CN113933790A - Inversion identification method, device and medium for working mode of phased array radar - Google Patents

Inversion identification method, device and medium for working mode of phased array radar Download PDF

Info

Publication number
CN113933790A
CN113933790A CN202111091991.5A CN202111091991A CN113933790A CN 113933790 A CN113933790 A CN 113933790A CN 202111091991 A CN202111091991 A CN 202111091991A CN 113933790 A CN113933790 A CN 113933790A
Authority
CN
China
Prior art keywords
phased array
array radar
radar
unmanned aerial
aerial vehicle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111091991.5A
Other languages
Chinese (zh)
Inventor
王伟
周永坤
饶彬
王涛
周颖
邹小海
徐峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sun Yat Sen University
Original Assignee
Sun Yat Sen University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sun Yat Sen University filed Critical Sun Yat Sen University
Priority to CN202111091991.5A priority Critical patent/CN113933790A/en
Publication of CN113933790A publication Critical patent/CN113933790A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/292—Extracting wanted echo-signals
    • G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S2013/0236—Special technical features
    • G01S2013/0245—Radar with phased array antenna

Abstract

The invention discloses an inversion identification method, device and medium for a phased array radar working mode, wherein the method comprises the following steps: determining the position of the phased array radar according to the lateral data acquired by the unmanned aerial vehicle cluster; controlling the unmanned aerial vehicle in the unmanned aerial vehicle cluster to move within a target range according to the position of the phased array radar; identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting, and determining signal parameters; carrying out multi-dimensional modeling analysis according to the signal parameters; and determining the working mode of the phased array radar according to the result of the modeling analysis. The invention utilizes the unmanned aerial vehicle cluster to quickly reconnaissance phased array radar signals in a complex electromagnetic environment, inverts and identifies the working mode of the phased array radar signals according to radar characteristics, overcomes the defects of a single-point reconnaissance mode of a traditional reconnaissance plane, provides a space-time frequency domain radar working mode identification model, and can be widely applied to the technical field of electronic countermeasure.

Description

Inversion identification method, device and medium for working mode of phased array radar
Technical Field
The invention relates to the technical field of electronic countermeasure, in particular to an inversion identification method, device and medium for a working mode of a phased array radar.
Background
Due to the flexibility and functional diversity of the phased array radar, the selection of the signal characteristic parameters of the phased array radar is different under different functions and working modes, and therefore the analysis of the characteristic parameters of the phased array radar has important significance for identifying the phased array radar. Generally, a reconnaissance machine carries out accumulation receiving on phased array radar signals, generation of a radar Pulse Description Word (PDW) is achieved through parameter estimation, and then a task of identifying the phased array radar is achieved through analysis of different characteristic parameters in the PDW Pulse Description Word. Parameters representing the basic characteristics of the radar signals mainly include intra-pulse parameters and inter-pulse parameters, the types of the phased array radar signals are rich and diverse, and the intra-pulse parameters are flexible and changeable. The operation mode and function of the phased array radar will vary according to the selection range of the characteristic parameters.
With the continuous development of radar technology, phased array radars are widely applied with unique advantages and flexibility, and great challenges are created for the identification of radar reconnaissance parties. Meanwhile, the traditional reconnaissance equipment can only detect the pulse description words of the radar on a single wave position, cannot acquire the information of the phased array radar on all the wave positions, and cannot perform inversion and identification on the working mode of the phased array radar.
Disclosure of Invention
In view of this, embodiments of the present invention provide an inversion identification method, an apparatus, and a medium for phased array radar working mode, which can perform fast reconnaissance on phased array radar signals in a complex electromagnetic environment, thereby implementing inversion identification on the phased array radar working mode.
The first aspect of the embodiment of the invention provides an inversion identification method for a phased array radar working mode, which comprises the following steps:
identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting, and determining signal parameters;
carrying out multi-dimensional modeling analysis according to the signal parameters;
and determining the working mode of the phased array radar according to the result of the modeling analysis.
Optionally, the method further comprises:
determining the position of the phased array radar according to the lateral data acquired by the unmanned aerial vehicle cluster;
and controlling the unmanned aerial vehicles in the unmanned aerial vehicle cluster to move within a target range according to the position of the phased array radar.
Optionally, the identifying the obtained radar pulse signal according to the unmanned aerial vehicle cluster sorting and determining the signal parameter includes:
acquiring radar pulse signals respectively intercepted by all unmanned aerial vehicles in the unmanned aerial vehicle cluster;
sorting out signal parameters according to the radar pulse signals;
normalizing the signal parameters and sequencing the signal parameters according to the arrival time;
clustering the sequenced signal parameters according to a preset algorithm;
the signal parameters comprise a PRF parameter, a CF parameter and a PW parameter, wherein the PRF represents the capability of the phased array radar for transmitting pulses in unit time, the CF represents the frequency characteristic of the radar transmission signals, the PW represents the time period from the beginning to the end of the radar pulse received by the reconnaissance machine, and the arrival time represents the arrival time of the radar pulse leading edge signals received by the reconnaissance receiver.
Optionally, the performing multidimensional modeling analysis according to the signal parameter includes:
and carrying out modeling analysis on the signal parameters from the multi-dimensional angles of a space domain, a time domain and a frequency domain according to the signal parameters.
Optionally, the determining an operation mode of the phased array radar according to the result of the modeling analysis includes:
determining a recognition confidence of the signal parameter;
and determining the working mode of the phased array radar according to the recognition confidence coefficient and the result of the modeling analysis.
Optionally, the determining the position of the phased array radar according to the lateral data acquired by the unmanned aerial vehicle cluster includes:
and determining the position of the phased array radar through lateral cross positioning according to the lateral data.
Optionally, in the step of controlling the drones of the drone cluster to move within the target range according to the phased array radar position:
the expression for the angular range of motion of the drone is:
Figure BDA0003267685670000021
wherein i represents the ith unmanned aerial vehicle, k represents the time, AiyRepresenting the angle of the drone in the horizontal direction, AizRepresenting the angle of the drone in the pitch direction, Δ AyDenotes the maximum angular extent, Δ A, in the horizontal directionzA maximum angular range representing a pitch direction;
the expression of the minimum safe distance of each unmanned aerial vehicle in the unmanned aerial vehicle cluster is as follows:
Figure BDA0003267685670000022
wherein (x)i(k),yi(k) Denotes the coordinate position of the ith drone at time k, (x)j(k),yj(k) Represents the coordinate position of the jth drone at time k.
The second aspect of the embodiments of the present invention provides an inversion identification apparatus for a phased array radar working mode, including:
the first module is used for identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting and determining signal parameters;
the second module is used for carrying out multi-dimensional modeling analysis according to the signal parameters;
and the third module is used for determining the working mode of the phased array radar according to the result of the modeling analysis.
A third aspect of embodiments of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a program for execution by a processor to implement the method as described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
The embodiment of the invention has the beneficial effects that: identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting, and determining signal parameters; carrying out multi-dimensional modeling analysis according to the signal parameters; and determining the working mode of the phased array radar according to the result of the modeling analysis. The number advantage of the unmanned aerial vehicle cluster is utilized, the scanning area of the phased array radar is covered, the current working mode of the phased array radar is identified through holographic sensing of the wave position scanning mode and the resource scheduling mode of the radar in multiple dimensions, the current threat level of the radar is further judged, and useful decision information is provided for the fusion center so as to take corresponding countermeasures.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of an inversion identification method for a phased array radar working mode according to an embodiment of the present invention;
fig. 2 is another flowchart of an inversion identification method for a working mode of a phased array radar according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a wave position scanning mode of a phased array radar tracking mode;
FIG. 4 is a schematic diagram of a wave position scanning mode of a phased array radar search and tracking mode;
FIG. 5 is a data rate diagram for a phased array radar tracking mode;
FIG. 6 is a schematic diagram of phased array radar search plus tracking pattern data rate;
FIG. 7 is a flowchart of fuzzy clustering implementation in radar pulse signal sorting identification according to an embodiment of the present invention;
fig. 8 is a block diagram of search pattern classification elements for phased array radar.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In order to make the content and technical solution of the present application more clear, the characteristics, terms and meanings of the related art are described:
1) phased array radar characteristic analysis based on characteristic parameters
Due to the flexibility and functional diversity of the phased array radar, the selection of the signal characteristic parameters of the phased array radar is different under different functions and working modes, and therefore the analysis of the characteristic parameters of the phased array radar has important significance for identifying the phased array radar. Generally, a reconnaissance machine carries out accumulation receiving on phased array radar signals, generation of a radar Pulse Description Word (PDW) is achieved through parameter estimation, and then a task of identifying the phased array radar is achieved through analysis of different characteristic parameters in the PDW Pulse Description Word. Parameters representing the basic characteristics of the radar signals mainly include intra-pulse parameters and inter-pulse parameters, the types of the phased array radar signals are rich and diverse, and the intra-pulse parameters are flexible and changeable. The operation mode and function of the phased array radar will vary according to the selection range of the characteristic parameters. The following detailed analysis of typical characteristic parameters in phased array radar:
direction of Arrival (DOA);
the DOA refers to the arrival direction of radar echo signals, and the position of the phased array radar is estimated by measuring the arrival direction of the phased array radar signals. In theory this estimation requires only two receiving elements to determine the position of the phased array radar, but in practice more than two elements are usually required due to limitations in angular resolution, multipath and noise.
Time of Arrival (TOA);
the TOA refers to the arrival time of the radar pulse leading edge signal received by the reconnaissance receiver, is one of the main parameters for characterizing the radar pulse signal, and is also an important basis for calculating the pulse repetition frequency. In a complex pulse stream, different phased array radar pulses can be distinguished according to different TOA parameters.
Pulse Repetition Frequency (PRF);
the PRF refers to the capability of the phased array radar to transmit pulses in unit time, and is one of the important parameters for determining the phased array radar, and the selection of the PRF characteristic parameters is also different in different modes. When the phased array radar works in a searching state, the PRF parameter value is lower, and when the phased array radar works in a tracking state, the PRF parameter value is higher.
Carrier Frequency (CF);
CF represents the frequency characteristics of the radar transmission signal, which is an important parameter characteristic for identifying and analyzing the radar signal. Due to the signal variability and flexibility of phased array radars, the frequency parameters often vary according to their operating modes, and even when a certain function is implemented, their frequencies may vary from pulse to pulse.
Pulse Amplitude (PA);
the PA is the size of the pulse power transmitted by the phased array radar received by the scout, the power size is generally represented by the pulse amplitude, and the identification of the phased array radar system can be realized by analyzing the pulse amplitude.
Pulse Width (PW);
PW refers to a time period from the beginning to the end of the radar pulse received by the reconnaissance machine, is an important characteristic parameter in a pulse description word, and is also an important basis for judging the working state of the phased array radar. Due to the diversity of phased array radar signals, the selection of PW characteristic parameters is different under different working modes, for example, the PW parameter value is generally larger in a search mode, and the PW parameter value is smaller in a tracking mode.
2) Phased array radar characteristic analysis based on wave position arrangement and data rate
Wave position arrangement mode
Phased array radar beams are realized by scanning different wave positions when scanning airspace intervals, and therefore scanning wave positions of phased array radars need to be arranged. The wave position arrangement of the phased array radar is to divide wave positions according to information such as monitoring airspace range, wave beam width, arrangement mode and the like of the radar so as to determine the direction of each wave position. The phased array radar has different wave position scanning sequences under different working modes.
When the phased array radar works in a Tracking While Searching (TWS) mode, the scanning mode of the wave beams on the arranged wave positions is one-time scanning in a row-by-row mode, the phased array radar belongs to remote searching due to the fact that an observation target is far away, and the searching range of the phased array radar to an airspace is set as follows: the azimuth direction is as follows: -6 degrees to 6 degrees, pitching 0 degrees to 8 degrees, assuming that the beam width of the beam direction position emitted by the transmitting array is 2 degrees, and pitching 2 degrees, it can be known according to calculation that 6 wave positions are needed to cover in the azimuth direction, and 4 wave positions are needed to cover in the pitching direction, the searched airspace is divided into 24 wave positions to search and track, and then the search requirement of the search space of the specific area can be met, and fig. 3 shows a wave position scanning mode of a phased array radar tracking mode.
The TWS working mode mainly searches a Target according to divided wave positions, stays on each wave position for a period of time, and switches the working mode of the phased array radar to a Tracking and Tracking (TAS) mode or a Single Target Tracking (STT) mode after the Target is locked.
When the phased array radar works in a TAS working mode, not only a current airspace needs to be searched, but also a target locked by the radar needs to be tracked, the time interval between the searching and the tracking is relatively fixed, and after the searching wave beam of 6 wave positions is transmitted in the searching mode, the wave beam of the tracking wave position is transmitted to the tracked target once, and so on until all the wave positions of the whole phased array radar searching airspace finish the transmission and scanning of the wave beam. Unlike the TWS mode, the target of TAS search is generally far away and belongs to long-distance search, and fig. 4 shows the wave position scanning mode of phased array radar search plus tracking mode.
Referring to fig. 4, the tracked wave position is in the third wave position in the first row, and the phased array radar still realizes the tracking task of the high data rate of the locked target while searching the target, and the tracking data rate in the working mode is higher than that in the TWS mode. After the search tasks of 6 wave positions are completed, the phase information of the wave beams is changed through the computer controller, the working mode is adjusted to be the tracking mode, the wave beam transmitting direction is irradiated to the airspace wave position where the tracked target is located, the searching mode is switched after the wave position stays for a period of time, the wave position is continuously scanned, and the process is repeated until the search tasks of the phased array radar in the searched airspace are completed.
Data Rate-
The data rate can be obtained by reciprocal calculation of the time interval between two adjacent exposures of the same target, i.e. the data rate can be measured by using the arrival time measurement principle. The data rates may also be different in TWS and TAS modes of operation.
FIG. 5 is a graph illustrating data rate in a tracking mode for a phased array radar, wherein TtiIndicating the tracking time, TsiIndicating the search time, the data rate is typically relatively high in TWS mode of operation. Taking a phased array radar as an example, the search mode can be further divided into a low elevation search mode and a high elevation search mode according to the range of the elevation angle.
Fig. 6 is a schematic diagram of data rate of search and tracking mode of the phased array radar, which is generally used for measuring the accurate position of a target in a TAS operating mode, the data rate of the search stage is substantially the same as that of the TWS mode, and the tracking stage may include target confirmation, target rough tracking, target fine tracking, and the like. Meanwhile, the targets can be divided into general targets, important targets and dangerous targets according to the types of the targets. The tracking of a common target adopts a mode of searching and tracking at the same time, the tracking data rate is generally 1Hz, the searching task can be completed without additionally distributing energy in a specified airspace, and the method is commonly used for monitoring the aerial target; for important targets, a search and tracking mode is adopted, a high tracking data rate is needed, at least 10Hz is needed for the important targets, and a data rate of more than 20Hz is needed for dangerous targets.
The following detailed description of the implementation principle of the method of the present invention is made with reference to the accompanying drawings:
fig. 1 is a flowchart illustrating an inversion identification method for a phased array radar operating mode according to an embodiment of the present invention, where the method includes:
identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting, and determining signal parameters;
carrying out multi-dimensional modeling analysis according to the signal parameters;
and determining the working mode of the phased array radar according to the result of the modeling analysis.
In some embodiments, as shown in fig. 2, a method comprises:
determining the position of the phased array radar according to the lateral data acquired by the unmanned aerial vehicle cluster;
controlling the unmanned aerial vehicle in the unmanned aerial vehicle cluster to move within a target range according to the position of the phased array radar;
identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting, and determining signal parameters;
carrying out multi-dimensional modeling analysis according to the signal parameters;
and determining the working mode of the phased array radar according to the result of the modeling analysis.
In some embodiments, identifying the acquired radar pulse signals according to unmanned aerial vehicle cluster sorting and determining signal parameters includes:
acquiring radar pulse signals respectively intercepted by all unmanned aerial vehicles in an unmanned aerial vehicle cluster;
specifically, unmanned aerial vehicles on each wave position respectively sort and identify radar pulse signals in a complex electromagnetic environment.
Sorting out signal parameters according to the radar pulse signals;
specifically, CF parameter, PRF parameter and PW parameter information of the radar pulse are obtained from the radar pulse signals identified by sorting.
Carrying out normalization processing on the signal parameters, and sequencing the signal parameters according to the arrival time;
specifically, in order to facilitate statistics and eliminate the influence of the magnitude of the sample parameter, each parameter needs to be subjected to standard normalization processing, so that the parameters are uniformly distributed in the interval [0,1 ]. The expression for the normalization process of the sample parameters is:
Figure BDA0003267685670000071
in the formula, PRFi、CFi、PWiRespectively representing the pulse repetition frequency, carrier frequency and pulse width, PRF, of the radar received by the ith unmanned aerial vehiclemax、CFmax、PWmaxRespectively representing the maximum values of the pulse repetition frequency, carrier frequency and pulse width, PRFmin、CFmin、PWminRespectively representing the minimum values of the pulse repetition frequency, carrier frequency and pulse width, prfi、cfi、pwiRespectively represent a pair of PRFi、CFi、PWiNormalized values.
Clustering the sorted signal parameters according to a preset algorithm;
specifically, as shown in fig. 7, the implementation process of fuzzy clustering is as follows: firstly, M unmanned aerial vehicles respectively capture pulse signals (N unmanned aerial vehicles possibly do not receive the pulse signals due to position); then, in order to eliminate the influence of the sample magnitude, the sorted parameters are subjected to normalization processing and are sorted according to the TOA parameters; and then, processing the pulse data based on a clustering algorithm of multi-dimensional parameters to realize the sorting of the pulses. The fuzzy clustering algorithm is specifically realized by the following steps:
it should be noted that, in the clustering algorithm, a weighted euclidean distance formula is required to represent the distance between each dimension characteristic parameter in the radar pulse descriptor and the class center, and the expression is as follows:
Figure BDA0003267685670000072
in the formula (I), the compound is shown in the specification,
Figure BDA0003267685670000073
represents the ith characteristic parameter in the sample,
Figure BDA0003267685670000074
representing the cluster center of the jth class element.
In the fuzzy clustering algorithm, a membership matrix in a sample set is constructed by defining the membership of each eigenvector in a data sample to each clustering center, the uncertainty attribute of each sample data to different clustering centers is represented, and the classification attribute of the sample data is reflected more objectively. In data sets
Figure BDA0003267685670000075
In the method, each data has three characteristic parameters, and for fuzzy clustering, a membership matrix U ═ U of a sample set for each clustering center is firstly constructedij]c×NAnd the sum of the membership of each element to all the cluster centers is 1, namely:
Figure BDA0003267685670000081
in the formula, N represents the number of samples, i represents the ith sample, uijRepresenting the degree of membership of each cluster center, j representing the jth cluster center, NcRepresenting the total number of cluster centers.
(1) Establishing a fuzzy clustering cost function J, namely:
Figure BDA0003267685670000082
wherein N represents the number of samples, i represents the ith sample, j represents the jth cluster center, NcIndicates the number of cluster centers, uijRepresents a sample xiThe membership degree of the first cluster center is 0-1, and the parameter m is a parameter describing the degree of algorithm model, which defines the ambiguity of cluster boundary, generally m>1，cjThe word vector is described for the pulse in the jth cluster center.
(2) Setting the number N of cluster centerscInitializing a membership matrix U, and randomly generating elements in the membership matrix between 0 and 1 according to uniform distribution.
(3) Calculating a clustering center value according to the membership matrix:
Figure BDA0003267685670000083
in the formula, xiRepresenting sample data.
(4) After the cluster center value is updated, updating a membership matrix U according to the following rule:
Figure BDA0003267685670000084
in the formula, ckThe word is described for the pulse in the k-th cluster center.
(5) Turning to the step (1), calculating the current cost function value, if the cost function J is smaller than a set threshold epsilon, stopping updating the membership matrix cluster, and otherwise, turning to the step (6);
(6) and (4) updating the new membership matrix according to the formula in the step (4), and realizing the most central clustering and the continuous optimization of the membership matrix through continuous iteration to finally realize the optimal clustering effect.
It should be noted that the optimal clustering effect is that the similarity between classes is minimum, and the similarity between classes is maximum, so that the sum of weighted distances from sample elements to the centers of all classes is minimum. When the optimal clustering effect is achieved, the identification accuracy of the CF parameters, the PRF parameters, and the PW parameters is improved.
In some embodiments, the multidimensional modeling analysis is performed according to signal parameters, including:
and carrying out modeling analysis on the signal parameters from the multi-dimensional angles of a space domain, a time domain and a frequency domain according to the signal parameters.
Specifically, from the angle of an airspace, because the unmanned aerial vehicle cluster covers the scanning wave position of the phased array radar, radar pulses can be sorted and identified at the same time, and information such as PRF, CF and PW in radar pulse description words is obtained. The PDW obtained by the unmanned aerial vehicle cluster reconnaissance at each moment can be regarded as a frame of electromagnetic panoramic image obtained by reconnaissance;
from the dimensionality of a time domain, PDW data obtained in a space domain is accumulated for a period of time, and PRF parameters on each wave position are calculated in a time coding mode, so that the residence time of a phased array radar wave beam on each wave position can be obtained, the scanning rule of the phased array radar wave beam is observed, and the current working mode of the radar can be identified. If the dwell time of the phased array radar wave beam on each wave position is approximately the same and the wave position jumping does not occur, the radar can be shown to be in a search mode, at this time, the penetration target can still continuously fly according to the original route, and meanwhile, the specific search mode type can be identified according to the size of the calculated data rate, referring to fig. 8, the search mode of the common phased array radar can be divided into three types of elements, namely a pitch angle range, an importance degree and a distance far and near range according to different classification modes, wherein the pitch angle range comprises a high-elevation angle search mode and a low-elevation angle search mode, the importance degree comprises a key area and other areas, and the distance far and near comprises near area search, far area search and simultaneous far area and near area search.
If the phased array radar jumps to a certain wave position for multiple times in the wave beam scanning process, so that the residence time on the certain wave position is increased, the jumped wave position is a tracking wave position, the radar tracks a target at the moment, and as a defense party, the working mode of the radar is predicted in advance, and certain countermeasures can be taken in advance, such as interference release and the like, so as to shield the target defense.
From the dimension of the frequency domain, in order to combat the mutual interference, the phased array radar usually adopts some anti-interference measures, such as frequency agility, that is, changes the transmitting frequency of the radar. By reasonably configuring the reconnaissance frequency range of each unit of the unmanned aerial vehicle cluster, the reconnaissance frequency can cover a larger range, the carrier frequency information of the radar can still be obtained at the moment, and the frequency change condition of the phased array radar is output in real time.
In some embodiments, determining an operating mode of the phased array radar based on results of the modeling analysis includes:
determining a recognition confidence of the signal parameter;
and determining the working mode of the phased array radar according to the recognition confidence coefficient and the result of the modeling analysis.
Specifically, the confidence is a parameter for measuring the confidence of the reconnaissance, and after a certain threshold is exceeded, the result of the modeling analysis, that is, the reconnaissance result, can be considered to be credible.
In some embodiments, determining a phased array radar location from lateral data acquired by the cluster of drones includes:
and determining the position of the phased array radar through lateral cross positioning according to the lateral data.
Specifically, each unmanned aerial vehicle needs to firstly execute a search task, and monopulse direction finding of the phased array radar is achieved by using the self-carried reconnaissance equipment. According to the direction-finding data obtained by the unmanned aerial vehicle cluster, the position of the phased array radar can be accurately determined by a direction-finding cross positioning method.
The working modes of the phased array radar have diversity, so that the characteristic parameters influencing the working modes of the phased array radar need to be analyzed, and the most suitable characteristic parameters are selected to identify different working modes of the phased array radar. The invention realizes the identification of the working mode of the phased array radar by combining the following aspects:
1) idea of algorithm
It is assumed that each individual in the drone cluster is homogeneous and the number density is large enough to cover each scan wave bit. Firstly, the unmanned aerial vehicle is released in a scanning airspace of the phased array radar, the unmanned aerial vehicle on each scanning wave position can be independently detected, and pulse description words of the radar phased array radar are obtained. And then, analyzing the sorted parameters from multiple dimensions such as a space domain, a time domain, a frequency domain and the like, and calculating to obtain the dwell time and the scanning period of the radar beam on each wave position. And finally, according to the length of the residence time, the working mode of the phased array radar can be inverted and identified in real time, the current threat situation is evaluated, and then a real target is guided to execute a penetration task.
2) Model assumptions
Electronic reconnaissance equipment carried by each unmanned aerial vehicle measures phased array radar pulse description words (TOA, CF, PRF and PW parameters);
the unmanned aerial vehicle clusters and the unmanned aerial vehicle-ground fusion center can realize mutual communication (transmitting phased array radar data);
assuming that the phased array radar is approximately considered to be motionless in a scanning period, mainly researching the reconnaissance effect of a cluster in the scanning airspace of the radar;
the radar scattering cross section (RCS) of the drone is assumed to be small enough to be in a silent state during reconnaissance, and the radar does not detect the drone swarm targets.
3) Constraint conditions
Assume that there is no a priori information (position of phased array radar) when a drone is first launched. On the premise of no prior information, each unmanned aerial vehicle needs to firstly execute a search task, and monopulse direction finding of the phased array radar is realized by utilizing the reconnaissance equipment carried by the unmanned aerial vehicle. According to the direction-finding data obtained by the unmanned aerial vehicle cluster, the position of the phased array radar can be accurately determined by a direction-finding cross positioning method. After the position of the phased array radar is obtained, the phased array radar can fly to a designated airspace, and a reconnaissance perception task is continuously executed.
Motion constraint
Motion constraint is in order to prevent two unmanned aerial vehicles from colliding. Maximum angle range (delta A) allowed by each unmanned aerial vehicle in phased array radar scanning period Ty,ΔAz) In which, Δ AyDenotes the maximum angular extent, Δ A, in the horizontal directionzRepresenting the maximum angular range in the pitch direction. Then the angular range of unmanned aerial vehicle motion should satisfy:
Figure BDA0003267685670000111
i denotes the ith unmanned plane, k denotes the time, AiyRepresenting the angle of the drone in the horizontal direction, AizRepresenting the angle of the drone in the pitch direction.
Suppose that at time k, the coordinate positions of the ith drone and the jth drone are (x)i(k),yi(k) And (x)j(k),yj(k) Then the interval of two arbitrary unmanned aerial vehicles at the same moment must be greater than minimum safe distance, promptly:
Figure BDA0003267685670000112
② reconnaissance of constraints
Suppose that a set of phased array radar pulses is received by an unmanned aerial vehicle scout
Figure BDA0003267685670000113
With a number of pulse samples of N, a set of characteristic parameters for each sample
Figure BDA0003267685670000114
Expressed as:
Figure BDA0003267685670000115
then the phased array radar pulse PDW matrix obtained by reconnaissance of the M unmanned aerial vehicles is as follows:
Figure BDA0003267685670000116
the embodiment of the invention provides an inversion identification device of a phased array radar working mode, which comprises:
the first module is used for identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting and determining signal parameters;
the second module is used for carrying out multi-dimensional modeling analysis according to the signal parameters;
and the third module is used for determining the working mode of the phased array radar according to the result of the modeling analysis.
The embodiment of the invention provides electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
and the processor executes the program to realize the inversion identification method of the working mode of the phased array radar.
The contents of the embodiment of the method of the present invention are all applicable to the embodiment of the electronic device, the functions specifically implemented by the embodiment of the electronic device are the same as those of the embodiment of the method, and the beneficial effects achieved by the embodiment of the electronic device are also the same as those achieved by the method.
In summary, the phased array radar signal can be rapidly detected in a complex electromagnetic environment, and the working mode of the phased array radar signal can be inverted and identified according to the radar characteristics, based on the distributed intelligent sensing technology, firstly, the radar pulse signal is sorted and identified by using unmanned aerial vehicle cluster detection equipment, then, the obtained radar pulse description word is modeled and analyzed in the airspace-time domain-frequency domain, so that the inversion identification of the working mode of the phased array radar is realized, and compared with the original technology:
(a) overcomes the defect of the single-point reconnaissance mode of the traditional reconnaissance aircraft
The traditional reconnaissance mode can only obtain radar pulse information of a single wave position or a small number of wave positions generally due to the limitation of the number of reconnaissance machines, the obtained information quantity is limited, and the working mode of a radar cannot be identified. The phased array radar mode identification method based on the unmanned aerial vehicle cluster technology can cover the scanning airspace of the phased array radar by using the number advantage of the unmanned aerial vehicle cluster after the position of the phased array radar is determined. Meanwhile, each reconnaissance aircraft can realize sorting and identification of radar pulses, obtain radar pulse description word information and form single-frame electromagnetic reconnaissance data;
(b) a model for recognizing the working mode of space-time frequency domain radar is disclosed
According to the method, the radar pulse description words obtained by sorting and identifying the reconnaissance aircraft can be used for judging that the unmanned aerial vehicle cluster covers the scanning wave position of the phased array, and the pulse description word information of the phased array radar on all the wave positions at each moment can be obtained in an airspace to form a real-time panoramic electromagnetic reconnaissance image; in the time domain, the scanning period and the scanning mode of the phased array radar and the residence time of each wave position can be obtained by storing data for a continuous period of time, and the current working mode of the phased array radar can be deduced according to the information; in the frequency domain, in the radar scanning process, in order to cope with enemy interference, a frequency agile mode may be adopted, and at this time, data of the same carrier frequency of each wave position needs to be counted, and the frequency agile rule of the phased array radar is obtained through analysis.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the method described above.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. An inversion identification method for a working mode of a phased array radar is characterized by comprising the following steps:
identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting, and determining signal parameters;
carrying out multi-dimensional modeling analysis according to the signal parameters;
and determining the working mode of the phased array radar according to the result of the modeling analysis.
2. The method of claim 1, further comprising:
determining the position of the phased array radar according to the lateral data acquired by the unmanned aerial vehicle cluster;
and controlling the unmanned aerial vehicles in the unmanned aerial vehicle cluster to move within a target range according to the position of the phased array radar.
3. The inversion identification method of phased array radar working mode according to claim 1, wherein the determining signal parameters according to the radar pulse signals obtained by sorting and identifying the unmanned aerial vehicle cluster comprises:
acquiring radar pulse signals respectively intercepted by all unmanned aerial vehicles in the unmanned aerial vehicle cluster;
sorting out signal parameters according to the radar pulse signals;
normalizing the signal parameters and sequencing the signal parameters according to the arrival time;
clustering the sequenced signal parameters according to a preset algorithm;
the signal parameters comprise a PRF parameter, a CF parameter and a PW parameter, wherein the PRF represents the capability of the phased array radar for transmitting pulses in unit time, the CF represents the frequency characteristic of the radar transmission signals, the PW represents the time period from the beginning to the end of the radar pulse received by the reconnaissance machine, and the arrival time represents the arrival time of the radar pulse leading edge signals received by the reconnaissance receiver.
4. The method for inverse identification of phased array radar operating modes according to claim 1, wherein the performing multidimensional modeling analysis according to the signal parameters comprises:
and carrying out modeling analysis on the signal parameters from the multi-dimensional angles of a space domain, a time domain and a frequency domain according to the signal parameters.
5. The method for identifying the inversion of the working mode of the phased array radar according to claim 1, wherein the determining the working mode of the phased array radar according to the result of the modeling analysis comprises:
determining a recognition confidence of the signal parameter;
and determining the working mode of the phased array radar according to the recognition confidence coefficient and the result of the modeling analysis.
6. The method for inversion identification of the working mode of the phased array radar according to claim 2, wherein the determining the position of the phased array radar according to the lateral data obtained by the unmanned aerial vehicle cluster comprises:
and determining the position of the phased array radar through lateral cross positioning according to the lateral data.
7. The method of claim 2, wherein the step of controlling the drones in the cluster of drones to move within a target range according to the phased array radar position comprises:
the expression for the angular range of motion of the drone is:
Figure FDA0003267685660000021
wherein i represents the ith unmanned aerial vehicle, k represents the time, AiyRepresenting the angle of the drone in the horizontal direction, AizRepresenting the angle of the drone in the pitch direction, Δ AyDenotes the maximum angular extent, Δ A, in the horizontal directionzA maximum angular range representing a pitch direction;
the expression of the minimum safe distance of each unmanned aerial vehicle in the unmanned aerial vehicle cluster is as follows:
Figure FDA0003267685660000022
wherein (x)i(k),yi(k) Denotes the coordinate position of the ith drone at time k, (x)j(k),yj(k) Represents the coordinate position of the jth drone at time k.
8. An inversion identification device for a phased array radar working mode is characterized by comprising:
the first module is used for identifying the obtained radar pulse signals according to unmanned aerial vehicle cluster sorting and determining signal parameters;
the second module is used for carrying out multi-dimensional modeling analysis according to the signal parameters;
and the third module is used for determining the working mode of the phased array radar according to the result of the modeling analysis.
9. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1 to 7.
CN202111091991.5A 2021-09-17 2021-09-17 Inversion identification method, device and medium for working mode of phased array radar Pending CN113933790A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111091991.5A CN113933790A (en) 2021-09-17 2021-09-17 Inversion identification method, device and medium for working mode of phased array radar

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111091991.5A CN113933790A (en) 2021-09-17 2021-09-17 Inversion identification method, device and medium for working mode of phased array radar

Publications (1)

Publication Number Publication Date
CN113933790A true CN113933790A (en) 2022-01-14

Family

ID=79276075

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111091991.5A Pending CN113933790A (en) 2021-09-17 2021-09-17 Inversion identification method, device and medium for working mode of phased array radar

Country Status (1)

Country Link
CN (1) CN113933790A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114624645A (en) * 2022-03-10 2022-06-14 扬州宇安电子科技有限公司 Miniature rotor unmanned aerial vehicle radar reconnaissance system based on micro antenna array
CN115144820A (en) * 2022-09-02 2022-10-04 北京轩涌科技发展有限公司 Space radar load signal analysis and evaluation system and evaluation method
CN115755963A (en) * 2022-11-15 2023-03-07 大连理工大学 Unmanned aerial vehicle group cooperative task planning method considering carrier delivery mode
CN115811377A (en) * 2022-10-17 2023-03-17 扬州宇安电子科技有限公司 Interference communication system based on airborne platform

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114624645A (en) * 2022-03-10 2022-06-14 扬州宇安电子科技有限公司 Miniature rotor unmanned aerial vehicle radar reconnaissance system based on micro antenna array
CN114624645B (en) * 2022-03-10 2022-09-30 扬州宇安电子科技有限公司 Miniature rotor unmanned aerial vehicle radar reconnaissance system based on micro antenna array
CN115144820A (en) * 2022-09-02 2022-10-04 北京轩涌科技发展有限公司 Space radar load signal analysis and evaluation system and evaluation method
CN115811377A (en) * 2022-10-17 2023-03-17 扬州宇安电子科技有限公司 Interference communication system based on airborne platform
CN115811377B (en) * 2022-10-17 2023-09-15 扬州宇安电子科技有限公司 Interference communication system based on airborne platform
CN115755963A (en) * 2022-11-15 2023-03-07 大连理工大学 Unmanned aerial vehicle group cooperative task planning method considering carrier delivery mode

Similar Documents

Publication Publication Date Title
CN113933790A (en) Inversion identification method, device and medium for working mode of phased array radar
CN109508000A (en) Isomery multi-sensor multi-target tracking method
US8718323B2 (en) Batch detection association for enhanced target descrimination in dense detection environments
Petrov et al. Radar emitter signals recognition and classification with feedforward networks
CN110780289B (en) Multi-target vehicle tracking method and device based on scene radar
CN109655822A (en) A kind of improved track initiation method
Cheng et al. A novel radar point cloud generation method for robot environment perception
CN108303684A (en) Ground surveillance radar multi-object tracking method based on radial velocity information
CN115061113B (en) Target detection model training method and device for radar and storage medium
Zhang et al. Research on decision-making system of cognitive jamming against multifunctional radar
Zhang et al. Multiple-target tracking with adaptive sampling intervals for phased-array radar
CN113702940A (en) Spatial cluster target resolution method based on multi-element characteristic information hierarchical fusion and application
CN111198366B (en) Method for quickly selecting finite array elements under distributed MIMO radar multitasking
Huang et al. Stif: A spatial-temporal integrated framework for end-to-end micro-uav trajectory tracking and prediction with 4d mimo radar
Schuster et al. Multi Detection Joint Integrated Probabilistic Data Association Using Random Matrices with Applications to Radar-Based Multi Object Tracking.
Farshchian et al. A multi-scale and adaptive track-before-detect technique for maritime environments
White et al. Lagrangian relaxation approaches to closed loop scheduling of track updates
Johnson et al. Adaptive beamsteering cognitive radar with integrated search-and-track of swarm targets
Zhang et al. Radar Operation Mode Recognition Via Multi-Feature Residual-and-Shrinkage ConvNet
Han et al. A New Method for Detecting Radar Target Electromagnetic Spectrum Shift
Torstensson et al. Particle filtering for track before detect applications
Deming et al. Track-before-detect of multiple slowly moving targets
Xu et al. A novel IMM filter for VideoSAR ground moving target tracking
Ye TARANUKHA V. Yu., PhD,(Phys.-Math.)
Sutharsan et al. Tracking and classification using aspect-dependent RCS and kinematic data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination