Bluetooth-Tracing RSSI Sampling Method as Basic Technology of Indoor Localization for Smart Grid

In recent years, Smart Grid have become the center of interest for IT companies and construction companies and various types of Smart Grids have been made currently available on the market. Yet, equipment is costly and it is not easy to convert existing equipment for Smart Grid application as they may require additional resources which could also inflict much costs. The extra costs involving the remodeling of existing housing structure and installment of new equipment can be avoided by using advanced wireless technologies. As an example, this book proposed an indoor localization system that adopts Bluetooth technology and uses RSSI (Received Signal Strength Indication) values for localization. Researchers have configured a system where the central control device will recognize all other devices or equipment in the system, communicate with each other, and respond to the commands or the information provided. However, despite the efforts of many researchers, existing RSSI-based indoor localization systems do not show a satisfactory level of accuracy such that we have devised a system that traces the trend in the RSSI samples.

Mobile Industrial Robots Localization Algorithm Based on Improved Multidimensional Scale and Received Signal Strength Indication

Aiming at the problem of large error in the location algorithm based on MDS-MAP when the distance between mobile industrial robots is not measurable, a mobile industrial robot location algorithm based on improved MDS-MAP is proposed. Experimental simulation shows that the algorithm can achieve good positioning effect. When the distance between mobile industrial robots is measurable, the positioning algorithm based on RSSI achieves good positioning effect. Therefore, this paper discusses the influence of different anchor robot selection methods on the positioning accuracy of RSSI positioning algorithm. The experimental simulation shows that when the selection method of anchoring robot is that the unknown robot with adjacent anchoring robot uses the original anchoring robot for positioning and the unknown robot without anchoring robot uses the adjacent positioning robot as the anchoring robot for positioning, its positioning effect is the best, and it can still achieve good positioning effect when there are few anchoring robots.

A Survey of Recent Indoor Localization Scenarios and Methodologies

Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

Research on a reference signal optimisation algorithm for indoor Bluetooth positioning

GPS has a sharp performance decline in terms of accuracy indoors due to the complex building structure. A combined algorithm, targeting at received signal strength indication (RSSI) calibration optimisation, depending on deep neural network training via input vector Γ and the target output vector Ψ, termed reference signal optimisation algorithm (RSOA) is proposed to improve the positioning accuracy in the indoor Bluetooth positioning networks. Experimental results show that the relative error of the proposed RSOA between the estimated results and the measured ones can reach as low as 0.2%, and the absolute errors can be reduced to 0.13 m at most within 10 m.

РАЗРАБОТКА RSSI-МИКРОСХЕМ СО ВСТРОЕННЫМ АЦП ДЛЯ ИЗГОТОВЛЕНИЯ ПО ОТЕЧЕСТВЕННОЙ ТЕХНОЛОГИИ

В работе представлены результаты разработки Received Signal Strength Indication (RSSI) для УПЧ в диапазоне 30-180 МГц и 300 кГц - 100 МГц с динамическим диапазоном свыше 70 дБ по технологии 180 нм БиКМОП SiGe с использованием двух оригинальных схемных решений: блока коррекции выходного тока детектора для непосредственного соединения RSSI с АЦП без использования внешних элементов и подстроек; схемы дополнительного усилительного каскада с отрицательной обратной связью (ОСС) по постоянному току для компенсации разброса технологических параметров усилителя.

Improved fingerprinting performance in indoor positioning by reducing duration of the training phase process

<span>Wireless <span>sensor network (WSN) can be used as a solution to find out the position of an object that cannot be reached by global positioning system (GPS), for example to find out the position of objects in a room known as Indoor Positioning. One method in indoor positioning that can be used is fingerprinting. Inside there are two main work phases, namely training and positioning. The training phase is the process of collecting received signal strength indication (RSSI) data levels from each sensor Node reference that will be used as a reference value for the positioning phase. The more sensor Nodes used, the longer the processing time needed in the training phase. This research focussed on the duration of the training phase, the implementation of which are used 4 sensor Nodes, namely Zigbee (IEEE 802.15.4 protocol) arranged according to mesh network topology, one as Node X (positioning target) and 3 as reference Nodes. There are two methods used in the training phase, namely fixed target parameter (FTP) and moving target parameter (MTP). MTP took 5 seconds faster than FTP in terms of the duration of RSSI data collection from each reference Node. </span></span>

Fingerprint Feature Extraction for Indoor Localization

This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.

Optimization of Indoor Bluetooth Ranging Model Based on RSSI

Because the received signal strength indication (RSSI) ranging technology has problems with line-of-sight and multipath effects in indoor environments, the actual received RSSI value is unstable. In order to reduce the influence of RSSI value volatility on ranging accuracy, according to the fluctuation characteristics of the signal itself, a combined filtering method of Gaussian, median and mean is proposed to process the collected RSSI values, and the least squares method is used to fit and optimize the ranging parameter. Experiments show that using the RSSI intensity value processed by the combined filtering method to establish a model to achieve ranging, the maximum absolute error is about 2 m, and the absolute average error is about 0.763 m. The accuracy of the ranging has been significantly improved, and the ranging model has been optimized.

A Method to Locate Tree Positions Using Ultrawideband Technology

Tree position plays an important role in research on forest resources and ecological functions, and quickly and accurately obtaining tree position data has long been the focus of investigators. However, the classical method is time-consuming and laborious; thus, a convenient method of measuring tree position is needed. The primary achievements of this study include the following: (1) a device was designed for precise location of trees; (2) a new location algorithm was proposed for pentagonal localization based on the received signal strength indication and ultrawideband technology; and (3) a PC software application was developed for automatically storing and uploading tree position data. The device was applied to 10 circular plots with a diameter of 24 m to test the positioning speed and accuracy. The results showed that the tree positions could be accurately estimated. On the x - and y -axes, the biases were -3.94 and 3.36 cm, respectively, and the root mean square errors (RMSEs) were 28.39 and 28.53 cm, respectively. The mean error (Ed) between the estimated and reference distances was 36.13 cm, and the standard deviation was 16.67 cm. The device is inexpensive and easy to use and carry in the field; thus, it is suitable for locating trees in environments with complex terrain.