Earticle

Although indoor positioning technologies have been extensively researched, many indoor positioning methods are limited by the need for actual measurement data to be collected within a specified environment. To address issues, this paper proposes an indoor positioning simulation to implement an indoor environment and measure object position. In Addition, it presents the architecture and method of simulation through simple situations.

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

본 논문에서는 IEEE802.15.4a 기술표준 기반의 UWB 실내 위치 인식 시스템의 다수 태그 수용을 위한 비동기식 알고리즘을 제안한다. 다수의 태그를 운용하기 위한 기존의 연구에서는 CSMA-CA, TDMA와 같은 방식들을 사용하여 임의 접근으로 인한 신호의 충돌을 회피하였다. 이러한 방식들은 구현을 위하여 모든 구성 요소들에 정교한 동기화를 요구한다. 또한 관측 영역 내에서 태그의 수에 관계없이 동일한 시간을 소모한다는 점에서 시간을 낭비하게 된다는 단점이 있다. 본 논문에서는 동기화방식에 비하여서 상대적으로 간단하게 구현이 가능한 비동기식 방식을 사용하며 태그의 수량에 따라 가변적으로 동작하여 시간의 낭비를 줄일 수 있는 알고리즘을 제안한다.

Previous studies for operating multi-tags to avoid the collision of the signals generally used CSMA-CA or TDMA. However, these methods require precise synchronization for implementation of all components. Moreover, there exists a drawback that these methods obligate equal time consumption regardless of the number of tags in the observation area. In this paper, we propose an asynchronous method that uses a master anchor to control multi-tag operation. The propsoed system can be more easily implemented than the synchronous systems. The proposed system operates adaptively according to the number of tags, which reduces time loss.

Users get unexpected position information with errors because of satellite clock, ionospheric delay, tropospheric delay and hot noise. Especially if there are crowded many buildings and tall buildings in a small area, larger errors are generated by multi path, reflection and refraction. Therefore, in this paper, we have proposed a post-processing algorithm based on Minimum distance maintenance, Bounce cancellation and Direction vector scheme considering distribution and direction of received position information. Through the experimental results of this paper, we have shown a low average error 5.87m compared to the GPS point positioning that shows an average error 23.4m.

최근에 BLE 비콘의 낮은 배터리 소모와 저렴한 인프라 비용의 특징 때문에 실내 정밀 측위 시스템에 폭넓게 활용되고 있다. 하지만 기존의 BLE 비콘 기반 실내 측위 알고리즘은 사용자의 이동 속도 변화에 따라 유동적인 위치 오차 보정이 어렵다. 따라서 본 논문은 BLE 비콘을 활용한 Bounced cancellation 및 최소 거리 유지 알고리즘과 방향벡터를 이용한 측위오차 보정 기법을 결합한 위치 오차 보정 알고리즘을 제안했다. 본 논문의 실험 결과는 제안된 알고리즘이 기존의 실내 측위 알고리즘에 비해 유저 이동속도가 변화함에도 우수한 측위 성능을 보장하며 개선된 위치 오차 보정 성능을 나타냈다.

Recently, BLE beacons are widely used in indoor precision positioning systems because of their low battery consumption and low infrastructure cost. However, existing BLE beacon based indoor positioning algorithms are difficult to compensate for position errors due to the user's moving speed. Therefore, we proposed a position error correction algorithm that combines bounced cancellation and minimum distance maintenance algorithm with a positioning error correction method using direction vectors. Experimental results show that the proposed algorithm guarantees superior positioning performance than the existing indoor positioning algorithm and also improves the performance of position error compensation.

자율주행 시스템을 비롯한 지능형 교통 시스템에서 측위는 매우 중요한 기술 요소이다. 본 연구는 정밀지도와 카메라의 차선 정보를 활용하여 차량 위치 정확도를 향상시키는 알고리즘 을 개발하고 평가하는 것을 목표로 한다. 이를 위해 정밀지도의 노면선 정보와 카메라 인식 차선을 비교하여 횡방향 오차를 보정 후, 확장 칼만 필터(Extend Kalamn Filter)를 이용하여 측 정 노이즈를 제거하는 알고리즘을 설계하였다. 실성능 검증을 위해 시험 차량을 구성하고 검 증하였다. 시험 차량을 이용한 시험 결과, 제안된 방법은 GNSS 단독 측위 대비 평균 2.74m 감소하였으며, 성능이 약 86.1% 개선됨을 확인하였다.

An algorithm was developed to enhance the vehicle localization accuracy using high-definition map lane information and camera-based lane detection. Lateral errors were corrected through lane matching, and measurement noise was reduced using an Extended Kalman Filter. The experimental results with a test vehicle showed that the proposed method reduced the positioning error from approximately 3.17m to 0.38m, corresponding to an 88.09% improvement compared to a standalone global navigation satellite system.

차로변경은 운전자의 숙련된 주변인식 및 운전기술이 요구되어 심각한 교통사고를 야기한다. 그리하여 우리는 불가피한 차로변경이 대두되는 고속도로 합류구간에서 본선으로 합류하는 차량의 차로변경을 보조하는 차량 자동제어 시스템 (ALCAS; Advanced Lane Change Assist System)을 개발한다. 본 연구에서는 ALCAS 중 조향이 수행되기 이전에 최적 차로변경 시작지점(Optimal Lane Change Start Point; OLCSP)을 생성하고 그 지점까지 도달하는 종방향 제어 알고리즘 개발에 초점을 두었다. 이를 위해 우선 고속도로 합류구간 의 차로변경 행태를 분석하였고, 실제 차량의 가속도 함수 형태를 통해 차로변경 특징모형(Lane Change Feature Model)을 설계하였다. 그 후 차로변경 수행 단계를 정립하였으며, 이 알고리즘 성능과 타당성을 검증하기 위하여 다양한 주변차량 주행환경에 따른 시나리오 시험을 수행하였다. 또한 개발된 알고리즘의 효과를 미시적 교통류 시뮬레이션 (VISSIM)을 통해 확인하였다. 개발한 알고리즘을 합류차 량에 적용한 결과 안정류 상태에서 합류성능이 두드러지게 개선되는 것을 확인하였다. 이 차량 자동제어 시스템은 교통 자동차 분야 융 합기술의 일환으로 개발되었으며, 운전자의 부하와 오류를 감소시켜 효율성과 안전성을 향상시킬 뿐만 아니라 교통류의 안정성, 임계용 량, 주행 효율성의 증대로 사회비용 감소를 기대할 수 있다.

A lane change maneuver which has a high driver cognitive workload and skills sometimes leads to severe traffic accidents. In this study, the Advanced Lane Change Assist System (ALCAS) was developed to assist with the automatic lane changes in merging sections which is mainly based on an automatic control algorithm for detecting an available gap, determining the Optimal Lane Change Start Point (OLCSP) in various traffic conditions, and positioning the merging vehicle at the OLCSP safely by longitudinal automatic controlling. The analysis of lane change behavior and modeling of fundamental lane change feature were performed for determining the default parameters and the boundary conditions of the algorithm. The algorithm was composed of six steps with closed-loop. In order to confirm the algorithm performance, numerical scenario tests were performed in various surrounding vehicles conditions. Moreover, feasibility of the developed system was verified in microscopic traffic simulation(VISSIM 5.3 version). The results showed that merging vehicles using the system had a tendency to find the OLCSP readily and precisely, so improved merging performance was observed when the system was applied. The system is also effective even during increases in vehicle volume of the mainline.

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.

In order to precisely predict the location of forest fire, a new algorithm based on improvement of gravity center scan method is introduced in this paper. The new algorithm suppressed error rate of InToOut and OutToIn. By using the neighbor nodes around the unknown nodes, it solved the problem of less signal flag nodes around unknown nodes, furthermore, it improved the coverage of nodes and suppressed error rate the location of node. The simulation result shows that, along with the increment of the density of signal flag nodes, the error rate of location of unknown nodes is gradually decreasing. In networks with limited signal flag nodes, the new algorithm based on improved gravity scan suppressed the error rate by 40%, compared with previous gravity scanning algorithm, which dramatically improved the precision of location.

The positioning system based on RSSI is extremely affected by the multipath transmission medium, the signal transmission and shadow. Even changing the angle of the antenna is likely to affect the ranging accuracy. In order to solve the problems above, this paper proposes an indoor optimal positioning algorithm based on Wi-Fi signal strength. It introduces positioning evaluation function, using the optimal algorithm to get a set of path loss exponent to improve the positioning accuracy. Experiment results show that the algorithm can obtain higher positioning accuracy without priori information of indoor environment or additional hardware investment.

Localization as a key problem of wireless sensor network technology, has been the subject of widespread attention. This paper presents an improved positioning algorithm of wireless sensor network based on differential evolution(DE). Firstly, the algorithm improves on each jump distance measurement method in the traditional DV-hop algorithm, thereby reducing distance estimation error between anchor nodes and the unknown nodes, and then through the DE algorithm to obtain better positioning accuracy. Simulation results show that improved positioning algorithm has improved significantly in positioning accuracy compared to the previous DE positioning algorithm and least-squares algorithm, under different error factor and the density of anchor nodes. The algorithm has features of good robustness, global search capability and the ability to inhibit the accumulation of errors, suitable for a variety of applications in wireless sensor networks localization.

The estimation error of the least square method in traditional Distance Vector-Hop (DV-Hop) algorithm is too large and the Particle Swarm Optimization (PSO) algorithm is easy to trap into local optimum. In order to overcome the problems, a fusion algorithm of improved particle swarm algorithm and DV-Hop algorithm was presented. Firstly, PSO algorithm was improved from aspects of particle velocity, inertia weight, learning strategy and variation, which enhanced the ability to jump out of local optimum of the algorithm and increased the search speed of the algorithm in later iterative stage. Then, the node localization result was optimized by using the improved PSO algorithm in the third stage of the DV-Hop algorithm. The simulation results show that compared with the traditional DV-Hop algorithm, the improved DV-Hop based on chaotic PSO algorithm and the DV-Hop algorithm based on improved PSO, the proposed algorithm has higher positioning accuracy and better stability, which is suitable for high positioning accuracy and stability requirements scenes.

In order to give full play to ability of existing devices, the combined positioning algorithm of volume image sequence is proposed, which is applied in large scale topographical map. Firstly, the observation equations of observable image sequence are analyzed, and the five different coordinates relating with space 3D coordinate of object can be calculated. Secondly, the combining positioning algorithm based the characteristics of hyperbolic curve and Kalman filter is put forward and the corresponding mathematical model is constructed. Finally, the dynamic measured data of a highway is used as basis, the corresponding simulation analysis is carried out, and results show that the new combined positioning algorithm can improve the precision of mobile measurement system effectively.

This paper proposes a new algorithm to position the area of a truck’s compartment in images for the intelligent sampling control system. The goal is to identify a truck’s compartment and assure that some works are done inside it. For example, while a mechanical arm automatically snatches goods carried by a truck, we must ensure that the goods must be obtained inside the truck’s compartment. Firstly, the compartment image is acquired by the camera overhead. Secondly, the Canny operator is used to extract the edge of the image, because it is sensitive to the image texture features. Thirdly, the Canny edge image is transformed to the Hough transform space. The peak points in the Hough parameter space indicate the straight lines which may be the elements of the compartment. The straight lines which meet the conditions of the actual size of the compartment make up the area of the compartment. Finally, converting the image coordinate to the physical compartment coordinate. The experiment result shows that the algorithm is effective and the coordinate conversion error analysis indicates that the converting algorithm is workable.

This paper proposed an improved particle swarm optimization positioning algorithm based on virtual force-directed method for node localization of wireless sensor networks. The improved algorithm adopted adaptive inertia weight and adaptive mutation operation on global optimum, which overcomes the disadvantage of traditional particle swarm optimization algorithm that is easy to be trapped in local optimum. Fast convergence to near optimal solutions can be achieved after inertia weight is adjusted to be bigger, and smaller inertia weight can result in high precision solution. Through adaptive mutation on the global optimum, the improved algorithm can jump out of the current search area to maximize the coverage of the network nodes and the convergence speed. Compared with the virtual force-directed particle swarm optimization algorithm, the simulation results indicate that the improved algorithm has the advantages of faster convergence speed, lower energy consumption, higher precision and better stability.

A location fingerprint positioning technology method is proposed based on bat algorithm to improve the defect of traditional indoor location fingerprint positioning technology. These shortcomings include offline heavy workload, limited accuracy and poor robustness. The fingerprint database can generate using the middle point interpolation method and the method that channel attenuation model which instead of offline training stage. This stage realize the function of timing automatic databased updates. After that the database combines K nearest neighbor algorithm with bat algorithm in the stage of matching algorithm to realize the positioning function. Compared with the traditional method, location fingerprint positioning technology using bat algorithm reduces the overall positioning of workload and rapidly respond to the effect of the changing environment. Finally, localization performance test is carried out under the given simulation environment. The result indicates that this method improves the average precision than other algorithms about 23.14%, the vast most of the blind node position error range within 1.5 meters, which shows the advantages of positioning accuracy, robustness and adaptation to the changing environment.