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.

Kalman Filtering-Aided Optical Localization of Mobile Robots: System Design and Experimental Validation

2017 ◽  
Author(s):  
Jason N. Greenberg ◽  
Xiaobo Tan
Keyword(s):  
Kalman Filter ◽  
Mobile Robots ◽  
Kalman Filtering ◽  
Experimental Validation ◽  
Line Of Sight ◽  
Localization Error ◽  
Two Dimensional ◽  
Prior Work ◽  
Localization Performance ◽  
Mobile Base

Localization of mobile robots in GPS-denied envrionments (e.g., underwater) is of great importance to achieving navigation and other missions for these robots. In our prior work a concept of Simultaneous Localization And Communication (SLAC) was proposed, where the line of sight (LOS) requirement in LED-based communication is exploited to extract the relative bearing of the two communicating parties for localization purposes. The concept further involves the use of Kalman filtering for prediction of the mobile robot’s position, to reduce the overhead in establishing LOS. In this work the design of such a SLAC system is presented and experimentally evaluated in a two-dimensional setting, where a mobile robot localizes itself through wireless LED links with two stationary base nodes. Experimental results are presented to demonstrate the feasibility of the proposed approach and the important role the Kalman filter plays in reducing the localization error. The effect of the distance between the base nodes on the localization performance is further studied, which bears implications in future SLAC systems where mobile base nodes can be reconfigured adaptively to maximize the localization performance.

Performance of LED for line-of-sight (LoS) underwater wireless optical communication system

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alaa G. Nasser ◽  
Mazin Ali A. Ali
Keyword(s):  
Optical Communication ◽  
Link Quality ◽  
Line Of Sight ◽  
Modulation Scheme ◽  
Underwater Communication ◽  
Optical Communication System ◽  
High Data ◽  
Wireless Optical Communication ◽  
Noise Rate ◽  
Wireless Optical Communications

AbstractUnderwater wireless optical communications (UWOC) recently emerge as a solution to the problem of underwater communication for link with a high data rate, low delay, safety, and high immunity. In this study, the line of sight (LoS) method based on LED used for UWOC with different modulation schemes. The bit error rate (BER), signal-to-noise rate, and quality factor are used for assessing system performance and link quality. Besides the effect of transmitting angle, distance link (d), and transmitting power (PT) are analysed. Results show that 8-pulse position modulation (PPM) is the best modulation scheme for achieving a good link in the LoS method.

Self-tuning Kalman filter for compensation of transmission delay and loss in line-of-sight guidance

2018 ◽  
Vol 233 (11) ◽  
pp. 4191-4201
Author(s):  
Akram Nikfetrat ◽  
Reza Mahboobi Esfanjani
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Random Variables ◽  
Measurement Model ◽  
Line Of Sight ◽  
Uncertain Parameters ◽  
Suggested Approach ◽  
Self Tuning ◽  
Simulation Results ◽  
Sequence Simulation

A self-tuning Kalman filter is introduced to reduce the destructive effects of the delayed and lost measurements in the guidance systems employing command to line-of-sight strategy. A sequence of Bernoulli distributed random variables with uncertain probabilities are used to model the delayed and lost observations. Besides the state estimation, the uncertain parameters of the measurement model are identified online using the covariance of innovation sequence. Simulation results are given to demonstrate the merits of the suggested approach.

Line-of-Sight Angle Rate Reconstruction for Phased Array Strapdown Seeker

2013 ◽  
Vol 645 ◽  
pp. 196-201
Author(s):  
Ying Liu ◽  
Wei Feng Tian ◽  
Jian Kang Zhao ◽  
Shi Qing Zhu ◽  
Ge Wen Yang
Keyword(s):  
Kalman Filter ◽  
Phased Array ◽  
Unscented Kalman Filter ◽  
Measurement Noise ◽  
Line Of Sight ◽  
Measurement Information ◽  
Reconstruction Method ◽  
Proportional Navigation ◽  
Filtering Algorithm ◽  
Simulation Results

The phased array strapdown radar seeker’s detecting information is coupled with missile attitude information. Hence, the measurement information can not be used for proportional navigation directly. The method of reconstructing inertial line of sight (LOS) rate in phased array strapdown seeker is presented using the missile-target relative motion geometric and filtering algorithm. Considering measurement noise and nonlinearity of the phased array strapdown radar guidance systems, the principle of unscented kalman filter (UKF) is introduced to estimate LOS rate. The simulation results show that the reconstruction method is correct and the extraction of LOS rate is effective.

Performance of OOK, 2PSK, QPSK Modulation Format in Downlink of Ground-to-Satellite Laser Communication under the Fluctuation of Atmosphere

2013 ◽  
Vol 411-414 ◽  
pp. 753-756
Author(s):  
Yan Li ◽  
Mi Li ◽  
Jia Chen Ding ◽  
Ming Hui Tang ◽  
Yuan Gang Lu
Keyword(s):  
Optical Communication ◽  
Communication System ◽  
Laser Communication ◽  
Optical Communication System ◽  
Modulation Format ◽  
Advantages And Disadvantages ◽  
Satellite Laser Communication ◽  
Atmosphere Turbulence ◽  
Simulation Results ◽  
Selection Of

With the consideration of the intensity scintillation caused by the atmosphere turbulence in the downlink of the ground-to-satellite optical communication system, BER performances of three different modulation schemes OOK, 2PSK, QPSK are explored and compared. Simulation results are given and conclusions are achieved through the discussions of the advantages and disadvantages of various schemes, which may cause contributions to the selection of the modulation format in the design of the ground-to-satellite laser communication system.

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

2018 ◽  
Vol 233 (11) ◽  
pp. 4017-4031
Author(s):  
Kai Xiong ◽  
Chunling Wei ◽  
Haoyu Zhang
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Autonomous Navigation ◽  
Adaptive Estimation ◽  
Line Of Sight ◽  
Parallel Model ◽  
Multiple Model ◽  
Filtering Algorithm ◽  
Robust Kalman Filter ◽  
Multiple Model Adaptive Estimation

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

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