Adaptive Filtration of Parameters of the UAV Movement Based on the TDOA-Measurement Sensor Networks

Abstract

In modern conditions unmanned aerial vehicles (UAVs) generate new classes of threats, including their use for terrorist purposes. A feature of modern UAVs is the ability to perform sudden maneuvers and to keep the same position in the point in space. For the description of the UAV movement with various types of maneuver it is used a rectangular coordinate system. We use the model in the form of stochastic dynamic system with random structure in the discrete time in which the change type UAV movement occurs at random times. When a UAV emits a sign, its location can be determined by wireless sensor networks (WSN) using the TDOA method. On the basis of a mathematical apparatus of the mixed Markov processes for in discrete time optimal and quasi-optimal adaptive algorithms for filtration of UAV movement parameters based on the TDOA-measurement, sensor networks are synthesized. Devices that realize these algorithms are multichannel and belong to the class of devices with feedback between channels. At the same time, in a quasi-optimal algorithm, a sequential procedure of the arriving measurements from sensors of a sensor network is realized, which allows to avoid the inversion of large-dimensional matrices. An analysis of the quasi-optimal adaptive algorithm is performed using statistical modeling. On the intervals of hovering and of the UAV movements without maneuver, the developed algorithm allows to increase significantly the accuracy of the estimation of the UAV coordinates, and also to recognize various types of its movement with high probability level.

Research on distributed radiation source collaborative localization technology based on unmanned aerial vehicle platform

“…indicates the distance between the target and the observation station, so the distance difference between the target and the main station and the target to other observation stations is [3][4][5][6][7][8][9][10][11][12][13][14][15][16] :…”

“…The above expressions have a total of i=1,2,..., M, which can be expressed in matrix form [3][4][5][6][7][8][9][10][11][12][13][14][15][16] :…”

“…The covariance matrix P of the positioning error is [3][4][5][6][7][8][9][10][11][12][13][14][15][16] :…”

“…Utilizing the advantages of UAV "bee colony" group platform, such as low cost, massive networking, large coverage, flexible use, and strong anti loss capability, it can realize real-time data sharing, multi crew network, and cooperative cooperation among UAVs, and complete operational tasks such as area search and attack, reconnaissance and suppression, psychological warfare, and tactical suppression under the guidance of operators. [1][2][3][4][5][6][7][8][9] The distributed collaborative reconnaissance and positioning methods based on unmanned aerial vehicle platforms mainly include dual station cross positioning, multi station time difference positioning, etc. Dual station cross positioning requires each platform to have independent angle measurement capabilities for target radiation sources, and the positioning accuracy is lower than that of multi station time difference positioning methods.…”

Аналіз Сучасних Хвилевідних Поляризаційних Пристроїв

“…Поляризатори виготовляються за допомогою штирів [44][45], діафрагм [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43], їх комбінацій [46][47][48], структур із коаксіалами та ребрами [49][50][51][52][53][54][55][56]. Сфера застування цих пристроїв дуже широка від супутникових систем до радіонавігаційних, радіолокаційних систем, різноманітних антенних решітках з обробкою сигналів [57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72].…”