Low-cost sensors based Multi-Sensor Data Fusion techniques for RPAS Navigation and Guidance

Cappello, Francesco; Ramasamy, Subramanian; Sabatini, Roberto; Liu, Jing

doi:10.1109/icuas.2015.7152354

Cited by 11 publications

(13 citation statements)

References 22 publications

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“…The measurement fusion methods directly fuse observations or sensor measurements to obtain a weighted or combined measurement and then use a single Kalman filter to obtain the final state estimate based upon the fused measurement [12]. These authors presented a Particle Filter (PF) based multisensor data fusion (MSDF) technique in an integrated Navigation and Guidance System (NGS) design based on low-cost avionics sensors [13]. Wang et al addressed the problem of tracking multiple targets using multisensor bearings-only measurements in the presence of noise and clutter.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Wang

Dong

2015

Mathematical Problems in Engineering

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In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

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Section: Introductionmentioning

confidence: 99%

Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Wang

Dong

2015

Mathematical Problems in Engineering

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“…The requirements for RPAS to be integrated into all classes of airspace are illustrated in Figure 1. Enhanced surveillance solutions based on Automatic Dependent Surveillance-Broadcast (ADS-B) system and Traffic Collision Avoidance System (TCAS) are required for addressing cooperative DAA functions [11][12][13][14][15][16][17][18]. The Performance Based Navigation (PBN) concept specifies that aircraft RNP and area navigation (RNAV) requirements have to be met for all flight phases [11].…”

Section: Introductionmentioning

confidence: 99%

“…These navigation specifications are defined at a sufficient level of detail in order to facilitate global harmonization by providing specific implementation guidance for national aviation regulators and operators. The RNP is in turn translated to technical requirements, which aid in the determination of specific airborne sensors that can be employed onboard the RPAS [19,20]. The sensor requirements dictate the following: physical characteristics of the sensors including size, weight and volume, support requirements such as electrical power, accuracy and precision [20].…”

Section: Introductionmentioning

confidence: 99%

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Low-Cost RPAS Navigation and Guidance System using Square Root Unscented Kalman Filter

Cappello¹,

Ramasamy²,

Sabatini³

2015

SAE Technical Paper Series

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Multi-Sensor Data Fusion (MSDF) techniques involving satellite and inertial-based sensors are widely adopted to improve the navigation solution of a number of mission-and safety-critical tasks. Current Navigation and Guidance Systems (NGS) employing MSDF algorithms do not meet the required level of performance in all flight phases of small Remotely Piloted Aircraft Systems (RPAS). Hence in order to satisfy the Required Navigation Performance (RNP), an innovative Square Root-Unscented Kalman Filter (SR-UKF) based NGS is implemented and compared with a conventional UKF design. The presented NGS architectures employ a number of state-of-the-art low-cost sensors including; Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Navigation (VBN) sensors. Additionally, an Aircraft Dynamics Model (ADM), which is essentially a knowledge based module, is employed to compensate for the MEMS-IMU sensor shortcomings in high-dynamics attitude determination tasks. The ADM acts as a virtual sensor and its measurements are processed with non-linear estimation techniques in order to increase the operational validity time. An improvement in the ADM navigation state vector (i.e., position, velocity and attitude) measurements is obtained, thanks to the accurate modeling of aircraft dynamics and advanced processing techniques. A novel SR-UKF based VBN-IMU-GNSS-ADM (SR-U-VIGA) architecture design is implemented and compared with a conventional UKF based design (U-VIGA) in a small RPAS (AEROSONDE) integration scheme exploring a representative cross-section of the operational flight envelope. The comparison of the state vector demonstrates the capability of SR-U-VIGA and U-VIGA systems to fulfill the relevant RNP criteria, including precision approach tasks. Furthermore, the computation time of SR-U-VIGA system is lower when compared to U-VIGA NGS allowing for an enhanced implementation in real-time applications.

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“…The establishment of a modern intelligent transportation system will be mentioned on the agenda. Intelligent Vehicles, as an important part of Intelligent Transportation Systems, is also the main body of the system, which not only improves driving safety and road traffic efficiency, but also reduces energy consumption [1][2][3]. Due to many advantages, the technology research has been increasingly concerned about the relevant institutions at home and abroad.…”

Section: Introductionmentioning

confidence: 99%

Application of Multi-sensor Information Fusion Based on Improved Particle Swarm Optimization in Unmanned System Path Planning

Shi¹,

He²,

Li³

et al. 2017

Int. J. Onl. Eng.

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Abstract-Intelligent vehicle driving performance is safe and stable, which can significantly improve the efficiency of road traffic and reduce energy consumption, and intelligent vehicle is also the development direction of modern transport. Its core technology is intelligent environment perception module, by using a variety of sensors on the car in which the surrounding environment for data collection, processing module to provide effective control for the basis. In this paper, a new SINS / CNS / GPS integrated navigation observation equation is proposed, and a new federated data fusion structure is designed for the integrated navigation system. The particle filter is used to fuse the multi-source data of the federated filter subsystem, thus eliminating the limitations of the classical Kalman filter. The traditional Kalman filter structure and the federal particle filter mechanism are designed. The comparison shows that the proposed algorithm is effective in the information fusion of the integrated navigation system, and the filtering effect is superior to the traditional filtering method. Keywords-particle filter; information fusion; intelligent vehicle; navigation IntroductionWith the progress of society, the car has become an essential travel means of transport, vehicle congestion, traffic accidents and other issues are increasingly apparent. The rapid growth in the number of cars is caused by the low efficiency of public transport which leads to frequent traffic accidents. The establishment of a modern intelligent transportation system will be mentioned on the agenda. Intelligent Vehicles, as an important part of Intelligent Transportation Systems, is also the main body of the system, which not only improves driving safety and road traffic efficiency, but also reduces energy consumption [1][2][3]. Due to many advantages, the technology research has been increasingly concerned about the relevant institutions at home and abroad. Intelligent transportation system can effectively relieve traffic pressure, rational allocation of public transport resources and road resources. Based on machine sensing technology and control technology, the driving system uses information transmission technology and computer vision technology helps to monitor the road 88

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Low-cost sensors based Multi-Sensor Data Fusion techniques for RPAS Navigation and Guidance

Abstract: Low-cost sensors based multi-sensor data fusion techniques for RPAS navigation and guidance.

Cited by 11 publications

References 22 publications

Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Low-Cost RPAS Navigation and Guidance System using Square Root Unscented Kalman Filter

Application of Multi-sensor Information Fusion Based on Improved Particle Swarm Optimization in Unmanned System Path Planning

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