An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values

Cooperatively Extending the Range of Indoor Localisation

̶Whilst access to location based information has been mostly possible in the\ud outdoor arena through the use of GPS, the provision of accurate positioning estimations and\ud broad coverage in the indoor environment has proven somewhat problematic to deliver.\ud Considering more time is spent in the indoor environment, the requirement for a solution is\ud obvious. The topography of an indoor location with its many walls, doors, pillars, ceilings\ud and floors etc. muffling the signals to \from mobile devices and their tracking devices, is one\ud of the many barriers to implementation. Moreover the cha racteristically noisy behaviour of\ud wireless devices such as Bluetooth headsets, cordless phones and microwaves can cause\ud interference as they all operate in the same band as Wi -Fi devices. The limited range of\ud tracking devices such as Wireless Access Point s (AP), and the restrictions surrounding their\ud positioning within a buildings’ infrastructure further exacerbate this issue, these difficulties\ud provide a fertile research area at present.\ud The genesis for this research is the inability of an indoor location based system (LBS) to\ud locate devices beyond the range of the fixed tracking devices. The hypothesis advocates a\ud solution that extends the range of Indoor LBS using Mobile Devices at the extremities of\ud Cells that have a priori knowledge of their location, and utilizing these devices to ascertain\ud the location of devices beyond the range of the fixed tracking device. This results in a\ud cooperative localisation technique where participating devices come together to aid in the\ud determination of location of device s which otherwise would be out of scope

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

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Cooperatively extending the range of indoor localisation

Whilst access to location based information has been mostly possible in the outdoor arena through the use of GPS, the provision of accurate positioning estimations and broad coverage in the indoor environment has proven somewhat problematic to deliver. Considering more time is spent in the indoor environment, the requirement for a solution is obvious. The topography of an indoor location with its many walls, doors, pillars, ceilings and floors etc. muffling the signals to from mobile devices and their tracking devices, is one of the many barriers to implementation. Moreover the characteristically noisy behaviour of wireless devices such as Bluetooth headsets, cordless phones and microwaves can cause interference as they all operate in the same band as Wi-Fi devices. The limited range of tracking devices such as Wireless Access Points (AP), and the restrictions surrounding their positioning within a buildings' infrastructure further exacerbate this issue, these difficulties provide a fertile research area at present. The genesis for this research is the inability of an indoor location based system (LBS) to locate devices beyond the range of the fixed tracking devices. The hypothesis advocates a solution that extends the range of Indoor LBS using Mobile Devices at the extremities of Cells that have a priori knowledge of their location, and utilizing these devices to ascertain the location of devices beyond the range of the fixed tracking device. This results in a cooperative localisation technique where participating devices come together to aid in the determination of location of devices which otherwise would be out of scope

D2D-based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey

Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research

Robust blimps formation using wireless sensor based on Received Signal Strength Indication (RSSI) localization method

This paper suggests the wireless communication technique used to determine the information of blimp localization (distance and orientation) via wireless sensor network (WSN) protocol. In cooperative decentralized system, information exchanges between the agents are crucial. Each agent is required to share data to enable individual decision making task. In this work, the WSN were used as the data communications protocol which provides robust communication using the mesh networking. In order to support the multi agent communication network, the reliability of data needs to be proved. The performances of the communication selection were studied through experimental and simulation approach. The experimental results showed that the RSSI value from the device provided good statically data fit using the R-square with value of 0.947. This paper also proposed a new mobile node arrangement with hexagonal anchor node arrangement based on water particles formation. The node arrangement was simulated using the Senelex™ and showed low absolute error position thus achieving the desired cooperative system requirement