RU2562616C1 - Method of acquiring radio information and radio system therefor - Google Patents

Abstract

FIELD: radio engineering, communication.

SUBSTANCE: when tracking an aerial object based on primary radio information, receiving stations perform simultaneous primary filtering of separate bearings according to the arrival time thereof, wherein movement of the aerial object is considered straight and uniform, otherwise the movement is considered a manoeuvre. Generation of an initial estimate of the approximate vector of trajectory parameters of the aerial object and a covariance error matrix at the receiving stations are carried out on the first bearing coming from one of the information sensors on a new aerial object. Further, the method includes final information filtering to obtain a refined vector of trajectory parameters of each aerial object and an algorithmic covariance error matrix of observation parameters of receiving stations, outputting an accurate estimate of trajectory parameters of each aerial object for accurate monitoring of the nature and parameters the flight of said object. Filtration of separate bearings of the aerial object at the receiving stations according to the arrival time thereof is carried out in a certain manner.

EFFECT: rapid evaluation of the presence and nature of the flight trajectory of an aerial object.

2 cl, 5 dwg

Description

The invention relates to the field of electronic intelligence and can be used to determine the location of the source of scattered radio emission by radar stations of airborne objects using receiving posts with parallel scanning of radio frequencies.

- для приемных постов с параллельным сканированием радиоволн по частоте, где: x_i, y_i, z_i - координаты приемного поста, x, y, z - координаты воздушного объекта, далее преобразованные результаты измерений отождествляют между собой и с построенными ранее траекториями и определяют принадлежность поступивших данных тем или иным воздушным объектам или уже имеющимся траекториям их полета, не отождествленные данные используют для завязки новых траекторий воздушных объектов [1].There is a method of obtaining radio information by radio intelligence stations that are part of a multi-positional passive location complex, which consists in the fact that radio emitted from airborne objects located on the ground are sent to a central receiving point, where they are transformed into a single central, for example, Cartesian coordinate system from the beginning in the central reception post, all newly received changes in the position of the airborne object are grouped by radio engineering signs and predelyayut location of air triangulation object decision task

- for receiving posts with parallel scanning of radio waves by frequency, where: x _i , y _i , z _i are the coordinates of the receiving post, x, y, z are the coordinates of the airborne object, then the converted measurement results are identified among themselves and with the previously constructed trajectories and determined whether the received data belong to one or another airborne object or their existing flight paths, unidentified data are used to set new trajectories of airborne objects [1].

A device for a multi-position passive location complex consisting of several information sensors of receiving posts of radio intelligence stations capable of measuring in the azimuthal and elevation planes the direction of movement of airborne objects with radiating electronic means and recording the moment of transition from radiating means when changing the direction of movement of airborne objects, receiving posts capable of scan in frequency in parallel and determine the location of an air object by and the angulation problem [1].

Implementation of synchronous scanning in frequency and instantaneous circular viewing in azimuth in radio intelligence reconnaissance stations allows, along with rough bearings - primary measurements of the azimuth type, to calculate at the central reception station of the passive location complex the difference in signal reception time. However, radiation from radio-electronic means of airborne objects is not detected by all receiving posts and there is an incompleteness of the processed information received. The identification and integration of observation vectors with coordinate information characterized by incompleteness of its composition is not provided. The irrational use of excess information in the trajectory tracking algorithms of airborne objects also occurs.

A known method of obtaining radio information by radio intelligence stations that are part of a multi-positional complex of a passive location, which consists in the fact that the receiving posts of radio emission from air objects spaced on the ground send data to a central receiving post, where they are transformed into a single central Cartesian coordinate system with the beginning at central reception post, all newly received bearings or differences in the time of radiation of signals from airborne objects are grouped by radio signs, after which they solve the problem of identifying private flight paths of air objects and radio engineering marks obtained as a result of solving the problem of determining the location of air objects by solving the triangulation problem

- для приемных постов с параллельным сканированием радиоволн по частоте, где x_i, y_i, z_i - координаты приемных постов, x, y, z - координаты воздушного объекта, при этом отождествление трасс и радиотехнических отметок производят расчетом попадания отметки воздушного объекта в строб автосопровождения при выполнении условия $$d\ge \sqrt{{\left(x^{э}-x\right)}^2+{\left(y^{э}-y\right)}^2}$$

, где d - строб автосопровождения, определяемый максимальной скоростью воздушного объекта и ошибками определения его координат, x^э, y^э - экстраполированные координаты воздушного объекта, причем в процессе трассового сопровождения воздушного объекта для снижения воздействия шума и для измерения положения объекта по каждой координате применяют α, β - фильтры, которые обеспечивают оценку положения и скорости воздушного объекта при его равномерном прямолинейном движении, далее результаты измерений отождествляют между собой и с построенными ранее траекториями и определяют принадлежность поступивших данных воздушным объектам или уже имеющимся траекториям их полета [2].

- for receiving posts with parallel scanning of radio waves by frequency, where x _i , y _i , z _i are the coordinates of the receiving posts, x, y, z are the coordinates of the airborne object, while the identification of the tracks and radio engineering marks is performed by calculating the hit of the airborne object’s strobe auto tracking when the condition is met $$d\ge \sqrt{{\left(x^{\mathrm{uh}}-x\right)}^2+{\left(y^{\mathrm{uh}}-y\right)}^2}$$

, where d is the auto tracking strobe determined by the maximum speed of the air object and errors in determining its coordinates, x ^e , y ^e are the extrapolated coordinates of the air object, and in the process of tracking the air object to reduce the impact of noise and to measure the position of the object at each coordinate, α , β - filters that provide an assessment of the position and speed of an air object during its uniform rectilinear movement, then the measurement results are identified with each other and with the built and the previously determined paths and received data belonging air existing objects or their flight paths [2].

However, α, β - filters in the block of track tracking of air objects do not allow to obtain clear trajectories of the movement of air objects and their coordinates at a given point in time. In multi-positional complexes of a passive location, where single measurements from the same airborne object can be recycled from different positions of the airborne object, moreover, irregularly in time and with different accuracy, α, β filters are not effective.

, а также из α, β - фильтров в блоке трассового сопровождения воздушных объектов [2].A device is known for a multi-position complex of a passive location, consisting of several information sensors of receiving posts of a radio reconnaissance station, capable of measuring in the azimuth and elevation planes the direction of motion of airborne objects with radiating electronic means and recording the moment of transfer of pulses from radiating means when changing the direction of movement of airborne objects, receiving posts capable of scanning in frequency and determining the location of an air object by solving triangulation problem, an electronic unit autotracking air objects in gate size $$d\ge \sqrt{{\left(x^{\mathrm{uh}}-x\right)}^2+{\left(y^{\mathrm{uh}}-y\right)}^2}$$

, as well as from α, β - filters in the track tracking unit of air objects [2].

In single-position survey radar stations, in which single measurements are sent to secondary processing regularly with a review period, and their accuracy for each air object is unchanged in several adjacent review periods, the simplest first-order filters (for each coordinate) with constant smoothing coefficients α, are widely used β (the so-called "α, β - filters"). These filters, with the appropriate choice of α, β, provide an estimate of the position and speed of the air object with its uniform rectilinear movement with a minimum mean square error.

, где x, y - координаты воздушного объекта, x^э, y^э - экстраполированные координаты воздушного объекта; производят завязку траекторий воздушных объектов путем вычисления начальных параметров возможной траектории нового воздушного объекта - координат, скорости, направления движения, ковариационной матрицы ошибок оценки этих параметров по отметкам, полученным в различных циклах сканирования воздушного объекта и содержащихся в стробах привязки; проверяют истинность завязываемых траекторий воздушных объектов и производят подтверждение их траекторий, принимают решение об обнаружении воздушного объекта в виде (k/n - l) при появлении k отметок в n смежных обзорах, при этом траекторию считают ложной при отсутствии отметок в n смежных обзорах и при отсутствии отметок в 1 смежных обзорах, а по измерениям, полученным многопозиционным комплексом пассивной локации, производят сопровождение воздушного объекта, причем в процессе трассового сопровождения воздушного объекта применяют фильтр Калмана, использующий вероятностную модель динамики воздушного объекта [3].The closest in technical essence to the proposed one is a method for receiving radio technical information by radio intelligence stations, which are part of a multi-positional passive location complex, which consists in the fact that data receiving stations located on the ground receiving radio emission from airborne objects are sent to a central receiving station, where they are converted to a central one Cartesian coordinate system with a start at the central receiving station, all the received primary radio engineering measurements are tied to rovozhdaemym radio communications paths air objects on the set of radio isolated marks formed during multiple scans, perform a procedure of detection of radio paths, which consists of the following steps: calculating the planar coordinates x, y, z object air; determine the size of the gates d of the anchor, based on the maximum speed of air objects and errors in determining coordinates in the strobe $$d\ge \sqrt{{\left(x^{\mathrm{uh}}-x\right)}^2+{\left(y^{\mathrm{uh}}-y\right)}^2}$$

where x, y are the coordinates of the air object, x ^e , y ^e are the extrapolated coordinates of the air object; set up the trajectories of airborne objects by calculating the initial parameters of the possible trajectory of a new airborne object — coordinates, speed, direction of movement, covariance matrix of errors in estimating these parameters from the marks obtained in various scanning cycles of the airborne object and contained in the reference strobes; they verify the truth of the trajectory of the airborne objects and confirm their paths, make a decision to detect the airborne object in the form (k / n - l) when k marks appear in n adjacent surveys, while the trajectory is considered false if there are no marks in n adjacent surveys and there are no marks in 1 adjacent reviews, and according to the measurements obtained by the multi-position complex of the passive location, an air object is tracked, and in the process of track tracking of an air object, filter K is used alman using a probabilistic model of the dynamics of an air object [3].

, где x, y - координаты воздушного объекта, x^э, y^э - экстраполированные координаты воздушного объекта, а также блока трассового сопровождения воздушных объектов с фильтром Калмана вероятностного моделирования динамики воздушного объекта [3].A device is known for a multi-position passive location complex consisting of several information sensors of receiving posts of a radio intelligence station capable of measuring in the azimuthal and elevation planes the direction of movement of airborne objects with radiating radioelectronic means, recording the moment of transition of pulses from radiating means when changing the direction of movement of airborne objects, scanning by frequency and determine the location of an air object by solving the triangulation problem, c ntralnogo electronic control unit fasting autotracking air objects in the gate $$d\ge \sqrt{{\left(x^{\mathrm{uh}}-x\right)}^2+{\left(y^{\mathrm{uh}}-y\right)}^2}$$

, where x, y are the coordinates of the air object, x ^e , y ^e are the extrapolated coordinates of the air object, as well as the track tracking unit of the air objects with the Kalman filter of probabilistic modeling of the dynamics of the air object [3].

In multi-position complexes of a passive location, where single measurements on the same airborne object can be re-processed from different positions irregularly in time and with different accuracy, the simplest “α, β-filters” are ineffective, therefore they use more complex Kalman filters in different modifications, although their implementation requires higher performance computing tools.

на основе оценки X (полученной по предыдущим k наблюдениям воздушного объекта в моменты t_k) и вновь поступившего замера $${\stackrel{\wedge }{X}}_{изм\left(k+1\right)}$$

, а именно P_изм(k+1) - корреляционная матрица замера X_изм(k+1); P_k+1 - корреляционная матрица оценки $${\stackrel{\wedge }{X}}_{k+1}$$

, определяемая рекуррентным соотношением

.For each moment in time t _{k + 1,} the Kalman filter generates a smoothed estimate of the state vector $${\stackrel{\wedge }{X}}_{k+\mathrm{one}}$$

based on an estimate of X (obtained from previous k observations of an air object at times t _k ) and a newly received measurement $${\stackrel{\wedge }{X}}_{\mathrm{and}sm\left(k+\mathrm{one}\right)}$$

, namely P _{ISM (k + 1)} - correlation matrix of measurement X _{ISM (k + 1)} ; P _{k + 1} - correlation matrix of estimation $${\stackrel{\wedge }{X}}_{k+\mathrm{one}}$$

defined by the recurrence relation

.

, P_k+1 с учетом модели движения полностью определяют алгоритм калмановской фильтрации при заданных начальных условиях. Результатами фильтрации на каждом шаге являются оптимальная сглаженная оценка вектора состояния $${\stackrel{\wedge }{X}}_{k+1}$$

и ее корреляционная матрица P_k+1.Expressions $${\stackrel{\wedge }{X}}_{k+\mathrm{one}}$$

, P _{k + 1} , taking into account the motion model, completely determine the Kalman filtering algorithm for given initial conditions. The filtration results at each step are the optimal smoothed estimate of the state vector $${\stackrel{\wedge }{X}}_{k+\mathrm{one}}$$

and its correlation matrix P _{k + 1} .

The disadvantage of this method of obtaining radio information on the radio complex is: the incompleteness received by the receiving posts and processed by the central receiving post of information about moving air objects; incomplete composition of coordinate information is not identified and combined with vectors of observation of airborne objects; redundant information is irrationally used in algorithms for trajectory tracking of air objects.

There is a delay in detecting the path of air objects, the failure of their tracking path. The rate of continuous tracking of the target is reduced. The root-mean-square error ratio of determining the coordinates and motion parameters of the trajectories of the tracking airborne objects is increasing, which significantly reduces the quality of tracking airborne objects in existing multi-positional passive location complexes. At the stage of tracking an air object according to radio technical information, it is not advisable to recalculate the observed parameters in the radio engineering marks with subsequent filtering of the results of solving the triangulation problem.

Existing algorithms for processing radio information are carried out in two stages with the subsequent stage of its integration. At the initial stage, signals are detected, signal parameters and observed coordinates are measured. At the secondary processing stage, the marks from the same target in time are referenced and the parameters of the target trajectory are calculated. In the process of secondary processing, the task of detecting and tracking target traces is solved. Such a division does not take into account the peculiarities of constructing a multi-position complex of a passive location. The radiation of the radio-electronic stations of airborne objects is not detected by all receiving posts, in this case an incomplete vector of the observed information is observed, in the absence of which it is impossible to determine all the spatial coordinates of the target. The incompleteness of the processed information leads to a delay in the detection of the route, disruption of the route from tracking, which leads to a decrease in the rate of continuous tracking, as well as to an increase in the standard deviation of the errors in determining the coordinates and motion parameters of the trajectory of the tracked target.

VO coordinates are determined only by the minimum required number of primary radio engineering measurements, other measurements are not taken into account when forming a mark and in filtering algorithms.

For existing filtering algorithms, one measurement of the bearing on the target at least 2 receiving posts in one review is necessary to determine the coordinate of the airborne object and only then evaluate it.

There are no methods for using in the trajectory tracking algorithms the redundancy of primary radio engineering measurements from the receiving post. Due to the lack of prioritization of RTI in determining the coordinates of HE (calculating the center of gravity of the figure), as well as in the subsequent filtering of the entire vector of the observed information, the motion parameters of the trajectory of the tracked target are deteriorated.

These factors lead to the need to create a bearing information filter in multi-positional complexes of a passive location, which takes into account the varied timing and incompleteness of the observed parameters.

The technical result of the proposed method for obtaining radio information by radio intelligence stations as part of a multi-positional passive location complex, which consists in the fact that the receiving posts of the radio intelligence station, spaced on the ground through sensors with parallel scanning in frequency, receive passive radio emission data from airborne objects - their bearing the frequency of the on-board radio-electronic means of an airborne object and the time point of receiving a bearing measurement, d The data are sent to the central reception post, converted into a single central Cartesian coordinate system with the origin in the central reception post and linked to the accompanying radio engineering trajectories of air objects, on the set of isolated radio engineering marks on the air object formed during scanning, they filter the results of solving the triangulation detection tasks of the radio trajectory of an air object in the following sequence:

воздушных объектов, где x, y - координаты воздушного объекта, x^э, y^э - экстраполированные координаты воздушного объекта, вычисляют начальные параметры траектории и их подтверждение по решению (k/n-l) об обнаружении при появлении k отметок в n смежных обзорах при отсутствии отметок в l смежных обзорах, устанавливают вектора S(t) состояния траектории воздушного объекта, составляют модель движения воздушного объекта как S(t+Δt)=F_Δt·S(t), где Δt=t_k+1-t_k - период обзора, F_Δt - матрица перехода траектории воздушного объекта при маневрировании, получают матрицу H_i(S) производной функции наблюдения β_i(x, y)=h(S) для каждого информационного датчика, вычисляют экстраполированные значения вектора состояния S_k+1=F_Δt·S(t) и алгоритмической ковариационной матрицы Q_k+1=F_Δt·Q_k·(F_Δt)^T, экстраполированное значение пеленга β_k+1(x,y), матрицу производной функции наблюдения в виде $$H_{i_{k+1}}\left(S_{k+1}\right)$$

, рассчитывают дисперсионную ошибку экстраполяции пеленга $$G_{k+1}=H_{i_{k+1}}\left(S_{k+1}\right)\cdot Q_{k+1}\cdot {\left[H_{i_{k+1}}\cdot \left(S_{k+1}\right)\right]}^T$$

, вычисляют разность экстраполированного пеленга и измеренного $$\Delta \beta ={\beta }_{k+1}-{\beta }_{t_{k+1}}^{i_{k+1}}$$

(при - π<Δβ≤π), определяют коэффициент усиления

, где σ_β - среднеквадратичная ошибка измерения пеленга, уточняют значение вектора состояния

и алгоритмическую ковариационную матрицу

, где E - диагональная единичная матрица, и производят оценку работы фильтра при сглаживании пеленга на станции радиотехнической разведки по частному показателю среднеквадратического отклонения ошибки измерения пеленга

, где N_реал - количество реализаций (N_реал=1000), достигается тем, что при сопровождении воздушного объекта по первичной радиотехнической информации на приемных постах производят одновременную первичную фильтрацию отдельных пеленгов по времени их поступления, при этом движение воздушного объекта принимают прямолинейным и равномерным, а в противном случае - принимают за маневр, а формирование начальной оценки приближенного вектора параметров траектории воздушного объекта и ковариационной матрицы ошибок на приемных постах производят по первому пеленгу, поступившему от одного из информационных датчиков по новому воздушному объекту, далее производят окончательную фильтрацию информации с получением уточненного вектора параметров траектории каждого воздушного объекта и алгоритмической ковариационной матрицы ошибок параметров наблюдения приемных постов, выдают точную оценку параметров траектории каждого воздушного объекта для четкого отслеживания характера и параметров его полета, при этом на приемных постах фильтрацию отдельных пеленгов воздушного объекта по времени их поступления производят следующим образом: задают вектор состояния траектории воздушного объекта в виде S(t)=(x, y, V_x, V_y, a _x, a _y), где V_x, V_y - проекции вектора скорости координат x, y; a _x, a _y - проекции ускорения координат x, y, фильтрацию координатной информации производят по зависимости

,

, где D - задаваемое расстояние от пеленгатора в направлении азимута при наличии априорной неопределенности по выбранной фиктивной дальности, выбираемое из предельных возможностей станции радиотехнической разведки, а измеренный азимут, поступивший на вход фильтра, пересчитывается в плоскостные координаты x, y; далее с учетом влияния ошибок экстраполяции производят обратный пересчет плоскостных координат в азимут при сглаживании пеленга, при этом матрицу производной функции наблюдения выражают в виде

.determine the size of the auto-capture gates $$d\ge \sqrt{{\left(x^{\mathrm{uh}}-x\right)}^2+{\left(y^{\mathrm{uh}}-y\right)}^2}$$

air objects, where x, y are the coordinates of the air object, x ^e , y ^e are the extrapolated coordinates of the air object, calculate the initial parameters of the trajectory and confirm them according to the decision (k / nl) to detect when k marks appear in n adjacent reviews in the absence of marks in l adjacent surveys, establish the state vector S (t) of the trajectory of the air object, make up the model of motion of the air object as S (t + Δt) = F _Δt · S (t), where Δt = t _{k + 1} -t _k is the period of the review , F _Δt is the transition matrix of the trajectory of an air object during maneuvering, a matrix is obtained H _i (S) of the derivative of the observation function β _i (x, y) = h (S) for each information sensor, the extrapolated values of the state vector S _{k + 1} = F _Δt · S (t) and the algorithmic covariance matrix Q _{k + 1} are calculated = F _Δt · Q _k · (F _Δt ) ^T , extrapolated bearing value β _{k + 1} (x, y), matrix of the derivative of the observation function in the form $$H_{i_{k+\mathrm{one}}}\left(S_{k+\mathrm{one}}\right)$$

, calculate the dispersion error of extrapolation of the bearing $$G_{k+\mathrm{one}}=H_{i_{k+\mathrm{one}}}\left(S_{k+\mathrm{one}}\right)\cdot Q_{k+\mathrm{one}}\cdot {\left[H_{i_{k+\mathrm{one}}}\cdot \left(S_{k+\mathrm{one}}\right)\right]}^T$$

, calculate the difference between the extrapolated bearing and the measured $$\Delta \beta ={\beta }_{k+\mathrm{one}}-{\beta }_{t_{k+\mathrm{one}}}^{i_{k+\mathrm{one}}}$$

(at - π <Δβ≤π), determine the gain

where σ _β is the standard error of the bearing measurement, the value of the state vector is refined

and algorithmic covariance matrix

, where E is the diagonal unit matrix, and the filter is evaluated when smoothing the bearing at the radio intelligence station according to a particular indicator of the standard deviation of the measurement error of the bearing

, where N _real is the number of implementations (N _real = 1000), is achieved by the fact that when tracking an air object according to primary radio technical information at the receiving posts, simultaneous primary filtering of individual bearings is made according to the time of their arrival, while the movement of the air object is assumed to be rectilinear and uniform, otherwise, they take it for a maneuver, and the formation of the initial estimate of the approximate vector of the parameters of the trajectory of the airborne object and the covariance matrix of errors at the receiving posts is carried out by The first bearing received from one of the information sensors for a new air object is then finally filtered to obtain the updated vector of the parameters of the trajectory of each air object and the algorithmic covariance matrix of errors of the observation parameters of the receiving posts, they give an accurate estimate of the parameters of the trajectory of each air object for clear tracking of the character and its flight parameters, while at the receiving posts, filtering individual bearings of an air object during nor their receipts produced as follows: setting a state vector trajectory air object in the form S (t) = (x, y, V x, V y, a x, a y), where V _x, V _y - projection of coordinates velocity x , y; a _x , a _y - projections of the acceleration of coordinates x, y, filtering of coordinate information is performed according to

,

where D is the specified distance from the direction finder in the azimuth direction in the presence of a priori uncertainty over the chosen fictitious range, selected from the limit capabilities of the radio intelligence station, and the measured azimuth received at the filter input is recalculated into the plane coordinates x, y; further, taking into account the influence of extrapolation errors, the plane coordinates are converted back to azimuth when smoothing the bearing, and the matrix of the derivative of the observation function is expressed as

.

The operation of the proposed data filtering at the receiving posts of the radio intelligence station allows you to quickly assess the presence and nature of the flight path of an air object, clearly monitor its flight path, and adjust the flight path of the air object until it is fully specified by coordinates and bearing.

The technical result of the proposed radio-technical complex of a passive location, consisting of reception posts with information sensors of a radio reconnaissance station, capable of measuring in the azimuthal and elevation planes the direction of movement of airborne objects with radiating radio equipment and recording the moment of arrival of pulses from radiating means when changing the direction of movement of an airborne object, scan by frequency and determine the location of an air object by solving the triangulation tasks, and equipped with an electronic unit for auto tracking of air objects

где x, y - координаты воздушного объекта, x^э, y^э - экстраполированные координаты воздушного объекта, а также блоком трассового сопровождения воздушных объектов с фильтром Калмана динамики воздушных объектов, достигается тем, что фильтр динамики воздушных объектов на каждом из приемных постов составлен из электронного блока установки вектора состояния траектории воздушного объекта S(t)=(x, y, V_x, V_y, a _x, a _y), где V_x, V_y - проекции вектора скорости координат x, y; a _х, a _y - проекции ускорения координат x, y, производящего фильтрацию координатной информации по зависимости

,

, где D - задаваемое расстояние от пеленгатора в направлении азимута, блока пересчета измеренного азимута воздушного объекта в плоскостные координаты x, y, блока расчета ошибок экстраполяции и измерений и обратного пересчета плоскостных координат в азимут, блока составления матрицы производной функции наблюдения в виде

, блока вычисления Δβ экстраполированного и измеренного пеленгов (при - π<Δβ≤π), блока определения коэффициента усиления k, блока уточнения вектора состояния S(t) и алгоритмической ковариационной матрицы, а также блока оценки работы фильтра при сглаживании пеленга.in the strobe size $$d\le \sqrt{{\left(x_i^{\mathrm{uh}}-x\right)}^2+{\left(y_i^{\mathrm{uh}}-y\right)}^2}$$

where x, y are the coordinates of the air object, x ^e , y ^e are the extrapolated coordinates of the air object, as well as the route tracking unit of the air objects with the Kalman filter of the dynamics of air objects, achieved by the fact that the filter of the dynamics of air objects at each of the receiving posts is composed of electronic unit for setting the state vector of the trajectory of an airborne object S (t) = (x, y, V _x , V _y , a _x , a _y ), where V _x , V _y are the projections of the velocity vector of coordinates x, y; a _x , a _y - projection of the acceleration of coordinates x, y, filtering the coordinate information according to

,

where D is the specified distance from the direction finder in the azimuth direction, the unit for converting the measured azimuth of the airborne object to the x, y plane coordinates, the unit for calculating extrapolation and measurement errors and the inverse of the plane coordinates in azimuth, the matrix composing unit of the derivative of the observation function in the form

, a unit for calculating Δβ of extrapolated and measured bearings (for - π <Δβ≤π), a unit for determining the gain k, a unit for refining the state vector S (t) and an algorithmic covariance matrix, as well as a unit for evaluating the filter operation when smoothing the bearing.

The proposed design of the filter of the dynamics of an air object, unlike the existing ones, makes it possible to estimate the coordinates of the tracking of the route of an air object according to information from one receiving station. Reception posts of the radio intelligence station allow you to quickly assess the presence and nature of the flight path of an airborne object, and clearly monitor its flight path. Filtering data at primary posts allows you to adjust the flight path of an air object to its full refinement.

The invention is illustrated by graphic materials, where in FIG. 1 is a flow chart of a filter of the proposed radio intelligence station; FIG. 2 - assessment of the trajectory of a rectilinear, uniform movement of an air object and a maneuvering air object; in FIG. 3 and 4 show the results of methods for obtaining radio technical information - the dashed line shows the measurement errors, the solid line shows the values of the estimated indicator of the proposed method, the dashed line (with a point) shows the method presented in the prototype, FIG. 3 shows the results of measuring the bearing from the time of observation of the rectilinear and uniform movement of an air object, FIG. 4 shows the results of measuring the bearing against time for a maneuvering object; FIG. 5 is a block diagram of a radio intelligence station device.

,

, где D - задаваемое расстояние от пеленгатора в направлении азимута, блока 9 пересчета измеренного азимута воздушного объекта при дальности D в плоскостные координаты x, y; блока 10 расчета ошибок $$Q_{k+1}^{э}$$

экстраполяции и измерений и обратного пересчета плоскостных координат в азимут; блока 11 моделирования движения воздушного объекта и составления матрицы перехода F_Δt, блока 12 расчета матрицы производной функции наблюдения H_i(S); блока 13 вычисления Δβ экстраполированного и измеренного пеленгов (при - π<Δβ≤π); блока 14 определения коэффициента усиления k; блока 15 уточнения вектора состояния S(t) и алгоритмической ковариационной матрицы ошибок $$Q_{k+1}^{э}$$

, а также устройства 17 вычисления уточненных координат (x, y) местоположения воздушного объекта.The device of the radio complex (Fig. 5) consists of three receiving posts with information sensors 1 of a radio intelligence station, capable of measuring in the azimuthal and elevation planes the direction of movement of an air object with radiating radio equipment and recording the moment of arrival of pulses from the emitting means when changing the direction of movement of an air object , scan by frequency and determine the location of an air object by solving the triangulation problem, and equipped with antennas 2 s building 3 of their control, with receivers 4 of radio signals emitted by radio-electronic means of air objects associated with a device 5 for measuring the shift of the received signals in time and with filters 6 of the dynamics of movement of air objects, consisting of: electronic unit 7 for measuring the bearing to an air object in the initial and subsequent point in time (t, t + 1); unit 8 for setting the state vector of the trajectory of an airborne object S (t) = (x, y, V _x , V _y , a _x , a _y ), where V _x , V _y are the projections of the velocity vector of coordinates x, y; a _x , a _y - projections of the acceleration of coordinates x, y, filtering coordinate information according to

,

where D is the specified distance from the direction finder in the direction of azimuth, block 9 conversion of the measured azimuth of an air object at a range of D in the plane coordinates x, y; block 10 error calculation $$Q_{k+\mathrm{one}}^{\mathrm{uh}}$$

extrapolation and measurements and the inverse translation of plane coordinates into azimuth; block 11 modeling the movement of an air object and compiling the transition matrix F _Δt , block 12 calculating the matrix of the derivative of the observation function H _i (S); block 13 calculating Δβ extrapolated and measured bearings (with - π <Δβ≤π); block 14 determine the gain k; unit 15 refinement of the state vector S (t) and algorithmic covariance error matrix $$Q_{k+\mathrm{one}}^{\mathrm{uh}}$$

as well as a device 17 for calculating the adjusted coordinates (x, y) of the location of the airborne object.

The essence of the presented method of obtaining radio information by information sensors with parallel scanning in frequency is as follows.

. На каждом i-м посту измеряют пеленг $${\beta }_{t_k}^{i_k}$$

на воздушный объект с координатами (x, y)^T, где t_k - момент времени получения измерения пеленга (t_k+1>t_k). Устанавливают вектор состояния траектории воздушного объекта S(t)=(x, y, V_x, V_y, a _x, a _y), где V_x, V_y - проекции вектора скорости координат x, y; a _х, a _y - проекции ускорения координат x, y. При начальной оценке принимает значение

где

, D - задаваемое расстояние от пеленгатора в направлении азимута.The multi-position passive location complex consists of three information sensors of the receiving posts V = {V ₁ , V ₂ , V ₃ } of radio intelligence, which are located at points with coordinates (x _i , y _i , x _i ) ^T , where $$i=\overline{1.3}$$

. Bearing is measured at every ith post $${\beta }_{t_k}^{i_k}$$

to an airborne object with coordinates (x, y) ^T , where t _k is the time instant of obtaining the bearing measurement (t _{k + 1} > t _k ). Set the state vector of the trajectory of the airborne object S (t) = (x, y, V _x , V _y , a _x , a _y ), where V _x , V _y are the projections of the velocity vector of coordinates x, y; a _x , a _y are the projections of the acceleration of coordinates x, y. At the initial assessment, it takes on the value

Where

, D is the specified distance from the direction finder in the azimuth direction.

For the initial algorithmic covariance error matrix, a diagonal matrix is selected, the diagonal elements of which are equal to the maximum possible dispersions of the speed and acceleration of the air object within a priori uncertainty, the elements of the upper part of the 2 × 2 matrix are calculated by the formula

.

.

The model of the target’s movement is made in the form S (t + Δt) = F _Δt · S (t), where Δt = t _{k + 1} -t _k is the period of review of the multi-positional complex of the passive location. The transition matrix has the form

.

.

. Матрица производной функции наблюдения имеет вид

.The observation function for each of the information sensors has the form:

. The matrix of the derivative of the observation function has the form

.

, дальнейшую фильтрацию данных и последующее уточнение оценки вектора состояния траектории цели выполняют в последовательности:Provided that there is some initial estimate of the state vector S _{k of} the target trajectory and an algorithmic covariance matrix of errors in estimating the state vector at time t _k , and a new bearing measurement is obtained $${\beta }_{t_{k+\mathrm{one}}}^{i_{k+\mathrm{one}}}$$

, further data filtering and subsequent refinement of the assessment of the state vector of the target path are performed in the sequence:

1) calculate the extrapolated values of the state vector and the algorithmic covariance matrix

;

;

;

;

2) calculate the extrapolated bearing value

;

;

3) calculate the matrix of the derivative of the observation function

;

;

4) calculate the variance of the bearing extrapolation error

;

;

, при условии, что - π<Δβ≤π;5) calculate the difference between the extrapolated bearing and the measured $$\Delta \beta ={\beta }_{k+\mathrm{one}}^E-{\beta }_{t_{k+\mathrm{one}}}^{i_{k+\mathrm{one}}}$$

provided that - π <Δβ≤π;

6) determine the gain

, где σ_β - среднеквадратическая ошибка измерения пеленга;

where σ _β is the standard error of the bearing measurement;

7) specify the value of the state vector

8) refine the algorithmic covariance matrix

, где E - диагональная единичная матрица.

, where E is the diagonal identity matrix.

, где N_реал - количество реализаций (N_реал=1000).Next, they evaluate the operation of the triangulation method for determining the location of the bearing directly at the radio intelligence station using a particular indicator of the standard deviation of the measurement error of the bearing

where N _real is the number of implementations (N _real = 1000).

Thus, the block diagram of the filtering of direction finding information obtained by information sensors by the proposed method has the form shown in FIG. one.

Consider two options for the movement of an air object relative to a multi-position complex of a passive location.

In FIG. 2 shows the paths of an air object moving rectilinearly and evenly (option 1) and an air object moving with a maneuver (option 2).

The following conditions for modeling are set: the rate of the frequency range survey by radio intelligence stations - 5 s; the speed of movement of an air object is 300 m / s, its course speed is ≤5 m / s; the standard error of the measurement of the bearing is σ _β = 0.5 °.

As a result of the evaluation of the planar coordinates of air objects during filtration by the proposed method and the method presented in the prototype, the results were obtained, which are presented in FIG. 3, for the rectilinear movement of an air object, and in FIG. 4 - for a maneuvering air object.

The presented graphs of the dependence of the standard errors of the bearing measurement on the time of observation of the airborne object in FIG. 2 - for rectilinear movement and in FIG. 4 - for a maneuvering air object.

According to the proposed method for obtaining radio technical information, block 8 of the passive location complex is characterized by the use of new operations that take into account, when forming the state vector of the trajectory of an air object, all radio engineering measurements received at reception posts without solving the triangulation problem of determining the coordinates of an air object.

Block 9 of the conversion of the measured azimuth of the airborne object at a range D to the plane coordinates x, y is characterized by the use of a new operation - the introduction of a dummy range D to determine the initial coordinates x, y of an airborne object from one radio engineering measurement, which allows us to identify and combine the observation vectors with an incomplete coordinate structure information with the trajectory of an air object, avoid the duration of transient filtering algorithms, in particular: the time of transition to the steady state, time t since the start of operation until the time of stabilization error filter value.

экстраполяции и измерений и обратного пересчета плоскостных координат в азимут отличается учетом в алгоритмической ковариационной матрице ошибок среднеквадратической ошибкой измерения пеленга заложенной в приемной антенне информационного датчика, а не взятого средневзвешенного квадратичного значения проекции угла на плоскость x, y - что приводит к дополнительной накладываемой ошибке.Block 10 error calculation $$Q_{k+\mathrm{one}}^{\mathrm{uh}}$$

extrapolation and measurements and the recalculation of plane coordinates in azimuth differs in taking into account in the algorithmic covariance matrix of errors the root-mean-square error of bearing measurement embedded in the receiving antenna of the information sensor, rather than the taken mean-square value of the projection of the angle onto the x, y plane - which leads to an additional superimposed error.

Block 12 of calculating the matrix of the derivative of the observation function H _i (S), characterized in that the observation function is set by the projection of the model for changing the angle of the bearing of an air object on the x, y coordinate axis.

The block 13 for calculating Δβ of extrapolated and measured bearings (for - π <Δβ≤π) differs in that the error is calculated directly between the received bearing measurement and its extrapolated value for the next review period, in existing ones this operation is performed for the result of the triangulation problem - which brings its error in the measurement received.

The device 17 for calculating the specified coordinates (x, y) of the location of the air object is distinguished by the use of new operations that take into account the features of constructing the complex and the flow of measurements received from radio intelligence stations with parallel scanning in the conditions of the predicted electronic situation and can improve the quality of the track being tracked (Fig. 3 and 4) - the standard deviation of the measurement error of the bearing in the proposed method decreased for rectilinear sections of the movement of an air object by 20%, and in maneuvering areas - by 30%.

The proposed method and radio-technical complex for obtaining radio-technical information allows for high-quality filtering and extrapolation of the parameters of the trajectory of radiating air objects at a multi-position passive location complex based on primary radio information obtained using even one of the posts of radio intelligence stations.

Information sources

1. Zaitsev D.V. Multiposition radar systems / Methods and algorithms for processing information in an interference environment. - M .: Radio engineering, 2007. - p. 16-20.

2. Smirnov Yu.A. Radio intelligence. - M., 1997. - p. 164, 165, 190-193, 203-205, 211.

3. Chernyak B.C., Zaslavsky L.P., Osipov L.V. Multiposition radar stations and systems "Foreign Radio Electronics" No. 1 - 1987 - p. 11, 15-17, 29-33, 42-54 (prototype).

Claims (2)

1. A method for obtaining radio technical information by radio intelligence stations as part of a multi-positional passive location complex, which consists in the fact that the receiving posts of radio intelligence stations spaced on the ground through sensors with parallel scanning in frequency receive passive radio emission data from airborne objects - their bearing bearing the onboard frequency electronic means of an airborne object and the time point of receiving a bearing measurement, the data is sent to the central reception the post is transformed into a single central Cartesian coordinate system with a beginning in the central reception post and is tied to the accompanying radio engineering trajectories of airborne objects, on a set of isolated radio engineering marks of an airborne object formed during scanning, the operation of filtering the results of solving the triangulation problem of detecting the radioengineering trajectory of the airborne object in the following sequence: determine the size of the auto-capture gates $$d\ge \sqrt{{\left(x^{\mathrm{uh}}-x\right)}^2+{\left(y^{\mathrm{uh}}-y\right)}^2}$$

air objects, where x, y are the coordinates of the air object, x ^e , y ^e are the extrapolated coordinates of the air object, calculate the initial parameters of the trajectory and confirm them according to the decision (k / nl) to detect when k marks appear in n adjacent reviews in the absence of marks in l adjacent surveys, establish the state vector S (t) of the trajectory of the air object, make up the model of motion of the air object as S (t + Δt) = F _Δt · S (t), where Δt = t _{k + 1} -t _k is the period of the review , F _Δt is the transition matrix of the trajectory of an air object during maneuvering, a matrix is obtained H _i (S) of the derivative of the observation function β _i (x, y) = h (S) for each information sensor, the extrapolated values of the state vector S _{k + 1} = F _Δt · S (t) and the algorithmic covariance matrix Q _{k + 1} are calculated = F _Δt · Q _k · (F _Δt ) ^T , extrapolated bearing value β _{k + 1} (x, y), matrix of the derivative of the observation function in the form $$H_{i_{k+\mathrm{one}}}\left(S_{k+\mathrm{one}}\right)$$

, calculate the dispersion error of extrapolation of the bearing $$G_{k+\mathrm{one}}=H_{i_{k+\mathrm{one}}}\left(S_{k+\mathrm{one}}\right)\cdot Q_{k+\mathrm{one}}\cdot {\left[H_{i_{k+\mathrm{one}}}\cdot \left(S_{k+\mathrm{one}}\right)\right]}^T$$

, calculate the difference between the extrapolated bearing and the measured $$\Delta \beta ={\beta }_{k+\mathrm{one}}-{\beta }_{t_{k+\mathrm{one}}}^{i_{k+\mathrm{one}}}$$

(at - π <Δβ≤π), determine the gain

where σ _β is the standard error of the bearing measurement, the value of the state vector is refined

and algorithmic covariance matrix

, where E is the diagonal unit matrix, and the filter is evaluated when smoothing the bearing at the radio intelligence station according to a particular indicator of the standard deviation of the measurement error of the bearing

, where N _real is the number of implementations (N _real = 1000), characterized in that when tracking the air object according to primary radio information at the receiving posts, simultaneous primary filtering of the individual bearings is made according to the time of their arrival, while the movement of the air object is assumed to be linear and uniform, otherwise, they take it for a maneuver, and the formation of the initial estimate of the approximate vector of the parameters of the trajectory of the airborne object and the covariance matrix of errors at the receiving posts is carried out by The first bearing received from one of the information sensors for a new air object is then finally filtered to obtain the updated vector of the parameters of the trajectory of each air object and the algorithmic covariance matrix of errors of the observation parameters of the receiving posts, they give an accurate estimate of the parameters of the trajectory of each air object for clear tracking of the character and its flight parameters, while at the receiving posts, filtering individual bearings of an air object during nor their receipts produced as follows: setting a state vector trajectory air object in the form S (t) = (x, y, V x, V y, a x, a y), where V _x, V _y - projection of coordinates velocity x , y; a _x , a _y - projections of the acceleration of coordinates x, y, filtering of coordinate information is performed according to

,

where D is the specified distance from the direction finder in the azimuth direction in the presence of a priori uncertainty over the chosen fictitious range, selected from the limit capabilities of the radio intelligence station, and the measured azimuth received at the filter input is recalculated into the plane coordinates x, y; further, taking into account the influence of extrapolation errors, the plane coordinates are converted back to azimuth when smoothing the bearing, and the matrix of the derivative of the observation function is expressed as

. 2. The device of the radio-technical complex of the passive location that implements the method according to claim 1, consisting of reception posts with information sensors of the radio intelligence station, capable of measuring in the azimuth and elevation planes the direction of movement of an air object with radiating radio equipment and recording the moment of arrival of pulses from the radiating means at change the direction of movement of an air object, scan in frequency and determine the location of the air object by solving the triangulation oh task, and equipped with an electronic unit for automatic tracking of air objects in a strobe size $$d\ge \sqrt{{\left(x_i^{\mathrm{uh}}-x\right)}^2+{\left(y_i^{\mathrm{uh}}-y\right)}^2}$$

, where x, y are the coordinates of the airborne object, x ^e , y ^e are the extrapolated coordinates of the airborne object, as well as the route tracking unit of the airborne objects with the Kalman filter of the dynamics of airborne objects, characterized in that the airborne object dynamics filter at each of the receiving posts is composed the electronic unit for setting the state vector of the trajectory of an airborne object S (t) = (x, y, V _x , V _y , a _x , a _y ), where V _x , V _y are the projections of the velocity vector of coordinates x, y; a _x , a _y - projections of the acceleration of coordinates x, y, filtering coordinate information according to

,

where D is the specified distance from the direction finder in the azimuth direction, the unit for converting the measured azimuth of the airborne object to the x, y plane coordinates, the unit for calculating extrapolation and measurement errors and the inverse of the plane coordinates in azimuth, the matrix composing unit of the derivative of the observation function in the form

, a unit for calculating Δβ of extrapolated and measured bearings (for - π <Δβ≤π), a unit for determining the gain k, a unit for refining the state vector S (t) and an algorithmic covariance matrix, as well as a unit for evaluating the filter operation when smoothing the bearing.

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