In this paper, aiming at the severe problems of UWB positioning in NLOS-interference circumstances, a complete method is proposed for NLOS/LOS classification, NLOS identification and mitigation, and a final accurate UWB coordinate solution through the integration of two machine learning algorithms and a hybrid localization algorithm, which is called the C-T-CNN-SVM algorithm. This algorithm consists of three basic processes: an LOS/NLOS signal classification method based on SVM, an NLOS signal recognition and error elimination method based on CNN, and an accurate coordinate solution based on the hybrid weighting of the Chan–Taylor method. Finally, the validity and accuracy of the C-T-CNN-SVM algorithm are proved through a comparison with traditional and state-of-the-art methods. (i) Focusing on four main prediction errors (range measurements, maxNoise, stdNoise and rangeError), the standard deviation decreases from 13.65 cm to 4.35 cm, while the mean error decreases from 3.65 cm to 0.27 cm, and the errors are practically distributed normally, demonstrating that after training a SVM for LOS/NLOS signal classification and a CNN for NLOS recognition and mitigation, the accuracy of UWB range measurements may be greatly increased. (ii) After target positioning, the proposed method can realize a one-dimensional X-axis and Y-axis accuracy within 175 mm, and a Z-axis accuracy within 200 mm; a 2D ($X,Y$) accuracy within 200 mm; and a 3D accuracy within 200 mm, most of which fall within (100 mm, 100 mm, 100 mm). (iii) Compared with the traditional algorithms, the proposed C-T-CNN-SVM algorithm performs better in location accuracy, cumulative error probability (CDF), and root-mean-square difference (RMSE): the 1D, 2D, and 3D accuracy of the proposed method is 2.5 times that of the traditional methods. When the location error is less than 10 cm, the CDF of the proposed algorithm only reaches a value of 0.17; when the positioning error reaches 30 cm, only the CDF of the proposed algorithm remains in an acceptable range. The RMSE of the proposed algorithm remains ideal when the distance error is greater than 30 cm. The results of this paper and the idea of a combination of machine learning methods with the classical locating algorithms for improved UWB positioning under NLOS interference could meet the growing need for wireless indoor locating and communication, which indicates the possibility for the practical deployment of such a method in the future.

To meet the urgent need for high-precision tracking and reliable cataloging of non-cooperative targets in the Earth–Moon space, this paper proposes a GNSS Inter-Satellite Link and Connected Element Interferometry (CEI)-based measurement method for high-value cislunar space targets. Firstly, the general flow and basic scenario of the proposed method are given, followed by the mathematical model which, mainly includes four parts: (i) dynamical constraint equations for targets; (ii) GNSS-based interplanetary link for irradiation of targets; (iii) transmission loss equation of GNSS inter-satellite link signal in Earth–Moon space; (iv) CEI-based precision measurements of targets. On this basis, the full process link budget analysis is carried out, followed by the performance evaluation, which includes the reception performance of CEI receiving arrays and the measurement accuracy of targets. The feasibility of the proposed method is evaluated and verified in experiments, and it is illustrated that (i) for inter-satellite link visibility analysis, at least 20 satellites can simultaneously provide inter-satellite link signals to the Earth–Moon space targets, with a single GEO satellite up to $8.5$ h continuously, while the chain access can be available at up to 73,000 km, with the angle ranging from $-{80}^{\circ }$ to ${360}^{\circ }$; (ii) the Max Duration of Chain Access for BD3-lunarprobe-CEI (from 24 March 2023 04:00:00.000 to 31 March 2023 10:00:00.000) is 50,998.804 s/day, with a Total Duration of 358,408.797 s in 7 days; (iii) for link budget and measurement accuracy analysis, even beyond the farthest Earth–Moon Lagrangian point, the $C/N_0$ will be above $56.1$ dBHZ, while even approaching the distances of $4.5\times 10^5\mathrm{km}$, the ${\sigma }_{DLL}$ and ${\sigma }_{FLL}$ will be below 5.345 m and $3.475\times {10}^{-4}$ m/s, respectively, and the final measurement error will remain at 62.5 m with the proposed method. The findings of this paper could play a key role in future increasingly serious space missions, such as Earth–Moon space situational awareness, and will have a broad application prospect, if put into actual testing and operations.

With the rapid increase of data generated by remote sensing satellites, the demand of real-time demodulation of ultra-high-speed remote sensing data by digital receivers has become a research hotspot and difficulty. Aiming at the international research gap of parallel demodulation of remote sensing satellite data using multi-channel channelization technology, a parallel matched filtering architecture of filter banks based on Weighted OverLap-Add (WOLA) structure is designed. This structure has the following advantages: (a) The decimation factor is flexible and adjustable when the signal is channelized, which is not restricted by the number of channels. (b) The signal is divided into several sub-bands according to the frequency spectrum, which is beneficial to the parallel analysis and processing of the signal. This paper completes the parallel matched filtering processing and provides ideas for the subsequent processing operation. (c) The structural channelization decomposition has strong expandability and can be flexibly applied to signal transmission of different systems. Aiming at the characteristics of high speed and wide bandwidth of remote sensing data transmission, this paper designs the channelization method of filter bank based on WOLA structure, and deduces and realizes the sub-band matching filtering operation. Taking QPSK modulation as an example, the simulation results show that parallel matched filtering has almost no loss in bit error performance. The research results of this paper have great reference value for designing multiphase parallel receivers of high-speed remote sensing data in the future.

Considering the problem that the timing synchronization calculation in high-speed remote sensing signal reception is complex and it is difficult for it to be parallel, this paper deduces and designs a parallel timing error estimation and correction scheme. This paper presents the design of polyphase DFT filter banks with non-maximum decimation. The feedforward timing error estimation and correction method is then improved to enhance synchronization performance. Finally, an implementation scheme for parallel timing error estimation and correction is proposed using the polyphase filter bank time domain decomposition method and the filter polyphase model. In the estimation module, the parallel implementation structure of the joint second-order and fourth-order cyclic statistics methods is designed, which improves the estimation accuracy. In the correction module, a fractional delay filtering method with higher accuracy is adopted in order to improve the calibration accuracy and reduce the computational complexity. The timing synchronization of a high-speed remote sensing signal with timing error is simulated and verified. The experimental results show that the parallel method proposed in this paper greatly reduces the processing speed of subband data, and has a good synchronization performance, which is close to the theoretical limit in the demodulation error rate. This paper utilizes a multi-phase DFT filter bank architecture to achieve parallel timing synchronization, which presents a novel approach for the future parallel reception of high-speed remote sensing signals.

As one of the most widespread and important types of terrestrial vegetation in the world, grasslands play an irreplaceable role in global climate change. The grasslands of Inner Mongolia, represented by the Xilin Gol League, are typical of Eurasian grasslands and have an important ecological status in the world. In this paper, taking the grassland of Xilin Gol League as the research object, based on the machine learning method, we mainly carry out two aspects of work: the prediction of grassland soil health and evaluation of grassland sustainable development. To address the issue of predicting soil health in grasslands, we focus on an important indicator in grasslands: soil moisture. By analyzing the characteristics of soil moisture time series values and related influencing factors, based on a NAR neural network model, three important factors of soil moisture were predicted: soil evaporation data, average air temperature, and precipitation. Subsequently, the corresponding soil moisture calculation model was trained using regression models based on hyperparameter optimization, and the final predicted soil moisture values were obtained for different months and depths in 2023 and 2024. To evaluate the sustainability of grassland development, we developed a model for the degree of grassland desertification based on the kernel principal component analysis, focusing on three dimensions: environmental factors, surface factors, and human factors. Based on this, a quantitative definition of soil denudation is given by analyzing the main influencing factors of grassland soil degradation. At the same time, a prediction model for the evaluation of soil slumping was established based on a fuzzy comprehensive evaluation matrix, and the evaluation weights of each major factor were given and analyzed. Based on the above research, this paper suggests a reasonable grazing strategy for the grassland areas of the Xilin Gol League: when the grazing intensity is medium and the total number of grazing days is [85, 104] days in a year, the degree of soil slumping and soil desertification in the pastures is minimized. The research results of this paper are useful for the future maintenance and management of the grasslands of Xilin Gol League and other similar areas.

With the development of low-orbit mega-constellations, low-orbit navigation augmentation systems, and other emerging LEO projects, the tracking accuracy requirement for low-orbit satellites is constantly increasing. However, existing methods have obvious shortcomings, and a new tracking and measurement method for LEO satellites is thus urgently needed. Given this, in this paper, a Connected Element Interferometry (CEI)-based “near-field” measurement model for low-orbit satellites is proposed. On this basis, the goniometric error formula of the model is derived, and the factors included in each error source are briefly discussed, followed by the simplification of the error formula. Furthermore, for the feasibility analysis of the proposed method, the common view time of CEI array on LEO satellites is analyzed in different regions and different baseline lengths. Finally, this paper simulates the effects of satellite–station distance, baseline length, and goniometric angle on the error coefficients in the goniometric error formula, and provides the theoretical goniometric accuracy of this model for different baseline lengths and goniometric angles. Under a baseline length of 240 km, the accuracy can reach 10 nrad. The research results of this paper could play the role of theoretical a priori in accuracy prediction in future low-orbit satellite tracking measurements.