CN115940993A - Network platform sorting and tracking method based on RB-Stream algorithm - Google Patents
Network platform sorting and tracking method based on RB-Stream algorithm Download PDFInfo
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Abstract
The invention discloses a network platform sorting and tracking method based on RB-Stream algorithm, belonging to the technical field of communication countermeasure. The method comprises the steps of caching frequency hopping signal parameter data flow, sequencing the cached data in an ascending order according to arrival time, dividing duration grouping, estimating hopping speed, forming/updating frequency hopping period grouping, estimating frequency hopping period, calculating clustering characteristics, clustering data flow by using RB-Stream algorithm, identifying network station state, calculating network station parameters and the like. The invention abandons the traditional batch processing mode, and clusters the frequency hopping signal parameter data stream by using the high-efficiency clustering model based on the representative point structure, thereby realizing the real-time sorting and tracking of the network station and improving the visual level of the sorting of the network station. The invention has advanced technology, moderate calculated amount, easy engineering realization and capability of realizing real-time processing of frequency hopping data, and is an important improvement on the prior art.
Description
Technical Field
The invention belongs to the technical field of communication countermeasure, and particularly relates to a network platform sorting and tracking method based on an RB-Stream algorithm.
Background
Frequency hopping communications have been widely used in the field of military communications as an anti-interference communication means. As a third party of non-cooperative communication, in order to perform reconnaissance and interference on frequency hopping communication, it is an important premise that network station sorting can be accurately performed, and parameters such as a frequency set and a hopping rate of a frequency hopping radio station can be acquired.
The existing network station sorting algorithm comprises a network station sorting algorithm based on blind source separation, a network station sorting algorithm based on frequency hopping characteristic clustering and the like. The network platform sorting algorithm based on blind source separation processes time domain signals for sorting by using the blind source separation algorithm, and the network platform sorting algorithm is complex in calculation, poor in real-time performance and difficult to adapt to complex electromagnetic environments. The network station sorting algorithm based on the frequency hopping characteristic clustering sorts according to the characteristics of the arrival time, the incoming wave direction and the like of a frequency hopping signal, if the clock consistency of different radio stations is poor, the arrival time of the different radio stations is slowly drifted, the extraction of the arrival time characteristics of the frequency hopping radio stations is influenced, and the performance of the network station sorting algorithm based on the frequency hopping characteristic clustering is reduced. The network platform sorting method is in a batch processing mode, sorting results output at different moments have no relevance, and no network platform tracking capability exists.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a network station sorting and tracking method based on an RB-Stream algorithm, which abandons the traditional batch processing mode, and clusters frequency hopping signal parameter data streams by using a high-efficiency clustering model based on a representative point structure, thereby realizing real-time sorting and tracking of the network station and providing technical support for frequency hopping communication countermeasure.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a network platform sorting and tracking method based on RB-Stream algorithm is characterized by comprising the following steps:
(1) Caching the frequency hopping signal parameter data stream; the frequency hopping signal parameter data stream comes from reconnaissance of a frequency hopping communication system, the frequency hopping communication system comprises a plurality of network stations, and radio stations in the network stations communicate through frequency hopping signals; the frequency hopping signal parameter data stream comprises a plurality of frequency hopping signal parameter vectors, and the frequency hopping signal parameter vectors comprise the frequency, the arrival time and the duration of a frequency hopping signal;
(2) Sequencing the cached frequency hopping signal parameter vectors in an ascending order according to arrival time;
(3) Setting the current time as the sum of the arrival time and the duration of the last frequency hopping signal in the frequency hopping signal parameter data stream;
(4) Normalizing the arrival time and the duration time in the frequency hopping signal parameter vector by using the time resolution;
(5) Dividing the frequency hopping signal parameter vectors according to the duration, dividing the frequency hopping signal parameter vectors with the same duration into the same duration group, and arranging the frequency hopping signal parameter vectors in the duration group according to the ascending order of the arrival time;
(6) Screening the duration time groups divided in the step (5), and if the number of the frequency hopping signal parameter vectors of a certain duration time group exceeds a threshold 1 and the time interval between the current time and the estimated hopping speed of the duration time group is greater than a threshold 2, estimating the hopping speed; if the speed hopping estimation is successful, setting the speed hopping of the duration grouping as a speed hopping estimation value, setting the time of the estimated speed hopping of the duration grouping as the current time, and if the speed hopping estimation is failed, setting the speed hopping of the duration grouping as an illegal value; the threshold 1 is a set value M; when the jumping speed of the duration grouping is an illegal value, the threshold 2 is set time A, and when the jumping speed of the duration grouping is a legal value, the threshold 2 is set time B, wherein the set time A is less than the set time B;
(7) Moving the hopping signal parameter vectors in the duration grouping with the hopping speed being a legal value and the hopping speed difference not exceeding a set value C to the same hopping cycle grouping, setting the hopping cycle of the just formed hopping cycle grouping as an illegal value, and arranging the hopping signal parameter vectors in the hopping cycle grouping in an ascending order according to the arrival time; if the hop rate of the duration grouping is an illegal value and the number of the frequency hopping signal parameter vectors in the duration grouping exceeds a threshold 3 or the time interval between the current time and the arrival time of the 1 st frequency hopping signal parameter vector in the duration grouping exceeds a threshold 4, clearing the frequency hopping signal parameter vectors of the duration grouping and setting the time of estimating the hop rate as the current time; the threshold 3 is a set value D, and the threshold 4 is a set value E;
(8) If the frequency hopping period of the frequency hopping period group is an illegal value and the number of the frequency hopping signal parameter vectors in the frequency hopping period group exceeds a threshold 5, calculating a frequency hopping period estimated value according to the arrival time of the frequency hopping signal parameter vectors in the frequency hopping period group and setting the frequency hopping period of the frequency hopping period group as the frequency hopping period estimated value; the threshold 5 is a set value F;
(9) For the frequency hopping period grouping of which each frequency hopping period is a legal value, calculating the sequence number and the relative arrival time of each frequency hopping signal parameter vector as clustering characteristics;
(10) For each frequency hopping cycle group with the frequency hopping cycle of a legal value, sequentially taking out frequency hopping signal parameter vectors with the time span of 1 according to the time sequence, and clustering the data streams by using an RB-Stream algorithm, wherein the frequency hopping signal parameter vectors in each cluster belong to the same net station; the duration 1 is greater than the product of the frequency hopping period group and a set value H;
(11) For each cluster, judging the state of the network station according to the density of data streams in the cluster, wherein the cluster is the appearance state of the network station from the absence to the presence, the cluster is the disappearance state of the network station from the presence to the absence, the density of the data streams is not lower than a threshold 6 and is the activity state of the network station, and the density of the data streams is lower than the threshold 6 and is the silence state of the network station; the threshold 6 is a set value N;
(12) For each cluster, the reciprocal of the frequency hopping cycle group to which the cluster belongs is the hopping speed of the corresponding network station, the occurrence times of the frequency of each frequency hopping signal parameter vector in the cluster are counted according to a set time interval, and the frequency with high occurrence times is selected to form a frequency set of the network station corresponding to the cluster;
(13) And (5) repeating the steps (1) to (12) to realize real-time sorting and tracking of the network stations.
Further, in the step (6), the calculation method of the jump speed estimation is a PRI pulse repetition interval transform method.
Further, in the step (8), the calculation method of the frequency hopping period estimation value is a frequency hopping period estimation algorithm based on a cost function.
Further, in the step (9), the sequence number of each hopping signal parameter vector is an integer part of a quotient obtained by dividing the arrival time of the hopping signal parameter vector by the hopping cycle of the hopping cycle group to which the hopping signal parameter vector belongs, and the relative arrival time of each hopping signal parameter vector is a remainder obtained by dividing the arrival time of the hopping signal parameter vector by the hopping cycle of the hopping cycle group to which the hopping signal parameter vector belongs.
Further, in the step (12), the specific manner of selecting the frequency with the high frequency of occurrence to form the frequency set of the network station corresponding to the cluster is to calculate a mean value of the frequency of occurrence of each vector, and select the frequency with the frequency of occurrence greater than one half of the mean value to form the frequency set of the network station corresponding to the cluster.
Compared with the prior art, the invention has the following advantages:
1. the method and the device abandon the traditional batch processing mode, and cluster the data stream of the frequency hopping signal parameter by using the high-efficiency clustering model based on the representative point structure, thereby realizing the real-time sorting and tracking of the network station and improving the visual level of the sorting of the network station.
2. The invention has advanced technology, moderate calculation amount, easy engineering realization and can realize the real-time processing of frequency hopping data.
Drawings
Fig. 1 is a flow chart of a network station sorting and tracking method according to an embodiment of the present invention.
Detailed Description
The present invention is described in further detail below with reference to fig. 1.
A network station sorting and tracking method based on RB-Stream algorithm is used for analyzing a frequency hopping signal parameter data Stream obtained by reconnaissance so as to sort and track the network station. The frequency hopping signal parameter data stream comes from reconnaissance of a frequency hopping communication system, the frequency hopping communication system comprises a plurality of network stations, and radio stations in the network stations are communicated through frequency hopping signals. The method comprises the following steps:
(1) Caching frequency hopping signal parameter data streams; the frequency hopping signal parameter data stream comprises a plurality of frequency hopping signal parameter vectors, and the frequency hopping signal parameter vectors comprise components of frequency, arrival time and duration of signals;
(2) Sequencing the cached frequency hopping signal parameter vectors in an ascending order according to arrival time;
(3) Setting the current time as the sum of the arrival time and the duration of the last frequency hopping signal in the frequency hopping signal parameter data stream;
(4) The time resolution is utilized to normalize the arrival time and the duration time in the frequency hopping signal parameter vector, and the calculation mode is as follows: normalized arrival time = arrival time/time resolution, normalized duration = duration/time resolution;
(5) Dividing the frequency hopping signal parameter vectors according to duration, dividing the frequency hopping signal parameter vectors with the same duration into the same duration grouping, and arranging the frequency hopping signal parameter vectors in the duration grouping according to the ascending order of arrival time;
(6) Screening the duration time groups divided in the step (5), and if the number of the frequency hopping signal parameter vectors of a certain duration time group exceeds a threshold 1 and the time interval between the current time and the estimated hopping speed of the duration time group is greater than a threshold 2, estimating the hopping speed; if the speed jumping estimation is successful, setting the speed jumping of the duration grouping as a speed jumping estimation value, setting the time of the estimated speed jumping of the duration grouping as the current time, and otherwise, setting the speed jumping of the duration grouping as an illegal value; the threshold 1 is greater than 150; when the jump speed of the duration grouping is an illegal value, the threshold 2 is more than 1 second, otherwise, the threshold 2 is more than 8 seconds; the calculation mode of the jump speed estimation is a PRI pulse repetition interval conversion method;
(7) Processing the data in the duration packet to form or update a frequency hopping period packet; specifically, the method comprises the following steps:
(7a) Moving the hopping signal parameter vectors in the duration grouping with the hopping speed being a legal value and the hopping speeds being close to each other to the same hopping cycle grouping;
(7b) If the hop period packet is just formed, setting its hop period to an illegal value;
(7c) The parameter vectors of the frequency hopping signals in the frequency hopping period group are arranged according to the ascending order of the arrival time;
(7d) If the hop rate of the duration grouping is an illegal value, and the number of the frequency hopping signal parameter vectors in the duration grouping exceeds a threshold 3 or the time interval between the current time and the arrival time of the 1 st frequency hopping signal parameter vector in the duration grouping exceeds a threshold 4, clearing the frequency hopping signal parameter vectors of the duration grouping and setting the time of the estimated hop rate as the current time; the threshold 3 is greater than 15000, and the threshold 4 is greater than 30 seconds;
(8) If the frequency hopping period of the frequency hopping period group is an illegal value and the number of the frequency hopping signal parameter vectors in the frequency hopping period group exceeds a threshold 5, calculating a frequency hopping period estimated value according to the arrival time of the frequency hopping signal parameter vectors in the frequency hopping period group and setting the frequency hopping period of the frequency hopping period group as the frequency hopping period estimated value; the threshold 5 is greater than 900; the calculation mode of the frequency hopping period estimation value is a frequency hopping period estimation algorithm based on a cost function;
(9) For the frequency hopping period grouping of which each frequency hopping period is a legal value, calculating the sequence number and the relative arrival time of each frequency hopping signal parameter vector as clustering characteristics; the calculation method of the sequence number and the relative arrival time is as follows:
RT i ＝mod(T i p), wherein N i Serial number, RT, of the ith hopping signal i For the relative arrival time of the ith frequency hopping signal, <' >>
Denotes the lower integer, mod denotes the remainder, T i For the arrival time of the ith hopping signal, P is the hopA hop period of the frequency period group;
(10) For each frequency hopping cycle group with the frequency hopping cycle of a legal value, sequentially taking out frequency hopping signal parameter vectors with the time span of 1 according to the time sequence, and clustering the data streams by using an RB-Stream algorithm, wherein the frequency hopping signal parameter vectors in each cluster belong to the same net station; the duration 1 is greater than the product of the hopping period group and 50;
(11) Identifying the network platform state and calculating the network platform parameters; specifically, the method comprises the following steps:
(11a) For each cluster, judging the state of the network station according to the density of the data stream in the cluster, wherein the cluster is the appearance state of the network station from the absence to the presence, the cluster is the disappearance state of the network station from the presence to the absence, the density of the data stream is not lower than a threshold 6 and is the activity state of the network station, and the density of the data stream is lower than the threshold 6 and is the silence state of the network station; the threshold 6 is greater than 0.01;
(11b) For each cluster, the reciprocal of the frequency hopping period group to which the cluster belongs is the hopping speed of the corresponding network station;
(11c) Counting the frequency occurrence times of each frequency hopping signal parameter vector in the cluster according to a certain time interval, and selecting the frequency with high frequency occurrence times to form a frequency set of a network station corresponding to the cluster; the time interval is greater than 1 second.
And (5) repeating the steps (1) to (11) to realize real-time sorting and tracking of the network stations.
The following is a more specific example:
referring to fig. 1, a network platform sorting and tracking method based on RB-Stream algorithm includes the following steps:
step 1) caching frequency hopping signal parameter data flow: the frequency hopping signal parameter data stream comprises a plurality of frequency hopping signal parameter vectors, and the frequency hopping signal parameter vectors comprise components of frequency, arrival time and duration of signals; in the embodiment, the parameters of the frequency hopping signal are obtained by a frequency hopping signal detection method based on short-time fourier transform, and the time resolution in the detection method is 10 microseconds;
step 2) sequencing the cached frequency hopping signal parameter vectors in an ascending order according to arrival time;
step 3) setting the current time as the sum of the arrival time and the duration of the last frequency hopping signal in the frequency hopping signal parameter data stream;
step 4) normalizing the arrival time and the duration time in the frequency hopping signal parameter vector by using the time resolution, wherein the calculation mode is as follows: normalized arrival time = arrival time/time resolution, normalized duration = duration/time resolution;
step 5) dividing the frequency hopping signal parameter vectors according to the duration, dividing the frequency hopping signal parameter vectors with the same duration into the same duration grouping, and arranging the frequency hopping signal parameter vectors in the duration grouping according to the ascending order of the arrival time;
step 6) screening the duration grouping divided in the step 5), and if the number of the frequency hopping signal parameter vectors of a certain duration grouping exceeds a threshold 1 and the time interval between the current time and the estimated hopping speed of the duration grouping is greater than a threshold 2, estimating the hopping speed by using a PRI pulse repetition interval conversion method; if the speed jumping estimation is successful, setting the speed jumping of the duration grouping as a speed jumping estimation value, setting the time of the estimated speed jumping of the duration grouping as the current time, and otherwise, setting the speed jumping of the duration grouping as an illegal value; in this embodiment, the threshold 1 is 200; when the jumping speed of the duration grouping is an illegal value, the threshold 2 is 3 seconds, otherwise, the threshold 2 is 8 seconds;
step 7), processing the data in the duration packet to form or update a frequency hopping period packet; specifically, the method comprises the following steps:
step 7 a) moving the hopping signal parameter vectors in the duration grouping with the hopping speed being legal and similar to the hopping speed to the same hopping cycle grouping;
step 7 b) if the frequency hopping period group is just formed, setting the frequency hopping period thereof to an illegal value;
step 7 c) the frequency hopping signal parameter vectors in the frequency hopping period grouping are arranged according to the ascending order of arrival time;
step 7 d) if the hopping rate of the duration grouping is an illegal value and the number of the hopping signal parameter vectors in the duration grouping exceeds a threshold 3 or the time interval between the current time and the arrival time of the 1 st hopping signal parameter vector in the duration grouping exceeds a threshold 4, clearing the hopping signal parameter vectors of the duration grouping and setting the time of the estimated hopping rate as the current time; in this embodiment, the threshold 3 is 20000, and the threshold 4 is 32 seconds;
step 8) if the frequency hopping period of the frequency hopping period group is an illegal value and the number of the frequency hopping signal parameter vectors in the frequency hopping period group exceeds a threshold 5, calculating a frequency hopping period estimation value by using a frequency hopping period estimation algorithm based on a cost function according to the arrival time of the frequency hopping signal parameter vectors in the frequency hopping period group, and setting the frequency hopping period of the frequency hopping period group as the frequency hopping period estimation value; in this embodiment, the threshold 5 is 1000;
step 9) for the frequency hopping cycle groups with each frequency hopping cycle being a legal value, calculating the sequence number and the relative arrival time of each frequency hopping signal parameter vector as clustering characteristics; the calculation mode of the sequence number and the relative arrival time is as follows:
RT i ＝mod(T i p) in which N i For the sequence number of the ith frequency hopping signal, RT i For the relative arrival time of the ith frequency hopping signal, <' >>
Denotes lower rounded, mod denotes remainder, T i For the arrival time of the ith hopping signal, P is the hopping period of the hopping period group;
step 10) for each frequency hopping cycle group with the frequency hopping cycle of a legal value, sequentially taking out frequency hopping signal parameter vectors with the time span of 1 according to the time sequence, and clustering the frequency hopping signal parameter vectors by using an RB-Stream algorithm, wherein the frequency hopping signal parameter vectors in each cluster belong to the same net station; in this embodiment, duration 1 is the product of the hop period packet and 60;
step 11) identifying the state of the network platform and calculating the parameters of the network platform; specifically, the method comprises the following steps:
step 11 a) for each cluster, judging the state of the network station according to the density of data streams in the cluster, wherein the cluster is the appearance state of the network station from the absence to the presence, the cluster is the disappearance state of the network station from the presence to the absence, the density of the data streams is not lower than a threshold 6 and is the activity state of the network station, and the density of the data streams is lower than the threshold 6 and is the silence state of the network station; in this embodiment, the threshold 6 is 0.05;
step 11 b), for each cluster, the reciprocal of the frequency hopping period group to which the cluster belongs is the hopping speed of the corresponding net station;
step 11 c) counting the frequency occurrence times of each frequency hopping signal parameter vector in the cluster according to a certain time interval, calculating the average value of the occurrence times, and selecting the frequencies with the occurrence times more than one half of the average value to form a frequency set; in the present embodiment, the time interval is 3 seconds.
And (5) repeating the steps 1) to 11) to realize real-time sorting and tracking of the network stations.
The invention abandons the traditional batch processing mode, and clusters the frequency hopping signal parameter data stream by using the high-efficiency clustering model based on the representative point structure, thereby realizing the real-time sorting and tracking of the network station and improving the visual level of the sorting of the network station. The invention has advanced technology and is an important improvement on the prior art.
It should be understood that the above description of the embodiments of the present patent is only an exemplary description for facilitating the understanding of the patent scheme by the person skilled in the art, and does not imply that the scope of protection of the patent is only limited to these examples, and that the person skilled in the art can obtain more embodiments by combining technical features, replacing some technical features, adding more technical features, and the like to the various embodiments listed in the patent without any inventive effort on the premise of fully understanding the patent scheme, and therefore, the new embodiments are also within the scope of protection of the patent.
Claims (5)
1. A network platform sorting and tracking method based on RB-Stream algorithm is characterized by comprising the following steps:
(1) Caching the frequency hopping signal parameter data stream; the frequency hopping signal parameter data stream comes from reconnaissance of a frequency hopping communication system, the frequency hopping communication system comprises a plurality of network stations, and radio stations in the network stations communicate through frequency hopping signals; the frequency hopping signal parameter data stream comprises a plurality of frequency hopping signal parameter vectors, and the frequency hopping signal parameter vectors comprise the frequency, the arrival time and the duration of a frequency hopping signal;
(2) Sequencing the cached frequency hopping signal parameter vectors in an ascending order according to arrival time;
(3) Setting the current time as the sum of the arrival time and the duration of the last frequency hopping signal in the frequency hopping signal parameter data stream;
(4) Normalizing the arrival time and the duration time in the frequency hopping signal parameter vector by using the time resolution;
(5) Dividing the frequency hopping signal parameter vectors according to duration, dividing the frequency hopping signal parameter vectors with the same duration into the same duration grouping, and arranging the frequency hopping signal parameter vectors in the duration grouping according to the ascending order of arrival time;
(6) Screening the duration time groups divided in the step (5), and if the number of the frequency hopping signal parameter vectors of a certain duration time group exceeds a threshold 1 and the time interval between the current time and the estimated hopping speed of the duration time group is greater than a threshold 2, estimating the hopping speed; if the speed hopping estimation is successful, setting the speed hopping of the duration grouping as a speed hopping estimation value, setting the time of the estimated speed hopping of the duration grouping as the current time, and if the speed hopping estimation is failed, setting the speed hopping of the duration grouping as an illegal value; the threshold 1 is a set value M; when the jumping speed of the duration grouping is an illegal value, the threshold 2 is set time A, and when the jumping speed of the duration grouping is a legal value, the threshold 2 is set time B, wherein the set time A is less than the set time B;
(7) Moving the hopping signal parameter vectors in the duration grouping with the hopping speed being a legal value and the hopping speed difference not exceeding a set value C to the same hopping cycle grouping, setting the hopping cycle of the just formed hopping cycle grouping as an illegal value, and arranging the hopping signal parameter vectors in the hopping cycle grouping in an ascending order according to the arrival time; if the hop rate of the duration grouping is an illegal value, and the number of the frequency hopping signal parameter vectors in the duration grouping exceeds a threshold 3 or the time interval between the current time and the arrival time of the 1 st frequency hopping signal parameter vector in the duration grouping exceeds a threshold 4, clearing the frequency hopping signal parameter vectors of the duration grouping and setting the time of the estimated hop rate as the current time; the threshold 3 is a set value D, and the threshold 4 is a set value E;
(8) If the frequency hopping period of the frequency hopping period group is an illegal value and the number of the frequency hopping signal parameter vectors in the frequency hopping period group exceeds a threshold 5, calculating a frequency hopping period estimated value according to the arrival time of the frequency hopping signal parameter vectors in the frequency hopping period group and setting the frequency hopping period of the frequency hopping period group as the frequency hopping period estimated value; the threshold 5 is a set value F;
(9) For the frequency hopping period grouping of which each frequency hopping period is a legal value, calculating the sequence number and the relative arrival time of each frequency hopping signal parameter vector as clustering characteristics;
(10) For each frequency hopping cycle group with the frequency hopping cycle of a legal value, sequentially taking out frequency hopping signal parameter vectors with the time span of 1 according to the time sequence, and clustering the data streams by using an RB-Stream algorithm, wherein the frequency hopping signal parameter vectors in each cluster belong to the same net station; the duration 1 is greater than the product of the frequency hopping period group and a set value H;
(11) For each cluster, judging the state of the network station according to the density of the data stream in the cluster, wherein the cluster is the appearance state of the network station from the absence to the presence, the cluster is the disappearance state of the network station from the presence to the absence, the density of the data stream is not lower than a threshold 6 and is the activity state of the network station, and the density of the data stream is lower than the threshold 6 and is the silence state of the network station; the threshold 6 is a set value N;
(12) For each cluster, the reciprocal of the frequency hopping cycle group to which the cluster belongs is the hopping speed of the corresponding network station, the occurrence times of the frequency of each frequency hopping signal parameter vector in the cluster are counted according to a set time interval, and the frequency with high occurrence times is selected to form a frequency set of the network station corresponding to the cluster;
(13) And (5) repeating the steps (1) to (12) to realize real-time sorting and tracking of the network stations.
2. The RB-Stream algorithm based network station sorting and tracking method according to claim 1, wherein in said step (6), the calculation method of the skip rate estimation is a PRI pulse repetition interval transform method.
3. The method for sorting and tracking network stations based on RB-Stream algorithm as claimed in claim 1, wherein in said step (8), the estimation value of the frequency hopping period is calculated by a cost function-based estimation algorithm of the frequency hopping period.
4. The method as claimed in claim 1, wherein in step (9), the sequence number of each hop signal parameter vector is an integer part of a quotient obtained by dividing the arrival time of the hop signal parameter vector by the hop period of the hop period group to which the hop signal parameter vector belongs, and the relative arrival time of each hop signal parameter vector is a remainder of the division of the arrival time of the hop signal parameter vector by the hop period of the hop period group to which the hop signal parameter vector belongs.
5. The method for sorting and tracking network stations based on the RB-Stream algorithm as claimed in claim 1, wherein in said step (12), the frequency with high occurrence is selected as the frequency set of the network station corresponding to the cluster by calculating the mean of the occurrence of each vector frequency and selecting the frequency with occurrence greater than one half of the mean to form the frequency set of the network station corresponding to the cluster.
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