Flight Path Measurement of Drones Using Microphone Array and Performance Improvement Method Using Unscented Kalman Filter

2018 ◽  
Vol 46 (12) ◽  
pp. 975-985
Author(s):  
Jiwon Lee ◽  
Yeong-Ju Go ◽  
Seungkeum Kim ◽  
Jong-Soo Choi
Keyword(s):  
Kalman Filter ◽  
Performance Improvement ◽  
Unscented Kalman Filter ◽  
Microphone Array ◽  
Flight Path ◽  
Improvement Method ◽  
Path Measurement ◽  
And Performance

scholarly journals An unscented Kalman filter-based rolling radius estimation methodology for railway vehicles with traction

2017 ◽  
Vol 232 (6) ◽  
pp. 1686-1702 ◽  
Author(s):  
Altan Onat ◽  
Petr Voltr ◽  
Michael Lata
Keyword(s):  
Kalman Filter ◽  
Condition Monitoring ◽  
Unscented Kalman Filter ◽  
Test Stand ◽  
Railway Vehicle ◽  
Wheel Wear ◽  
Railway Vehicles ◽  
Wheel Profile ◽  
Roller Configuration ◽  
And Performance

Monitoring the conditions of railway vehicle systems plays an important role in the maintenance of safety and performance of railway vehicles. Rolling radius is one of the properties that should be monitored continuously for the predictive maintenance of a railway vehicle since it changes with time due to wheel wear. In this study, a model-based condition monitoring methodology, which is based on an unscented Kalman filter, is proposed. The model includes the torsional dynamics of an independently rotating tram wheel with a traction motor and a contact model. The rolling radius is estimated by considering the traction effort of the motor and the angular velocity measurements. The proposed methodology is tested on a tram wheel test stand (roller rig), which has a wheel on roller configuration. First, a mathematical model is validated by the measurements taken from the test stand. Second, the unscented Kalman filter is applied as a parameter estimator. The results demonstrate that the proposed scheme is a promising option to be used in the predictive condition monitoring of the wheel profile for traction vehicles.

scholarly journals The UKF Based Approach to Improving Attitude and Position of Quadcopter Through Autonomous and Non-Autonomous Flight

2020 ◽  
Vol 16 (2) ◽  
pp. 1-9
Author(s):  
Ahmed Abdulkareem ◽  
Basil Jasim ◽  
Safanah Raafat
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Nonlinear Filter ◽  
Linear Estimation ◽  
Autonomous Flight ◽  
Physical Sensors ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
And Performance ◽  
The Stability

The gyroscope and accelerometer are the basic sensors used by most Unmanned Aerial Vehicle (UAV) like quadcopter to control itself. In this paper, the fault detection of measured angular and linear states by gyroscope and accelerometer sensors are present. Uncertainties in measurement and physical sensors itself are the main reasons that lead to generate noise and cause the fault in measured states. Most previous solutions are process angular or linear states to improving the performance of quadcopter. Also, in most of the previous solutions, KF and EKF filters are used, which are inefficient in dealing with high nonlinearity systems such as quadcopter. The proposed algorithm is developed by the robust nonlinear filter, Unscented Kalman Filter (UKF), as an angular and linear estimation filter. Simulation results show that the proposed algorithm is efficient to decrease the effect of sensors noise and estimate accurate angular and linear states. Also, improving the stability and performance properties of the quadcopter. In addition, the new algorithm leads to increasing the range of nonlinearity movements that quadcopter can perform it.

scholarly journals A Method of Rescue Flight Path Plan Correction Based on the Fusion of Predicted Low-altitude Wind Data

2016 ◽  
Vol 28 (5) ◽  
pp. 479-485 ◽  
Author(s):  
Ming Zhang ◽  
Shuo Wang ◽  
Hui Yu
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Unscented Kalman Filter ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Prediction Method ◽  
Flight Path ◽  
Wind Data ◽  
Observation Data ◽  
Low Altitude

This study proposes a low-altitude wind prediction model for correcting the flight path plans of low-altitude aircraft. To solve large errors in numerical weather prediction (NWP) data and the inapplicability of high-altitude meteorological data to low altitude conditions, the model fuses the low-altitude lattice prediction data and the observation data of a specified ground international exchange station through the unscented Kalman filter (UKF)-based NWP interpretation technology to acquire the predicted low-altitude wind data. Subsequently, the model corrects the arrival times at the route points by combining the performance parameters of the aircraft according to the principle of velocity vector composition. Simulation experiment shows that the RMSEs of wind speed and direction acquired with the UKF prediction method are reduced by 12.88% and 17.50%, respectively, compared with the values obtained with the traditional Kalman filter prediction method. The proposed prediction model thus improves the accuracy of flight path planning in terms of time and space.

Cognitive Factors Mediate the Relation Between Age and Flight Path Maintenance in General Aviation

2016 ◽  
Vol 6 (2) ◽  
pp. 81-90 ◽  
Author(s):  
Kathleen Van Benthem ◽  
Chris M. Herdman
Keyword(s):  
Cognitive Functioning ◽  
Flight Path ◽  
General Aviation ◽  
Cognitive Factors ◽  
Pilot Performance ◽  
Pilot Error ◽  
Age Related ◽  
And Performance ◽  
Relationship Of ◽  
The Relationship

Abstract. Identifying pilot attributes associated with risk is important, especially in general aviation where pilot error is implicated in most accidents. This research examined the relationship of pilot age, expertise, and cognitive functioning to deviations from an ideal circuit trajectory. In all, 54 pilots, of varying age, flew a Cessna 172 simulator. Cognitive measures were obtained using the CogScreen-AE ( Kay, 1995 ). Older age and lower levels of expertise and cognitive functioning were associated with significantly greater flight path deviations. The relationship between age and performance was fully mediated by a cluster of cognitive factors: speed and working memory, visual attention, and cognitive flexibility. These findings add to the literature showing that age-related changes in cognition may impact pilot performance.

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