We present a two-dimensional relative pose estimation framework for cooperative unmanned ground vehicles that fuses two-way ranging with phase-difference-of-arrival (PDoA) measurements from multi-antenna UWB modules. The fusion significantly improves observability by exploiting mutual orientation information. To resolve PDoA phase ambiguities, we introduce a wavevector-norm-consistency-based unwrapping algorithm that outputs per-pair quality metrics. These metrics are integrated into a robust adaptive optimization combining quality-weighted Huber loss with a shortest-path initialization, effectively mitigating outliers and initialization sensitivity. Simulations and real-world experiments demonstrate superior robustness and accuracy over conventional approaches, especially under phase ambiguity and outlier conditions.

In indoor environments, conventional Global Navigation Satellite System (GNSS) is difficult to provide accurate localization due to signal interference. In this paper, we propose a robust single-point localization technique using downlink TDOAAOA fusion for self-localization systems, which can be applied to hierarchical self-organized wireless sensor network (WSN) to provide tags with localization that does not consume communication bandwidth. The positioning algorithm proposed in this paper utilizes a planar antenna array to receive the broadcast signal from base stations (BS), and constructs a nonlinear least-squares optimization problem to achieve single-point localization by fusing the time difference of arrival (TDOA) and angle of arrival (AOA) measurements. Aiming at the non-line-of-sight (NLOS) problem in real indoor scenarios, we establish an outlier isolation strategy (OIS) based on the geometrical constraints between the tag and BS, which is combined with M-estimation to realize the robust localization algorithm. Real-world experiments conducted in an underground parking lot verify the feasibility and localization accuracy of the proposed technique.

Carrier phase positioning with indoor pseudolite can achieve high-precision positioning and has received extensive attention. Correct ambiguity resolution (AR) is the key to carrier phase positioning. However, the complex indoor environment has serious blocking, which can lead to signal interference and cycle slips. Existing methods, like the OTF method and the KPI method, are unable to deal with these problems. This paper proposes a single epoch carrier phase positioning algorithm for indoor pseudolite systems, which is more resistant to interference and cycle slips. The algorithm consists of a positioning algorithm and a validation algorithm. The positioning algorithm searches in the ambiguity domain for the smallest carrier phase residuals to achieve AR, and then realizes high-precision positioning. The validation algorithm evaluates positioning results through the residual ratio and eliminates the positioning results that may have errors. Numerical simulation proves the effectiveness of this algorithm.

In this paper, we propose a simple method, termed TWO, of directly closing the loop for LiDAR odometry. TWO suggests assigning high weights to the LIDAR observations corresponding to the old parts of the map; since these parts are built with the low-drift poses from the early odometry and can help drag the drifted odometry back to the correct global position when the LiDAR scans the points of these parts again. Also, we present the method of checking the consistency of the plane normal to address the two-side problem that may cause damage when using TWO. Moreover, we show that the proposed method is lightweight and needs little extra computation and storage space compared to the original odometry. The proposed TWO is integrated into the state-of-the-art LiDAR odometry A-LOAM and LiDAR-inertial odometry FAST-LIO2, and it is tested thoroughly on five public datasets and our private handheld dataset. The experiments show that the TWO can effectively help these two methods directly close most loops and produce localization results with apparently lower drifts.

The self-localization of wireless sensor networks (WSNs) is facing the problem of insufficient positioning accuracy in indoor environment due to multipath and interference issues. At the same time, without external references, through mutual measurement nodes can calculate only a set of relative coordinates. Therefore, it is difficult to achieve the mapping of coordinate values to the physical world. The development of simultaneous localization and mapping (SLAM) technologies has provided new opportunities to solve the above problems by making it easier to obtain real-time indoor maps. This article proposes a Lidar-assisted self-localization (LASL) technique to further improve the localization accuracy of WSNs in indoor scenes by combining spatial constraint information obtained from real-time maps, and to place the relative node coordinate network in the visualized maps. Based on the general assumption that the nodes are deployed on the surface of the object, the proposed technique combines the spatial constraints obtained by plane fitting of a local point cloud map (PCM) and finite-area approximation of object surfaces with the distance constraints provided by radio ranging. Subsequently, the self-localization results under the joint constraints are solved by the alternating coordinate descent (ACD) method. Simulations and experiments demonstrate that the proposed technique can effectively combine the spatial constraints provided by the Lidar PCM to further improve the self-localization accuracy of the sensor nodes, and further optimize the relative position relationship between each node and the map environment to achieve better matching and integration of the WSN and the real-time map.

Based on carrier phase positioning, narrow band systems such as pseudolites and cellular networks can achieve decimeter-level to centimeter-level positioning accuracy. In this paper, we propose a new carrier phase positioning method based on dual-antenna transmitters (TXs). All TXs maintain frequency synchronization, while each TX broadcasts distinguishable signals through two antennas respectively. The two signals from the same TX maintain time synchronization, and single difference makes the partial fixed solutions available. In this way, the proposed method can realize single point positioning (SPP) and avoid data transmission brought by double difference. It is shown by the simulation results that our method has better accuracy than traditional SPP methods with floating-point solutions.

Simultaneous Localization and Mapping (SLAM) is essential for robots, especially in unfamiliar indoor environments where other localization methods such as GNSS, UWB are unavailable. LOAM, as a state-of-the-art LiDAR SLAM method, works by extracting corner and surf points from raw point clouds and matching them with accumulated maps. However, the bisquare weight it uses for each observation is derived from the observation residual, which cannot reflect the actual observation quality and is of little help in improving the system accuracy. In this paper, we propose a novel method termed OW-LOAM, which takes the difference in the observation qualities into account by replacing the bisquare weight in LOAM with the inverse of the estimated variance of the observation noise based on Bayesian estimation theory. We conduct a series of experiments in various indoor environments of different scales, and the results show that the proposed OW-LOAM outperforms the original LOAM in both accuracy and robustness.

With the widespread application of wireless sensor networks, localization issue has attracted much attention. It is a major challenge for many sensor network tasks to locate a large number of asynchronous nodes in an unknown environment without external references. In this article, we present an asynchronous collaborative localization system (ACLS) to address the localization challenge for large-capacity sensor networks (LCSNs). ACLS exploits a hierarchical architecture, under which the wireless sensor nodes in the network are divided into parent nodes that can communicate with each other through wireless signals and child nodes that can only passively receive signals. Specific protocols and nonlinear distance estimators for this broadcast communication ranging technique are proposed. These characteristics are verified through theoretical analyses to have a strong suppression effect on local clock errors and are not sensitive to measurement noise. The simulation experiments further illustrate that the proposed ACLS can achieve high-rate and high-precision ranging and localization for asynchronous LCSN without preinstalled infrastructures.

Local positioning systems (LPSs) serve as a feasible alternative to provide positioning service in global navigation satellite system (GNSS)-denied environments. When the area of interest is unknown and potentially dangerous, e.g., urban search and rescue (USAR), or unreachable, e.g., extraterrestrial exploration, the autonomous establishment of LPSs by a robot is an attractive approach to coping with the demand for positioning service. In this article, we investigate the autonomous establishment problem in indoor scenarios, where a robot carrying several positioning beacons intends to place them sequentially to establish high-quality positioning services for the area of interest. To solve the complicated sequential decision problem, we first model the optimal positioning beacon configuration problem and then model the autonomous establishment process as a partially observable Markov decision process (POMDP). We apply deep reinforcement learning (DRL) to solve the POMDP. Extensive simulations, including comparisons with other baselines and generalization experiments, demonstrate the advantages of the proposed DRL-based autonomous establishment of LPSs.

Ground-based positioning systems (GBPSs), such as cellular networks and pseudolites, can provide high-precision positioning services through carrier phase positioning (CPP). Most existing methods are based on having accurate coordinates of all transmitters (TXs). In some applications, however, it could be too expensive and time-consuming to measure the coordinates of all TXs accurately, and requiring precise manual measurements severely degrades the flexibility of GBPSs. In this paper, we propose a carrier phase based autonomous coordinate evolution (CPACE) method for GBPSs, which uses the observations of multiple users to continuously improve the accuracy of TX coordinates. We first propose a Batch CPACE (B-CPACE) method and analyze its theoretical advantages over traditional single-point positioning (SPP). In the case of a large number of users, to avoid heavy burden on data transmission and calculation, we propose two distributed CPACE (D-CPACE) methods which have the same asymptotic performance with B-CPACE. Our numerical simulations prove that both B-CPACE and D-CPACE have better accuracy than SPP and reach the Cramer-Rao lower bounds. A real-world experiment shows that the proposed method can achieve centimeter-level accuracy.

In this letter, a side-peak cancellation unambiguous acquisition technique is proposed for sine-phased binary offset carrier (BOC) modulated signals. The test criterion used in this technique is based on a synthesized correlation function which has no major positive side peak. This synthesized correlation function is obtained by subtracting the cross-correlation between the received sin-BOC signal and an auxiliary signal from the autocorrelation of sin-BOC signal. For different types of BOC signal, the proposed technique employs corresponding modulated symbols of the auxiliary signal. The common solution of the symbol shape vector is derived, and the theoretical false alarm and detection performance formulas are given. Theoretical and simulation results show that at the expense of some performance degradation this technique completely removes the ambiguity threat in acquisition process.

In this letter, we develop a fully automatic robust linear receiver technique for joint space-time decoding and interference rejection in multi-access MIMO systems that use orthogonal space-time block codes and erroneous channel state information (CSI). The proposed receiver does not need any a priori knowledge of channel estimation errors and has a simple closed form. Numerical examples show that our method usually gives good performance in case of non-perfect CSI and/or low sample sizes when compared with other tested linear receivers.

In this letter, we propose an unambiguous tracking technique for the new multiplexed binary offset carrier (MBOC) modulated signals, which will most likely be employed in both European Galileo system and modernized global positioning system (GPS). The discriminator used in this technique is based on a pseudo correlation function. It uses two kinds of gating correlators and a novel combination function, which completely removes side peaks from the correlation function while keeping the sharp main peak. Results demonstrate that this technique is totally unambiguous while maintaining the same level of tracking performance with respect to thermal noise as the traditional MBOC tracking method.