scholarly journals A Novel Relative Position Estimation Method for Capsule Robot Moving in Gastrointestinal Tract

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2746 ◽  
Author(s):  
Min Wang ◽  
Qinyuan Shi ◽  
Shuang Song ◽  
Chao Hu ◽  
Max Q.-H. Meng

Recently, a variety of positioning and tracking methods have been proposed for capsule robots moving in the gastrointestinal (GI) tract to provide real-time unobstructed spatial pose results. However, the current absolute position-based result cannot match the GI structure due to its unstructured environment. To overcome this disadvantage and provide a proper position description method to match the GI tract, we here present a relative position estimation method for tracking the capsule robot, which uses the moving distance of the robot along the GI tract to indicate the position result. The procedure of the proposed method is as follows: firstly, the absolute position results of the capsule robot are obtained with the magnetic tracking method; then, the moving status of the robot along the GI tract is determined according to the moving direction; and finally, the movement trajectory of the capsule robot is fitted with the Bézier curve, where the moving distance can then be evaluated using the integral method. Compared to state-of-the-art capsule tracking methods, the proposed method can directly help to guide medical instruments by providing physicians the insertion distance in patients’ bodies, which cannot be done based on absolute position results. Moreover, as relative distance information was used, no reference tracking objects needed to be mounted onto the human body. The experimental results prove that the proposed method achieves a good distance estimation of the capsule robot moving in the simulation platform.

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.


Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5231 ◽  
Author(s):  
Felipe Lobo ◽  
Danilo Grael ◽  
Horacio Oliveira ◽  
Leandro Villas ◽  
Abdulaziz Almehmadi ◽  
...  

In vehicular ad hoc networks (VANets), a precise localization system is a crucial factor for several critical safety applications. The global positioning system (GPS) is commonly used to determine the vehicles’ position estimation. However, it has unwanted errors yet that can be worse in some areas, such as urban street canyons and indoor parking lots, making it inaccurate for most critical safety applications. In this work, we present a new position estimation method called cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS positions of nearby vehicles and minimize their errors through an extended Kalman filter to execute Data Fusion using GPS and distance information. Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in distance information given by neighbors closer to the target. We implement and evaluate the performance of CoVaLID using real-world data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that CoVaLID is capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet location improve (VLOCI) algorithm.


2021 ◽  
Vol 13 (15) ◽  
pp. 2997
Author(s):  
Zheng Zhao ◽  
Weiming Tian ◽  
Yunkai Deng ◽  
Cheng Hu ◽  
Tao Zeng

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.


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