Joint angular and spectral estimation technique using nonlinear Kalman filters for cognitive radio

The joint estimation of direction of arrivals (DOA) and carrier frequencies of band-limited source signals is considered in this paper. A novel technique based on nonlinear Kalman filters is proposed for this joint angular and spectral estimation problem for cognitive radio (CR). Since sampling a wideband spectrum at Nyquist rate increases the analog-to-digital converter (ADC) requirements, we propose executing Kalman filter algorithm over a spatial state space model. Thus, one time sample is required and hardware complexity is reduced. Two types of nonlinear Kalman filters, extended Kalman filter (EKF) and unscented Kalman filter (UKF), are proposed. We consider their sub-optimal performance and show how to control their convergence. However, the proposed algorithms can detect a number of source signals limited to the number of elements in employing arrays.     

基于聚类思想的弹道外推算法研究

为了提高炮位侦察校射雷达中炮位侦察定位精度同时提升外推结果一致性,本文引入聚类思想,建立了基于K-均值聚类的弹道外推模型。该模型采用七态无迹卡尔曼滤波算法对量测数据进行多次滤波,然后利用4 阶龙格-库塔积分方法对火炮位置进行外推,最后对多次外推结果进行K-均值聚类处理,采用综合多因子方法计算簇品质,选取最优簇对应的聚类中心作为最终的火炮位置进行输出。实验结果表明,该弹道外推算法显著提升了外推结果的一致性及定位精度。     

一种针对全海深载人潜水器的异步融合组合导航算法

全海深载人潜水器（HOV）组合导航中会产生异步融合现象,传统的组合导航算法在处理时会产生较大的误差．针对这一问题,提出了一种基于机器学习和无迹卡尔曼滤波（UKF）的异步融合组合导航算法．首先建立了针对超短基线（USBL）声学定位系统预测的机器学习模型,通过USBL声学定位系统的观测数据集来训练该模型,并用得到的模型来预测更新间隔内的数据．最后使用UKF将已更新的数据集进行融合．仿真结果表明,相比传统的组合导航算法,本文的异步融合组合导航算法可以将USBL声学定位系统数据异步问题所引起的误差降低17%,有效提高了组合导航系统的精度．     

Nonlinear sequential fusion estimation for clustered sensor networks

This paper is concerned with distributed fusion estimation problem for discrete‐time nonlinear systems with asynchronous sampling data in the clustered sensor networks. A nonlinear sequential fusion method consisting of a sequential measurement fusion (SMF) method and a sequential state fusion (SSF) method is proposed. It is shown that the SMF estimator using the Unscented Kalman Filtering (UKF) method can handle the asynchronous measurement data sequentially and the SSF estimator which uses sequential matrix weighting method can get a close performance as the centralized batch matrix weighting method but has a lower computational complexity. The proposed measurement fusion method is able to deal with asynchronous measurements and has lower computational complexity as compared with the augmentation method. A simulation example shows the effectiveness of the proposed nonlinear sequential fusion method.     

基于无味粒子滤波的动态场景下高机动目标跟踪

针对传统粒子滤波的目标跟踪算法存在粒子退化问题,提出了基于无味粒子滤波(UPF)的目标跟踪算法。为了将当前观测信息融入,采用无味卡尔曼滤波(UKF)生成粒子滤波的提议分布,以改善滤波效果。针对目标在机动过程中引起的视觉形变以及背景的变化,又采用了颜色直方图作为目标的颜色分布模型,并与UPF相融合。仿真结果表明,该算法对动态场景下的高机动目标有较好的跟踪效果。     

一种基于神经网络和UKF的自适应目标误差配准方法

针对多传感器数据融合的误差配准问题,提出了一种自适应的误差配准方法。该方法使用无迹卡尔曼滤波方法训练神经网络,在偏差先验模型未知的条件下,通过学习待配准目标量测与配准目标之间的误差变化,实时估计配准误差并同时将其应用于目标状态估计。仿真实验表明,该方法能够实时有效地估计目标配准误差和目标状态。     

基于BAP-UKF方法的微弱GPS信号多径参数估计

在室内、城市中心等恶劣环境下,多径效应已成为码跟踪的主要误差源。采用一种数据块平均预处理—无迹卡尔曼滤波器(BAP-UKF)方法来估计微弱GPS信号的多径参数,该方法采用数据BAP方法处理接收到的微弱GPS信号,以抑制噪声和干扰,提高接收信号的信噪比;经过BAP的信号通过多径相关器后的同相或正交相值,将作为UKF的观测量来估计码跟踪时的多路径的幅值、码延时、相位、载波多普勒等参数.仿真结果表明:该方法能够有效地估计出载噪比低至21 dB-Hz的微弱信号多径参数,从而证明了其有效性。     

基于EMMAP的MEMS陀螺噪声估计与滤波方法研究

针对MEMS陀螺仪测量精度低、随机噪声具有不确定性和非线性的问题,提出一种基于最大期望算法(Expectation maximum,EM)和极大后验估计(Maximum a posterion,MAP)的无迹卡尔曼滤波(Unscented Kalman filter,UKF)——EMMAP-UKF的陀螺噪声估计与滤波方法。根据极大后验估计原理,构造出一种次优无偏MAP噪声统计估计模型,并在此基础上引入最大期望算法将噪声估计问题转换为数学期望极大化问题,实现对观测噪声方差的动态调整,最终实现陀螺仪随机漂移误差的估计与滤波处理。最后通过Allan方差对陀螺噪声滤波方法的性能进行评估,通过半实物仿真验证了本方法的有效性。     

杂波环境密集机动目标跟踪PCIMM-UKF算法

杂波环境密集机动目标跟踪对于数据关联和滤波始终是一个挑战性问题,本文提出的PC（投影收缩）IMM（交互多模型）-UKF（无迹卡尔曼滤波）算法,利用优化领域前沿研究成果之一的投影收缩算法,解决了全局最优关联问题,同时将IMM和UKF算法结合,克服了非线性机动目标跟踪困难的缺点,大量数值仿真结果也表明该算法可行且有效。