Cooperative localization is widely used to solve the network localization problem in global navigation satellite system (GNSS) denied environments. In particular, gathering the measurement results of all nodes on a specific node requires substantial time for aircraft networks. Therefore, methods simultaneously calculating all the target node positions are challenging to apply due to information delay. This study uses only two hops measurements information relying on the time of arrival (TOA) and angle of arrival (AOA) to propose a weighted least squares (WLS) method that decentralizes the calculation of a specific single target node position in a 3D environment. Moreover, the effect of TOA and AOA measurement errors on localization accuracy is analyzed based on the geometric relationship. Consequently, a novel weight-factor calculation method is designed. The simulation results show that the proposed cooperative localization method can improve localization accuracy compared with the decentralized non-cooperative method. Furthermore, the proposed hybrid approach provides superior localization accuracy than the weight-factor calculation method which only considers the distance.

This paper addresses multi-rigid-body localization problems in three-dimensional 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 multi-rigid-body localization scenarios. For the stationary case, we first reformulate the cooperative localization problem as a non-convex 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.

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.

The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry systems to improve transmission efficiency and reduce the burden of required spacecraft resources, in particular of their transmitter power. In this paper, a differential-clustering (D-CLU) compression algorithm for lossless compression of real-time aerospace telemetry data is proposed. Because of the temporal-spatial correlation characteristics of telemetry data, the use of a differential compression strategy can efficiently improve compression performance. However, differential compression faces two non-negligible problems, reliability and compression ratio, both of which may be solved by clustering. This is the approach pursued in the proposed D-CLU compression algorithm. The algorithm involves both clustering and coding. In the clustering stage, a one-pass clustering method based on a similarity metric is used to group the original data into clusters. In the coding stage, two traditional encoding algorithms, Lempel–Ziv–Welch and run-length encoding, are used to encode the data, based on the clustering results. Compared with the direct use of differential compression, the clustering-based differential compression algorithm can reduce the error propagation range, thus increasing reliability. The experimental results demonstrate that the proposed D-CLU algorithm can also achieve better compression performance than the other existing algorithms.

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.

Polynomial interpolation is widely used in channel modeling for aerospace tracking, telemetry, and command (TT&C) channel simulators, to accurately regenerate the satellite motion parameters with a high sampling rate from the available low sampling rate motion data. In this paper, a novel piecewise osculating interpolation approach is proposed to improve modeling precision. It uses the available distance and its higherorder derivatives at each pair of adjacent knots to construct the piecewise interpolation polynomial, thus achieving a better precision and continuity of the resulting motion parameters. Moreover, it is efficiently implemented with an interpolation filter based on a modified Farrow structure, and the designed filter can interpolate the motion data obtained both through uniform or non-uniform sampling. Additionally, a sampling optimization algorithm is proposed. It optimizes the motion data sampling by minimizing the maximum interpolation error given the average sampling interval and interpolation polynomial order, and therefore, improves the precision further without increasing the costs. The validity of the proposed approach is verified by numerical simulation, and its advantages are demonstrated by comparison with existing methods.