Research on Indoor Positioning Algorithm Based on SAGA-BP Neural Network

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

The location method based on Received Signal Strength Indication (RSSI) ranging has the advantages of low development cost and simple implementation mechanism, and is the mainstream indoor location method nowadays. However, at present, there is a lack of systematic research on the characteristics of Access Point (AP) received signal strength, which cannot meet the signal strength characteristics required by specific indoor positioning environment. Detailedly, in this study, the indoor distribution characteristics of 2.4 GHz RSSI were analyzed experimentally from three influencing factors including antenna orientation of the receiver, type of wireless network card of the receiver and height difference of the transmitting and receiving antenna. The experimental results show that: (1) The RSSI value measured is the strongest When the antenna of the receiver is vertically oriented to the antenna of the transmitter, while the antenna is vertically backward to the transmitter, it is the opposite. The difference between the strongest signal and the weakest signal is 20%–25% at the same test point. (2) The network card with a large measurement range should be selected for data collection on the premise that the quantization step size is 1 and the smoothness is small. Avoiding using the network card for positioning with the maximum limit of measurement range is proposed also. (3) The path loss index of the ranging model is affected by the height difference of the antenna, resulting in a deviation of 0.1–0.2.

Nowadays, location-based services are common in our daily lives. Traditional Global Positioning System (GPS) location can provide real-time location function in outdoor complex environments, but it is insufficient for indoor location. There are many indoor location technologies, such as ultrasound, Zigbee, RFID and WIFI. RFID location technology has attracted the attention of researchers due to its high precision and low cost. Most existing RFID location algorithms are based on RSSI (Received Signal Strength Indicator) measurement. When converting RSSI to distance, the inaccurate estimation of the path loss parameter may lead to large error. In order to reduce the deviation, this paper proposes a new RFID location algorithm. Specifically, the RSSI of the target tag is read in different directions of the antenna, and the position information is predicted by the general regression neural network, which is optimized by the optimization algorithm. The experimental results show the efficiency of our proposed algorithm.

A comparative study of performance is conducted between two indoor positioning algorithms described in the Literature, the Vector and Ecolocation algorithms. Both use received signal strength (RSS) to implement a radio frequency fingerprinting technique. The experiment is conducted using commercial ray tracing software called Wireless Insite and Matlab. The Vector algorithm performs better than the Ecolocation algorithm, which suffers heavily from an ambiguity issue. For the Vector algorithm, the median position error was 2.79 m and 90% of errors were less than 5.72 m.

This paper focuses on the review of the recent advancement and development of indoor localization focusing on device-free indoor localization (DFIL). DFIL offers more flexibility for the user or object point-of-view. The basic idea is to free the object from the device attached for localization process. The most popular technologies for DFIL are based on wireless technology. However, if we discuss the low-cost system, we have to discuss the fusion or hybrid of many sensors output data to increase accuracy. Some of the accessible technology used for these low-cost systems are wireless-based, i.e., Wi-Fi, ZigBee, radio frequency identification (RFID) and an optical-based, visible light communication (VLC). Wireless-based probably the most used in the low-cost and simple configuration of an indoor localization since the parameter used can be directly obtained from its device. The most used parameter in the wireless-based system is received signal strength indicator (RSSI), which merely the derivation of power by the distance. By this parameter, the technique such as trilateration, min-max, and RSSI database can be used as the location. In the DFIL, RSSI can be applied as the Wi-Fi -based radio map from two or more links and applying an algorithm such as the Bayes Inference, resulting in the accuracy of object tracking up to 86%. On the other hand, ZigBee applies to mesh network and need more nodes to gather more information for higher accuracy results. The RFID has advantages in no-contact, and a good accuracy even in non-line-of-sight (LoS) condition. Optical-based VLC can provide high accuracy but dependent on the light noise and shadow effect in the object. From these benefits and disadvantages of some low-cost systems, we can make the best of them by fusing the sensor or developing the hybrid system.

In this paper, we propose an indoor positioning system based on Bluetooth low energy (BLE), and the localization tag is with characteristics of low power, low cost, and high portability. The proposed system consists of BLE tags, BLE/WiFi repeaters, and a fusion server. The BLE tag is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to the fusion server through WiFi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose an indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO) to estimate the BLE tag's location. Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy. The test results show 1.2 m to 1.4 m positioning accuracy. The size of the BLE tag is 1.7 cm in radius and 0.5 cm thick. Each BLE tag costs 3 US dollars. The current consumption of the tag is 50 μA which can be used without charge for 136 days with a CR2025 battery.

The localization of wireless sensor networks (WSN) is an important field of research study, that is gaining considerable attention. This growth in wireless sensor network installations is expected to expand more in the future. Indoor localization based on the Received Signal Strength Indicators (RSSI) is a well-known localization technique used in WSN systems due to its availability and simplicity. Radio location fingerprinting is one of the most effective of the many indoor positioning techniques due to its accuracy and low cost. In this study, we investigate the possibility of applying RSSI fingerprinting based Artificial Neural Network (ANN) jointly to determine the correct location of an object or person in indoor environments. According to our analysis and experiment result, Fingerprinting techniques can achieve accurate localization with high interference in a computer laboratory. In addition, this research uses a small-structure neural network (one hidden layer) to provide efficient computation with restricted resource requirements.

Increasing the location accuracy of the Indoor Positioning System (IPS) is an important research area in localization.Utilizing mobile beacons in IPS environment has made localization more accurate and cost-effective.This paper proposes an Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) values using a Kalman filter.This algorithm takes both the distance and improved RSSI measurements between beacon nodes into consideration.Kalman filter applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm.The proposed algorithm was applied using eight beacons.The results show that this FRBW approach has better positioning accuracy and minimum location error, and can be applied in IoT applications in smart city.

With the increasing demand for location-based services, indoor localization technology based on wireless sensor networks (WSNs) has recently received significant attention. Bluetooth low energy (BLE) has become a research hotspot among the numerous WSNs due to its ubiquitous deployment, low-power consumption, and low cost. Many previous BLE-based indoor localization technologies can only use received signal strength indication (RSSI) for localization in the 2-D plane and achieve coarse accuracy. However, high-accuracy location information is required in an increasing number of applications, e.g., elderly fall detection for medical applications, which urgently requires a new BLE-based localization technology in 3-D space. To this end, we propose Spotlight, an angle-based fine-grained indoor localization system in 3-D space using BLE sensors. The critical idea of the Spotlight is to measure the angle-of-arrival (AoA) and elevation-of-angle (EoA) of the signal from the target to the BLE receivers and utilize them to achieve accurate 3-D localization. Specifically, we develop an offset elimination algorithm and data stitching algorithm, utilizing the second-order cone (SoC) programming to obtain accurate angle estimates. Then, the simulated annealing algorithm (SAA) is used to search for the target location in 3-D space. Finally, we develop the prototype system with commercial off-the-shelf (COTS) BLE sensors and conduct extensive experiment measurements in real-world scenarios. The results demonstrate that the proposed system can achieve a median localization error of 36 cm in the 2-D plane and 48 cm in the 3-D space.

In this study, we present an indoor localization system leveraging Zigbee-based hardware for real-time positioning. We conduct in-house measurements in a controlled environment to generate a robust dataset for the system. A six-step methodology is employed, encompassing received signal strength indicator (RSSI)-to-image transformation, feature extraction using a pre-trained convolutional neural network (CNN) model, dimensionality reduction, clustering, and classification. By converting RSSI values into images, we capture spatial patterns more effectively. A multi-power level fusion strategy combines RSSI data from three distinct power levels into RGB images, improving localization accuracy and robustness against interference. K-means clustering segments the environment into distinct zones, streamlining classification. Experiments demonstrate the significant contributions of power level fusion, clustering, and classification, with the multi-power level fusion yielding improved performance compared to single power levels. Our approach, based on a custom CNN architecture, offers enhanced precision and efficiency for indoor localization tasks, providing valuable insights into the integration of advanced signal processing and deep learning.

This paper presents of study of ultra-wide band (UWB) indoor positioning considering different type of obstacles that can affect the localization accuracy. In the actual warehouse, a variety of obstacles including metal, board, worker and other obstacles will have NLOS (non-line-of-sight) impact on the positioning of the logistics package, which influence the measurement of the distance between the logistics package and the anchor, thereby affecting positioning accuracy. A new developed method attempts to improve the accuracy of UWB indoor positioning, through and improved positioning algorithm and filtering algorithm. In this project, simulate the warehouse environment in the laboratory, several simulation proves that the used Kalman filter algorithm and Markov algorithm can effectively reduce the error of NLOS. Experimental validation is carried out considering a mobile tag mounted on a robot platform.