RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

Super-Resolution TOA Estimation with Diversity Techniques for Indoor Geolocation Applications

Recently, there are great interests in the location-based applications and the location-awareness of mobile wireless systems in indoor areas, which require accurate location estimation in indoor environments. The traditional geolocation systems such as the GPS are not designed for indoor applications, and cannot provide accurate location estimation in indoor environments. Therefore, there is a need for new location finding techniques and systems for indoor geolocation applications. In this thesis, a wide variety of technical aspects and challenging issues involved in the design and performance evaluation of indoor geolocation systems are presented first. Then the TOA estimation techniques are studied in details for use in indoor multipath channels, including the maximum-likelihood technique, the MUSIC super-resolution technique, and diversity techniques as well as various issues involved in the practical implementation. It is shown that due to the complexity of indoor radio propagation channels, dramatically large estimation errors may occur with the traditional techniques, and the super-resolution techniques can significantly improve the performance of the TOA estimation in indoor environments. Also, diversity techniques, especially the frequency-diversity with the CMDCS, can further improve the performance of the super-resolution techniques

Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation

New techniques in cross-layer wireless networks are building demand for ubiquitous channel sounding, that is, the capability to measure channel impulse response (CIR) with any standard wireless network and node. Towards that goal, we present a software-defined IEEE 802.11b receiver and CIR estimation system with little additional computational complexity compared to 802.11b reception alone. The system implementation, using the universal software radio peripheral (USRP) and GNU Radio, is described and compared to previous work. By overcoming computational limitations and performing direct-sequence spread-spectrum (DS-SS) matched filtering on the USRP, we enable high-quality yet inexpensive CIR estimation. We validate the channel sounder and present a drive test campaign which measures hundreds of channels between WiFi access points and an in-vehicle receiver in urban and suburban areas

Performance of TOA Estimation Algorithms in Different Indoor Multipath Conditions

Using Time of Arrival (TOA) as ranging metric is the most popular technique for accurate indoor positioning. Accuracy of measuring the distance using TOA is sensitive to the bandwidth of the system and the multipath condition between the wireless terminal and the access point. In a telecommunication-specific application, the channel is divided into Line of Sight (LOS) and Obstructed Line of Sight (OLOS) based on the existence of physical obstruction between the transmitter and receiver. In indoor geolocation application, with extensive multipath conditions, the emphasis is placed on the behavior of the first path and the channel conditions are classified as Dominant Direct Path (DDP), Nondominant Direct Path (NDDP) and Undetected Direct Path (UDP). In general, as the bandwidth increases the distance measurement error decreases. However, for the so called UDP conditions the system exhibits substantially high distance measurement errors that can not be eliminated with the increase in the bandwidth of the system. Based on existing measurements performed in CWINS, WPI a measurement database that contains adequate number of measurement samples of all the different classification is created. Comparative analysis of TOA estimation in different multipath conditions is carried out using the measurement database. The performance of super-resolution and traditional TOA estimation algorithms are then compared in LOS, OLOS DDP, NDDP and UDP conditions. Finally, the analysis of the effect of system bandwidth on the behavior of the TOA of the first path is presented

Super resolution WiFi indoor localization and tracking

In this paper, we present a complete framework for accurate indoor positioning and tracking using the 802.11a WiFi network. Channel frequency response is first estimated via the least squares (LS) method using an orthogonal frequency division multiplexing (OFDM) pilot symbol. For accurate time of arrival (ToA) distance estimates in multipath environments, super resolution technique i.e. Multiple Signal Classification (MUSIC) is used which capitalizes on the autocorrelation matrix of the estimated channel frequency response. The estimated distances from the base stations (BSs) are then used in the observation model for particle filter (PF) tracking for which a constant velocity motion model is used, depicting indoor mobile movement. The tracking performance of the combined MUSIC-PF is compared with PF performance when a conventional cross correlator (CC) is used for delay estimates. It is shown via simulation that the MUSIC-PF performance is superior to the CC-PF performance

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin