Multi-Sensor Integrated Navigation System for Ships Based on Adaptive Kalman Filter

Fu, Bo; Jun-sheng, Liu; Wang, Qishuang

doi:10.1109/icma.2019.8816392

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…In this way, the adapted ship model is increasingly approaching the real one. For example, Fu et al (2019) [7] in their paper proposed such an integrated system algorithm. The algorithm proved to be suitable for exploiting the characteristics of all measurement systems and to perform a satisfactory evaluation of the results in cases where the GPS did not provide enough data.…”

Section: Signal Filtration and Reconstructionmentioning

confidence: 99%

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Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Lerga

Prpić-Oršić

et al. 2020

JMSE

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Various operations at sea, such as maintaining a constant ship position and direction, require a complex control system. Under such conditions, the ship needs an efficient positioning technique. Dynamic positioning (DP) systems provide such an application with a combination of the actuators mechanism, analyses of crucial ship variables, and environmental conditions. The natural forces of induced nonlinear waves acting on a ship’s hull interfere with the systems. To generate control signals for actuators accurately, sensor measurements should be filtered and processed. Furthermore, for safe and green routing, the forces and moments acting on the ship’s hull should be taken into account in terms of their prediction. Thus, the design of such systems takes into account the problem of obtaining data about the directional wave spectra (DWS). Sensor systems individually cannot provide high accuracy and reliability, so their measurements need to be combined and complemented. Techniques based on the recursive Kalman filter (KF) are used for this purpose. When some measurements are unavailable, the estimation procedure should predict them and, based on the comparison of theoretical and measured states, reduce the error variance of the analyzed signals. Different approaches for improving estimation algorithms have evolved over the years with the indication of improvement. This paper gives an overview of the state-of-the-art estimation and filtering techniques for providing optimum estimation states in DP systems.

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Section: Signal Filtration and Reconstructionmentioning

confidence: 99%

“…Therefore, measurement systems are observed simultaneously, forming an integrated navigation system. Advanced estimation techniques use multiple various sensors together [7]. Missing information from one of the sensors is provided by measurements of other sensors.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Lerga

Prpić-Oršić

et al. 2020

JMSE

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“…For instance, [22] has developed a multi-sensor data fusion methodology for INS/GPS/SAR integrated navigation systems. In [23], a new adaptive Kalman filter has studied for multi-sensor integrated navigation system. Recently, a new technique has been designed in [24] to handle unknown noise statistical characteristics of system and outliers in the measurement information.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Multi-Sensor State Estimation for Systems Subject to Multiplicative and Cross-Correlated Noises With Measurement Packet Dropping

Zhang

Wang

et al. 2021

IEEE Access

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This paper is concerned with the optimal state estimation problem for linear discrete-time systems with both multiplicative and cross-correlated noises. The measurement outputs for state estimation are collected from multiple sensors whose sampling rates are different that provide asynchronous data. In addition, the noises that affect the measurement information are correlated among different sensors and also coupled with the process noises as well. The aim of the addressed problem is to propose an optimal state estimation algorithm such that the estimation error is minimized in the mean-square sense with the existence of asynchronous data, possible sensor faults and correlated noises. In order to mitigate the impact of measurement missing, this paper utilizes neural networks to compensate the state estimation when measurement packets are dropping. Then, a fault detection mechanism that utilizes normalized innovation test is adopted to ensure that the abnormal data would be detected and removed. By resorting to the projection theorem and mathematical induction approach, sufficient conditions are derived for the existence of the desired optimal state estimator where the optimized estimation gains are formulated and can be computed iteratively at each time step. The proposed theoretical results are demonstrated via an illustrative numerical example. INDEX TERMS Correlated noises, multiplicative noises, asynchronous data, multi-sensors, multi-rate systems, neural networks.

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“…For integrated navigation, the most widely used in the world is the global positioning system (GPS)/inertial navigation system (INS) integrated navigation positioning system [7]. The Kalman filter is the most common method of data fusion [8]. Gao et al [9] developed an INS/GPS/LiDAR integrated navigation system through an extended Kalman filter (EKF) using a hybrid scan matching algorithm.…”

Section: Introductionmentioning

confidence: 99%

UAV-Borne LiDAR Crop Point Cloud Enhancement Using Grasshopper Optimization and Point Cloud Up-Sampling Network

Chen

Zhang

et al. 2020

Remote Sensing

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Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper optimization algorithm (GOA) and a point cloud density enhancement method were proposed. Firstly, a global positioning system (GPS)/inertial navigation system (INS) integrated navigation and positioning information fusion method based on a Kalman filter was constructed. Then, the GOA was employed to find the optimal solution by iterating the system noise variance matrix Q and measurement noise variance matrix R of Kalman filter. By feeding the optimal solution into the Kalman filter, the error variances of longitude were reduced to 0.00046 from 0.0091, and the error variances of latitude were reduced to 0.00034 from 0.0047. Based on the integrated navigation, an UAV-borne LiDAR scanning system was built for obtaining the crop point. During offline processing, the crop point cloud was filtered and transformed into WGS-84, the density clustering algorithm improved by the particle swarm optimization (PSO) algorithm was employed to the clustering segment. After the clustering segment, the pre-trained Point Cloud Up-Sampling Network (PU-net) was used for density enhancement of point cloud data and to carry out three-dimensional reconstruction. The features of the crop point cloud were kept under the processing of reconstruction model; meanwhile, the density of the crop point cloud was quadrupled.

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Multi-Sensor Integrated Navigation System for Ships Based on Adaptive Kalman Filter

Cited by 6 publications

References 2 publications

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Asynchronous Multi-Sensor State Estimation for Systems Subject to Multiplicative and Cross-Correlated Noises With Measurement Packet Dropping

UAV-Borne LiDAR Crop Point Cloud Enhancement Using Grasshopper Optimization and Point Cloud Up-Sampling Network

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