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1. Millimeter Wave Position Location using Multipath Differentiation for 3GPP using Field Measurements

   Ojas Kanhere, Theodore S. ( December 2020 , 2020 IEEE Global Communciations conference)

   null (Ed.)

   3GPP air interface standards support meter-level position location of a user in a cellular network. With wider bandwidths and narrow antenna beamwidths available at mmWave frequencies, cellular networks now have the potential to provide sub-meter position location for each user. In this work, we provide an overview of 3GPP position location techniques that are designed for line-of-sight propagation. We discuss additional measurements required in the 3GPP standard that enable multipath-based non-line-of-sight position location. Further, we validate the concepts in this paper by using field data to test a map-based position location algorithm in an indoor office environment which has dimensions of 35 m by 65.5 m. We demonstrate how the fusion of angle of arrival and time of flight information in concert with a 3-D map of the office provides a mean accuracy of 5.72 cm at 28 GHz and 6.29 cm at 140 GHz, over 23 receiver distances ranging from 4.2 m to 32.3 m, using a single base station in line-of-sight and non-line-of-sight. We also conduct a theoretical analysis of the typical error experienced in the map-based position location algorithm and show that the complexity of the map-based algorithm is low enough to allow real-time implementation. 

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2. A wireless signal-based sensing framework for robotics

   https://doi.org/10.1177/02783649221097989  

   Jadhav, Ninad ; Wang, Weiying ; Zhang, Diana ; Khatib, Oussama ; Kumar, Swarun ; Gil, Stephanie ( August 2022 , The International Journal of Robotics Research)

   In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10^◦for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m²environment with occlusions.

    

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3. Map-Assisted Millimeter Wave Localization for Accurate Position Location

   https://doi.org/10.1109/globecom38437.2019.9013365  

   Kanhere, Ojas ; Ju, Shihao ; Xing, Yunchou ; Rappaport, Theodore S. ( December 2019 , 2019 IEEE Global Communications Conference)

   null (Ed.)

   Accurate precise positioning at millimeter wave frequencies is possible due to the large available bandwidth that permits precise on-the-fly time of flight measurements using conventional air interface standards. In addition, narrow antenna beamwidths may be used to determine the angles of arrival and departure of the multipath components between the base station and mobile users. By combining accurate temporal and angular information of multipath components with a 3- D map of the environment (that may be built by each user or downloaded a-priori), robust localization is possible, even in non-line-of-sight environments. In this work, we develop an accurate 3-D ray tracer for an indoor office environment and demonstrate how the fusion of angle of departure and time of flight information in concert with a 3-D map of a typical large office environment provides a mean accuracy of 12.6 cm in line-of-sight and 16.3 cm in non-line-of-sight, over 100 receiver distances ranging from 1.5 m to 24.5 m using a single base station. We show how increasing the number of base stations improves the average non-line-of-sight position location accuracy to 5.5 cm at 21 locations with a maximum propagation distance of 24.5 m. Index Terms—localization; positioning; position location; navigation; mmWave; 5G; ray tracing; site-specific propagation; map-based 

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4. Machine learning regression-based RETRO-VLP for real-time and stabilized indoor positioning

   https://doi.org/10.1007/s10586-022-03884-w  

   Alenezi, Ali H. ; Nazzal, Mahmoud ; Sawalmeh, Ahmed ; Khreishah, Abdallah ; Shao, Sihua ; Almutiry, Muhannad ( December 2022 , Cluster Computing)

   Many real-world applications require real-time and robust positioning of Internet of Things (IoT) devices. In this context, visible light communication (VLC) is a promising approach due to its advantages in terms of high accuracy, low cost, ubiquitous infrastructure, and freedom from RF interference. Nevertheless, there is a growing need to improve positioning speed and accuracy. In this paper, we propose and prototype a VLC-based positioning solution using retroreflectors attached to the IoT device of interest. The proposed algorithm uses the retroreflected power received by multiple photodiodes to estimate the euclidean and directional coordinates of the underlying IoT device. In particular, the relative relationship between reflected light magnitude and reflected power is used as input to trainable machine learning regression models. Such models are trained to estimate the coordinates. The proposed algorithm excels in its simplicity and fast computation. It also reduces the need for sensory devices and active operation. Additionally, after regression, Kalman filtering is applied as a post-processing operation to further stabilize the obtained estimates. The proposed algorithm is shown to provide stable, accurate, and fast. This has been verified by extensive experiments performed on a prototype in real-world environments. Experiments confirm a high level of positioning accuracy and the added benefit of Kalman filtering stabilization. 

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5. UAV wildlife radiotelemetry: System and methods of localization

   https://doi.org/10.1111/2041-210X.13261  

   Shafer, Michael W. ; Vega, Gabriel ; Rothfus, Kellan ; Flikkema, Paul ; Photopoulou, ed., Theoni ( August 2019 , Methods in Ecology and Evolution)

   The majority of bird and bat species are incapable of carrying tags that transmit their position to satellites. Given fundamental power requirements for such communication, burdened mass guidelines and battery technology, this constraint necessitates the continued use of very high frequency (VHF) radio beacons. As such, efforts should be made to mitigate their primary deficiencies: detection range, localization time and localization accuracy.

   The integration of a radiotelemetry system with an unmanned aerial vehicle (UAV) could significantly improve the capacity for data collection from VHF tags. We present a UAV‐integrated radiotelemetry system that relies on open source hardware and software. Localization methods, including signal processing, bearing estimation based on principal component analysis, localization techniques and test results, are discussed.

   Using a low‐power beacon applicable for bats and small birds, testing showed that the improved vantage of the UAV‐radiotelemetry system (UAV‐RT) provided significantly higher received signal power compared to the low‐level flights (maximum range beyond 1.4 km). Flight testing of localization methods showed median bearing errors between 2.3° and 6.8°, with localization errors of between 5% and 14% of the distance to the tag. In a direct comparison to an experienced radiotelemetry user, the UAV‐RT system provided bearing and localization estimates with 53% less error.

   This paper introduces the core functionality and use methods of the UAV‐RT system, while presenting baseline localization performance metrics. An associated website hosts plans for assembly and software installation. The methods of UAV‐RT use for tag detection will be further developed in future works. For both the detection and localization problems, the mobility of a flying asset drastically reduces tracker time requirements. A 7‐min flight would be sufficient to collect five equally spaced bearing estimates over a 1‐km transect. The use of a software‐defined radio on the UAV‐RT system will allow for the simultaneous detection and localization of multiple tags.

    

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