Design and Analysis of a Pose Estimator for Quadrotor MAVs With Modified Dynamics and Range Measurements

2017 ◽  
Author(s):  
Eranga Fernando ◽  
George K. Mann ◽  
Oscar De Silva ◽  
Raymond G. Gosine
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Position Estimation ◽  
Estimation Task ◽  
Range Measurements ◽  
Aerial Vehicles ◽  
Accurate Position ◽  
Inertial Measurements ◽  
Modified Dynamics ◽  
Estimation Range

This paper presents the design and analysis of a pose estimator for quadrotor micro aerial vehicles (MAVs). The proposed design uses the dynamic model of the quadrotor with aerodynamic effects and uses the extended Kalman filter (EKF) formulation for state estimation. Range measurements to known locations, inertial measurements and height measurements are used for the estimation task. The purpose of the study is to evaluate the performance of the estimator when navigating through a changing indoor setting. The study investigates the effect of changing number of rannge measurements, different geometrical arrangements of range sensors and changing availability of confident height information on the performance of the estimator. Performance of the estimator for each scenario is numerically analyzed. Finally a criteria is proposed for selecting the sensors, number of range measurements, geometric location of sensors which facilitates accurate position estimation using the proposed method.

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 Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages

2021 ◽  
Author(s):  
Meharoon Shaik
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Error ◽  
Position Estimation ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Range Data ◽  
Radio Communications ◽  
Collision Warning ◽  
Range Measurements

The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.

scholarly journals Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages

2021 ◽  
Author(s):  
Meharoon Shaik
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Error ◽  
Position Estimation ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Range Data ◽  
Radio Communications ◽  
Collision Warning ◽  
Range Measurements

The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

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