Integrated factor graph algorithm for DOA-based geolocation and tracking

Abstract This paper proposes a new position tracking algorithm by integrating extended Kalman filter (EKF) and direction-of-arrival (DOA)-based geolocation into one factor graph (FG) framework. A distributed sensor network is assumed for detecting an anonymous target, where the process and observation equations in the state space model (SSM) are unknown. Importantly, the predicted state information can be utilized not only for filtering, but also for enhancing the observation process. To be specific, by taking the prediction into account as the a priori, a new FG scheme is proposed for GEolocation, denoted by FG-GE. The benefits are two-fold, compared with the conventional geolocation scheme which does not rely on the a priori information. First of all, significant performance improvement can be observed, in terms of the root mean square error (RMSE), when severe sensing errors are suddenly encountered. Furthermore, the proposed FG-GE can achieve dramatic reduction of computational complexity. In addition, this paper also proposes the use of a predicted Cramer-Rao lower bound (P-CRLB) to dynamically estimate the observation error variance, which demonstrates more robust tracking performance than that with only fixed average variance approximation

A DOA-based factor graph technique for 3D multi-target geolocation

Abstract The primary goal of this paper is to propose a new factor graph (FG) technique for the direction-of-arrival (DOA)-based three-dimensional (3D) multi-target geolocation. The proposed FG detector uses only the mean and the variance of the DOA measurement including both the azimuth and the elevation, assuming that they are suffering from errors following a Gaussian probability density function (PDF). Therefore, both the up-link (UL) transmission load and the detection complexity can be significantly reduced. The Cramer-Rao lower bound (CRLB) of the proposed DOA-based 3D geolocation system is mathematically derived. According to the root mean square error (RMSE) results obtained by simulations, the proposed FG algorithm is found to outperform the conventional linear least square (LS) approach, which achieves a very close performance to the derived CRLB. Moreover, we propose a sensor separation algorithm to solve the target-DOAs matching problem such that the DOAs, measured by each sensor, can be matched to their corresponding targets. With this technique, additional target identification is not needed, and the multi-target geolocation can be decomposed into multiple independent single-target detections

A PTDOA-DRSS hybrid factor graph-based unknown radio wave geolocation

Abstract We propose a hybrid Pythagorean Time Difference of Arrival and Differential Received Signal Strength based Factor Graph (PTDOA-DRSS-FG) to estimate the location of unknown radio wave emitter in outdoor environments. The term of “unknown” indicates that the knowledge of neither time of departure (TOD) nor absolute transmit power of the radio wave emitter are required. The PTDOA-FG can eliminate the necessity of TOD knowledge of the target signal transmission and DRSS-FG can eliminate the necessity of the knowledge of target absolute transmit power. However, PTDOA-FG alone requires perfect time synchronism between sensors, which is difficult in practice. On the other hand, DRSS-FG alone requires the most suitable monitoring spots. In this paper, PTDOA-FG is used to provide the rough estimation of the target position to select the most suitable monitoring spots for DRSS-FG technique. It is shown that, in terms of root mean square error (RMSE) vs. iteration times, the achieved RMSE of the proposed technique is better than the PTDOA-FG alone and very close to the DRSS reference, i.e., the DRSS-FG technique with the idealistic monitoring spots. Performing the proposed technique in the framework of factor graph (FG) does not require excessive computational complexity due to the fact that using the Gaussian distribution model, it uses mean and variance only with the sum-product algorithm

6G white paper on localization and sensing

Executive Summary This white paper explores future localization and sensing opportunities for beyond fifth generation (5G) wireless communication systems by identifying key technology enablers and discussing their underlying challenges, implementation issues, and identifying potential solutions. In addition, we present exciting new opportunities for localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. In contrast to 5G and earlier generations, localization and sensing will be built-in from the outset to both cope with specific applications and use cases, and to support flexible and seamless connectivity. Following the trend initiated in the 5G new radio (NR) systems, sixth generation (6G) will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler and angular resolutions, as well as localization to cm-level degree of accuracy. Moreover, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. 6G systems will be truly intelligent wireless systems that will not only provide ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency and space. Applications such as THz imaging and spectroscopy have the potential to provide continuous, real-time physiological information via dynamic, non-invasive, contactless measurements for future digital health technologies. 6G simultaneous localization and mapping (SLAM) methods will not only enable advanced cross reality (XR) applications but also enhance the navigation of autonomous objects such as vehicles and drones. In convergent 6G radar and communication systems, both passive and active radars will simultaneously use and share information, to provide a rich and accurate virtual image of the environment. In 6G, intelligent context-aware networks will be capable of exploiting localization and sensing information to optimize deployment, operation, and energy usage with no or limited human intervention. This white paper concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust. Addressing these challenges will undoubtedly require an interdisciplinary and concerted effort from the research community

Convergent communication, sensing and localization in 6G systems:an overview of technologies, opportunities and challenges

Abstract Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust