Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the ma...Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system.展开更多
Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQP...Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQPSK) and Gaussian Frequency Shift Keying (GFSK) can be non-coherently demodulated with simple algorithms. However, these types of demodulation are not robust and suffer from poor performance. This paper proposes a new method to enhance the performance of DQPSK and GFSK using Interactive Kalman Filtering (IKF) technique, in which a one Unscented Kalman Filter (UKF) and two Kalman Filters (KF) are coupled to optimize the demodulated signals. This method consists of simple but very effective algorithms without adding complexity to the demodulators comparing to other very complex methods. UKF is used in this method due to its superiority in approximating and estimating nonlinear systems and its ability to handle non-Gaussian noise environments. The proposed method has been validated by creating a MATLAB/SIMULINK Bluetooth system model, in which the IKF is integrated into the receiver, which implement both DQPSK and GFSK, and run simulation in Gaussian and Non-Gaussian noise environments. Results have shown the effectiveness of this method in optimizing the received signals, and that the UKF outperforms the Extended Kalman Filter (EKF).展开更多
The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an inte...The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay.The proposed algorithm fixes the maximum delay(problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.展开更多
The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering as an effective alternative. In this paper, linear second-order st...The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering as an effective alternative. In this paper, linear second-order state space Kalman Filtering is further investigated and tested for applicability. This is important to optimize estimates of received power signals to improve control of handoffs. Simulation models were used extensively in the initial stage of this research to validate the proposed theory. Recently, we managed to further confirm validation of the concept through experiments supported by data from real scenarios. Our results have shown that the linear second-order state space Kalman Filter (KF) can be more accurate in predicting local shadow power profiles than the first-order Kalman Filter, even in channels with imposed non-Gaussian measurement noise.展开更多
This paper presents a novel and cost effective method to be used in the optimization of the Gaussian Frequency Shift Keying (GFSK) at the receiver of the Bluetooth communication system. The proposed method enhances th...This paper presents a novel and cost effective method to be used in the optimization of the Gaussian Frequency Shift Keying (GFSK) at the receiver of the Bluetooth communication system. The proposed method enhances the performance of the noncoherent demodulation schemes by improving the Bit Error Rate (BER) and Frame Error Rate (FER) outcomes. Linear, Extended, and Unscented Kalman Filters are utilized in this technique. A simulation model, using Simulink, has been created to simulate the Bluetooth voice transmission system with the integrated filters. Results have shown improvements in the BER and FER, and that the Unscented Kalman Filters (UKF) have shown superior performance in comparison to the linear Kalman Filter (KF) and the Extended Kalman Filter (EKF). To the best of our knowledge, this research is the first to propose the usage of the UKF in the optimization of the Bluetooth System receivers in the presence of additive white Gaussian noise (AWGN), as well as interferences.展开更多
在高动态与强干扰条件下,卫星导航接收机码跟踪环路与载波跟踪环路会出现异常抖动进而影响卫星导航接收机的授时精度,针对这一问题提出了一种新的基于抗野值Kalman滤波技术的卫星导航接收机授时方法。该方法可以有效消除码环与载波环的...在高动态与强干扰条件下,卫星导航接收机码跟踪环路与载波跟踪环路会出现异常抖动进而影响卫星导航接收机的授时精度,针对这一问题提出了一种新的基于抗野值Kalman滤波技术的卫星导航接收机授时方法。该方法可以有效消除码环与载波环的异常抖动对解算出的卫星钟差的影响,并且对连续出现的野值也有很好的剔除效果。同时该授时方法还可以有效地对卫星导航接收机的晶振频率误差进行估计,进而对1PPS(pulse per second)信号发生器的频率控制字进行调整,提高系统的授时精度。经过实验验证,该授时方法可以有效提高卫星导航接收机的授时精度以及授时系统的鲁棒性。展开更多
为解决单个高斯过程回归无法对来自多个信息源的数据进行整体建模的问题,提出了卡尔曼滤波优化的高斯过程回归模型(Gaussian process regression model based on Kalman filtering,KF-GPR).该模型首先根据多个传感器获取的离散样本数据...为解决单个高斯过程回归无法对来自多个信息源的数据进行整体建模的问题,提出了卡尔曼滤波优化的高斯过程回归模型(Gaussian process regression model based on Kalman filtering,KF-GPR).该模型首先根据多个传感器获取的离散样本数据分别进行高斯过程回归,预测关键参数的均值和方差,并将其视作软传感器输出的测量值和噪声.然后利用卡尔曼滤波算法对软传感器的输出进行融合,在最小均方误差准则下,实现对多个高斯过程回归结果的融合优化,获得优化后模型的输出结果.仿真实验将KF-GPR与平均值融合方法进行对比,结果表明KFGPR能够获得拟合精度更高的预测曲线,验证了模型的有效性.最后,将KF-GPR应用于温度随纬度变化的实例分析中,分季节给出了纬度−温度预测曲线.展开更多
基金Supported by the National Natural Science Foundation of China(No.61771006)the Open Foundation of Key Laboratory of Spectral Imaging Technology of the Chinese Academy of Sciences(No.LSIT201711D)+1 种基金the Outstanding Young Cultivation Foundation of Henan University(No.0000A40366)the Excellent Chinese and Foreign Youth Exchange Programme of China Science and Technology Association(2017CASTQNJL046)
文摘Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system.
文摘Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQPSK) and Gaussian Frequency Shift Keying (GFSK) can be non-coherently demodulated with simple algorithms. However, these types of demodulation are not robust and suffer from poor performance. This paper proposes a new method to enhance the performance of DQPSK and GFSK using Interactive Kalman Filtering (IKF) technique, in which a one Unscented Kalman Filter (UKF) and two Kalman Filters (KF) are coupled to optimize the demodulated signals. This method consists of simple but very effective algorithms without adding complexity to the demodulators comparing to other very complex methods. UKF is used in this method due to its superiority in approximating and estimating nonlinear systems and its ability to handle non-Gaussian noise environments. The proposed method has been validated by creating a MATLAB/SIMULINK Bluetooth system model, in which the IKF is integrated into the receiver, which implement both DQPSK and GFSK, and run simulation in Gaussian and Non-Gaussian noise environments. Results have shown the effectiveness of this method in optimizing the received signals, and that the UKF outperforms the Extended Kalman Filter (EKF).
基金supported by the Department of Science and Technology,Government of India under the Inspire Faculty Award
文摘The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay.The proposed algorithm fixes the maximum delay(problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.
文摘The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering as an effective alternative. In this paper, linear second-order state space Kalman Filtering is further investigated and tested for applicability. This is important to optimize estimates of received power signals to improve control of handoffs. Simulation models were used extensively in the initial stage of this research to validate the proposed theory. Recently, we managed to further confirm validation of the concept through experiments supported by data from real scenarios. Our results have shown that the linear second-order state space Kalman Filter (KF) can be more accurate in predicting local shadow power profiles than the first-order Kalman Filter, even in channels with imposed non-Gaussian measurement noise.
文摘This paper presents a novel and cost effective method to be used in the optimization of the Gaussian Frequency Shift Keying (GFSK) at the receiver of the Bluetooth communication system. The proposed method enhances the performance of the noncoherent demodulation schemes by improving the Bit Error Rate (BER) and Frame Error Rate (FER) outcomes. Linear, Extended, and Unscented Kalman Filters are utilized in this technique. A simulation model, using Simulink, has been created to simulate the Bluetooth voice transmission system with the integrated filters. Results have shown improvements in the BER and FER, and that the Unscented Kalman Filters (UKF) have shown superior performance in comparison to the linear Kalman Filter (KF) and the Extended Kalman Filter (EKF). To the best of our knowledge, this research is the first to propose the usage of the UKF in the optimization of the Bluetooth System receivers in the presence of additive white Gaussian noise (AWGN), as well as interferences.
文摘在高动态与强干扰条件下,卫星导航接收机码跟踪环路与载波跟踪环路会出现异常抖动进而影响卫星导航接收机的授时精度,针对这一问题提出了一种新的基于抗野值Kalman滤波技术的卫星导航接收机授时方法。该方法可以有效消除码环与载波环的异常抖动对解算出的卫星钟差的影响,并且对连续出现的野值也有很好的剔除效果。同时该授时方法还可以有效地对卫星导航接收机的晶振频率误差进行估计,进而对1PPS(pulse per second)信号发生器的频率控制字进行调整,提高系统的授时精度。经过实验验证,该授时方法可以有效提高卫星导航接收机的授时精度以及授时系统的鲁棒性。
文摘为解决单个高斯过程回归无法对来自多个信息源的数据进行整体建模的问题,提出了卡尔曼滤波优化的高斯过程回归模型(Gaussian process regression model based on Kalman filtering,KF-GPR).该模型首先根据多个传感器获取的离散样本数据分别进行高斯过程回归,预测关键参数的均值和方差,并将其视作软传感器输出的测量值和噪声.然后利用卡尔曼滤波算法对软传感器的输出进行融合,在最小均方误差准则下,实现对多个高斯过程回归结果的融合优化,获得优化后模型的输出结果.仿真实验将KF-GPR与平均值融合方法进行对比,结果表明KFGPR能够获得拟合精度更高的预测曲线,验证了模型的有效性.最后,将KF-GPR应用于温度随纬度变化的实例分析中,分季节给出了纬度−温度预测曲线.
