Wifi bluetooth based combined positioning algorithm

Nowadays positioning have become very important for many services (Localization based services) and positioning have become more accurate, despite, there are some territories that basic positioning systems like GPS or even hybrid ones like GPS-WiFi or GPS-WiFi-gsm can’t cover, specially indoor enviroments. In this paper we propose a positioning method merging WLAN and Bluetooth technologies based on trilateration technique. Simulated sceneario demostrate accuracy gains, even when we use a high signal attenuation parameter. A simulated sceneario taken from a real home with the real WLAN and Bluetooth stations validates our WLAN-Bluetooth method. Firstly we calculate each equation from each available station, then we decide how to overdetermine the generated equation system in a reason of 4 to 1 (4 equations for one unknown) and finally solve the system using mathematical methods. This work is a step more to better position in indoor enviroments and localization based service

Comparison of Localization Methods Using Calibrated and Simulated Fingerprints for Indoor Systems Based on Bluetooth and WLAN Technologies

This paper compares two different localization algorithms to face the problem of indoor positioning using Bluetooth and WLAN technologies, which we have called: the fusion algorithm and the combination algorithm. The first algorithm is based on the construction of a fusion map using WiFi and Bluetooth power values. Considering the three lowest values of a defined distance, we compute the coordinates of the target point that we want to localize. In the second algorithm, the location determination is carried out independently with every single technology; then, results are combined to obtain a final estimated position. The performance of these methods has been tested experimentally using a simulated map and a real calibrated one. Using a real calibrated map, the localization errors obtained with the fusion algorithm are smaller than with the combination one, while when using a simulated map there is almost no difference between both algorithms. The results of the experiments made with the real calibrated map are a little better than using the simulated map, but the improvement obtained using the real map is not enough to confirm that using this one is worth, because of the effort necessary to build it

Indoor localization based on hybrid Wi-Fi hotspots

Most existing indoor localization algorithms basedon Wi-Fi signals mainly rely on wireless access points (APs), i.e. hotspots, with fixed deployment, which are easily affected by the non-line of sight (NLOS) factors and the multipath effect. There also exist many other problems, such as positioning stability and blind spots, which can cause decline in positioning accuracy at certain positions, or even failure of positioning. However, it will increase the hardware cost by adding more static APs; if the localization mechanism integrates different wireless signals is adopted, it tends to cause high cost of positioning and long complex positioning process, etc. In this paper, we proposed a novel hybrid Wi-Fi access point-based localization algorithm (HAPLA), which utilizes the received signal strength indications(RSSI) from static APs and dynamic APs to determine location scenes. It flexibly selects available AP signals and dynamically switches the positioning methods, thus to achieve efficient positioning. HAPLA only relies on the Wi-Fi signal strength values, which can reduce the cost of hardware and the complexity of localization system. The proposed method can also be able to effectively prevent interference from different signal sources. Inour test scenario, we deployed typical indoor scenes with the NLOS factors and the multipath effect for experiments. The experiments demonstrate the effectiveness of proposed method and the results show that, compared with the classic K nearest neighbor-based location algorithm (KNN) and the variance-based fingerprint distance adjustment algorithm (VFDA), HAPLA has better adaptability and higher positioning accuracy, and can effectively solve the problem of positioning blind spots

Dual-sensor fusion for indoor user localisation

In this paper we address the automatic identification of in- door locations using a combination of WLAN and image sensing. Our motivation is the increasing prevalence of wear- able cameras, some of which can also capture WLAN data. We propose to use image-based and WLAN-based localisa- tion individually and then fuse the results to obtain better performance overall. We demonstrate the effectiveness of our fusion algorithm for localisation to within a 8.9m2 room on very challenging data both for WLAN and image-based algorithms. We envisage the potential usefulness of our ap- proach in a range of ambient assisted living applications

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

3D Indoor Positioning in 5G networks

Over the past two decades, the challenge of accurately positioning objects or users indoors, especially in areas where Global Navigation Satellite Systems (GNSS) are not available, has been a significant focus for the research community. With the rise of 5G IoT networks, the quest for precise 3D positioning in various industries has driven researchers to explore various machine learning-based positioning techniques. Within this context, researchers are leveraging a mix of existing and emerging wireless communication technologies such as cellular, Wi-Fi, Bluetooth, Zigbee, Visible Light Communication (VLC), etc., as well as integrating any available useful data to enhance the speed and accuracy of indoor positioning. Methods for indoor positioning involve combining various parameters such as received signal strength (RSS), time of flight (TOF), time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA) and more. Among these, fingerprint-based positioning stands out as a popular technique in Real Time Localisation Systems (RTLS) due to its simplicity and cost-effectiveness. Positioning systems based on fingerprint maps or other relevant methods find applications in diverse scenarios, including malls for indoor navigation and geo-marketing, hospitals for monitoring patients, doctors, and critical equipment, logistics for asset tracking and optimising storage spaces, and homes for providing Ambient Assisted Living (AAL) services. A significant challenge facing all indoor positioning systems is the objective evaluation of their performance. This challenge is compounded by the coexistence of heterogeneous technologies and the rapid advancement of computation. There is a vast potential for information fusion to be explored. These observations have led to the motivation behind our work. As a result, two novel algorithms and a framework are introduced in this thesis

Identification and Mitigation of NLOS based on Channel Information Rules for Indoor UWB Localization

Indoor localization is an emerging technology that can be utilized for developing products and services for commercial usage, public safety, military applications and so forth. Commercially it can be applied to track children, people with special needs, help navigate blind people, locate equipment, mobile robots, etc. The objective of this thesis is to enable an indoor mobile vehicle to determine its location and thereby making it capable of autonomous localization under Non-light of sight (NLOS) conditions. The solution developed is based on Ultra Wideband (UWB) based Indoor Positioning System (IPS) in the building. The proposed method increases robustness, scalability, and accuracy of location. The out of the box system of DecaWave TREK1000 provides tag tracking features but has no method to detect and mitigate location inaccuracies due to the multipath effect from physical obstacles found in an indoor environment. This NLOS condition causes ranges to be positively biased, hence the wrong location is reported. Our approach to deal with the NLOS problem is based on the use of Rules Classifier, which is based on channel information. Once better range readings are achieved, approximate location is calculated based on Time of Flight (TOF). Moreover, the proposed rule based IPS can be easily implemented on hardware due to the low complexity. The measurement results, which was obtained using the proposed mitigation algorithm, show considerable improvements in the accuracy of the location estimation which can be used in different IPS applications requiring centimeter level precision. The performance of the proposed algorithm is evaluated experimentally using an indoor positioning platform in a laboratory environment, and is shown to be significantly better than conventional approaches. The maximum positioning error is reduced to 15 cm for NLOS using both an offline and real time tracking algorithm extended from the proposed approach

Using bluetooth to implement a pervasive indoor positioning system with minimal requirements at the application level

Proyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de MadridPublicad