SATELLITE SELECTION ALGORITHMS FOR POSITIONING, NAVIGATION AND TIMING USERS

Проведен обзор методов и алгоритмов формирования рабочего созвездия навигационных космических аппаратов при решении задач определения местоположения потребителя ГНСС. Появление новых орбитальных группировок и развитие прошлых поколений глобальных навигационных спутниковых систем (ГНСС) способствует увеличению как количества навигационных аппаратов, так и навигационных радиосигналов, излучаемых каждым спутником, в связи с чем решение проблемы выбора навигационных аппаратов является важной составляющей навигационной задачи. Рассмотрены исследования, посвященные типовым алгоритмам формирования рабочего созвездия, а также современным алгоритмам, построенным с привлечением элементов теории машинного обучения. Представлена связь ошибок определения координат потребителя, погрешностей определения псевдодальностей и пространственного расположения навигационных аппаратов и потребителя. Среди рассмотренных алгоритмов выделены три направления исследований: 1) нацеленных на поиск оптимального рабочего созвездия, обеспечивающего минимальную оценку выбранного геометрического фактора снижения точности; 2) нацеленных на поиск квазиоптимальных рабочих созвездий с целью уменьшения вычислительной сложности алгоритма ввиду большого количества видимых спутников; 3) позволяющих одновременно работать в совмещенном режиме по нескольким ГНСС. Приводятся особенности реализаций алгоритмов, их преимущества и недостатки. В заключении приведены рекомендации по изменению подхода к оценке эффективности алгоритмов, а также делается вывод о необходимости учета как геометрического расположения космических аппаратов, так и погрешности определения псевдодальности при выборе космического аппарата в рабочее созвездие The article provides an overview of methods and algorithms for forming a satellite constellation as a part of the navigation problem for the positioning, navigation and timing service. The emergence of new orbital constellations and the development of past GNSS generations increase both the number of navigation satellites and radio signals emitted by every satellite, and therefore the proper solution of satellite selection problem is an important component of the positioning, navigation and timing service. We considered the works devoted to typical algorithms of working constellation formation, as well as to modern algorithms built with the use of machine-learning theory elements. We present the relationship between user coordinates errors, pseudorange errors and the influence of spatial location of satellites and the user. Three directions of researche among reviewed algorithms are outlined: 1) finding the best satellite constellation that provides the minimum geometric dilution of precision; 2) finding quasi-optimal satellite constellation in order to reduce the computational complexity of the algorithm due to the large number of visible satellites; 3) possibility to work in a combined mode using radio signals of multiple GNSS simultaneously. The article presents the features of the algorithms' implementations, their advantages and disadvantages. The conclusion presents the recommendations to change the approach to assessing the performance of the algorithms, and concludes that it is necessary to take into account both the satellite geometric configuration, and pseudorange errors when satellite constellation is being formed

The first deep-learning search for radio technosignatures from 820 nearby stars

The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their “technosignatures". One theorized technosignature are narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radio frequency interference (RFI) that dominate the features across the band in searches for technosignatures. Here, we present the first comprehensive deep-learning based technosignature search to date, returning 8 promising ETI signals-of-interest for re-observation as part of the Breakthrough Listen initiative. The search comprises 820 unique targets observed with the Robert C. Byrd Green Bank Telescope, totaling over 480 hr of on-sky data. We implement a novel β−Convolutional Variational Autoencoder with an embedded discriminator combined with Random Forest Decision Trees to classify technosignature candidates in a semiunsupervised manner. We compare our results with prior classical techniques on the same dataset and conclude that our algorithm returns more convincing and novel signals-of-interest with a manageable false positive rate. This new approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.

Design o f Wireless Ad Hoc System u sing Adaptive Multi Hop Link State Optimal Routing Protocol

Now a days for the radio network communication multi-hop routing is used. This multi-hop routing technique covers larger coverage area. Therefore to reach at specific location data is transferred in form of packets from one node to other node. But for the transmission of radio signals over the large distance, large number of transreceivers are required and these transreceivers requires large power to operate. As a result, multi-hop routing can saves energy over separate routing. Therefore it is necessity to design a cost effective multi-hop routing technique for successful transmission of ratio packet data. In this paper a hop by hop adaptive link state optional routing (HALO) is explained. It is the first packet transmitting solution with hop by hop and link state routing, which reduces the cost of transporting data across a packet switch network[3]. The triple model is designed for multi hop packet routing. In this work each node of network iteratively and separately improves the small part of traffic bound. This algorithm finds the shortest path of specific location for every iteration and it is calculated by the marginal cost of the various links of network. The marginal link cost is used to calculate the shortest path between the node and the destination location. This marginal link cost is gathered from link state updates. The various networks changes are automatically identified by the adaptive method which is used in this paper. Due to this the exchange between the packets on wrong node is reduced over the overhead traffic. To validate these theoretical results the experimental evaluations and mathematical calculations are also reported in this work. Net beans java is the programmed use in this proposed project.

How to make a multi-billion dollar thermometer

The United States’ Global Positioning System (GPS), and similar geolocation systems such as Galileo, GLONASS, and Beidou are used by people all over the globe. Modern receivers of these global navigation satellite systems can track multiple satellites from different constellations. Casual, non-technical users are probably aware that the positional information provided is typically accurate to within a few meters. We could expect physics students to infer that, because these systems rely on the travel time of radio signals, this implies time measurement accuracy on the scale of tens of nanoseconds. This feature has led to GPS-enabled Internet time servers providing stratum 1 accuracy for under $1000. In this paper, we will show that we can couple a GPS unit to a field programmable gate array (FPGA) to determine the temperature in a room. The more serious application of this GPS-FPGA pairing is to provide precise time-stamping of events, thereby synchronizing data collection between stations across a room or across the globe.

OPTIMAL SOLUTION OF LINEAR EQUATION SYSTEM FOR RADIO SYSTEMS WITH MULTIPOLARIZATION MULTIPLEXING

Приводятся результаты исследования возможности увеличения эффективности использования радиочастотного спектра более чем в два раза за счет использования нескольких (N>2)радиосигналов с неортогональными поляризациями,передаваемых на общей несущей частоте. Предложены метод выявления такой возможности для заданной комбинации поляризаций радиосигналов, а также структурная схема и процедура определения параметров устройства поляризационного разделения радиосигналов на приемной стороне. Выполнен детальный анализ определения оптимальных коэффициентов элементов системы поляризационного разделения на приемной стороне четырех радиосигналов с учетом иррациональности коэффициентов анализируемой системы линейных уравнений. The results of the study of the possibility of increasing the efficiency of using the radio frequency spectrum by more than twice due to several (N>2) non-orthogonal polarizations of carrier radio signals are presented. The method to determine such possibility for any given set of polarizations, as well as a block diagram and a procedure for determining the parameters of a device for polarization separation of radio signals on the receiving side are proposed. The detailed analysis of the determination of optimal coefficients of elements of polarization separation system on receiving side of four radio signals is performed taking into account the irrationality of coefficients of analyzed linear equation system.

Outbreak of Negative Narrow Bipolar Events in Two Mid-Latitude Thunderstorms Featuring Overshooting Tops

Lightning discharges are the electrical production in thunderclouds. They radiate the bulk of radio signals in the very low-frequency and low-frequency (VLF/LF) that can be detected by ground-based receivers. One kind of special intra-cloud lightning discharges known as narrow bipolar events (NBEs) have been shown to be rare but closely linked to the convective activity that leads to hazardous weather. However, there is still lack of understanding on the meteorological conditions for thunderstorm-producing NBEs, especially for those of negative polarity, due to their rare occurrence. In this work, we aim to investigate what meteorological and electrical conditions of thunderclouds favor the production of negative NBEs. Combining with the VLF/LF radio signal measured by Jianghuai Area Sferic Array (JASA), S-band Doppler radar observation and balloon sounding data, two mid-latitude thunderstorms with outbreaks of negative NBEs at midnight in East China were analyzed. The comparison with the vertical radar profile shows that the bursts of negative NBEs occurred near thunderclouds with overshooting tops higher than 18 km. Manifestation of negative NBEs is observed with a relatively low spectrum width near thundercloud tops. Our findings suggest that the detection of negative NBEs would provide a unique electrical means to remotely probe overshooting tops with implications for the exchange of troposphere and stratosphere.

Detection of radio signals against the background of strong electromagnetic noise in transport

The problem of detecting a useful signal in the presence of a strong background noise is considered. To solve it, a statistical approach is used, based on a change in the level of chaos in the system when an additional random or deterministic process occurs, which is probabilistically independent from a set of stochastic phenomena that form background noise. It is shown that the occurrence of this process changes the level of entropy of the measured signal; this fact is the basis of the applied mathematical algorithm. It is based on the elements of the Fourier transform apparatus for the probability density with an appropriate choice of a nonlinear function of the random process under study. The proposed approach, based on variations in the randomness in the system in the presence of a useful signal, makes it possible to record its presence against the background of noise components even at low signal-to-noise ratios. The effectiveness of the method is confirmed both by theoretical justification and by the calculations presented in this work. The condition for the implementation of the technique described in the article, which does not impose restrictions on the studied physical fields and frequency ranges, is the comparability of the width of the probabilistic distribution of the desired useful signal with several intervals of discreteness of the measuring equipment. One of the results of this work is a high sensitivity to the emergence of independent random components.

Fingerprinting/indoor positioning using complex planar splines

The rapid development of various LBS-based applications and services that operate on the basis of the user’s current location, both global GPS and local LBS, today require the development of new and improved methods. This concerns, first of all, methods for determining the local location of LPS users in premises, if there is a high concentration of users and the presence of difficulties in the propagation of radio signals. the use of local methods of location determination based on the fingerprinting method is considered. It is shown that to improve the user positioning accuracy, it is expedient to use a combination of several methods. to determine the local location of the user, a method based on the finite element method and linear complex planar splines is proposed. the construction of linear complex planar splines is considered, their coefficients are found. finding the error in determining the coordinates of the user’s UE location is shown. The use of the proposed method will improve the accuracy of determining the coordinates of the user’s location and will ensure the provision of LBS services and applications to users in the premises under various conditions of their provision.

No Need of Data Pre-processing

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.