An Unscented Kalman Filter based wave filtering algorithm for dynamic ship positioning

2011 ◽  
Author(s):  
Xiaocheng Shi ◽  
Xingyan Sun ◽  
Mingyu Fu ◽  
Wenbo Xie ◽  
Dawei Zhao
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Filtering Algorithm

An Innovative Navigation Strategy for Autonomous Underwater Vehicles: An Unscented Kalman Filter Based Approach

2015 ◽  
Author(s):  
Benedetto Allotta ◽  
Riccardo Costanzi ◽  
Enrico Meli ◽  
Alessandro Ridolfi ◽  
Luigi Chisci ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Underwater Robotics ◽  
Filtering Algorithm ◽  
Navigation Algorithms ◽  
Underwater Navigation

Developing reliable navigation strategies is mandatory in the field of Underwater Robotics and in particular for Autonomous Underwater Vehicles (AUVs) to ensure the correct achievement of a mission. Underwater navigation is still nowadays critical, e.g. due to lack of access to satellite navigation systems (e.g. the Global Positioning System, GPS): an AUV typically proceeds for long time intervals only relying on the measurements of its on-board sensors, without any communication with the outside environment. In this context, the filtering algorithm for the estimation of the AUV state is a key factor for the performance of the system; i.e. the filtering algorithm used to estimate the state of the AUV has to guarantee a satisfactory underwater navigation accuracy. In this paper, the authors present an underwater navigation system which exploits measurements from an Inertial Measurement Unit (IMU), Doppler Velocity Log (DVL) and a Pressure Sensor (PS) for the depth, and relies on either an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) for state estimation. A comparison between the EKF approach, classically adopted in the field of underwater robotics and the UKF is given. These navigation algorithms have been experimentally validated through the data related to some sea tests with the Typhoon class AUVs, designed and assembled by the Department of Industrial Engineering of the Florence University (DIEF) for exploration and surveillance of underwater archaeological sites in the framework of the THESAURUS and European ARROWS projects. The comparison results are significant as the two filtering strategies are based on the same process and sensors models. At this initial stage of the research activity, the navigation algorithms have been tested offline. The presented results rely on the experimental navigation data acquired during two different sea missions: in the first one, Typhoon AUV #1 navigated in a Remotely Operated Vehicle (ROV) mode near Livorno, Italy, during the final demo of THESAURUS project (held in August 2013); in the latter Typhoon AUV #2 autonomously navigated near La Spezia in the framework of the NATO CommsNet13 experiment, Italy (held in September 2013). The achieved results demonstrate the effectiveness of both navigation algorithms and the superiority of the UKF without increasing the computational load. The algorithms are both affordable for online on-board AUV implementation and new tests at sea are planned for spring 2015.

Line-of-Sight Angle Rate Reconstruction for Phased Array Strapdown Seeker

2013 ◽  
Vol 645 ◽  
pp. 196-201
Author(s):  
Ying Liu ◽  
Wei Feng Tian ◽  
Jian Kang Zhao ◽  
Shi Qing Zhu ◽  
Ge Wen Yang
Keyword(s):  
Kalman Filter ◽  
Phased Array ◽  
Unscented Kalman Filter ◽  
Measurement Noise ◽  
Line Of Sight ◽  
Measurement Information ◽  
Reconstruction Method ◽  
Proportional Navigation ◽  
Filtering Algorithm ◽  
Simulation Results

The phased array strapdown radar seeker’s detecting information is coupled with missile attitude information. Hence, the measurement information can not be used for proportional navigation directly. The method of reconstructing inertial line of sight (LOS) rate in phased array strapdown seeker is presented using the missile-target relative motion geometric and filtering algorithm. Considering measurement noise and nonlinearity of the phased array strapdown radar guidance systems, the principle of unscented kalman filter (UKF) is introduced to estimate LOS rate. The simulation results show that the reconstruction method is correct and the extraction of LOS rate is effective.

scholarly journals Study of the Effectiveness of Different Kalman Filtering Methods and Smoothers in Object Tracking Based on Simulation Tests

2015 ◽  
Vol 97 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Marcin Malinowski ◽  
Janusz Kwiecień
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Measuring Instruments ◽  
Central Difference ◽  
Filtering Algorithm ◽  
Kalman Smoother ◽  
Derivative Free ◽  
Integration Strategies ◽  
Central Difference Kalman Filter

AbstractIn navigation practice, there are various navigational architecture and integration strategies of measuring instruments that affect the choice of the Kalman filtering algorithm. The analysis of different methods of Kalman filtration and associated smoothers applied in object tracing was made on the grounds of simulation tests of algorithms designed and presented in this paper. EKF (Extended Kalman Filter) filter based on approximation with (jacobians) partial derivations and derivative-free filters like UKF (Unscented Kalman Filter) and CDKF (Central Difference Kalman Filter) were implemented in comparison. For each method of filtration, appropriate smoothers EKS (Extended Kalman Smoother), UKS (Unscented Kalman Smoother) and CDKS (Central Difference Kalman Smoother) were presented as well. Algorithms performance is discussed on the theoretical base and simulation results of two cases are presented.

Application of Anti-Outlier UKF Algorithm in Integrated Navigation

2014 ◽  
Vol 641-642 ◽  
pp. 1307-1311
Author(s):  
Wen Chao Liu ◽  
Hong Wei Bian ◽  
Rong Ying Wang
Keyword(s):  
Kalman Filter ◽  
Nonlinear Problem ◽  
Unscented Kalman Filter ◽  
System Model ◽  
Integrated Navigation ◽  
Outlier Rejection ◽  
Newton Interpolation ◽  
Filtering Algorithm ◽  
The Impact

In view of the problems on nonlinearity of system model and robustness of filtering algorithm in Integrated Navigation, the algorithm of Unscented Kalman Filter (UKF) for outlier rejection is studied. The algorithm identifies outliers firstly by using the new observation rate, and then rectifies the observation outliers by using of Newton interpolation and finally gets the relatively more accurate estimated value. The combination of Newton interpolation and UKF resolves the nonlinear problem of the system model as well as effectively suppresses the impact of outliers to filtering algorithm. And effectiveness of the methodology has been proved by simulation.

Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 931-935
Author(s):  
She Sheng Gao ◽  
Wen Hui Wei ◽  
Li Xue
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Particle Filtering ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Navigation Systems ◽  
Filtering Algorithm ◽  
Near Space ◽  
Autonomous Navigation System

This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.

scholarly journals Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2406 ◽  
Author(s):  
Zhihong Deng ◽  
Lijian Yin ◽  
Baoyu Huo ◽  
Yuanqing Xia
Keyword(s):  
Kalman Filter ◽  
Cost Function ◽  
Unscented Kalman Filter ◽  
Practical Applications ◽  
Filtering Algorithm ◽  
Tracking Process ◽  
Robust Adaptive ◽  
Fading Factor ◽  
Maximum Correntropy Criterion ◽  
The Impact

In most practical applications, the tracking process needs to update the data constantly. However, outliers may occur frequently in the process of sensors’ data collection and sending, which affects the performance of the system state estimate. In order to suppress the impact of observation outliers in the process of target tracking, a novel filtering algorithm, namely a robust adaptive unscented Kalman filter, is proposed. The cost function of the proposed filtering algorithm is derived based on fading factor and maximum correntropy criterion. In this paper, the derivations of cost function and fading factor are given in detail, which enables the proposed algorithm to be robust. Finally, the simulation results show that the presented algorithm has good performance, and it improves the robustness of a general unscented Kalman filter and solves the problem of outliers in system.

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