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.

This study focuses on Doppler frequency search strategy for signal acquisition in direct sequence spread spectrum (DSSS) systems. According to DSSS signal accumulation energy vibration characteristics, the whole two-dimension search region can be divided into non-vicinity region, transition region and vicinity region. If only one search strategy is used for the whole region, it is difficult to balance the frequency search times and the frequency estimation accuracy. To solve the above problem, this study proposes an intelligent Doppler frequency search (IDFS) strategy. A fuzzy logic (FL) controller with two sets of fuzzy parameters is utilized to adjust the frequency search step size in the non-vicinity region and vicinity region, respectively. In the non-vicinity region, by jointly considering signal amplitude and code phase to design the fuzzy parameters of the FL controller, the IDFS strategy can reduce the frequency search times while reducing frequency false detection caused by abnormal change of signal energy. In the vicinity region, the fuzzy parameters are designed based on our previous work which aims to achieve higher frequency estimation accuracy. The effectiveness of the IDFS strategy is evaluated through Monte Carlo simulations. Simulation results show that the proposed intelligent search strategy can reduce the frequency search times while ensuring frequency estimation accuracy compared to other existing search strategies. Moreover, the proposed search strategy can reduce false detection in the non-vicinity region.

The study on the frequency estimation of a real sinusoid has been addressed in this article. Discrete‐Fourier‐transform (DFT)‐based frequency estimation always uses the maximum bin of DFT as a coarse estimation followed by a fine estimation algorithm, which computes a correction term to compensate for the initial frequency offset caused by the coarse estimation. To enhance the frequency estimation accuracy, a fine estimation algorithm based on DFT samples and fuzzy logic (FL) is proposed in this study. Firstly, a modified phase‐based algorithm is presented to estimate the sign of the correction term. Then, a main‐lobe coefficient and a side‐lobe coefficient are constructed using the amplitude of three spectral lines to calculate the correction term. A FL controller is utilised to generate a weighted factor to adjust the weight of the main‐lobe coefficient and the side‐lobe coefficient in the formula of the correction term. Compared with other existing fine algorithms, the proposed algorithm improves the correct probability of sign estimation by 10%–20%, and improves the estimation accuracy by 5%–8% at lower carrier‐to‐noise ratios (CNRs). In addition, the performance of the proposed algorithm is less affected by the initial frequency offset than other algorithms.

The conventional phase deviation discriminator used in the decision feedback loop for carrier synchronization of APSK signal requires symbol decision for every constellation symbol. When the number of constellation symbols used for coherent integration becomes larger, the complexity would increase greatly. To solve this problem, this work proposes an improved phase deviation discriminator based on symbol decisions. It firstly executes doubling frequency on the APSK signal to reduce the modulation order and eliminate the modulation phase of the received signal. Then, it rotates the constellation clockwise and selects the constellation symbols that on the X-axis to execute coherent integration and phase deviation extraction. Compared to the conventional discriminator in the decision feedback loop, the proposed discriminator can reduce the symbol decision times and steps. Therefore, when a large number of constellation symbols are used for coherent integration, it can reduce the computational complexity significantly. Moreover, with a large number of constellation symbols, the proposed discriminator achieves better BER and RMSE performance than other existing feedback loops.

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 regarding their transmitter power. In our primary study, a D-CLU algorithm was proposed to perform lossless compression for telemetry data, and achieve better performance. However, a limitation of this algorithm is that the compression time may become longer when the clustering head (CH) and outlier (which are compressed by LZW algorithm) numbers increase. To reduce compression delay, this paper proposed a modified character string (MCS) parallel search strategy for LZW algorithm (denoted by MCS-based LZW). The proposed MCS-based LZW algorithm designs coding principle, dictionary update rule and search strategy according to the character string matching results. Example verification and simulation results show that the proposed algorithm can effectively decrease the dictionary search times, and thus reduce the compression time.