scholarly journals Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 31 ◽  
Author(s):  
Angelo Coluccia ◽  
Alessio Fascista
Keyword(s):  
Low Cost ◽  
Computational Cost ◽  
Closed Form Solution ◽  
Target Position ◽  
Form Solution ◽  
Position Estimation ◽  
Model Parameters ◽  
Time Of Arrival ◽  
Synchronization Errors ◽  
Step Algorithm

The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated “on the field”, with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost.

scholarly journals X-RAY CT Reconstruction by using Spatially Non Homogeneous ICD Optimization

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2043-2046
Keyword(s):  
Search Algorithm ◽  
Computational Cost ◽  
Selection Criterion ◽  
Closed Form Solution ◽  
Helical Ct ◽  
Reconstruction Algorithm ◽  
Form Solution ◽  
Ct Reconstruction ◽  
Practical Applications ◽  
Voxel Selection

Recent applications of conventional iterative coordinate descent (ICD) algorithms to multislice helical CT reconstructions have shown that conventional ICD can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of conventional algorithm in the practical applications. Among the various iterative methods that have been studied for conventional, ICD has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 3-D optimization algorithm that uses a quadratic substitute function to upper bound the local 3-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

Toward a Minimal Dynamic Model of a 6-DOF Parallel Robot

1997 ◽  
Author(s):  
L. Beji ◽  
M. Pascal ◽  
P. Joli
Keyword(s):  
Dynamic Model ◽  
Degrees Of Freedom ◽  
Equations Of Motion ◽  
Computational Cost ◽  
Closed Form Solution ◽  
Parallel Robot ◽  
Form Solution ◽  
Lagrangian Formalism ◽  
Inverse Kinematic Problem ◽  
Geometrical Properties

Abstract In this paper, an architecture of a six degrees of freedom (dof) parallel robot and three limbs is described. The robot is called Space Manipulator (SM). In a first step, the inverse kinematic problem for the robot is solved in closed form solution. Further, we need to inverse only a 3 × 3 passive jacobian matrix to solve the direct kinematic problem. In a second step, the dynamic equations are derived by using the Lagrangian formalism where the coordinates are the passive and active joint coordinates. Based on geometrical properties of the robot, the equations of motion are derived in terms of only 9 coordinates related by 3 kinematic constraints. The computational cost of the obtained dynamic model is reduced by using a minimum set of base inertial parameters.

scholarly journals Robustness Can Be Cheap: A Highly Efficient Approach to Discover Outliers under High Outlier Ratios

2019 ◽  
Vol 33 ◽  
pp. 5313-5320
Author(s):  
Siqi Wang ◽  
En Zhu ◽  
Xiping Hu ◽  
Xinwang Liu ◽  
Qiang Liu ◽  
...  
Keyword(s):  
Computational Cost ◽  
Solution Process ◽  
Closed Form Solution ◽  
Form Solution ◽  
Low Rank ◽  
Alternative Formulation ◽  
Efficient Detection ◽  
Comparable Performance ◽  
High Space ◽  
High Outlier

Efficient detection of outliers from massive data with a high outlier ratio is challenging but not explicitly discussed yet. In such a case, existing methods either suffer from poor robustness or require expensive computations. This paper proposes a Low-rank based Efficient Outlier Detection (LEOD) framework to achieve favorable robustness against high outlier ratios with much cheaper computations. Specifically, it is worth highlighting the following aspects of LEOD: (1) Our framework exploits the low-rank structure embedded in the similarity matrix and considers inliers/outliers equally based on this low-rank structure, which facilitates us to encourage satisfying robustness with low computational cost later; (2) A novel re-weighting algorithm is derived as a new general solution to the constrained eigenvalue problem, which is a major bottleneck for the optimization process. Instead of the high space and time complexity (O((2n)2)/O((2n)3)) required by the classic solution, our algorithm enjoys O(n) space complexity and a faster optimization speed in the experiments; (3) A new alternative formulation is proposed for further acceleration of the solution process, where a cheap closed-form solution can be obtained. Experiments show that LEOD achieves strong robustness under an outlier ratio from 20% to 60%, while it is at most 100 times more memory efficient and 1000 times faster than its previous counterpart that attains comparable performance. The codes of LEOD are publicly available at https://github.com/demonzyj56/LEOD.

scholarly journals A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 272 ◽  
Author(s):  
Ajmal Hinas ◽  
Roshan Ragel ◽  
Jonathan Roberts ◽  
Felipe Gonzalez
Keyword(s):  
Target Detection ◽  
Low Cost ◽  
Target Position ◽  
Position Estimation ◽  
Quality Data ◽  
Unmanned Aerial Systems ◽  
Modular Software ◽  
Ground Target ◽  
High Level ◽  
Aerial Systems

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

scholarly journals A Block-Based Regularized Approach for Image Interpolation

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Li Chen ◽  
Xiaotong Huang ◽  
Jing Tian
Keyword(s):  
Cost Function ◽  
Regularization Parameter ◽  
Computational Cost ◽  
Closed Form Solution ◽  
Image Interpolation ◽  
Adaptive Strategy ◽  
Form Solution ◽  
Interpolation Techniques ◽  
Block Based ◽  
Value Decomposition

This paper presents a new efficient algorithm for image interpolation based on regularization theory. To render ahigh-resolution(HR) image from alow-resolution(LR) image, classical interpolation techniques estimate the missing pixels from the surrounding pixels based on a pixel-by-pixel basis. In contrast, the proposed approach formulates the interpolation problem into the optimization of a cost function. The proposed cost function consists of a data fidelity term and regularization functional. The closed-form solution to the optimization problem is derived using the framework of constrained least squares minimization, by incorporating Kronecker product andsingular value decomposition(SVD) to reduce the computational cost of the algorithm. The effect of regularization on the interpolation results is analyzed, and an adaptive strategy is proposed for selecting the regularization parameter. Experimental results show that the proposed approach is able to reconstruct high-fidelity HR images, while suppressing artifacts such as edge distortion and blurring, to produce superior interpolation results to that of conventional image interpolation techniques.

scholarly journals Performance Evaluation of Alternative Relative Orientation Procedures for UAV-based Imagery with Prior Flight Trajectory Information

2016 ◽  
Vol XLI-B3 ◽  
pp. 21-25 ◽  
Author(s):  
F. He ◽  
A. Habib
Keyword(s):  
Low Cost ◽  
Closed Form Solution ◽  
Experimental Tests ◽  
Form Solution ◽  
Relative Orientation ◽  
Conjugate Points ◽  
Flying Height ◽  
Reliable Estimate ◽  
Comparative Performance ◽  
The Impact

Thanks to recent advances at the hardware (e.g., emergence of reliable platforms at low cost) and software (e.g., automated identification of conjugate points in overlapping images) levels, UAV-based 3D reconstruction has been widely used in various applications. However, mitigating the impact of outliers in automatically matched points in UAV imagery, especially when dealing with scenes that has poor and/or repetitive texture, remains to be a challenging task. In spite of the fact that existing literature has already demonstrated that incorporating prior motion information can play an important role in increasing the reliability of the matching process, there is a lack of methodologies that are mainly suited for UAV imagery. Assuming the availability of prior information regarding the trajectory of a UAV-platform, this paper presents a two-point approach for reliable estimation of Relative Orientation Parameters (ROPs) of UAV-based images. This approach is based on the assumption that the UAV platform is moving at a constant flying height while maintaining the camera in a nadir-looking orientation. For this flight scenario, a closed-form solution that can be derived using a minimum of two pairs of conjugate points is established. In order to evaluate the performance of the proposed approach, experimental tests using real stereo-pairs acquired from different UAV platforms have been conducted. The derived results from the comparative performance analysis against the Nistér five-point approach demonstrate that the proposed two-point approach is capable of providing reliable estimate of the ROPs from UAV-based imagery in the presence of poor and/or repetitive texture with high percentage of matching outliers.

A Closed Form Solution of Dual-Phase Lag Heat Conduction Problem With Time Periodic Boundary Conditions

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Pranay Biswas ◽  
Suneet Singh ◽  
Hitesh Bindra
Keyword(s):  
Heat Conduction ◽  
Boundary Conditions ◽  
Closed Form ◽  
Computational Cost ◽  
Closed Form Solution ◽  
Form Solution ◽  
Fourier Series Expansion ◽  
Phase Lag ◽  
Dual Phase ◽  
Time Periodic

The Laplace transform (LT) is a widely used methodology for analytical solutions of dual phase lag (DPL) heat conduction problems with consistent DPL boundary conditions (BCs). However, the inversion of LT requires a series summation with large number of terms for reasonably converged solution, thereby, increasing computational cost. In this work, an alternative approach is proposed for this inversion which is valid only for time-periodic BCs. In this approach, an approximate convolution integral is used to get an analytical closed-form solution for sinusoidal BCs (which is obviously free of numerical inversion or series summation). The ease of implementation and simplicity of the proposed alternative LT approach is demonstrated through illustrative examples for different kind of sinusoidal BCs. It is noted that the solution has very small error only during the very short initial transient and is (almost) exact for longer time. Moreover, it is seen from the illustrative examples that for high frequency periodic BCs the Fourier and DPL model give quite different results; however, for low frequency BCs the results are almost identical. For nonsinusoidal periodic function as BCs, Fourier series expansion of the function in time can be obtained and then present approach can be used for each term of the series. An illustrative example with a triangular periodic wave as one of the BC is solved and the error with different number of terms in the expansion is shown. It is observed that quite accurate solutions can be obtained with a fewer number of terms.

UWB Positioning Performance Tests Based on Chan Algorithm in IEEE 802.15.4a Channel Model

2012 ◽  
Vol 433-440 ◽  
pp. 2663-2669 ◽  
Author(s):  
Xiao Long Mu ◽  
Xue Rong Cui ◽  
Hao Zhang ◽  
T. Aaron Gulliver
Keyword(s):  
Channel Model ◽  
Dimensional Space ◽  
Moving Average ◽  
Weighted Least Squares ◽  
Three Dimensional ◽  
Closed Form Solution ◽  
Form Solution ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
High Degree

Chan algorithm is a closed form solution to the non-recursive equation set. This algorithm needs only a small amount of calculations but has a high degree of precision on positioning. It is valuable for academic reference. Firstly, it obtains the preliminary solution by using WLS (Weighted Least Squares) twice. Then, it uses the preliminary solution to linearise the nonlinear equation and finally makes the estimation of the position. The channel model can provide the model of indoor office environment ranging from 2 GHz to 10 GHz. Through the UWB (Ultra WideBand) positioning system of the channel model, the LOS(line-of-sight) environment can be simulated and TOA(Time-Of-Arrival) data measured by distance can also be obtained by sampling. However, small LOS errors included in the TOA data may lead to big ones in the positioning of 3D(three-dimensional) space and the precision of positioning may be undermined, when the data are directly applied to the Chan algorithm which is based on the TOA. In order to solve this issue, the TOA data obtained can be processed with MA(Moving Average) algorithm and the precision can be improved.

A closed-form algorithm for the least-squares trilateration problem

Robotica ◽  
2010 ◽  
Vol 29 (3) ◽  
pp. 375-389 ◽  
Author(s):  
Yu Zhou
Keyword(s):  
Least Squares ◽  
Closed Form ◽  
Closed Form Solution ◽  
Nonlinear Least Squares ◽  
Form Solution ◽  
Position Estimation ◽  
Reference Points ◽  
Distance Measurements ◽  
Optimal Position ◽  
General Number

SUMMARYTrilateration is the most adopted external reference-based localization technique for mobile robots, given the correspondence of external references. The nonlinear least-squares trilateration formulation provides an optimal position estimate from a general number (greater than or equal to the dimension of the environment) of reference points and corresponding distance measurements. This paper presents a novel closed-form solution to the nonlinear least-squares trilateration problem. The performance of the proposed algorithm in dealing with erroneous inputs of reference points and distance measurements has been analyzed through representative examples. The proposed trilateration algorithm has low computational complexity, high operational robustness, and reduced systematic error and uncertainty in position estimation. The effectiveness of the proposed algorithm has been further verified through an experimental test.

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