号分别进行逆滤波处理,恢复未经传递系统"污染"的反射信号,计算待测样品的反射系数和吸声系数。仿真实验分析了"后置"逆滤波相对于传统"前置"逆滤波的在低频测试方面优势。对橡胶材料样品进行了实验测试,无论在低频段还是中高频段,宽带脉冲法和CW (Continuous Wave)脉冲法测试结果均吻合较好。宽带脉冲法是一种有效的测试方法,其低频测试能达到350 Hz,能有效拓展低频测试范围。      

Filtering skill for turbulent signals for a suite of nonlinear and linear extended Kalman filters

The filtering skill for turbulent signals from nature is often limited by errors due to utilizing an imperfect forecast model. In particular, real-time filtering and prediction when very limited or no a posteriori analysis is possible (e.g. spread of pollutants, storm surges, tsunami detection, etc.) introduces a number of additional challenges to the problem. Here, a suite of filters implementing stochastic parameter estimation for mitigating model error through additive and multiplicative bias correction is examined on a nonlinear, exactly solvable, stochastic test model mimicking turbulent signals in regimes ranging from configurations with strongly intermittent, transient instabilities associated with positive finite-time Lyapunov exponents to laminar behavior. Stochastic Parameterization Extended Kalman Filter (SPEKF), used as a benchmark here, involves exact formulas for propagating the mean and covariance of the augmented forecast model including the unresolved parameters. The remaining filters use the same nonlinear forecast model but they introduce model error through different moment closure approximations and/or linear tangent approximation used for computing the second-order statistics of the augmented stochastic forecast model. A comprehensive study of filter performance is carried out in the presence of various moment closure errors which are enhanced by additional model errors due to incorrect parameters inducing additive and multiplicative stochastic biases. The estimation skill of the unresolved stochastic parameters is also discussed and it is shown that the linear tangent filter, despite its popularity, is completely unreliable in many turbulent regimes for both parameter estimation and filtering; moreover, regimes of filter divergence for the linear tangent filter are identified. The results presented here provide useful guidelines for filtering turbulent, high-dimensional, spatially extended systems with more general model errors, as well as for designing more skillful methods for superparameterization of unresolved intermittent processes in complex multi-scale models. They also provide unambiguous benchmarks for the capabilities of linear and nonlinear extended Kalman filters using incorrect statistics on an exactly solvable test bed with rich and realistic dynamics.     

光电跟踪系统计算机辅助控制实现

建立了基于数值微分的目标运动状态滤波预测模型,用s-函数实现了卡尔曼滤波预测算法。利用目标位置拟合方法给出角位置信息,并以此为观测信息通过卡尔曼滤波预测出当前角位置和角速度信息,将其引入跟踪控制系统中以克服脱靶量滞后问题,同时也实现了系统的等效复合控制。仿真结果表明,基于数值微分的模型适用于角度跟踪,滤波预测具有较好的鲁棒性。通过对两个不同等效正弦输入的验证,可知角位置合成精度对共轴跟踪影响较大,在脱靶量和跟踪架角位置采样匹配对应时,跟踪仿真精度较高,而对等效复合控制跟踪误差影响较小,输入信号角频率增大时误差增大。     

二级Kalman滤波的被动声定位后置处理方法

阵列定位的计算结果中往往存在大量的野值,需要用后置处理方法从结果中选取真实目标信息。本文提出了一种基于Kalman滤波的二级后置处理方法,建立了相应的滤波模型,并对主要参数进行讨论选取,同时给出了外场实验结果。结果表明此方法后置处理效果明显,并可以满足系统实时性的要求。     

Performance analysis of direct-sequence spread-spectrum underwater acoustic communications with low signal-to-noise-ratio input signals

Direct-sequence spread-spectrum signals collected from the TREX04 experiment are analyzed to determine the bit-error-rate (BER) as a function of the input signal-to-noise ratio (SNR) for a single receiver. A total of 1160 packets of data are generated by adding ambient noise data collected at sea to the signal data (in postprocessing) to create signals with different input-SNR, some as low as -15 dB. Two methods are analyzed in detail, both using a time-updated channel impulse-response estimate as a (matched) filter to mitigate the multipath-induced interferences. The first method requires an independent estimate of the time-varying channel impulse-response function; the second method uses the channel impulse-response estimated from the previous symbol as the matched filter. The first method yields an average BER <10(-2) for input-SNR as low as -12 dB and the second method yields a similar performance for input-SNR as low as -8 dB. The measured BERs are modeled using the measured signal amplitude fluctuation statistics and processing gain obtained by de-spreading the received signal with the transmitted code sequence. Performance losses caused by imprecise symbol synchronization at low input-SNR, uncertainty in channel estimation, and signal fading are quantitatively modeled and compared with data.     

一种破译混沌直接序列扩频保密通信的方法

提出了一种新型的混沌保密通信破译方法,并破译了混沌直接序列扩频保密通信(简称混沌直扩).针对混沌直扩信号中只有一个混沌吸引子的特点,基于混沌系统广义同步的思想,提出了混沌拟合方法;针对混沌直扩中混沌实值序列和数字信号相乘的特点,充分利用混沌直扩的基本原理和信息码是慢变信号的特性,提出了用无先导卡尔曼滤波混沌拟合的方法估计信息码的破译方法;进一步针对无先导卡尔曼滤波的过程噪声和混沌拟合的拟合误差共同导致的跟踪误差,提出了跟踪误差控制因子的方法,从而将跟踪误差转变成有利因素并加以利用,根据跟踪误差的值域范围破