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Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

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Defence Technology（防务技术） 2024年第7期37卷 1-13页

作者： Guangbo Xu Bingting Zha Hailu Yuan Zhen Zheng He Zhang Ministerial Key Laboratory of ZNDY Nanjing University of Science and TechnologyNanjing 210094China Science and Technology on Electromechanical Dynamic Control Laboratory Beijing 100081China Shanghai Radio Equipment Research Institute Shanghai 201109China

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser *** overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering *** enhances the algorithm loading capability of the *** order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)*** also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the ***,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo *** experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

关键词： Laser fuze Underwater laser detection Backscatter adaptive filter Spline least mean square algorithm nonlinear filtering algorithm

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Underwater target tracking in three-dimensional environment using intelligent sensor technique

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INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2022年第3期18卷 319-334页

作者： Jagan, Omkar Lakshmi B. Rao, Koteswara S. Lakshmi, Kavitha M. Koneru Lakshmaiah Educ Fdn Dept Elect & Elect Engn Guntur Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

Purpose This paper aims to target tracking in the marine environment is typically obtained by considering the measurement parameters like frequency, elevation and bearing. Marine environmental surveillance provides critical information and assistance for the exploitation and maintenance of marine resources. Design/methodology/approach With the use of intelligent sensor techniques like Hull-mounted and towed array sensors, convenient, precise and dependable three-dimensional (3D) underwater target tracking is introduced. Findings This research investigates a method to develop a reliable Unscented Kalman Filter (UKF) algorithm for enhanced underwater target tracking in a 3D scenario by using bearing, frequency and elevation measurements. In applications for underwater target tracking, uncertainty and inaccuracies are typically described by using Gaussian additive noise. Originality/value The proposed UKF algorithm is tested and analyzed using 100 Monte Carlo simulations with the Gaussian generated noise.

关键词： Passive target tracking Doppler angles tracking Statistical signal processing nonlinear filtering algorithm Unscented Kalman filter (UKF)

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nonlinear Filtration in the Problem of Joint Estimation of Channel Factors and OFDM Signal Distortion Arising in Direct Conversion Receiver

Nonlinear Filtration in the Problem of Joint Estimation of C...

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Conference on Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO)

作者： Poborchaya, N. E. Moscow Tech Univ Commun & Informat Moscow Russia

ISBN: (纸本)9781728132389

A combined algorithm for joint estimation the distortions of the OFDM signal and channel factors is proposed in the article. The algorithm is based on two procedures: 1) estimating the generalized channel factors using a polynomial spline approximation, the least squares method and finding the signal distortion estimates for them, 2) a nonlinear filtering algorithm to refine the parameters values such as DC components and frequency offset. The algorithm operates on two reference OFDM symbols under conditions of a priori uncertainty regarding phase noise and observation noise, and also allows to increase noise immunity and reduce computational complexity, as compared with the case of using only procedure 1).

关键词： OFDM estimation modified least squares method nonlinear filtering algorithm a priori uncertainty phase noise IQ-imbalance frequency offset DC drift

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Distributed event-triggered cubature information filtering based on weighted average consensus

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IET CONTROL THEORY AND APPLICATIONS 2018年第1期12卷 78-86页

作者： Tan, Qingke Dong, Xiwang Li, Qingdong Ren, Zhang Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China

To deal with the distributed estimation problem for mobile sensor networks with non-linear systems and a large amount of data transfer, the distributed event-triggered cubature information filtering based on weighted average consensus is proposed. The filter benefits from the non-linear filtering algorithm with consensus technique and event-triggered mechanism which reduces the amount of data transfer. The triggering decision is based on the data transmission mechanism, which is that each sensor makes a request to exchange information with its neighbours only if the difference between the most recent transmitted estimate and the current estimate exceeds a tolerable threshold. The estimation error of the proposed filter is proved to be bounded in mean square. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results.

关键词： nonlinear filters graph theory set theory mean square error methods estimation error information exchange data transmission mechanism triggering decision nonlinear filtering algorithm data transfer mobile sensor network distributed estimation problem weighted average consensus distributed event-triggered cubature information filtering

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Online Features in the MATLAB® System Identification ToolboxTM

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IFAC-PapersOnLine 2018年第15期51卷 700-705页

作者： Ljung, Lennart Ozdemir, Ahmet Arda Singh, Rajiv Div. of Automatic Control Linköping University Sweden Math Works NatickMA United States

Because of the increased demand on fault detection, monitoring and predictive maintenance, online or recursive identification is playing a more important role in systems engineering. In the recent releases of System Identification Toolbox™ for MATLAB®, this has been reflected in a more substantial support for online techniques. This contribution gives an account of these improvements. It covers the addition of nonlinear filtering algorithms, such as the extended Kalman filter, the unscented Kalman filter and particle filters. The traditional recursive estimation techniques for polynomial models have also been enhanced with a more versatile syntax. Several new Simulink® blocks have been developed to support Simulink® models with online estimation. © 2018

关键词： Kalman filters Bearings (machine parts) Fault detection MATLAB Online systems Parameter estimation Religious buildings nonlinear filtering algorithm On line algorithms On line estimation Predictive maintenance Recursive estimation Recursive identification Simulation Unscented Kalman Filter

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Continuous Passive TDOA Location Method Based on Multi-Flight-Vehicle Cooperation 30

Continuous Passive TDOA Location Method Based on Multi-Fligh...

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30th Chinese Control and Decision Conference (CCDC)

作者： Zhang Jie Zhang Dong Huang Xue-mei Shi Xiao-rong Beijing Inst Control & Elect Technol Beijing 100038 Peoples R China

ISBN: (纸本)9781538612446

In order to detect passively targets fixed on the ground or targets on the sea surface with multi-flight-vehicle cooperation, a continuous passive TDOA (Tune difference of arrival) location method is presented which can he applied to multi-flight-vehicle platforrn. Measurement information used in the method includes time difference, angle of target, and distance between receiving stations. nonlinear system model is established in the method. In order to continuously estimate the target's accurate position, Unscented Kalman Filter (UKF) is used to solve the nonlinear model. The method solves the problem that mobile platforms in the air cannot give accurate passive TDOA location of targets, when the receiving stations' positioning is inaccurate and the configuration of the stations is unfixed. Simulation results show that the continuous passive TDOA location method has high location accuracy and strong convergence, and change of spatial configuration of multi-tlight-vehicle cooperative flight has little influence on the positioning accuracy.

关键词： Passive TDOA Location Multi-flight-vehicle Cooperation nonlinear filtering algorithm Unscented Kalman Filter

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Continuous Passive TDOA Location Method Based on Multi-Flight-Vehicle Cooperation

Continuous Passive TDOA Location Method Based on Multi-Fligh...

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第30届中国控制与决策会议

作者： Zhang Jie Zhang Dong Huang Xue-mei Shi Xiao-rong Beijing Institute of Control and Electronics Technology

In order to detect passively targets fixed on the ground or targets on the sea surface with multi-flight-vehicle cooperation, a continuous passive TDOA(Time difference of arrival) location method is presented which can be applied to multi-flight-vehicle platform. Measurement information used in the method includes time difference, angle of target, and distance between receiving stations. nonlinear system model is established in the method. In order to continuously estimate the target's accurate position, Unscented Kalman Filter(UKF) is used to solve the nonlinear model. The method solves the problem that mobile platforms in the air cannot give accurate passive TDOA location of targets, when the receiving stations' positioning is inaccurate and the configuration of the stations is unfixed. Simulation results show that the continuous passive TDOA location method has high location accuracy and strong convergence, and change of spatial configuration of multi-flight-vehicle cooperative flight has little influence on the positioning accuracy.

关键词： Passive TDOA Location Multi-flight-vehicle Cooperation nonlinear filtering algorithm Unscented Kalman Filter

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UAV Attitude Estimation Using nonlinear filtering and Low-Cost Mems Sensors

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IFAC-PapersOnLine 2016年第21期49卷 521-528页

作者： Kada, B. Munawar, K. Shaikh, M.S. Hussaini, M.A. Al-Saggaf, U.M. Department of Aeronautical Engineering Faculty of Engineering King Abdul-Aziz University Jeddah21589 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineering King Abdul-Aziz University Jeddah21589 Saudi Arabia

Integration of low-cost Micro-Electro-Mechanical Systems (MEMS) in the design of Inertial Navigation Systems (INS) for real-time guidance, navigation, and control of flying vehicles often results in low precision, poor accuracy, and degraded performance. Most of existing filtering and data fusion methods that have been applied to the problem of attitude estimation with use of low-cost MEMS-based INS fail in cases that have highly nonlinear process dynamics, nonlinear measurement models, and long-range navigation. This paper presents two nonlinear filtering algorithms for low-cost Unmanned Aerial Vehicles (UAVs) attitude and measurement biases estimation. An Unscented Kalman Filter (UKF) and a Minimum Energy Kalman Filter (MEKF) are designed using two different attitude parameterizations i.e. Euler Angle (EA) and Unit Quaternion (UQ). The two filters AE-UKF and UQ-MEKF use highly nonlinear process and measurements estimators, zero-mean Gaussian noises, and first-order Gauss-Markov biases models. Field experiments, adding systems, and data fusion algorithms are used to achieve real-time tracking and estimation. The results show that both AE-UKF and UQ-MEKF demonstrate high accuracy estimation and robustness for UAVs attitude as well as MEMS biases filtering. © 2016

关键词： Gaussian noise (electronic) Antennas Cost estimating Data fusion Electromechanical filters Inertial navigation systems Kalman filters Machine design MEMS nonlinear analysis nonlinear filtering Real time systems Unmanned aerial vehicles (UAV) Data fusion algorithm Guidance , navigation , and controls Inertial navigation systems (INS) Long range navigations Micro electromechanical system (MEMS) nonlinear filtering algorithm nonlinear measurement Unscented Kalman Filter

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Kalman particle filtering algorithm for symmetric alpha-stable distribution signals with application to high frequency time difference of arrival geolocation

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IET SIGNAL PROCESSING 2016年第6期10卷 619-625页

作者： Xia, Nan Wei, Wen Li, Jingchun Zhang, Xiaofei State Radio Monitoring Ctr Beijing 100037 Peoples R China

In this study, a non-linear filtering algorithm for state estimation with symmetric alpha-stable (SS) noise is presented. The dynamic system model investigated here can be described by a linear state-space equation and a non-linear observation equation. The contribution of this study can be summarised as follows. First, particle filtering approach is employed for coarse estimation of the unknown parameters and then Kalman filter is performed to achieve better estimation. Second, SS noise is considered as the additive disturbance in the observed signal and Gaussian approximation is used to compute the characteristics. Third, the calculation complexity is analysed according to the proposed algorithm. The proposed method is compared with the standard particle filter, extended Kalman filter and unscented Kalman filter for static parameter estimation of a periodic signal. As a practical application, the proposed method is used in high frequency source localisation based on time difference of arrival measurements.

关键词： Kalman filters time-of-arrival estimation navigation nonlinear filters state estimation particle filtering (numerical methods) Gaussian processes approximation theory parameter estimation Kalman particle filtering algorithm symmetric alpha-stable distribution signals high frequency time difference of arrival geolocation nonlinear filtering algorithm state estimation symmetric alpha-stable noise dynamic system model linear state-space equation nonlinear observation equation particle filtering approach coarse estimation SS noise Gaussian approximation calculation complexity complexity unscented Kalman filter static parameter estimation periodic signal time difference of arrival measurements high frequency source localisation

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An Effective Single Passive Location Method Based on Near Space Vehicle for Ground Fixed Emitter 3

An Effective Single Passive Location Method Based on Near Sp...

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3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Liu, Fulai Jia, Hui Northeastern Univ Qinhuangdao Engn Optimizat & Smart Antenna Inst Shenyang Peoples R China NE Univ Sch Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783038352679

Ground target position estimation of near space vehicle is the key technology in military and civilian field. In this paper, we propose an effective method using the cubature Kalman filter (CKF) based on direction of arrival (DOA) and its rate of change. Comparisons with the location method only using DOA and the classic EKF algorithm are conducted respectively. Simulation results indicate that the proposed method has faster convergence speed and better location precision.

关键词： near space vehicle single passive location CKF nonlinear filtering algorithm

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