A low-cost and high performance navigation system for small RPAS applications

Cappello, Francesco; Ramasamy, Subramanian; Sabatini, Roberto

doi:10.1016/j.ast.2016.09.002

Cited by 27 publications

(11 citation statements)

References 8 publications

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“…The performance of VBN sensors is affected by low visibility conditions which could be due to night/low light conditions, smoke, fog, precipitation as well as presence of transparent or opaque objects [ 70 ]. An ADM virtual sensor is dependent on the aircraft’s physical sensors and is based on the assumption of the aircraft being a rigid body with a constant and static mass distribution [ 71 ]. An integration of sensor data achieved through Multi-Sensor Data Fusion algorithms can lead to an improved positioning solution for air and ground platforms.…”

Section: Integration Of Acoustic Sensors In Multi-sensor Navigatiomentioning

confidence: 99%

Acoustic Sensors for Air and Surface Navigation Applications

Kapoor

Ramasamy

Gardi

et al. 2018

Sensors

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This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for intelligent transport systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems.

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Section: Integration Of Acoustic Sensors In Multi-sensor Navigatiomentioning

confidence: 99%

Acoustic Sensors for Air and Surface Navigation Applications

Kapoor

Ramasamy

Gardi

et al. 2018

Sensors

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“…The European Commission's Directorate General for Mobility and Transport (DG MOVE), the European Defence Agency (EDA), the European Aviation Safety Agency (EASA), and the Single European Sky Air Traffic Management (ATM) Research (SESAR) Joint Undertaking (SJU) are also stepping up the efforts to safely accommodate UAS into the European aviation and ATM system [6]. In parallel with these government and industry-led initiatives, the aerospace research community has been continuously working on several challenges of integrating UAS into non-segregated airspaces including separation thresholds and methods for small UAS [7,8], UAS encounter modelling and collision avoidance [9][10][11], 3D obstacle avoidance strategies for UAS [12][13][14][15] dynamic model augmentation [16], Global Navigation Satellite Systems (GNSS) integrity augmentation for UAS [17], surveillance sensor integration in the UAS platform [18,19] and well-clear boundary models for UAS DAA [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Safety Assessment for UAS Separation Assurance and Collision Avoidance Systems

2019

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The airworthiness certification of aerospace cyber-physical systems traditionally relies on the probabilistic safety assessment as a standard engineering methodology to quantify the potential risks associated with faults in system components. This paper presents and discusses the probabilistic safety assessment of detect and avoid (DAA) systems relying on multiple cooperative and non-cooperative tracking technologies to identify the risk of collision of unmanned aircraft systems (UAS) with other flight vehicles. In particular, fault tree analysis (FTA) is utilized to measure the overall system unavailability for each basic component failure. Considering the inter-dependencies of navigation and surveillance systems, the common cause failure (CCF)-beta model is applied to calculate the system risk associated with common failures. Additionally, an importance analysis is conducted to quantify the safety measures and identify the most significant component failures. Results indicate that the failure in traffic detection by cooperative surveillance systems contribute more to the overall DAA system functionality and that the probability of failure for ownship locatability in cooperative surveillance is greater than its traffic detection function. Although all the sensors individually yield 99.9% operational availability, the implementation of adequate multi-sensor DAA system relying on both cooperative and non-cooperative technologies is shown to be necessary to achieve the desired levels of safety in all possible encounters. These results strongly support the adoption of a unified analytical framework for cooperative/non-cooperative UAS DAA and elicits an evolution of the current certification framework to properly account for artificial intelligence and machine-learning based systems.

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“…Figure 1 describes the above discrete Kalman filter algorithm, we can see from the figure Kalman filter which has two calculation loops [19,20]. Among them, the gain calculation circuit is an independent calculation circuit, and the filter calculation circuit depends on the gain calculation circuit.…”

Section: Combined Positioning Path Planningmentioning

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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A low-cost and high performance navigation system for small RPAS applications

Cited by 27 publications

References 8 publications

Acoustic Sensors for Air and Surface Navigation Applications

Acoustic Sensors for Air and Surface Navigation Applications

Probabilistic Safety Assessment for UAS Separation Assurance and Collision Avoidance Systems

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

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