Many applications such as intelligent transportation systems, spacecraft or autonomous aerial vehicles formation are very dependent on high-precision relative navigation solutions. This paper proposes a two-way ranging and timing (TRT) method that can provide high-precision inter-node relative ranging and timing results for relative navigation. Affected by the high-dynamics between nodes and nonuniform measuring interval caused by measurement instant jitter, the conventional TRT methods cannot achieve high-precision ranging and timing. To this end, we propose a TRT method using osculating polynomial timestamp unification. In this method, we first use the piecewise osculating function to construct the generalized-order timestamp unification model, and then use binomial theorem and boundary conditions to solve the unknown coefficients. Next, we implement time-varying interval processing and symmetry processing skills to reduce computational burden. Finally, we provide the estimator of the actual distance and clock offset (considering clock drift and initial clock offset) between two asynchronous nodes. Based on the proposed TRT method, we further propose a measuring precision improvement strategy based on the measurement interval adaptive optimization. This strategy can adaptively adjust the measurement interval according to the dynamics. The superiority of the proposed TRT method in terms of precision is verified by simulations under different measurement instant jitter, dynamics, and standard deviation of measurement noise.

GNSS-based cooperative positioning offers advantages such as high accuracy, robustness, and availability, making it highly effective in enhancing the positioning performance of intelligent vehicles in urban environments. Due to the complex signal propagation conditions in urban settings, GNSS and inter-vehicle measurements often include uncertainties caused by non-ideal factors. These uncertainties introduce anomalous measurement biases and noise with unknown characteristics, degrading positioning accuracy. To address this issue, this paper proposes a robust distributed Bayesian cooperative positioning algorithm. We first introduce latent variables to characterize unknown uncertainties in GNSS and V2V measurements. These latent variables are modeled using Gaussian-Gamma conjugate distributions, with the shape of the distribution determined by hyperparameters. Based on the Variational Bayesian (VB) theory, we then decompose the robust cooperative positioning problem into an alternating estimation of vehicle states and measurement uncertainties. We derive message-passing-based closed-form solutions for updating the variational posteriors of vehicle states and latent variables in a distributed manner, allowing all parameters to be estimated algebraically. Additionally, the computational complexity and communication overhead are also analyzed. Performance evaluation results using datasets from real urban environments show that the proposed algorithm achieves higher positioning accuracy compared to existing methods and is more robust to anomalous measurements. Furthermore, the proposed algorithm is insensitive to nominal parameter settings, featuring low computational complexity and communication overhead.

Satellite networks play important roles in fields such as communication, meteorology, and the Internet of Things. However, in highly dynamic multilayer heterogeneous satellite networks, the diverse satellite transmission demands pose challenges for network management. In large-scale satellite networks, conventional centralized network management methods have various adverse effects, such as high latency, high communication overhead, and high computational complexity. In this article, to overcome these challenges, we propose a distributed scheme for allocating intersatellite links between satellites at different orbital heights considering their different transmission demands. Specifically, we model the distributed link allocation framework as a Stackelberg game. Low Earth orbit (LEO) satellites, as leaders, apply for access to medium Earth orbit (MEO) satellites using a proposed link selection algorithm based on a stochastic best response strategy. The MEO satellites, as followers, allocate link resources using a proposed heuristic-based time slot resource allocation algorithm in accordance with the accessing applications. Simulation results show that the proposed algorithm outperforms benchmark algorithms in terms of the degree of matching between the transmission capacity and transmission requirements.

Aircraft network has a wide range of applications and plays an important role in various fields. However, the wide distribution and high dynamic motion of aircraft pose challenges to the multiple access management in aircraft networks. On the one hand, different relative distance of aircraft lead to different channel delays, and when the distance between aircraft is far, time-domain multiple access protocols require large guard intervals; On the other hand, the high-speed movement of aircraft leads to Doppler frequency shift on the received signal, resulting in inter carrier interference in frequency domain multiple access methods. These disadvantages extremely reduce the overall performance of the network. In this article, we use delay Doppler (DD) communication technology to provide multiple access for dynamic aircraft networks. Specifically, in the uplink channel of the aircraft network, we distinguish aircraft with different relative distances and velocities on the DD domain, and proposed a heuristic based resource allocation algorithm to allocate DD domain resources to each aircraft, thereby reducing the interference between aircraft. The simulation results show that compared to the benchmarking algorithm, the proposed algorithm can effectively improve the transmission rate of the aircraft network.

The direct position determination (DPD) technique utilizes raw received signals to localize agents in a single step, eliminating the need for intermediary measurements. The DPD is recognized for its accuracy superiority over the two-step approach, especially under low signal-noise-ratio (SNR) condition. However, few existing DPD research has focused on scenarios involving moving or unsynchronized agents. In this article, we develop a novel and extended problem, direct localization and synchronization (DLAS) for highly mobile agents with unsynchronized frequency shifts in collocated multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The base stations (BSs) sequentially broadcast signals in a time-division multiple access (TDMA) manner, and both Doppler effect and oscillator’s nondeterminism lead to frequency shifts at the agent side. In order to compensate for the position variation of the fast-moving agent, we construct a motion model with uniform acceleration. Next, we propose a computationally efficient DLAS method based on the maximum-likelihood (ML) principle. Specifically, we first decouple the frequency shifts from other unknowns by exploiting the periodicity of block-type pilots and determine a nonlinear optimization problem. We then develop an iterative solution using the frequency shifts to optimally extract real DLAS parameters from complex signal observables. Moreover, we present the closed-form Cramér-Rao lower bound (CRLB) for our estimators determined from the derived general bounding result in complex field. We theoretically analyze the performance gain owing to prior information, and compare the computational complexity among different algorithms. Finally, we provide extensive numerical results to establish the superiority of our proposed method.

Location awareness is a key enabling technology for many applications and services of the Internet of Things (IoT). Since densely deployed heterogeneous agents in IoT typically have mobility and different qualities of internal clocks, as well as limited computation and communication capabilities, high-precision network localization is a challenging problem. Existing methods do not compensate the position variation caused by the agent mobility during a measurement phase, which will result in estimation error, and have high-computational complexity. In this article, we propose a cooperative, distributed, and low-complexity algorithm for network simultaneous localization and synchronization (SLAS), which is suitable for large-scale network consisting of heterogeneous agents with mobility, time-varying clock and time-varying connectivity. We first propose a new measurement model based on the asymmetric time-stamped communication scheme, which compensates for the position variation of each agent within a measurement phase. Second, we construct a factor graph (FG) to represent the underlying Bayesian estimation problem, and apply belief propagation to obtain the marginal distribution of each agent’s state. To deal with the complex nonlinear measurements, we extend the posterior linearization technique by using iterative statistical linear regression with respect to the joint posterior of neighboring agents. All the messages on FG are derived in Gaussian form and the computational complexity at each agent is linear in the number of neighboring agents, which has significant advantages in large-scale networks. Simulation results demonstrate that the proposed algorithm has better estimation performance and lower average running time compared to existing methods.

The joint localization and synchronization (JLAS) of moving agents with clock offsets is critical to facilitating location services for Internet of Things (IoT). Existing methods using one-way time-of-arrival measurements require anchors to be synchronized and the agent’s motion to be modeled with a constant velocity. However, the requirement of synchronization between anchors limits the flexibility and scalability of IoT networks. Moreover, existing methods are inapplicable for agents that perform arbitrary motions. In this study, we developed a set of methods to solve the JLAS problem in asynchronous networks. First, we modeled the motion of the moving agent and classified it into two categories: 1) acceleration and 2) time-varying motion. We showed that the existing modeling motion is a special case of acceleration motion. Second, for the case of acceleration motion with priori information, we proposed the optimal JLAS method, namely, JLAS-KAM, to compensate for the movement-caused estimation error. Next, we developed a maximum likelihood estimator, namely, JLAS-UAM, to jointly estimate the position, velocity, and acceleration of the agent in the absence of priori information. Third, for the case of time-varying motion, we developed the optimal JLAS method, namely, JLAS-TVM, to jointly estimate the agent position and velocity at each time instant. Moreover, iterative algorithms were proposed to solve optimization problems. We derived the Cramer–Rao lower bound for three proposed methods and analyzed their performance. Simulation results verified the theoretical analysis of the estimation performance and revealed the characteristics and advantages of the proposed methods.

This article addresses multirigid-body localization problems in 3-D wireless sensor networks for both stationary and moving cases using range and Doppler measurements. The challenge of stationary (moving) rigid bodies localization is that not only the position (velocity) but also the rotation angles (angular velocity) need to be estimated, resulting in a nonlinear optimization problem with nonlinear constraints. Existing methods are limited to the localization of a single rigid body in a regular network and are difficult to extend to cooperative multirigid-body localization scenarios. For the stationary case, we first reformulate the cooperative localization problem as a nonconvex and smooth optimization problem with respect to two uncoupled blocks of unknown parameters and auxiliary variables. Next we propose an alternating minimization (AM)-based algorithm, which achieves localization in the absence of accurate prior information by setting the value of one block to be a minimizer of the objective with respect to the chosen block alternately. Subsequently, we propose an online updating algorithm that achieves precise localization by solving the maximum likelihood estimation problem with nonlinear constraints using the Gauss–Newton method on the orthogonal group. Both range and Doppler measurements are used to solve the localization problem in the moving case. Simulation results show that the proposed algorithms achieve better estimation accuracy and anti-noise performance than existing methods for both the stationary and moving cases, and they are suitable for both regular and irregular networks.

Existing trajectory data reconstruction algorithms for tracking, telemetry, and command (TT&C) channel simulators cannot achieve continuity and noise reduction simultaneously. By combining the B-spline interpolation algorithm with the least-square (LS) approximation algorithm, we propose a novel trajectory data reconstruction algorithm using LS B-spline approximation. In addition, a polyphase structure is proposed to implement the algorithm. Compared with existing algorithms, the proposed algorithm achieves higher reconstruction precision when the known trajectory data include measurement errors; moreover, it preserves continuity. Numerical simulation results verified the validity and effectiveness of our proposed algorithm.

The study on the Doppler frequency search strategy in direct sequence spread spectrum (DSSS) systems has been addressed in this article. To reduce the estimation error and alleviate the possible false detection, a novel Doppler search algorithm that combines the serial-based search and the fuzzy-logic (FL)-based search is presented. At first, the serial-based search is used until the decision variable exceeds a preset threshold; the FL-based search is then performed for a second search and the final decision is made. In the second search stage, Doppler search step size is adaptively adjusted by an FL controller. To design a robust FL controller, the characteristics of the spectrum distribution and several possible abnormal conditions are analyzed, input and output fuzzy membership functions are derived, and a set of fuzzy rules is formulated. Finally, the effectiveness of the proposed approach is evaluated through Monte Carlo simulations. The obtained results show that a smaller estimation error is achieved when the proposed algorithm is used; further, reasonable mean acquisition times are attained compared with other existing search techniques. Furthermore, the proposed algorithm can alleviate possible false detection in the abnormal conditions.

Tracking, telemetry, and command (TT&C) channel simulators need to accurately simulate the satellite motion parameters and dynamic transmission delays in TT&C channels. In this letter, a generalized high-precision simulation method using B-spline interpolation and differentiation is proposed. It regenerates the motion parameters with a high sampling rate using only the available distance data with a low sampling rate, and delays the arbitrary band-limited TT&C signals by the transmission delay resulting from the obtained distance. A modified recursive filter structure is constructed to implement simultaneous B-spline interpolation and differentiation with low implementation complexity. Compared with conventional methods, the proposed approach simulates the motion parameters with higher precision and preserves their continuity.