scholarly journals Distributed Multistatic Sky-Wave Over-the-Horizon Radar’s Positioning Algorithm for the Marine Target

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fangyu Ren ◽  
Huotao Gao ◽  
Lijuan Yang ◽  
Sang Zhou
Keyword(s):  
Optimization Problem ◽  
Doppler Frequency ◽  
Propagation Path ◽  
Frequency Error ◽  
Convex Optimization Problem ◽  
Positioning Accuracy ◽  
Straight Line ◽  
Highly Nonlinear ◽  
Signal Path ◽  
Positioning Algorithm

This paper establishes a distributed multistatic sky-wave over-the-horizon radar (DMOTHR) model and proposes a semidefinite relaxation positioning (SDP) algorithm to locate marine ship targets. In the DMOTHR, it is difficult to locate the target due to the complexity of the signal path propagation. Therefore, this paper uses the ionosphere as the reflector to convert the propagation path from a polyline to a straight line for establishing the model, and then the SDP algorithm will be used to transform a highly nonlinear positioning optimization problem into a convex optimization problem. Finally, it is concluded through the simulations that the SDP algorithm can obtain better positioning accuracy under a certain Doppler frequency error and ionospheric measurement error.

scholarly journals Inverse reinforcement learning in contextual MDPs

2021 ◽  
Author(s):  
Stav Belogolovsky ◽  
Philip Korsunsky ◽  
Shie Mannor ◽  
Chen Tessler ◽  
Tom Zahavy
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problem ◽  
Decision Processes ◽  
Inverse Reinforcement Learning ◽  
Convex Optimization Problem ◽  
Reward Function ◽  
Dynamic Treatment Regime ◽  
Markov Decision ◽  
Dynamic Treatment ◽  
Recorded Data

AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the reward mapping, such that the agent will act optimally even when encountering previously unseen contexts, also known as zero-shot transfer. We formulate this problem as a non-differential convex optimization problem and propose a novel algorithm to compute its subgradients. Based on this scheme, we analyze several methods both theoretically, where we compare the sample complexity and scalability, and empirically. Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts). Specifically, we present empirical experiments in a dynamic treatment regime, where the goal is to learn a reward function which explains the behavior of expert physicians based on recorded data of them treating patients diagnosed with sepsis.

scholarly journals Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3748 ◽  
Author(s):  
Chengkai Tang ◽  
Lingling Zhang ◽  
Yi Zhang ◽  
Houbing Song
Keyword(s):  
Smart Cities ◽  
Factor Graph ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Measured Range ◽  
Ranging Error ◽  
Positioning Algorithm ◽  
Range Error ◽  
Cooperative Nodes

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

scholarly journals Mapping ionospheric backscatter measured by the SuperDARN HF radars – Part 1: A new empirical virtual height model

2008 ◽  
Vol 26 (4) ◽  
pp. 823-841 ◽  
Author(s):  
G. Chisham ◽  
T. K. Yeoman ◽  
G. J. Sofko
Keyword(s):  
Elevation Angle ◽  
Propagation Path ◽  
Angle Of Arrival ◽  
Velocity Measurements ◽  
Actual Distribution ◽  
Virtual Height ◽  
Radar Network ◽  
Straight Line ◽  
Target Locations ◽  
Height Model

Abstract. Accurately mapping the location of ionospheric backscatter targets (density irregularities) identified by the Super Dual Auroral Radar Network (SuperDARN) HF radars can be a major problem, particularly at far ranges for which the radio propagation paths are longer and more uncertain. Assessing and increasing the accuracy of the mapping of scattering locations is crucial for the measurement of two-dimensional velocity structures on the small and meso-scale, for which overlapping velocity measurements from two radars need to be combined, and for studies in which SuperDARN data are used in conjunction with measurements from other instruments. The co-ordinates of scattering locations are presently estimated using a combination of the measured range and a model virtual height, assuming a straight line virtual propagation path. By studying elevation angle of arrival information of backscatterred signals from 5 years of data (1997–2001) from the Saskatoon SuperDARN radar we have determined the actual distribution of the backscatter target locations in range-virtual height space. This has allowed the derivation of a new empirical virtual height model that allows for a more accurate mapping of the locations of backscatter targets.

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

Extended state observer-based robust non-linear integral dynamic surface control for triaxial MEMS gyroscope

Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 481-501 ◽  
Author(s):  
Mehran Hosseini-Pishrobat ◽  
Jafar Keighobadi
Keyword(s):  
Optimization Problem ◽  
Dynamic Surface Control ◽  
Extended State ◽  
Gain Function ◽  
Dynamic Surface ◽  
Convex Optimization Problem ◽  
Mems Gyroscope ◽  
Surface Control ◽  
Non Linear ◽  
Linear Integral

SUMMARYThis paper reports an extended state observer (ESO)-based robust dynamic surface control (DSC) method for triaxial MEMS gyroscope applications. An ESO with non-linear gain function is designed to estimate both velocity and disturbance vectors of the gyroscope dynamics via measured position signals. Using the sector-bounded property of the non-linear gain function, the design of an $\mathcal{L}_2$-robust ESO is phrased as a convex optimization problem in terms of linear matrix inequalities (LMIs). Next, by using the estimated velocity and disturbance, a certainty equivalence tracking controller is designed based on DSC. To achieve an improved robustness and to remove static steady-state tracking errors, new non-linear integral error surfaces are incorporated into the DSC. Based on the energy-to-peak ($\mathcal{L}_2$-$\mathcal{L}_\infty$) performance criterion, a finite number of LMIs are derived to obtain the DSC gains. In order to prevent amplification of the measurement noise in the DSC error dynamics, a multi-objective convex optimization problem, which guarantees a prescribed $\mathcal{L}_2$-$\mathcal{L}_\infty$ performance bound, is considered. Finally, the efficacy of the proposed control method is illustrated by detailed software simulations.

scholarly journals Shearlet-based regularized reconstruction in region-of-interest computed tomography

2018 ◽  
Vol 13 (4) ◽  
pp. 34
Author(s):  
T.A. Bubba ◽  
D. Labate ◽  
G. Zanghirati ◽  
S. Bonettini
Keyword(s):  
Optimization Problem ◽  
Ad Hoc ◽  
Iterative Scheme ◽  
Tomographic Reconstruction ◽  
Region Of Interest ◽  
Projection Data ◽  
Reconstruction Problem ◽  
Convex Optimization Problem ◽  
Scanning Time ◽  
Novel Approach

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, bothad hocanalytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.

scholarly journals Indoor Positioning Method Based on Infrared Vision and UWB Fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012070
Author(s):  
Qianrong Zhang ◽  
Yi Li
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Low Cost ◽  
Ultra Wideband ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Mobile Vehicle ◽  
Positioning Method ◽  
Positioning Algorithm ◽  
Infrared Vision

Abstract Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals Research on Indoor Positioning Algorithm of Single Reader Based on Gated Recurrent Unit

2021 ◽  
pp. 1-10
Author(s):  
Jintao Tang ◽  
Lvqing Yang ◽  
Jiangsheng Zhao ◽  
Yishu Qiu ◽  
Yihui Deng
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Indoor Positioning ◽  
The Internet ◽  
Positioning Accuracy ◽  
Frequency Identification ◽  
The Cost ◽  
Positioning Algorithm ◽  
Gated Recurrent Unit ◽  
The Internet Of Things

With the development of the Internet of Things and Radio Frequency Identification (RFID), indoor positioning technology as an important part of positioning technology, has been attracting much attention in recent years. In order to solve the problems of low precision, high cost and signal collision between readers, a new indoor positioning algorithm based on a single RFID reader combined with a Double-order Gated Recurrent Unit (GRU) are proposed in this paper. Firstly, the reader is moved along the specified direction to collect the sequential tag data. Then, the tag’s coordinate is taken as the target value to train models and compare them with existing algorithms. Finally, the best Gated Recurrent Unit positioning model is used to estimate the position of the tags. Experiment results show that the proposed algorithm can effectively improve positioning accuracy, reduce the number of readers, cut down the cost and eliminate the collisions of reader signals.

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