Extended Kalman filter-aided alignment control for maintaining line of sight in optical communication

2016 ◽  
Author(s):  
Pratap Bhanu Solanki ◽  
Mohammed Al-Rubaiai ◽  
Xiaobo Tan
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Optical Communication ◽  
Line Of Sight

scholarly journals Extraction and Filter Algorithm of Guidance Information for Full-strapdown Seeker on Rotation Missile

2018 ◽  
Vol 214 ◽  
pp. 03008 ◽  
Author(s):  
YongShan Liu ◽  
Li Song ◽  
JingLong Li
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Simple Structure ◽  
Unscented Kalman Filter ◽  
High Reliability ◽  
Engineering Application ◽  
Line Of Sight ◽  
Light Weight ◽  
Rate Information ◽  
Guidance Information

Strapdown seekers are superior to platform seekers for their simple structure, high reliability and light weight but cannot measure the line-of-sight angle rate information for the guidance of rotation missile directly. This paper aims at the engineering application of full-strapdown seekers on rotation missile problem. Firstly, a line-of-sight angle rate solution model is established. Based on the MATLAB, the extended Kalman filter (EKF) algorithm and unscented Kalman filter (UKF) algorithm are used to estimate the line-of-sight angle rate information of the full-strapdown seekers. The results show that using EKF filter and UKF filter both can obtain effective guidance information and the UKF’s effect is better.

Efficient Optical Localization for Mobile Robots via Kalman Filtering-Based Location Prediction

2016 ◽  
Author(s):  
Jason N. Greenberg ◽  
Xiaobo Tan
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Mobile Agent ◽  
Optical Communication ◽  
Line Of Sight ◽  
Location Prediction ◽  
Two Dimensional ◽  
Optical Communication System ◽  
Search Range ◽  
Simulation Results

Localization and communication are both essential functionalities of any practical mobile sensor network. Achieving both capabilities through a single Simultaneous Localization And Communication (SLAC) would greatly reduce the complexity of system implementation. In this paper a technique for localizing a mobile agent using the line of sight (LOS) detection of an LED-based optical communication system is proposed. Specifically, in a two-dimensional (2D) setting, the lines of sight between a mobile robot and two base nodes enable the latter to acquire bearing information of the robot and compute its location. However, due to the mobile nature of the robot, establishing its LOS with the base nodes would require extensive scan for all parties, severely limiting the temporal resolution and spatial precision of the localization. We propose the use of a Kalman filter to predict the position of the robot based on past localization results, which allows the nodes to significantly reduce the search range in establishing LOS. Simulation results and preliminary experimental results are presented to illustrate and support the proposed approach.

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 The Vehicles Positioning in Ultra-Dense 5G/V2X Radio Access Networks Using the Extended Kalman Filter

2020 ◽  
Vol 6 (4) ◽  
pp. 45-59
Author(s):  
G. Fokin ◽  
A. Vladyko
Keyword(s):  
Kalman Filter ◽  
Simulation Model ◽  
Extended Kalman Filter ◽  
Traffic Control ◽  
Access Networks ◽  
Line Of Sight ◽  
Angle Of Arrival ◽  
Radio Access Networks ◽  
Radio Access ◽  
Vehicle Positioning

This work is devoted to the study of mathematical models of vehicle positioning in ultra-dense V2X / 5G radio access networks using the extended Kalman filter. Based on the study of the probability of line-of-sight availability in the conditions of ultra-dense distribution of reference radio access stations and vehicles, as well as existing mathematical prototype positioning models, a new simulation model for constructing the trajectory of a vehicle has been developed to assess compliance with the requirements for the accuracy of coordinate assessment on the example of the scenario of priority passage of intersections. The simulation model implements the procedures for collecting primary angle and rangefinder measurements by reference stations received from the vehicle for subsequent secondary processing using the extended Kalman filter, as a result of which the vehicle trajectory is built in real time. In contrast to the existing prototype models, the simulation model developed in this work makes it possible to assess compliance with the specified requirements and other specifications depending on the current conditions of line-of-sight availability, as well as the accuracy of collecting primary angle measurements determined by the antenna array installed on the support device. The results of simulation are consistent with the known estimates of prototype models and confirm the possibility of achieving an accuracy of up to 1 m for a traffic control scenario with an error in determining the angle of arrival of a signal of 2 °.

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.

Extended Kalman Filter-Based Active Alignment Control for LED Optical Communication

2018 ◽  
Vol 23 (4) ◽  
pp. 1501-1511 ◽  
Author(s):  
Pratap Bhanu Solanki ◽  
Mohammed Al-Rubaiai ◽  
Xiaobo Tan
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Optical Communication

scholarly journals A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6634
Author(s):  
Long Cheng ◽  
Sihang Huang ◽  
Mingkun Xue ◽  
Yangyang Bi
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Extended Kalman Filter ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Robust Localization

With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms.

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