Earticle

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 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.

This paper presents an intelligent positioning algorithm for transmitting station. By using GPS and RSSI, the influence of RSSI value can be reduced. The advantages of multi angle positioning method and centroid localization method are combined. The algorithm required less precision positioning device, also the least square method is used to reduce the error of location in urban areas. The method is simple and convenient, and the simulation results show the practicability 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.

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.

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.

To improve the speed of iris localization, an iris localization algorithm based on the morphological processing is proposed with fast speed. Firstly, pupil area is segmented from eye image by thresholding, to remove eyelash noise and other noises from binaryzation pupil area by morphological open operation. Then, a series of structure element of radius increasing is used to make morphological erode operation on pupil area to localize roughly the inner boundary of the iris. Finally, calculus operator is employed to accurately localize the inner and outer iris boundary. 108 iris images from CASIA (Version 1.0) iris database are used to do iris localization experiments. The localization accurate rate of the proposed algorithm, calculus operator and hough transform is 97.2%, 90.3% and 92.1% respectively. Experiment results have showed that the proposed algorithm has a high performance on speed and precision with strong robustness to the different quality iris images.

With the rapid development of wireless communication and embedded system, wireless positioning systems are paid more and more attention to. Radio Frequency Identification (RFID) localization system is getting more important, due to its own advantages, such as no contact, non-line-of-sight nature, promising transmission range and cost-effectiveness. To improve the accuracy of active RFID indoor location system, some traditional RFID indoor localization systems were studied, such as LANDMARC. On this basis, an adaptive self-correction location algorithm was presented, which uses a positioning correction value to correct the positioning result. N minimum errors and position results are obtained by using adaptive K-nearest neighbor algorithm N times. The positioning correction value calculated with N minimum errors in weighted way. The sum of the positioning average value and the positioning correction value would be the final positioning results. Experimental results show that compared with adaptive K-nearest neighbor algorithm and error self-correction algorithm, the proposed method provides a higher accuracy and stability.

Position information is the foundation of massive applications in Wireless Sensor Network(WSN). Three improved positioning algorithms based on DV-Hop are proposed in order to enhance the positioning accuracy of wireless sensor nodes. First improved algorithm is distance compensation algorithm (DCA) that creates a triangle model to compensate the estimated distance. The second improved algorithm creates a new chain table for all anchor nodes to record and compute the average hop distance. The third improved algorithm is weighting different anchor nodes with anchor nodes’ nearest unknown nodes. The second and third improved algorithms are based on the DCA. The simulation results show that the three improved algorithms are better than the original DV-Hop in localization accuracy. Compared to the original DV-Hop algorithm, the simulation results shows that the three improved algorithms proposed in the paper increase the positioning accuracy of the unknown nodes.