Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

Map matching by using inertial sensors: literature review

This literature review aims to clarify what is known about map matching by using inertial sensors and what are the requirements for map matching, inertial sensors, placement and possible complementary position technology. The target is to develop a wearable location system that can position itself within a complex construction environment automatically with the aid of an accurate building model. The wearable location system should work on a tablet computer which is running an augmented reality (AR) solution and is capable of track and visualize 3D-CAD models in real environment. The wearable location system is needed to support the system in initialization of the accurate camera pose calculation and automatically finding the right location in the 3D-CAD model. One type of sensor which does seem applicable to people tracking is inertial measurement unit (IMU). The IMU sensors in aerospace applications, based on laser based gyroscopes, are big but provide a very accurate position estimation with a limited drift. Small and light units such as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very popular, but they have a significant bias and therefore suffer from large drifts and require method for calibration like map matching. The system requires very little fixed infrastructure, the monetary cost is proportional to the number of users, rather than to the coverage area as is the case for traditional absolute indoor location systems.Siirretty Doriast

Collaborative Indoor Positioning Systems: A Systematic Review

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified

Combining Mobile Technologies For Accurate, Open Source, Privacy Sensitive, Zero Cost, Location Determination

Determining the location of an object or individual using a mobile device (e.g. cell phone) is an important aspect of modern information gathering. Various solutions have been proposed which all have their strengths and weaknesses. To date, no solution has been devised for a mobile device that will work effectively in multiple environments and without assistance from network-provider connections1. To address this, it is argued that the current state of the art can be advanced using a hybrid approach that combines a number of sensor technologies to provide a more reliable, and accurate mobile location determination that functions in multiple environments (indoors and outdoors). This thesis examines in detail current relevant available technology, calculation techniques for location determination, the Global Navigation Satellite System (GNSS) and other noteworthy location determination research. It then introduces our solution of a hybrid positioning system that is an open-source, provider-network independent, privacy sensitive, zero-cost and accurate software component. First the overall system design is described and then individual modules are described in detail. It describes in full an algorithm that intelligently combines signals from various technologies, applies weights to these signals and also leverages past signal readings to enhance current calculations. Next, the evaluation section is introduced which discusses how and why the test bed was chosen and deployed. It then discusses individual test results and finally the overall tests are analysed, discussed and summarised. Finally, the conclusions are prepared in detail, the three initial questions raised in the introduction are answered and discussed and the contributions to the body of knowledge are reaffirmed. Future work finishes the thesis and looks at several research paths that can be pursued from this research

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

INDOOR-WIRELESS LOCATION TECHNIQUES AND ALGORITHMS UTILIZING UHF RFID AND BLE TECHNOLOGIES

The work presented herein explores the ability of Ultra High Frequency Radio Frequency (UHF RF) devices, specifically (Radio Frequency Identification) RFID passive tags and Bluetooth Low Energy (BLE) to be used as tools to locate items of interest inside a building. Localization Systems based on these technologies are commercially available, but have failed to be widely adopted due to significant drawbacks in the accuracy and reliability of state of the art systems. It is the goal of this work to address that issue by identifying and potentially improving upon localization algorithms. The work presented here breaks the process of localization into distance estimations and trilateration algorithms to use those estimations to determine a 2D location. Distance estimations are the largest error source in trilateration. Several methods are proposed to improve speed and accuracy of measurements using additional information from frequency variations and phase angle information. Adding information from the characteristic signature of multipath signals allowed for a significant reduction in distance estimation error for both BLE and RFID which was quantified using neural network optimization techniques. The resulting error reduction algorithm was generalizable to completely new environments with very different multipath behavior and was a significant contribution of this work. Another significant contribution of this work is the experimental comparison of trilateration algorithms, which tested new and existing methods of trilateration for accuracy in a controlled environment using the same data sets. Several new or improved methods of triangulation are presented as well as traditional methods from the literature in the analysis. The Antenna Pattern Method represents a new way of compensating for the antenna radiation pattern and its potential impact on signal strength, which is also an important contribution of this effort. The performance of each algorithm for multiple types of inputs are compared and the resulting error matrix allows a potential system designer to select the best option given the particular system constraints

Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesGPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is BLE-based IPS. BLE-has low cost, low power consumption, and tit is are compatible with newer smartphones. These factors make it suitable for mass market applications with an estimated market of 10 billion USD by 2020. Although, BLEbased IPS have advantages over its counterparts, it has not solved the position accuracy problem yet. More research is needed to meet the position accuracy required for indoor LBS. In this thesis, two ways for accuracy improvement were tested i) a new algorithm for BLE-based IPS was proposed and ii) fusion of BLE position estimates with IMU position estimates was implemented. The first way exploits a concept from control survey called well-conditioned triangle. Theoretically, a well-conditioned triangle is an equilateral triangle but for in practice, triangles whose angles are greater than 30° and less than 120° are considered well-conditioned. Triangles which do not satisfy well-condition are illconditioned. An estimated position has the least error if the geometry from which it is estimated satisfy well-condition. Ill-conditioned triangle should not be used for position estimation. The proposed algorithm checked for well-condition among the closest detected beacons and output estimates only when the beacons geometry satisfied well-condition. The proposed algorithm was compared with weighted centroid (WC) algorithm. Proposed algorithm did not improve on the accuracy but the variance in error was highly reduced. The second way tested was fusion of BLE and IMU using Kálmán filter. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy. More research is required to improve accuracy of BLE-based IPS. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, prepossessing, methods, computational environment, results)

Detection of UWB ranging measurement quality for collaborative indoor positioning

Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning