Decorrelated state estimation for distributed tracking using multiple sensors in cluttered environments

2004 ◽  
Author(s):  
W. Khawsuk ◽  
L.Y. Pao
Keyword(s):  
State Estimation ◽  
Cluttered Environments ◽  
Multiple Sensors ◽  
Distributed Tracking

Extended Kalman Filter Based Quadrotor State Estimation Based on Asynchronous Multisensor Data

2015 ◽  
Author(s):  
Mohammad Sarim ◽  
Alireza Nemati ◽  
Manish Kumar ◽  
Kelly Cohen
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Extended Kalman Filter ◽  
Real World ◽  
Flight Test ◽  
Communication Delays ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Multisensor Data

For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario.

scholarly journals Control Loop Sensor Calibration Using Neural Networks for Robotic Control

2011 ◽  
Vol 2011 ◽  
pp. 1-8
Author(s):  
Kathleen A. Kramer ◽  
Stephen C. Stubberud
Keyword(s):  
Control System ◽  
State Estimation ◽  
Autonomous Navigation ◽  
Sensor Calibration ◽  
Control Loop ◽  
Robotic Control ◽  
Multiple Sensors ◽  
Sensor Model ◽  
On Line ◽  
The Difference

Whether sensor model’s inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just as detrimental. Sensor modeling errors result in poor state estimation. This, in turn, can cause a control system relying upon the sensor’s measurements to become unstable, such as in robotics where the control system is applied to allow autonomous navigation. A technique referred to as a neural extended Kalman filter (NEKF) is developed to provide both state estimation in a control loop and to learn the difference between the true sensor dynamics and the sensor model. The technique requires multiple sensors on the control system so that the properly operating and modeled sensors can be used as truth. The NEKF trains a neural network on-line using the same residuals as the state estimation. The resulting sensor model can then be reincorporated fully into the system to provide the added estimation capability and redundancy.

scholarly journals Jump state estimation with multiple sensors with packet dropping and delaying channels

2014 ◽  
Vol 47 (4) ◽  
pp. 982-993 ◽  
Author(s):  
Daniel Dolz ◽  
Ignacio Peñarrocha ◽  
Roberto Sanchis
Keyword(s):  
State Estimation ◽  
Packet Dropping ◽  
Multiple Sensors

scholarly journals Outage Minimization For State Estimation Using Multiple Sensors*

2009 ◽  
Vol 42 (20) ◽  
pp. 216-221
Author(s):  
Alex S. Leong ◽  
Subhrakanti Dey ◽  
Girish N. Nair ◽  
Priyank Sharma
Keyword(s):  
State Estimation ◽  
Multiple Sensors
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