A second-order divided difference filter (SDDF) is derived for integrating line of sight measurement from vision sensor with acceleration and angular rate measurements of the follower to estimate the precise relative ...A second-order divided difference filter (SDDF) is derived for integrating line of sight measurement from vision sensor with acceleration and angular rate measurements of the follower to estimate the precise relative position,velocity and attitude of two unmanned aerial vehicles (UAVs).The second-order divided difference filter which makes use of multidimensional interpolation formulations to approximate the nonlinear transformations could achieve more accurate estimation and faster convergence from inaccurate initial conditions than standard extended Kalman filter.The filter formulation is based on relative motion equations.The global attitude parameterization is given by quarternion,while a generalized three-dimensional attitude representation is used to define the local attitude error.Simulation results are shown to compare the performance of the second-order divided difference filter with a standard extended Kalman filter approach.展开更多
A current-mode low input and high output impedances first-order allpass filter using two multiple output second-generation current conveyors (MOCCIIs), one grounded capacitor and one grounded resistor is presented. Th...A current-mode low input and high output impedances first-order allpass filter using two multiple output second-generation current conveyors (MOCCIIs), one grounded capacitor and one grounded resistor is presented. The suggested filter uses a canonical number of passive components without requiring any component matching condition. The frequency responses simulation results of the proposed filter confirm the theoretical analysis.展开更多
为应对复杂多变的未知环境对海洋航行器运动预测所造成的挑战,提出了一种融合级联滤波与误差触发支持向量回归(error-triggered support vector regression,ETSVR)的智能预测系统。首先,该系统基于移动平均滤波对原始数据进行预处理,以...为应对复杂多变的未知环境对海洋航行器运动预测所造成的挑战,提出了一种融合级联滤波与误差触发支持向量回归(error-triggered support vector regression,ETSVR)的智能预测系统。首先,该系统基于移动平均滤波对原始数据进行预处理,以剔除异常值并抑制高频噪声,为后续预测提供高质量的数据集;其次,引入二阶扩展卡尔曼滤波对系统状态进行精确估计,进一步增强数据的平稳度和可靠性;最后,设计ETSVR算法对处理后的高质量数据集进行学习,以构建海洋航行器的运动预测模型,实现精准运动预测,并借助误差触发机制提升系统的实时性与计算效率。基于湖试数据的实验结果表明,所提出的智能运动预测系统在多项误差指标上均显著优于传统的线性回归算法。例如,在侧向速度预测中,均方误差较线性回归算法降低约53.2%;在转艏角速度预测中,最大误差减少了约58.2%。这些结果表明,提出的级联滤波与ETSVR算法相结合的智能预测系统,能够显著提升海洋航行器在复杂未知环境中的运动预测精度,具有较好的应用前景和重要的研究意义。展开更多
为解决软件无线电(Software Defined Radio,SDR)中采样后信号混叠问题,提出了一种改进的相位调整滤波算法。依托具备可调时间延迟的二阶射频带通采样前端,设计了一种支持多分点分段滤波的抗混叠滤波器,可以根据实际需求灵活设置多个频...为解决软件无线电(Software Defined Radio,SDR)中采样后信号混叠问题,提出了一种改进的相位调整滤波算法。依托具备可调时间延迟的二阶射频带通采样前端,设计了一种支持多分点分段滤波的抗混叠滤波器,可以根据实际需求灵活设置多个频率分点,从而实现针对不同频段的精确滤波。通过在MATLAB SIMULINK中进行仿真验证,该方法相比同类方法的滤波器抑制效果更佳,达到38 dB以上,并能有效滤除所需信号而不影响其他信号的完整性,简化了接收前端,具备较强的灵活性和适应性,能够更好地支持未来的通信技术和高密度通信连接。展开更多
高倍率磷酸铁锂电池在充放电过程中,大电流引发的内部温度升高及化学反应速率加剧,导致其荷电状态(state of charge,SOC)的精确估算较为困难。本文在高倍率工况下,基于Matlab/Simulink平台构建二阶等效电路模型,并结合扩展卡尔曼滤波(ex...高倍率磷酸铁锂电池在充放电过程中,大电流引发的内部温度升高及化学反应速率加剧,导致其荷电状态(state of charge,SOC)的精确估算较为困难。本文在高倍率工况下,基于Matlab/Simulink平台构建二阶等效电路模型,并结合扩展卡尔曼滤波(extended kalman filter,EKF)算法,对磷酸铁锂电池动态SOC进行估算。结果表明:EKF算法能够准确估算高倍率磷酸铁锂电池的动态SOC,将误差控制在5%以内。本文选用的EKF算法在高倍率磷酸铁锂电池动态SOC估算中具有良好的有效性和可靠性,可为SOC估算方法的优化提供理论依据与技术支撑。展开更多
Current methods of order tracking, such as synchronous resampling, Gabor filtering, and Vold-Kalman filtering have undesirable traits. Each method has two or more of the following deficiencies: requires measurement or...Current methods of order tracking, such as synchronous resampling, Gabor filtering, and Vold-Kalman filtering have undesirable traits. Each method has two or more of the following deficiencies: requires measurement or estimate of rotational speed over time, failure to isolate the contribution of crossing orders in the vicinity of the crossing time, large computational expense, end effects. In this work a new approach to the order tracking problem is taken. The Second Order Blind Identification (SOBI) algorithm is applied to synthesized data. The technique is shown to be very successful at isolating crossing orders and circumvents all of the above deficiencies. The method has its own restric-tions: multiple sensors are required and sensors must be mounted on a structure that responds quasi-statically to exci-tation of the rotational system.展开更多
锂离子电池具有无记忆效应、轻量化、环保等特点,因此常作为电动交通工具、电子设备的能源来源,并适用于各种规模的能源存储。在锂离子电池管理系统中,电池的荷电状态(state of charge,SOC)是最关键的指标之一,其准确估计对于实现电池...锂离子电池具有无记忆效应、轻量化、环保等特点,因此常作为电动交通工具、电子设备的能源来源,并适用于各种规模的能源存储。在锂离子电池管理系统中,电池的荷电状态(state of charge,SOC)是最关键的指标之一,其准确估计对于实现电池系统的高效能量管理和优化控制至关重要。因此本文提出了一种基于动态噪声自适应无迹卡尔曼滤波的SOC估计方法。首先,通过间歇放电实验获取电池不同SOC下的开路电压,并进一步拟合得到电池的OCV-SOC曲线,接着采用二阶RC等效电路模型对锂离子电池建模,然后通过混合功率脉冲特性工况测试对电池模型参数进行辨识。由于实际应用中锂离子电池为非线性系统且SOC估计精度容易受到噪声的影响,本文在卡尔曼滤波算法的基础上采用无迹变换处理,加入噪声自适应过程,以实现噪声特性自适应估计,动态调整测量噪声与过程噪声,提高算法鲁棒性以及估计精度。最后选取DST与FUDS工况进行验证,结果表明在不同工况下动态噪声自适应无迹卡尔曼滤波算法的估计平均绝对误差、最大绝对误差以及均方根误差相较于自适应无迹卡尔曼滤波、无迹卡尔曼滤波算法均有降低,其平均绝对误差小于0.59%。本文提出的动态噪声自适应无迹卡尔曼滤波算法能够更准确地估计锂离子电池SOC。展开更多
基金Sponsored by the Aerospace Technology Innovation Funding(Grant No. CASC0209)
文摘A second-order divided difference filter (SDDF) is derived for integrating line of sight measurement from vision sensor with acceleration and angular rate measurements of the follower to estimate the precise relative position,velocity and attitude of two unmanned aerial vehicles (UAVs).The second-order divided difference filter which makes use of multidimensional interpolation formulations to approximate the nonlinear transformations could achieve more accurate estimation and faster convergence from inaccurate initial conditions than standard extended Kalman filter.The filter formulation is based on relative motion equations.The global attitude parameterization is given by quarternion,while a generalized three-dimensional attitude representation is used to define the local attitude error.Simulation results are shown to compare the performance of the second-order divided difference filter with a standard extended Kalman filter approach.
文摘A current-mode low input and high output impedances first-order allpass filter using two multiple output second-generation current conveyors (MOCCIIs), one grounded capacitor and one grounded resistor is presented. The suggested filter uses a canonical number of passive components without requiring any component matching condition. The frequency responses simulation results of the proposed filter confirm the theoretical analysis.
文摘为应对复杂多变的未知环境对海洋航行器运动预测所造成的挑战,提出了一种融合级联滤波与误差触发支持向量回归(error-triggered support vector regression,ETSVR)的智能预测系统。首先,该系统基于移动平均滤波对原始数据进行预处理,以剔除异常值并抑制高频噪声,为后续预测提供高质量的数据集;其次,引入二阶扩展卡尔曼滤波对系统状态进行精确估计,进一步增强数据的平稳度和可靠性;最后,设计ETSVR算法对处理后的高质量数据集进行学习,以构建海洋航行器的运动预测模型,实现精准运动预测,并借助误差触发机制提升系统的实时性与计算效率。基于湖试数据的实验结果表明,所提出的智能运动预测系统在多项误差指标上均显著优于传统的线性回归算法。例如,在侧向速度预测中,均方误差较线性回归算法降低约53.2%;在转艏角速度预测中,最大误差减少了约58.2%。这些结果表明,提出的级联滤波与ETSVR算法相结合的智能预测系统,能够显著提升海洋航行器在复杂未知环境中的运动预测精度,具有较好的应用前景和重要的研究意义。
文摘为解决软件无线电(Software Defined Radio,SDR)中采样后信号混叠问题,提出了一种改进的相位调整滤波算法。依托具备可调时间延迟的二阶射频带通采样前端,设计了一种支持多分点分段滤波的抗混叠滤波器,可以根据实际需求灵活设置多个频率分点,从而实现针对不同频段的精确滤波。通过在MATLAB SIMULINK中进行仿真验证,该方法相比同类方法的滤波器抑制效果更佳,达到38 dB以上,并能有效滤除所需信号而不影响其他信号的完整性,简化了接收前端,具备较强的灵活性和适应性,能够更好地支持未来的通信技术和高密度通信连接。
文摘高倍率磷酸铁锂电池在充放电过程中,大电流引发的内部温度升高及化学反应速率加剧,导致其荷电状态(state of charge,SOC)的精确估算较为困难。本文在高倍率工况下,基于Matlab/Simulink平台构建二阶等效电路模型,并结合扩展卡尔曼滤波(extended kalman filter,EKF)算法,对磷酸铁锂电池动态SOC进行估算。结果表明:EKF算法能够准确估算高倍率磷酸铁锂电池的动态SOC,将误差控制在5%以内。本文选用的EKF算法在高倍率磷酸铁锂电池动态SOC估算中具有良好的有效性和可靠性,可为SOC估算方法的优化提供理论依据与技术支撑。
文摘Current methods of order tracking, such as synchronous resampling, Gabor filtering, and Vold-Kalman filtering have undesirable traits. Each method has two or more of the following deficiencies: requires measurement or estimate of rotational speed over time, failure to isolate the contribution of crossing orders in the vicinity of the crossing time, large computational expense, end effects. In this work a new approach to the order tracking problem is taken. The Second Order Blind Identification (SOBI) algorithm is applied to synthesized data. The technique is shown to be very successful at isolating crossing orders and circumvents all of the above deficiencies. The method has its own restric-tions: multiple sensors are required and sensors must be mounted on a structure that responds quasi-statically to exci-tation of the rotational system.
文摘锂离子电池具有无记忆效应、轻量化、环保等特点,因此常作为电动交通工具、电子设备的能源来源,并适用于各种规模的能源存储。在锂离子电池管理系统中,电池的荷电状态(state of charge,SOC)是最关键的指标之一,其准确估计对于实现电池系统的高效能量管理和优化控制至关重要。因此本文提出了一种基于动态噪声自适应无迹卡尔曼滤波的SOC估计方法。首先,通过间歇放电实验获取电池不同SOC下的开路电压,并进一步拟合得到电池的OCV-SOC曲线,接着采用二阶RC等效电路模型对锂离子电池建模,然后通过混合功率脉冲特性工况测试对电池模型参数进行辨识。由于实际应用中锂离子电池为非线性系统且SOC估计精度容易受到噪声的影响,本文在卡尔曼滤波算法的基础上采用无迹变换处理,加入噪声自适应过程,以实现噪声特性自适应估计,动态调整测量噪声与过程噪声,提高算法鲁棒性以及估计精度。最后选取DST与FUDS工况进行验证,结果表明在不同工况下动态噪声自适应无迹卡尔曼滤波算法的估计平均绝对误差、最大绝对误差以及均方根误差相较于自适应无迹卡尔曼滤波、无迹卡尔曼滤波算法均有降低,其平均绝对误差小于0.59%。本文提出的动态噪声自适应无迹卡尔曼滤波算法能够更准确地估计锂离子电池SOC。
