Earticle

The distance vector-hop wireless sensor node location method is one of typical range-free location methods. In distance vector-hop location method, if a wireless node A can directly communicate with wireless sensor network nodes B and C at its communication range, the hop count from wireless sensor nodes A to B is considered to be the same as that form wireless sensor nodes A to C. However, the real distance between wireless sensor nodes A and B may be dissimilar to that between wireless sensor nodes A and C. Therefore, there may be a discrepancy between the real distance and the estimated hop count distance, and this will affect wireless sensor node location error of distance vector-hop method. To overcome this problem, it proposes a wireless sensor network node location method by modifying the method of distance estimation in the distance vector-hop method. Firstly, we set three different communication powers for each node. Different hop counts correspond to different communication powers; and so this makes the corresponding relationship between the real distance and hop count more accurate, and also reduces the distance error between the real and estimated distance in wireless sensor network. Secondly, distance difference between the estimated distance between wireless sensor network anchor nodes and their corresponding real distance is computed. The average value of distance errors that is computed in the second step is used to modify the estimated distance from the wireless sensor network anchor node to the unknown sensor node. The improved node location method has smaller node location error than the distance vector-hop algorithm and other improved location methods, which is proved by simulations.

Over a wireless sensor network (WSN), accurate localization of sensor nodes is an important factor in enhancing the association between location information and sensory data. There are many research works on the development of a localization algorithm over three-dimensional (3D) space. Recently, the complexity-reduced 3D trilateration localization approach (COLA), simplifying the 3D computational overhead to 2D trilateration, was proposed. The method provides proper accuracy of location, but it has a high computational cost. Considering practical applications over resource constrained devices, it is necessary to strike a balance between accuracy and computational cost. In this paper, we present a novel 3D localization method based on the received signal strength indicator (RSSI) values of four anchor nodes, which are deployed in the initial setup process. This method provides accurate location estimation results with a reduced computational cost and a smaller number of anchor nodes.

This paper proposes a fast sound source localization method using a TDOA sign–based algorithm. We present an L-shaped microphone set-up which creates four major regions in the range of 0°~360° by the intersection of the positive and negative regions of the individual microphone pairs. Then, we make an initial source region prediction based on the signs of two TDOA estimates before computing the azimuth value. Also, we apply a threshold and angle comparison to tackle the existing front-back confusion problem. Our experimental results show that the proposed method is comparable in accuracy to previous three microphone array methods; however, it takes a shorter computation time because we compute only two TDOA values.

본 논문에서는 센서 네트워크를 위한 Radical line을 기반으로 한 센서 노드간의 Range-free 지역화 알고리 즘에 대해 연구한다. 무선 센서 네트워크에서 라우팅 기법은 센서 네트워크의 전체적인 에너지 소모량을 감소시키거 나 모든 센서 노드들의 균등한 에너지 소비를 유도해야 한다. 특히 데이터가 전송할 데이터의 양이 많아지면 에너지 소모가 심해지는데 이를 극복하기 위한 새로운 방식들이 제안되었다. 그 결과 전체적인 에너지 소모량을 균등하게 조절할 수 있게 되었다. 이를 위해 논문에서도 적은 연산으로 주변 노드의 위치정보를 획득할 수 있는 지역화 알고 리즘을 설계한다. 알고리즘의 연산을 위해 Radical Line을 적용한다. 실험환경은 운영체제는 윈도우 7, 플랫폼은 Visual C++ 2010으로 실험하였다. 실험결과 0.1837의 에러율로 지역화를 수행할 수 있었다.

In this paper, we studied the range-free localization algorithm between sensor nodes based on the Radical Line for sensor networks. Routing in wireless sensor networks should reduce the overall energy consumption of the sensor network, or induce equivalent energy consumption of all the sensor nodes. In particular, when the amount of data to send more data, the energy consumption becomes worse. New methods have been proposed to address this. So as to allow evenly control the overall energy consumption. For this, the paper covers designing a localization algorithm that can obtain the location information of the peripheral nodes with fewer operations. For the operation of the algorithm is applicable Radical Line. The experimental environment is windows 7, the Visual C ++ 2010, MSSQL 2008. The experimental results could be localized to perform an error rate of 0.1837.

본 논문에서는 위성항법장치 (GPS; global positioning system)를 보조센서로 사용하는 위치추정알고리즘의 성능을 검증하기 위해 호버링타입 무인잠수정에 알고리즘을 적용하여 실제 해역에서 실험을 수행하였다. 적용된 알고리즘은 무인잠수정에 탑재된 도플러 속도계 (DVL; doppler velocity logger), TCM (tilt-compensated compass module)을 이용한 추측항법의 시간에 따라 누적되는 위치오차를 개선하기 위한 알고리즘이다. 수면에서 GPS 위치정보를 수신하여 무인잠수정의 위치와 진북에 대한 TCM의 방향각 바이어스 오차를 추정하고, 이를 통해 진북 (geodetic north) 기준의 좌표계에 대한 추측항법을 수행한다. 실제 해역에서 방향각 제어 실험을 수행한 결과, 위치추정알고리즘을 통해 기존 추측항법의 위치오차가 개선되고 TCM의 방향각 바이어스 오차를 추정함을 확인하였다.

In this paper, in order to verify the performance of a localization algorithm using GPS as an auxiliary sensor, the algorithm was applied to a hovering-type autonomous underwater vehicle (AUV) to perform a field test. The applied algorithm is an algorithm to improve the accumulated positional error of dead reckoning using doppler velocity logger(DVL) and tilt-compensated compass module (TCM) mounted on the AUV. GPS when surfaced helps the algorithm to estimate the position and the heading bias error of TCM for geodetic north, which makes it possible to perform dead reckoning on north-east-down (NED) coordinates. As a result of field test performing heading control, it was judged that the algorithm could improve the positional error, enhance the operational capability of AUV and contribute to the research of underwater navigation depending on a magnetic compass.

본 논문은 재난 환경에 적합한 경량화된 UWB 측위에 대한 연구를 소개한다. UWB는 디바이스간 정 밀한 거리 측정 기능을 제공하여 고정밀 측위에 활용된다. 하지만 재난 환경과 같이 무선 채널이 복잡 한 경우, UWB 측위 성능이 급격히 하락하는 문제가 발생한다. 최근 UWB 신호에 딥러닝 기술을 이용 하여 성능 하락 문제를 해결하는 연구들이 제안된다. 하지만 딥러닝 기술은 고성능 컴퓨팅 자원을 요 구하여 자원 제약이 있는 재난 상황에서 활용되기 어렵다. 본 논문에서는 XAI 기술을 적용하여 딥러 닝 기술의 높은 정확도를 유지하면서 동시에 연산 복잡도를 낮추기 위한 XLNet을 제안한다.

전파 교란신호가 발생할 경우, GPS 기반의 정밀위치제공시스템에 오작동을 일으킬 수 있으며 특히, 항공분야에서는 더 심각한 피해가 발생할 수 있다. 하나의 전파교란원에 대한 대응 기법이나 위치 추정 기법의 연구결과는 잘 알려져 있으나, 다수의 전파교란원이 다양한 형태의 신호를 송출할 경우에는 대응 및 위치 추정이 어렵다. 따라서 본 논문에서는 다수의 전파위협원이 CW, DSSS, SCW 형태의 신호를 송출할 경우에 위치 추정을 수행할 수 있도록 하기 위하여 신호 식별 및 측정치 획득 방법을 제안한다. 또한 제안한 방법을 검증하기 위하여 MATLAB을 이용하여 모의실험을 수행하였다. 실험 결과로부터 제안한 방법은 CW, DSSS, SCW 형태의 교란신호를 정상적으로 식별하고 측정치를 정확하게 획득하는 것을 검증하였다.

When jamming signal is received, there would be malfunctions in GPS-based precise location systems. Especially, in aviation fields, these malfunctions may lead to more serious damages. Naturally, there are some research results about the prevention or location method for one jammer, but it is hard to identify and obtain measurements when multiple and various type signals are received. Therefore, we propose a method of identification and measurements acquisition algorithm in order to localize the multiple jammers which transmit CW, DSSS and SCW type signals. Also, a computer simulation is carried out so as to validate the feasibility of the proposed method by using MATLAB. From the simulation results, it is confirmed that the proposed method successfully identified the signal type and acquired the measurements of CW, DSSS and SCW type signals.

Due to the complexity of the algorithm own limitations and environmental parameters, makes for complex work in the process of localization is bigger, computational complexity, a larger error. In order to ensure the reliability and effectiveness of communication data, ensure that improve the localization accuracy, the proposed multi-channel data transmission mechanism, to realize the collection of real-time data information effectively. Based on the traditional localization algorithm principle and error source is analyzed, combined with Bayesian filtering probability model for RSSI localization algorithm was improved, the received signal strength indicator effectively restrain random fluctuations. Through to the node coordinates between areas corrections, make the final balance of signal strength. Experiments show that improved the reliability of the improved algorithm has higher localization accuracy, and shows the validity and the correctness of the algorithm.

In this paper, we propose an algorithm to locate an object with unknown coordinates based on the positive semi-definite programming in the wireless sensor networks, assuming that the squared error of the measured distance follows Gaussian distribution. We first obtain the estimator of the object location based on the maximum likelihood criterion; then considering that the estimator is a non-convex function with respect to the measured distances between the object and the anchors with known coordinates, we transform the non-convex optimization to convex one by the positive semi-definite relaxation; and finally we take the optimal solution of the convex optimization as the estimated value of the object location. Simulations results show that our algorithm is superior to the R-LS algorithm regardless of whether the object is located within the convex hull composed of the anchors.

The particularity of the prison decides the importance of its security system, and how to improve the accuracy of personnel positioning for prison monitoring system is the problem to be solved to realize intelligent prison monitoring management. RFID positioning technology with non line-of-sight transmission and large transmission range is widely applied in the personnel positioning system. RFID technology is used to design the monitoring and management system of prison in this paper. The Trilateral Ranging Method was adopted to realize personnel positioning. RSSI positioning algorithm its technology is relatively simple was adopted when calculating the trilateral distance and correcting the errors in order to improve the positioning precision. Simulation test results show that the positioning precision for the distance between a single reader and the tag is increased by 2.17% on average, up to 4.54% after the error correction. The personnel location coordinates, X direction average error is 2.96%, average error is 3.12%, Y direction, and meeting the requirements of the system design.

Node localization algorithm of NLOS (Non-line-of-sight) environment based on PSO (particle swarm optimization) is proposed aiming at NLOS range error. PSO algorithm is quoted in wireless sensor network localization. First of all, the parameter of PSO algorithm is improved and nonlinear adjustment to inertia weight is made to boost convergence rate of algorithm, at the same time, target value is in rank ordering to decrease calculated quantity. Simulation results demonstrate that proposed algorithm reduced error influence of NLOS and improved location accuracy.

Aiming at the inherent deficiencies of large-scale wireless sensor network for distributed positioning algorithm, the DV-hop algorithm is mainly studied and improved, and two kinds of improved algorithms DV-hop and DV-hop+Lastdist DV- hop-MinMax are proposed. The DV- hop+Lastdist improves the positioning accuracy of the node by only adding a message variable, while the DV-hop-MinMax effectively reduces the costs of floating-point operations and power consumption, and can select the appropriate algorithm according to actual application environment. Finally, the paper gives the method to realize OMNeT++ in the simulation environment, and carries out the simulation analysis.

An iris localization algorithm with the fast speed is proposed based on geometric features of the circle combing coarse localization with fine localization to avoid the problem of the slow speed of the classical iris localization algorithm. First, the row and column scanning method is employed to find four strings in the pupil. Four couples of tangency points are located by using the perpendicular bisector of strings and the threshold of the pupil boundary. The mean value of the coordinate parameters of the tangency points is regarded as the parameters for inner edge rough localization parameters. Then the calculus method is used for precise localization of the inner edge. The priori knowledge of the close distance between inner and outer edge centers of the circle is used to narrow the search range of the calculus method localizing outside edge, to significantly improving the speed of iris localization. The experimental results show that the proposed algorithm can improve the speed of iris localization with the high localizing accuracy.