Explore the vast range of titles available.

With the development of science and technology, more accurate and stable location information can better serve people’s lives. As people spend more time indoors, traditional positioning technologies such as Wi-Fi can no longer meet their needs due to a lack of positioning accuracy and interference resistance. UWB is an emerging positioning technology with high accuracy, robustness, and stability. This paper compares the advantages and disadvantages of UWB technology with those of Bluetooth and Wi-Fi. Common UWB geometric positioning methods such as TOA, TDOA, etc. are introduced. By introducing the latest research progress in UWB indoor positioning, an outlook on the future development of UWB indoor positioning is made. Compared with Bluetooth and other indoor positioning technologies, UWB positioning technology has the advantages of high positioning accuracy and good anti-interference. The UWB positioning system usually uses TOA, TDOA, AOA, and other positioning algorithms. In the future, UWB positioning technology will serve more and more complex positioning environments.

Time difference of arrival (TDoA) based on a group of sensor nodes with known locations has been widely used to locate targets. Two-step weighted least squares (TSWLS), constrained weighted least squares (CWLS), and Newton–Raphson (NR) iteration are commonly used passive location methods, among which the initial position is needed and the complexity is high. This paper proposes a hybrid firefly algorithm (hybrid-FA) method, combining the weighted least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high accuracy. The WLS algorithm is performed first, the result of which is used to restrict the search region for the FA method. Simulations showed that the hybrid-FA method required far fewer iterations than the FA method alone to achieve the same accuracy. Additionally, two experiments were conducted to compare the results of hybrid-FA with other methods. The findings indicated that the root-mean-square error (RMSE) and mean distance error of the hybrid-FA method were lower than that of the NR, TSWLS, and genetic algorithm (GA). On the whole, the hybrid-FA outperformed the NR, TSWLS, and GA for TDoA measurement.

Radio frequency (RF) technologies are often used to track assets in indoor environments. Among others, ultra-wideband (UWB) has constantly gained interest thanks to its capability to obtain typical errors of 30 cm or lower, making it more accurate than other wireless technologies such as WiFi, which normally can predict the location with several meters accuracy. However, mainly due to technical requirements that are part of the standard, conventional medium access strategies such as clear channel assessment, are not straightforward to implement. Since most scientific papers focus on UWB accuracy improvements of a single user, it is not clear to which extend this limitation and other design choices impact the scalability of UWB indoor positioning systems. We investigated the scalability of indoor localization solutions, to prove that UWB can be used when hundreds of tags are active in the same system. This paper provides mathematical models that calculate the theoretical supported user density for multiple localization approaches, namely Time Difference of Arrival (TDoA) and Two-Way Ranging (TWR) with different MAC protocol combinations, i.e., ALOHA and TDMA. Moreover, this paper applies these formulas to a number of realistic UWB configurations to study the impact of different UWB schemes and settings. When applied to the 802.15.4a compliant Decawave DW1000 chip, the scalability dramatically degrades if the system operates with uncoordinated protocols and two-way communication schemes. In the best case scenario, UWB DW1000 chips can actively support up to 6171 tags in a single domain cell (no handover) with well-selected settings and choices, i.e., when adopting the combination of TDoA (one-way link) and TDMA. As a consequence, UWB can be used to simultaneously localize thousands of nodes in a dense network. However, we also show that the number of supported devices varies greatly depending on the MAC and PHY configuration choices.

Finding the position of a radiative source based on time-difference-of-arrival (TDOA) measurements from spatially separated receivers has been widely applied in sonar, radar, mobile communications and sensor networks. For the nonlinear model in the process of positioning, Taylor series and other novel methods are proposed. The idea of cone constraint provides a new way of solving this problem. However, these approaches do not always perform well and are away from the Cramer-Rao-Lower-Bound (CRLB) in the situations when the source is set at the array edge, the noise in measurement is loud, or the initial position is biased. This paper presents a weighted-least-squares (WLS) algorithm with the cone tangent plane constraint for hyperbolic positioning. The method adds the range between the source and the reference sensor as a dimension. So, the space-range frame is established. Different from other cone theories, this paper sets the reference sensor as the apex and finds the optimal source estimation on the cone. WLS is used for the optimal result from the measurement plane equations, a vertical constraint and a cone constraint. The cone constraint equation is linearized by a tangent plane. This method iterates through loops and updates the tangent plane, which approximates the truth-value on the cone. The proposed algorithm was simulated and verified under various conditions of different source positions and noises. Besides, some state-of-the-art algorithms were compared in these simulations. The results show that this algorithm is accurate and robust under poor external environment.

We study wireless indoor positioning systems where multiple synchronized infrastructure devices simultaneously receive signals from an object of interest whose arrival times are measured. The positioning performance is degraded by unresolvable channel multipath and non-line-of-sight (NLOS) reflctions which cause a bias in the time difference of arrival (TDOA) measurements. In order to reduce the negative effect of multi-path, a Multi-Path Map (MPM) method based on spatial domain modeling principle in the reverse positioning framework with good robustness is proposed. Meanwhile, an improved non-linear iterative algorithm with height component constrained which reduces the complexity is introduced to calculate the coordinates so that the performance of the MPM can be verified. By using the MPM measurements as pre-calibration information to compensate the TDOA observed value, the accuracy of the cooperative location based on a UWB device is 6.45 cm, which achieves 63% improvement than that of none MPM used.

Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two different solutions that cannot be discarded according to mathematical standards. In this paper, a new methodology to solve the 3D TDOA problems in a sensor network with four beacons is proposed. A confidence interval, which is defined in this paper as a sphere, is defined to use positioning algorithms with four different nodes. It is proven that the separation between solutions in the four-beacon TDOA problem allows the transformation of the problem into an analogous one in which more receivers are implied due to the geometric properties of the intersection of hyperboloids. The achievement of the distance between solutions needs the application of genetic algorithms in order to find an optimized sensor distribution. Results show that positioning algorithms can be used 96.7% of the time with total security in cases where vehicles travel at less than 25 m/s.

To provide high-precision positioning for Internet of Things (IoT) scenarios, we optimize the indoor positioning technique based on Ultra-Wideband (UWB) Time Difference of Arrival (TDOA) equipment. This paper analyzes sources of positioning error and improves the time synchronization algorithm based on the synchronization packet. Then we use the labels of the known position to further optimize the time synchronization performance, and hence improve TDOA measurements. After time synchronization optimization, a Weighted Least Square (WLS) and Taylor coordination algorithm is derived. Experiments show that our optimization reduces the average positioning error from 54.8 cm to 12.6 cm.

This paper investigates the problem of optimizing the selection of radar site in three-dimensional terrain for multi-site passive time-difference positioning system. First, the directness of the radar to the surveillance airspace is investigated. A judgment method based on planar convex closure is given to determine whether a terrain point obscures the radar site to be selected, based on it, a screening algorithm is given to find the region where the received signals are not blocked. Second, the problem of optimal position combination of multiple radar stations is investigated. Taking a convex polygonal surveillance airspace as the object of study, a planning model with the objective of selecting the optimal combination of radar positions is given, which takes into account the constraints on the radar’s directness to the surveillance airspace, as well as the constraints on the far bounds of the detections, and proposes a genetic algorithm for solving the model, which uses an individual coding of the real type and introduces the penalty coefficients, which transforms the constrained planning problem to an unconstrained planning problem.

The Internet of Things (IoT) for smart industry requires the surveillance and management of people and objects. The ultra-wideband positioning system is an attractive solution for achieving centimeter-level accuracy in target location. While many studies have focused on improving the accuracy of the anchor coverage range, it is important to note that in practical applications, positioning areas are often limited and obstructed by furniture, shelves, pillars, or walls, which can restrict the placement of anchors. Furthermore, some positioning regions are located beyond anchor coverage, and a single group with few anchors may not be able to cover all rooms and aisles on a floor due to non-line-of-sight errors causing severe positioning errors. In this work, we propose a dynamic-reference anchor time difference of arrival (TDOA) compensation algorithm to enhance accuracy beyond anchor coverage by eliminating local minima of the TDOA loss function near anchors. We designed a multidimensional and multigroup TDOA positioning system with the aim of broadening the coverage of indoor positioning and accommodating complex indoor environments. By employing an address-filter technique and group-switching process, tags can seamlessly move between groups with a high positioning rate, low latency, and high accuracy. We deployed the system in a medical center to locate and manage researchers with infectious medical waste, demonstrating its usefulness for practical healthcare institutions. Our proposed positioning system can thus facilitate precise and wide-range indoor and outdoor wireless localization.

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object’s position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.