CN115496114B - TDMA burst length estimation method based on K-means clustering - Google Patents

TDMA burst length estimation method based on K-means clustering Download PDF

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CN115496114B
CN115496114B CN202211444853.5A CN202211444853A CN115496114B CN 115496114 B CN115496114 B CN 115496114B CN 202211444853 A CN202211444853 A CN 202211444853A CN 115496114 B CN115496114 B CN 115496114B
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burst length
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杜健
姚慰
龚珊
张海
黄增泽
张占来
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Chengdu Rongxing Technology Co ltd
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Abstract

The invention discloses a TDMA burst length estimation method based on K-means clustering, which relates to the technical field of satellite communication signal processing, and comprises the steps of carrying out burst detection on TDMA data, detecting a burst starting point according to a burst unique word, detecting a burst ending point through signal energy change, and outputting the burst length of each burst; sending each burst length into a K-means clustering device for clustering; and selecting the set with the maximum clustering element amount in the clustering result, and outputting the estimated burst length. The invention applies the K-mean clustering algorithm to the burst length estimation of the TDMA signals with equal length frames, and carries out the K-mean clustering on the burst length roughly estimated by the conventional method, thereby improving the burst length estimation precision and improving the integral interpretation error code performance of the TDMA signals.

Description

TDMA burst length estimation method based on K-means clustering
Technical Field
The invention relates to the technical field of satellite communication signal processing, in particular to a TDMA burst length estimation method based on K-means clustering.
Background
The satellite VSAT (Very Small antenna earth station) system is widely applied due to the advantages of flexible networking, high power utilization rate and the like, currently, a VSAT satellite communication network system, a coast monitoring system, a navigation network, an air network system and the like all adopt a VSAT network, which is called VSAT network for short, and an important link in signal processing of a reverse TDMA (Time Division Multiple Access) link in the VSAT network is to detect the starting point and burst length of a burst, and the error of burst length estimation can directly influence the error code performance of system interpretation, so the correct estimation of the burst length is Very important for the performance of TDMA interpretation. At present, the estimation of the TDMA signal burst completely depends on the energy of a signal to determine the end of the burst, and when the signal quality is poor, the situation of burst end judgment error often occurs, so that the burst interpretation error is caused.
Disclosure of Invention
The invention aims to provide a TDMA burst length estimation method based on K-means clustering, which can accurately estimate the burst length of a long-frame TDMA signal.
The invention solves the problems through the following technical scheme:
a TDMA burst length estimation method based on K-means clustering comprises the following steps:
step S10, carrying out burst detection on the TDMA data, detecting a burst starting point according to the burst unique word, detecting a burst ending point through signal energy change, and outputting the burst length of each burst;
s20, sending each burst length into a K-means clustering device for clustering;
and S30, selecting a set with the maximum clustering element amount in the clustering result, and outputting the estimated burst length.
The step S10 specifically includes:
step S11, order the event
Figure DEST_PATH_IMAGE001
Event->
Figure 854290DEST_PATH_IMAGE002
：
Figure DEST_PATH_IMAGE003
Figure 348594DEST_PATH_IMAGE004
Wherein,
Figure DEST_PATH_IMAGE005
is a known burst unique word, is asserted>
Figure 738118DEST_PATH_IMAGE006
Is that the variance is pick>
Figure DEST_PATH_IMAGE007
White gaussian noise of (1);
Figure 41317DEST_PATH_IMAGE008
For the signal sampling value sampled n>
Figure DEST_PATH_IMAGE009
N is the sampling times;
step S12 of determining likelihood ratio
Figure 652427DEST_PATH_IMAGE010
：
Figure DEST_PATH_IMAGE011
Wherein,
Figure 881414DEST_PATH_IMAGE012
is a signal sampling value>
Figure DEST_PATH_IMAGE013
Is an initial decision threshold;
Figure 928874DEST_PATH_IMAGE014
is an event>
Figure 190091DEST_PATH_IMAGE002
The probability of (d);
Figure DEST_PATH_IMAGE015
Is an event>
Figure 468756DEST_PATH_IMAGE001
The probability of (d);
Figure 585091DEST_PATH_IMAGE016
Figure DEST_PATH_IMAGE017
the likelihood ratios further translate to:
Figure 870579DEST_PATH_IMAGE018
s13, taking logarithms at two sides of the above formula simultaneously to obtain a judgment criterion:
Figure DEST_PATH_IMAGE019
simplification yields the inequality:
Figure 217377DEST_PATH_IMAGE020
wherein, the unique word
Figure DEST_PATH_IMAGE021
Is known, therefore->
Figure 645822DEST_PATH_IMAGE022
As is known, the decision criteria are further simplified to:
Figure DEST_PATH_IMAGE023
wherein,
Figure 623137DEST_PATH_IMAGE024
is the final decision threshold;
step S14: and judging the burst starting point and the burst ending point according to the final judgment threshold, and outputting the burst length.
The step S20 specifically includes:
step S21: selecting m, such as 3, from the input burst lengths as initial clustering centers, and initializing a K-means clustering device;
step S22: calculating the distance between each burst length and the clustering centers according to the value of each clustering center, and re-dividing the burst lengths into sets according to the minimum distance;
step S23: recalculating the clustering center of each set by calculating the average value of the burst length in the set;
step S24: and repeating the step S22 and the step S23 until the cluster center value of the set is not changed any more, so as to obtain a cluster set.
The step S30 specifically includes: and traversing each cluster set, selecting the set with the largest cluster element amount, setting a threshold, rejecting the elements with larger deviation in the cluster set again, averaging the rest elements, and outputting the average value as the estimated burst length.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention applies the K-mean value clustering algorithm to the TDMA signal burst length estimation of the equal-length frame, and carries out the K-mean value clustering on the burst length roughly estimated by the conventional method, thereby improving the burst length estimation precision and further improving the integral interpretation error code performance of the TDMA signal.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a K-means clustering flow chart.
Detailed Description
The present invention will be described in further detail with reference to examples, but the embodiments of the present invention are not limited thereto.
Example 1:
referring to fig. 1, a TDMA burst length estimation method based on K-means clustering includes:
step S10, carrying out burst detection on the TDMA data, detecting a burst starting point by means of the unique word, and detecting a burst ending point by means of signal energy change;
s20, sending the detected burst lengths to a K-means clustering device for clustering;
and S30, selecting the set with the maximum clustering element amount in the clustering result, and outputting the estimated burst length.
Where a burst is a term well known in the art and refers to a segment of a signal in which a burst occurs.
Example 2:
on the basis of embodiment 1, burst detection is performed on TDMA data, a burst start point is detected by means of a unique word, and a burst end is detected by means of a signal energy change, including the steps of:
step S1, order event
Figure 661500DEST_PATH_IMAGE001
Event->
Figure 251138DEST_PATH_IMAGE002
：
Figure 753663DEST_PATH_IMAGE003
Figure 557671DEST_PATH_IMAGE004
Wherein,
Figure DEST_PATH_IMAGE025
is a known burst unique word, is asserted>
Figure 896380DEST_PATH_IMAGE026
Is that the variance is pick>
Figure 286779DEST_PATH_IMAGE007
White gaussian noise of (1);
Figure 112652DEST_PATH_IMAGE008
For the signal sampling value sampled n>
Figure 431769DEST_PATH_IMAGE009
N is the sampling times;
step S2, judging likelihood ratio
Figure 444725DEST_PATH_IMAGE010
：
Figure DEST_PATH_IMAGE027
Wherein,
Figure 235219DEST_PATH_IMAGE028
is a signal sampling value>
Figure 322124DEST_PATH_IMAGE013
Is an initial judgment threshold;
Figure 812142DEST_PATH_IMAGE014
is an event>
Figure 312394DEST_PATH_IMAGE002
The probability of (d);
Figure 841333DEST_PATH_IMAGE015
Is an event>
Figure 110640DEST_PATH_IMAGE001
The probability of (d);
Figure DEST_PATH_IMAGE029
Figure 505981DEST_PATH_IMAGE030
the likelihood ratios further translate into:
Figure DEST_PATH_IMAGE031
s3, taking logarithms at two sides of the above formula at the same time to obtain a judgment criterion:
Figure 604780DEST_PATH_IMAGE032
simplification yields the inequality:
Figure DEST_PATH_IMAGE033
wherein, the unique word
Figure 360247DEST_PATH_IMAGE034
Is known, therefore->
Figure DEST_PATH_IMAGE035
As is known, the decision criteria are further simplified to:
Figure 31531DEST_PATH_IMAGE036
wherein,
Figure 627466DEST_PATH_IMAGE024
is the final decision threshold;
and 4, step 4: and judging the burst start and end according to the final judgment threshold, and outputting the burst length.
Example 3:
on the basis of embodiment 2, as shown in fig. 2, the step of sending the detected burst lengths to a K-means clustering device for clustering includes the steps of:
step 1: selecting 3 from a plurality of input burst lengths as initial clustering centers, and initializing a K-means clustering device;
step 2: calculating the distance between each burst length and the center values according to each clustering center value, and dividing the burst length set again according to the minimum distance;
and step 3: recalculating the center of each cluster by averaging the burst lengths in the set;
and 4, step 4: and repeating the steps 2 and 3 until the cluster center value of each set is not changed any more, and stopping.
Example 4:
on the basis of embodiment 3, the selecting a set with the largest clustering element amount in the clustering result and outputting the estimated burst length includes the steps of: and traversing each cluster set, selecting the set with the largest cluster element amount, setting a threshold, rejecting the elements with larger deviation in the set again, averaging the rest elements, and outputting the average value as the estimated burst length.
Although the present invention has been described herein with reference to the illustrated embodiments thereof, which are intended to be preferred embodiments of the present invention, it is to be understood that the invention is not limited thereto, and that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure.

Claims (3)

1. A TDMA burst length estimation method based on K-means clustering is characterized by comprising the following steps:
step S10, carrying out burst detection on time division multiple access TDMA data, detecting a burst starting point according to burst unique words, detecting a burst ending point through signal energy change, and outputting the burst length of each burst; the method specifically comprises the following steps:
step S11, order the event
Figure QLYQS_2
Event->
Figure QLYQS_5
：
Figure QLYQS_6
Figure QLYQS_3
Wherein +>
Figure QLYQS_7
Is a known burst unique word, is asserted>
Figure QLYQS_8
Is variance +>
Figure QLYQS_9
White gaussian noise;
Figure QLYQS_1
For the signal sampling value sampled n>
Figure QLYQS_4
N is the sampling frequency;
step S12 of determining likelihood ratio
Figure QLYQS_10
：
Figure QLYQS_13
Wherein it is present>
Figure QLYQS_16
Is a signal sampling value>
Figure QLYQS_12
Is an initial decision threshold;
Figure QLYQS_14
Is event->
Figure QLYQS_17
Probability;
Figure QLYQS_19
Is an event>
Figure QLYQS_11
The probability of (d);
Figure QLYQS_15
；
Figure QLYQS_18
；
the likelihood ratios further translate into:
Figure QLYQS_20
；
s13, taking logarithms at two sides of the above formula simultaneously to obtain a judgment criterion:
Figure QLYQS_21
；
simplification yields the inequality:
Figure QLYQS_22
；
wherein, the unique word
Figure QLYQS_23
Is known and therefore is->
Figure QLYQS_24
As is known, the decision criteria are further simplified to:
Figure QLYQS_25
；
Wherein,
Figure QLYQS_26
a final decision threshold;
step S14: judging the burst starting point and the burst ending point according to the final judgment threshold, and outputting the burst length;
s20, sending each burst length to a K-means clustering device for clustering;
and S30, selecting the set with the maximum clustering element amount in the clustering result, and outputting the estimated burst length.
2. The method for estimating the TDMA burst length based on K-means clustering according to claim 1, wherein said step S20 specifically comprises:
step S21: selecting m from a plurality of input burst lengths as initial clustering centers, and initializing a K-means clustering device;
step S22: calculating the distance between each burst length and the clustering centers according to the value of each clustering center, and re-dividing the burst lengths into sets according to the minimum distance;
step S23: recalculating the clustering center of each set by calculating the average value of the burst lengths in the sets;
step S24: and repeating the step S22 and the step S23 until the cluster center value of the set is not changed any more, so as to obtain a cluster set.
3. The method for estimating the TDMA burst length based on K-means clustering according to claim 2, wherein said step S30 specifically comprises: and traversing each cluster set, selecting the set with the largest cluster element amount, setting a threshold, rejecting the elements with larger deviation in the cluster set again, averaging the rest elements, and outputting the average value as the estimated burst length.
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