Estimation of Altitude and Vertical Velocity for Multirotor Aerial Vehicle Using Kalman Filter

Ga̧sior, Przemysław; Gardecki, Stanisław; Gośliński, Jarosław; Giernacki, Wojciech

doi:10.1007/978-3-319-05353-0_36

Cited by 18 publications

(6 citation statements)

References 2 publications

(2 reference statements)

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“…The vertical velocity can be gathered on the basis on the available vertical acceleration and altitude measurements [39]. …”

Section: Simulation Framework and Resultsmentioning

confidence: 99%

“…In mode one, the observations for velocity and position are obtained from GPS, while in mode two, the position and velocity in the north and east directions are acquired from the IMU and the height from a barometric pressure sensor and finally the down velocity from the fusion between barometer and vertical accelerometer measurements. The vertical velocity can be gathered on the basis on the available vertical acceleration and altitude measurements [ 39 ].…”

Section: Simulation Framework and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

Elzoghby

Arafa³

et al. 2017

Sensors

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Information fusion from multiple sensors ensures the accuracy and robustness of a navigation system, especially in the absence of global positioning system (GPS) data which gets degraded in many cases. A way to deal with multi-mode estimation for a small fixed wing unmanned aerial vehicle (UAV) localization framework is proposed, which depends on utilizing a Luenberger observer-based linear matrix inequality (LMI) approach. The proposed estimation technique relies on the interaction between multiple measurement modes and a continuous observer. The state estimation is performed in a switching environment between multiple active sensors to exploit the available information as much as possible, especially in GPS-denied environments. Luenberger observer-based projection is implemented as a continuous observer to optimize the estimation performance. The observer gain might be chosen by solving a Lyapunov equation by means of a LMI algorithm. Convergence is achieved by utilizing the linear matrix inequality (LMI), based on Lyapunov stability which keeps the dynamic estimation error bounded by selecting the observer gain matrix (L). Simulation results are presented for a small UAV fixed wing localization problem. The results obtained using the proposed approach are compared with a single mode Extended Kalman Filter (EKF). Simulation results are presented to demonstrate the viability of the proposed strategy.

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“…The vertical velocity can be gathered on the basis on the available vertical acceleration and altitude measurements [39]. …”

Section: Simulation Framework and Resultsmentioning

confidence: 99%

Section: Simulation Framework and Resultsmentioning

confidence: 99%

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

Elzoghby

Arafa³

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…A drone usually uses state estimation methods to process sensor readings from accelerometers, gyroscopes, magnetometers, GPS signals, and barometers. Extended Kalman Filters (EKF) and its variants are the most popular state estimation methods [24], [25], [26], [27]. ArduPilot EKF system estimates 24 states for drone control.…”

Section: Drone Control Backgroundmentioning

confidence: 99%

DPM: Towards Accurate Drone Position Manipulation

Chen

Yang

Duan

2023

IEEE Trans. Dependable and Secure Comput.

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While existing methods can disrupt drone invasions in a protected airspace, none of them is able to accurately guide a drone to a desired location for safe handling. To address this issue, the Drone Position Manipulation (DPM) attack is proposed in this paper to utilize weaknesses in common navigation algorithms of consumer drones. The main advantage of DPM is that it can accurately redirect an invading drone to a desired location, by carefully crafting the spoofed guidance inputs of the drone to exploit the adjustment in the path-following navigation algorithms. Compared with existing methods, this is the first work to achieve such accurate quantitative control. Another main contribution of this work is that it explores three fundamental components (i.e., guidance sensing, state estimation, and navigation control) together to enable the quantitative manipulation of flight paths, different from all existing methods. Furthermore, a formal analysis of the attack range is presented for investigating where a drone can be redirected from its target under given constraints. The evaluation on the Software-in-the-loop (SITL) module of ArduPilot system shows that the proposed attack is able to not only accurately lead a drone to a redirected destination but also make it fairly far away from its target. Lastly, the proposed attack can be applied to many autopiloted systems, because it exploits common weaknesses in these systems.

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“…(u, f, c) updated by q is presented in (3). Transformation matrix C n b is presented in (4), while a U can be computed by C n b as (5).…”

Section: Accelerometermentioning

confidence: 99%

“…In the area of altitude estimation, Kalman filter is used to fuse altitude of barometer and acceleration in some literatures [5,6]. Altitude estimated result with Kalman filter has drift error in [6], and the pressure sensor used in this work and our work is the same.…”

Section: Introductionmentioning

confidence: 98%

Altitude data fusion utilising differential measurement and complementary filter

Wei

Chen

2016

IET Science, Measurement & Technology

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Estimation of Altitude and Vertical Velocity for Multirotor Aerial Vehicle Using Kalman Filter

Cited by 18 publications

References 2 publications

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

DPM: Towards Accurate Drone Position Manipulation

Altitude data fusion utilising differential measurement and complementary filter

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