CN103117823A - Short wave channel model building method - Google Patents

Short wave channel model building method Download PDF

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CN103117823A
CN103117823A CN2013100326622A CN201310032662A CN103117823A CN 103117823 A CN103117823 A CN 103117823A CN 2013100326622 A CN2013100326622 A CN 2013100326622A CN 201310032662 A CN201310032662 A CN 201310032662A CN 103117823 A CN103117823 A CN 103117823A
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CN103117823B (en
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金珠
李颖
张跃宝
管英祥
任源博
蒋宏奎
王程林
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CETC 22 Research Institute
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Abstract

本发明公开了一种短波信道模型建模方法,包括:在存储有短波信道参数样本的数据库中,提取出某链路的M个样本,并获取各样本中的多径展宽参数和多普勒展宽参数;将各样本的多径展宽参数和多普勒展宽参数作为列向量,构造信道参数矩阵,并对矩阵中的各向量进行归一化处理;将信道参数矩阵的每列定义为一个数组点,利用二维聚类组合算法计算各数组点中的聚类中心;检测各聚类中心的邻域半径范围内覆盖的数组点的数量是否满足聚类要求,若满足,则将聚类中心进行去归一化处理,以去归一化处理后的聚类中心对应的多径展宽和多普勒展宽参数作为短波信道模型。本发明解决了已有信道建模技术得到的模型误差较大,无法准确描述链路信道特性的问题。

The invention discloses a modeling method of a shortwave channel model, comprising: extracting M samples of a certain link from a database storing shortwave channel parameter samples, and obtaining multipath broadening parameters and Doppler parameters in each sample Stretch parameters; use the multipath spread parameters and Doppler spread parameters of each sample as column vectors to construct a channel parameter matrix, and normalize each vector in the matrix; define each column of the channel parameter matrix as an array Points, use the two-dimensional clustering combination algorithm to calculate the cluster centers in each array point; detect whether the number of array points covered within the neighborhood radius of each cluster center meets the clustering requirements, and if so, the cluster center Denormalization processing is performed, and the multipath broadening and Doppler broadening parameters corresponding to the cluster centers after denormalization processing are used as the shortwave channel model. The invention solves the problem that the model error obtained by the existing channel modeling technology is relatively large, and the characteristics of the link channel cannot be accurately described.

Description

一种短波信道模型建模方法A Modeling Method for HF Channel Modeling

技术领域technical field

本发明涉及短波信道建模和数理统计领域,尤其涉及一种短波信道模型建模方法。The invention relates to the fields of shortwave channel modeling and mathematical statistics, in particular to a shortwave channel model modeling method.

背景技术Background technique

短波信道是一种典型的时变参数信道,电离层在时域、空域和频域都是随机变化的,使得短波信道参数产生随机变化,从而无法用一个准确的信道模型来描述特定时域、频域和空域的信道特性。目前,国际上通用的办法是使用ITU-RF.1487根据高、中、低纬度的典型信道参数组成的信道模型。然而,由于短波信道参数的时变特性,使用ITU建议的信道模型与实际信道模型误差太大,而使用信道测量得出的实时信道参数具有很大的随机性,无法完整描述链路的信道特性。The shortwave channel is a typical time-varying parameter channel. The ionosphere changes randomly in the time domain, space domain and frequency domain, which makes the shortwave channel parameters change randomly, so that it is impossible to use an accurate channel model to describe the specific time domain, Channel characteristics in frequency and space domains. At present, the common method in the world is to use the channel model formed by ITU-RF.1487 according to the typical channel parameters of high, middle and low latitudes. However, due to the time-varying characteristics of short-wave channel parameters, the error between the channel model suggested by ITU and the actual channel model is too large, and the real-time channel parameters obtained by channel measurement have great randomness, which cannot fully describe the channel characteristics of the link. .

发明内容Contents of the invention

本发明提供一种短波信道模型建模方法,用以解决现有技术中受短波信道参数时变特性的影响,已有信道建模技术得到的模型误差较大,无法准确描述链路信道特性的问题。The present invention provides a short-wave channel model modeling method, which is used to solve the problems in the prior art that are affected by the time-varying characteristics of short-wave channel parameters, the model error obtained by the existing channel modeling technology is relatively large, and the link channel characteristics cannot be accurately described. question.

为了解决上述问题,本发明采用的技术方案如下:In order to solve the above problems, the technical scheme adopted in the present invention is as follows:

本发明提供了一种短波信道模型建模方法,包括:The invention provides a shortwave channel model modeling method, comprising:

步骤1,在存储有短波信道参数样本的数据库中,提取出某链路的M个样本,并获取每个样本中的多径展宽参数和多普勒展宽参数;Step 1, extracting M samples of a certain link in the database storing HF channel parameter samples, and obtaining the multipath broadening parameters and Doppler broadening parameters in each sample;

步骤2,将每个样本的多径展宽参数和多普勒展宽参数作为列向量,构造2行M列的信道参数矩阵,并对矩阵中的各向量进行归一化处理;Step 2, using the multipath broadening parameters and Doppler broadening parameters of each sample as column vectors, constructing a channel parameter matrix with 2 rows and M columns, and normalizing each vector in the matrix;

步骤3,将信道参数矩阵的每列定义为一个数组点,并利用二维聚类组合算法计算各数组点中的聚类中心;Step 3, define each column of the channel parameter matrix as an array point, and use the two-dimensional clustering combination algorithm to calculate the cluster center in each array point;

步骤4,检测各聚类中心的邻域半径范围内覆盖的数组点的数量是否满足聚类要求,若满足,则将所述聚类中心进行去归一化处理,以去归一化处理后的聚类中心对应的多径展宽参数和多普勒展宽参数作为短波信道模型。Step 4, detect whether the number of array points covered within the neighborhood radius of each cluster center meets the clustering requirements, and if so, denormalize the cluster centers to obtain The multipath broadening parameters and Doppler broadening parameters corresponding to the cluster centers are used as the shortwave channel model.

可选地,本发明所述方法步骤1中,数据库中的短波信道参数样本为通过短波信道测量实验获取的参数样本。Optionally, in step 1 of the method of the present invention, the shortwave channel parameter samples in the database are parameter samples obtained through shortwave channel measurement experiments.

可选地,本发明所述方法步骤1中,在所述数据库中,按照预先设定的时间条件、信噪比条件和太阳黑子数条件,提取出某链路符合条件的M个样本。Optionally, in step 1 of the method of the present invention, in the database, M samples that meet the conditions of a certain link are extracted according to preset time conditions, signal-to-noise ratio conditions, and sunspot number conditions.

可选地,本发明所述方法步骤2中,对矩阵中的各向量进行归一化处理,具体包括:Optionally, in step 2 of the method of the present invention, normalization processing is performed on each vector in the matrix, specifically including:

将各多普勒展宽参数进行相互比较,获取满足条件1时fx的最小值

Figure BDA00002785819700021
以及将各多径展宽参数进行相互比较,获取满足条件2时τy的最小值
Figure BDA00002785819700022
Compare each Doppler broadening parameter with each other to obtain the minimum value of f x when condition 1 is satisfied
Figure BDA00002785819700021
And compare the multipath broadening parameters with each other to obtain the minimum value of τ y when condition 2 is satisfied
Figure BDA00002785819700022

分别以所述

Figure BDA00002785819700023
Figure BDA00002785819700024
为多普勒展宽参数和多径展宽参数的归一化基数,对矩阵中的相应参数向量进行归一化处理;respectively as described
Figure BDA00002785819700023
and
Figure BDA00002785819700024
is the normalized base of the Doppler broadening parameter and the multipath broadening parameter, and normalizes the corresponding parameter vector in the matrix;

其中,所述条件1为:多普勒展宽参数

Figure BDA00002785819700025
大于fx的参数数量满足预先设定的阈值;所述条件2为:多径展宽参数
Figure BDA00002785819700026
大于τy的参数数量满足设定的阈值。Wherein, the condition 1 is: Doppler broadening parameter
Figure BDA00002785819700025
The number of parameters greater than f x satisfies a preset threshold; the second condition is: multipath broadening parameters
Figure BDA00002785819700026
The number of parameters larger than τ y satisfies a set threshold.

可选地,本发明所述方法步骤3中,利用二维聚类组合算法计算各数组点中的聚类中心,具体包括:Optionally, in step 3 of the method of the present invention, a two-dimensional clustering combination algorithm is used to calculate the cluster centers in each array point, specifically including:

步骤31,计算每个数组点的密度指标,获取各密度指标中最高密度指标对应的数组点,判定其为第一个聚类中心;Step 31, calculating the density index of each array point, obtaining the array point corresponding to the highest density index in each density index, and determining it as the first cluster center;

步骤32,利用第k个聚类中心的密度指标,对每个数组点的密度指标进行修正,并获取修正后的密度指标中最高值对应的数组点;Step 32, using the density index of the kth cluster center to correct the density index of each array point, and obtain the array point corresponding to the highest value in the corrected density index;

步骤33,根据设定的聚类判决门限,对获取的数组点进行聚类中心判断,当判断出该数组点为聚类中心时,得到第k+1个聚类中心,并令k=k+1,返回步骤32。Step 33, according to the set clustering judgment threshold, judge the clustering center of the obtained array point, when it is judged that the array point is the clustering center, get the k+1th clustering center, and set k=k +1, back to step 32.

其中,所述步骤33,具体包括:Wherein, the step 33 specifically includes:

步骤331,判断数组点对应的

Figure BDA00002785819700031
大于
Figure BDA00002785819700032
是否成立,若是,判定该数组点为一个聚类中心;否则,执行步骤332;Step 331, determine the array point corresponding
Figure BDA00002785819700031
more than the
Figure BDA00002785819700032
Whether it is established, if so, determine that the array point is a cluster center; otherwise, execute step 332;

步骤332,判断数组点对应的

Figure BDA00002785819700033
小于
Figure BDA00002785819700034
是否成立,若是,判定该数组点不是聚类中心,终止聚类过程;否则,执行步骤333;Step 332, determine the array point corresponding
Figure BDA00002785819700033
less than
Figure BDA00002785819700034
Whether it is established, if so, determine that the array point is not a cluster center, and terminate the clustering process; otherwise, execute step 333;

步骤333,在

Figure BDA00002785819700035
大于小于
Figure BDA00002785819700037
时,判断
Figure BDA00002785819700038
是否成立,若是,判定该数组点为一个聚类中心;否则,判定该数组点不是聚类中心,将该数组点对应的密度指标设为零,并选择余下数组点中具有最高密度指标对应的数组点为待确认点,返回步骤331;Step 333, in
Figure BDA00002785819700035
more than the less than
Figure BDA00002785819700037
time, judge
Figure BDA00002785819700038
Whether it is true, if so, determine that the array point is a cluster center; otherwise, determine that the array point is not a cluster center, set the density index corresponding to the array point to zero, and select the one with the highest density index corresponding to the remaining array points Array points are points to be confirmed, return to step 331;

其中,

Figure BDA00002785819700039
ε分别为预先设定的判决门限上限和下限,
Figure BDA000027858197000310
为第一个聚类中心的密度指标;dmin为当前待确认数组点距离第一个聚类中心的距离,ra为设定的数组点的邻域半径。in,
Figure BDA00002785819700039
ε are the preset upper and lower limits of the decision threshold, respectively,
Figure BDA000027858197000310
is the density index of the first cluster center; d min is the distance between the current array point to be confirmed and the first cluster center, and r a is the neighborhood radius of the set array point.

进一步地,所述步骤32中,利用公式

Figure BDA000027858197000311
实现对每个数组点的密度指标进行修正;式中,
Figure BDA000027858197000312
为第k个聚类中心的密度指标,
Figure BDA000027858197000313
Figure BDA000027858197000314
分别为经过归一化处理后的第i个样本的多普勒展宽参数和多径展宽参数,
Figure BDA000027858197000315
Figure BDA000027858197000316
分别为经过归一化处理后的第k个聚类中心的多普勒展宽参数和多径展宽参数,rb为正数,且满足rb大于数组点的邻域半径。Further, in the step 32, using the formula
Figure BDA000027858197000311
Realize the correction of the density index of each array point; where,
Figure BDA000027858197000312
is the density index of the kth cluster center,
Figure BDA000027858197000313
and
Figure BDA000027858197000314
are the Doppler broadening parameters and multipath broadening parameters of the i-th sample after normalization, respectively,
Figure BDA000027858197000315
and
Figure BDA000027858197000316
are the Doppler broadening parameters and multipath broadening parameters of the k-th cluster center after normalization, respectively, r b is a positive number, and r b is greater than the neighborhood radius of the array point.

可选地,本发明所述方法步骤4具体包括:Optionally, step 4 of the method of the present invention specifically includes:

获取每个聚类中心的邻域半径范围内落入的数组点个数Nck,检测

Figure BDA000027858197000317
是否大于等于设定的聚类阈值,以及
Figure BDA000027858197000318
是否大于等于设定的平均聚类阈值,当二者均大于等于对应的阈值时,将所述聚类中心进行去归一化处理,以去归一化处理后的聚类中心对应的多径展宽参数和多普勒展宽参数作为短波信道模型;其中,K为聚类中心的总个数,
Figure BDA00002785819700041
表示任意给定的。Get the number of array points N ck falling within the neighborhood radius of each cluster center, and detect
Figure BDA000027858197000317
Is it greater than or equal to the set clustering threshold, and
Figure BDA000027858197000318
Whether it is greater than or equal to the set average clustering threshold, when both are greater than or equal to the corresponding threshold, the clustering center is denormalized, and the multipath corresponding to the denormalized clustering center The broadening parameter and the Doppler broadening parameter are used as the shortwave channel model; where K is the total number of cluster centers,
Figure BDA00002785819700041
means any given.

其中,落入所述聚类中心的邻域半径范围内的数组点为满足如下条件的数组点: ( f σ i ′ - f σ k ′ ) 2 + ( τ σ i ′ - τ σ k ′ ) 2 ≤ ( r a ) 2 ; Among them, the array points falling within the neighborhood radius of the cluster center are the array points satisfying the following conditions: ( f σ i ′ - f σ k ′ ) 2 + ( τ σ i ′ - τ σ k ′ ) 2 ≤ ( r a ) 2 ;

式中,

Figure BDA00002785819700043
Figure BDA00002785819700044
分别为归一化处理后的第i个数组点的多普勒展宽参数和多径展宽参数,
Figure BDA00002785819700045
Figure BDA00002785819700046
分别为归一化处理后的第k个聚类中心的多普勒展宽参数和多径展宽参数,ra为设定的数组点的邻域半径。In the formula,
Figure BDA00002785819700043
and
Figure BDA00002785819700044
are the Doppler broadening parameters and multipath broadening parameters of the i-th array point after normalization, respectively,
Figure BDA00002785819700045
and
Figure BDA00002785819700046
are the Doppler broadening parameter and the multipath broadening parameter of the k-th cluster center after normalization, respectively, and r a is the neighborhood radius of the set array point.

可选地,本发明所述方法步骤4进一步包括:Optionally, step 4 of the method of the present invention further includes:

当检测到各聚类中心半径范围内覆盖的数组点数量不满足聚类要求时,结束建模流程,或者,调整二维聚类组合算法使用的变量参数,重新执行步骤3;其中,所述变量参数包括:设置的数组点的邻域半径ra、设置的密度指标函数显著减小的邻域rb、以及设置的聚类判决门限的上下限

Figure BDA00002785819700047
ε。When it is detected that the number of array points covered within the radius of each cluster center does not meet the clustering requirements, the modeling process is ended, or, the variable parameters used by the two-dimensional clustering combination algorithm are adjusted, and step 3 is re-executed; wherein, the The variable parameters include: the set neighborhood radius r a of the array points, the set neighborhood r b whose density index function is significantly reduced, and the set upper and lower limits of the cluster decision threshold
Figure BDA00002785819700047
and ε .

本发明有益效果如下:The beneficial effects of the present invention are as follows:

本发明建立了一种通过对离散信道参数的有效分类,进行短波信道模型建模的方法,使用本发明提供的方法,可以建立特定时域、空域和特定太阳黑子数下的短波信道模型,相比ITU-RF.1487提供的信道模型更贴近实际信道特性,并且依据此方法建立的信道模型可为短波通信及短波频率打分提供有效支撑。The present invention establishes a method for modeling short-wave channel models by effectively classifying discrete channel parameters. Using the method provided by the present invention, short-wave channel models under specific time domains, air domains, and specific sunspot numbers can be established. Compared with the channel model provided by ITU-RF.1487, it is closer to the actual channel characteristics, and the channel model established according to this method can provide effective support for short-wave communication and short-wave frequency scoring.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

图1为本发明实施例提供的一种短波信道模型建模方法的流程图;Fig. 1 is the flow chart of a kind of shortwave channel model modeling method that the embodiment of the present invention provides;

图2为本发明实施例中对信道参数进行二维聚类组合的算法流程图。FIG. 2 is a flow chart of an algorithm for performing two-dimensional clustering and combination of channel parameters in an embodiment of the present invention.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

为了解决现有技术中已有信道建模技术得到的模型误差较大,无法准确描述链路信道特性的问题,本发明实施例提供一种短波信道模型建模方法,所述方法提出在特定链路上的测量数据中分时、分段的提取出指定的信道参数,并使用二维聚类组合算法,将提取的离散的信道参数进行有效分类,从而得到特定时域、空域链路上信道模型。下面就通过几个具体实施例对本发明所述技术方案进行详细说明。In order to solve the problem that the model error obtained by the existing channel modeling technology in the prior art is relatively large, and the channel characteristics of the link cannot be accurately described, the embodiment of the present invention provides a shortwave channel model modeling method. From the measurement data on the road, the specified channel parameters are extracted in time division and segment, and the two-dimensional clustering combination algorithm is used to effectively classify the extracted discrete channel parameters, so as to obtain the channel on the specific time domain and air domain link. Model. The technical solution of the present invention will be described in detail below through several specific examples.

实施例一Embodiment one

如图1所示,本发明实施例提供一种短波信道模型建模方法,包括:As shown in Figure 1, an embodiment of the present invention provides a shortwave channel model modeling method, including:

步骤S101,在存储有短波信道参数样本的数据库中,提取出某链路的M个样本,并获取每个样本中的多径展宽参数和多普勒展宽参数;Step S101, extracting M samples of a link from a database storing shortwave channel parameter samples, and obtaining multipath broadening parameters and Doppler broadening parameters in each sample;

该步骤中,数据库中的短波信道参数样本为通过短波信道测量实验获取的参数样本。In this step, the shortwave channel parameter samples in the database are parameter samples obtained through shortwave channel measurement experiments.

进一步地,该步骤中,在所述数据库中,按照预先设定的时间条件、信噪比条件和太阳黑子数条件,提取出某链路符合条件的M个样本。其中,各条件可以根据具体建模需求进行灵活设定。Further, in this step, in the database, according to preset time conditions, signal-to-noise ratio conditions, and sunspot number conditions, M samples that meet the conditions of a certain link are extracted. Among them, each condition can be flexibly set according to specific modeling requirements.

步骤S102,将每个样本的多径展宽参数和多普勒展宽参数作为列向量,构造2行M列的信道参数矩阵,并对矩阵中的各向量进行归一化处理;Step S102, using the multipath spreading parameters and Doppler spreading parameters of each sample as column vectors, constructing a channel parameter matrix with 2 rows and M columns, and normalizing each vector in the matrix;

该步骤中,对矩阵中的各向量进行归一化处理的方式,包括:In this step, the way of normalizing each vector in the matrix includes:

方式一:method one:

(1)将各多普勒展宽参数进行相互比较,获取满足条件1时fx的最小值以及将各多径展宽参数进行相互比较,获取满足条件2时τy的最小值

Figure BDA00002785819700061
(1) Compare the Doppler broadening parameters with each other to obtain the minimum value of f x when condition 1 is satisfied And compare the multipath broadening parameters with each other to obtain the minimum value of τ y when condition 2 is satisfied
Figure BDA00002785819700061

(2)分别以所述

Figure BDA00002785819700062
Figure BDA00002785819700063
为多普勒展宽参数和多径展宽参数的归一化基数,对矩阵中的相应参数向量进行归一化处理;(2) Respectively as described
Figure BDA00002785819700062
and
Figure BDA00002785819700063
is the normalized base of the Doppler broadening parameter and the multipath broadening parameter, and normalizes the corresponding parameter vector in the matrix;

上述方式中,条件1为:多普勒展宽参数大于fx的参数数量满足预先设定的阈值;条件2为:多径展宽参数

Figure BDA00002785819700065
大于τy的参数数量满足设定的阈值。In the above method, condition 1 is: Doppler broadening parameter The number of parameters greater than f x satisfies a preset threshold; condition 2 is: multipath broadening parameter
Figure BDA00002785819700065
The number of parameters larger than τ y satisfies a set threshold.

方式二:Method 2:

按特定步长,设置多普勒展宽参数判定阈值

Figure BDA00002785819700066
i=1,2,...n,计算满足条件
Figure BDA00002785819700067
时,多普勒展宽参数的个数Lf,并求出Lf满足大于等于设定阈值的各fx中的最小值
Figure BDA00002785819700068
以所述
Figure BDA00002785819700069
为多普勒展宽参数的归一化基数,对矩阵中的多普勒展宽参数进行归一化处理;Set the judgment threshold of Doppler broadening parameters according to a specific step size
Figure BDA00002785819700066
i=1,2,...n, the calculation satisfies the condition
Figure BDA00002785819700067
, the number of Doppler broadening parameters L f , and find the minimum value of each f x that L f satisfies greater than or equal to the set threshold
Figure BDA00002785819700068
as stated
Figure BDA00002785819700069
is the normalization base of the Doppler broadening parameter, and normalizes the Doppler broadening parameter in the matrix;

按特定步长,设置多径展宽参数判定阈值i=1,2,...n,计算满足条件

Figure BDA000027858197000611
时,多普勒展宽参数的个数τl,并求出τl满足大于设定阈值的各τy中的最小值
Figure BDA000027858197000612
以所述为多径展宽参数的归一化基数,对矩阵中的多径展宽参数进行归一化处理。According to a specific step size, set the multipath broadening parameter judgment threshold i=1,2,...n, the calculation satisfies the condition
Figure BDA000027858197000611
, the number of Doppler broadening parameters τ l , and the minimum value of each τ y that τ l satisfies greater than the set threshold
Figure BDA000027858197000612
as stated is the normalized base of the multipath broadening parameters, and normalizes the multipath widening parameters in the matrix.

关于上述阈值的设置,本发明中建议阈值为大于等于M·0.95,当然,本领域技术人员也可以根据具体建模需求进行灵活设定。Regarding the setting of the above threshold, it is suggested in the present invention that the threshold is greater than or equal to M·0.95. Of course, those skilled in the art can also flexibly set it according to specific modeling requirements.

步骤S103,将信道参数矩阵的每列定义为一个数组点,并利用二维聚类组合算法计算各数组点中的聚类中心;Step S103, defining each column of the channel parameter matrix as an array point, and using a two-dimensional clustering combination algorithm to calculate the cluster centers in each array point;

步骤S104,检测各聚类中心的邻域半径范围内覆盖的数组点的数量是否满足聚类要求,若满足,则将所述聚类中心进行去归一化处理,以去归一化处理后的聚类中心对应的多径展宽参数和多普勒展宽参数作为短波信道模型。Step S104, detecting whether the number of array points covered within the neighborhood radius of each cluster center satisfies the clustering requirements, and if so, denormalizes the cluster centers to obtain The multipath broadening parameters and Doppler broadening parameters corresponding to the cluster centers are used as the shortwave channel model.

该步骤具体为:获取每个聚类中心的邻域半径范围内落入的数组点个数Nck,检测是否大于等于设定的聚类阈值,以及

Figure BDA000027858197000615
是否大于等于设定的平均聚类阈值,当二者均大于等于对应的阈值时,将所述聚类中心进行去归一化处理,以去归一化处理后的聚类中心对应的多径展宽参数和多普勒展宽参数作为短波信道模型;其中,K为聚类中心的总个数,
Figure BDA00002785819700071
表示任意给定的。This step is specifically as follows: obtain the number N ck of the array points falling within the neighborhood radius of each cluster center, and detect Is it greater than or equal to the set clustering threshold, and
Figure BDA000027858197000615
Whether it is greater than or equal to the set average clustering threshold, when both are greater than or equal to the corresponding threshold, the clustering center is denormalized, and the multipath corresponding to the denormalized clustering center The broadening parameter and the Doppler broadening parameter are used as the shortwave channel model; where K is the total number of cluster centers,
Figure BDA00002785819700071
means any given.

其中,聚类阈值和平均聚类阈值的设置表示计算得到的聚类中心是否具有典型性,二者的具体取值不唯一,本实施例建议平均聚类阈值要大于等于60%,聚类阈值要大于等于12%。Among them, the settings of the clustering threshold and the average clustering threshold indicate whether the calculated cluster centers are typical, and the specific values of the two are not unique. This embodiment suggests that the average clustering threshold should be greater than or equal to 60%, and the clustering threshold It must be greater than or equal to 12%.

进一步地,所述步骤S104中,当检测到各聚类中心半径范围内覆盖的数组点数量不满足聚类要求时,结束建模流程,或者,调整二维聚类组合算法使用的变量参数,重新执行步骤S103;其中,所述变量参数包括但不限于为:设置的数组点的邻域半径ra、设置的密度指标函数显著减小的邻域rb、以及设置的聚类判决门限的上下限

Figure BDA00002785819700072
ε。Further, in the step S104, when it is detected that the number of array points covered within the radius of each cluster center does not meet the clustering requirements, the modeling process is ended, or the variable parameters used by the two-dimensional clustering combination algorithm are adjusted, Re-execute step S103; wherein, the variable parameters include but are not limited to: the set neighborhood radius r a of the array points, the set neighborhood r b where the density index function is significantly reduced, and the set clustering decision threshold Upper and lower limits
Figure BDA00002785819700072
and ε .

实施例二Embodiment two

本发明实施例提供一种短波信道模型建模方法,与实施例一所述方法的原理相同,其是结合具体实施细节对实施例一所述方法的进一步详细阐述,所述方法包括:The embodiment of the present invention provides a shortwave channel model modeling method, the principle of which is the same as that of the method described in Embodiment 1. It is a further detailed elaboration of the method described in Embodiment 1 in combination with specific implementation details. The method includes:

步骤A:获取信道参数样本;Step A: Obtain channel parameter samples;

步骤B:对信道参数进行二维聚类组合;Step B: performing two-dimensional clustering combination on channel parameters;

步骤C:将聚类中心与信道模型对应起来;Step C: Corresponding the cluster center to the channel model;

对于上述步骤A,具体实现过程如下:For the above step A, the specific implementation process is as follows:

根据建模需求,获取能完整反映信道特性的不相关的两类信道参数:多径展宽和多普勒展宽。其中,信道参数来源:在某个特定链路上完成信道测量实验后,从实测数据中分析计算出的信道参数,每一个实测数据样本对应一组信道参数,如表1所示为信道参数表。According to modeling requirements, two types of uncorrelated channel parameters that can fully reflect channel characteristics are obtained: multipath broadening and Doppler broadening. Among them, the source of channel parameters: after completing the channel measurement experiment on a specific link, the channel parameters are analyzed and calculated from the measured data. Each measured data sample corresponds to a set of channel parameters, as shown in Table 1. The channel parameter table .

表1Table 1

Figure BDA00002785819700073
Figure BDA00002785819700073

步骤A1:根据短波信道特性,设置信道参数提取条件;Step A1: According to the characteristics of the shortwave channel, set the channel parameter extraction conditions;

(1)设置测量时间条件:Tmin<t<Tmax,一般情况下,时间间隔优选为1个月<Tmax-Tmin<3个月(1) Set the measurement time condition: T min <t<T max , in general, the time interval is preferably 1 month <T max -T min <3 months

(2)设置信噪比条件:Snr>xdB,一般情况下,x选择5dB。(2) Set the signal-to-noise ratio condition: Snr>xdB, under normal circumstances, select 5dB for x.

(3)设置太阳黑子数条件:Nmin<n<Nmax,一般情况下,太阳黑子数间隔Nmax-Nmin选择25。(3) Set the sunspot number condition: N min <n<N max , in general, the sunspot number interval N max -N min is 25.

步骤A2:提取信道参数;Step A2: extracting channel parameters;

使用步骤A1的三个条件,求出满足条件的数据样本交集,假设数据样本交集为M个,将其对应的多普勒展宽参数fσ和多径展宽信道参数τσ分别放入信道参数矩阵中。Use the three conditions in step A1 to find the intersection of data samples that meet the conditions. Assuming that the intersection of data samples is M, put the corresponding Doppler broadening parameter f σ and multipath broadening channel parameter τ σ into the channel parameter matrix middle.

ChannelChannel -- ParametParamet == ff &sigma;&sigma; 11 ,, ff &sigma;&sigma; 22 ,, .. .. .. ,, ff &sigma;&sigma; Mm &tau;&tau; &sigma;&sigma; 11 ,, &tau;&tau; &sigma;&sigma; 22 ,, .. .. .. ,, &tau;&tau; &sigma;&sigma; Mm

步骤A3:将信道参数做归一化处理,具体步骤如下:Step A3: Normalize the channel parameters, the specific steps are as follows:

步骤A31:计算满足条件

Figure BDA00002785819700083
τσy时多普勒展宽和多径展宽参数的个数Lf和τl,生成参数数值与参数个数的对应表,如表2所示。Step A31: Computing to satisfy the condition
Figure BDA00002785819700083
When τ σy , the number of Doppler broadening and multipath broadening parameters L f and τ l , generate a corresponding table of parameter values and parameter numbers, as shown in Table 2.

表2Table 2

Figure BDA00002785819700091
Figure BDA00002785819700091

步骤A32:从表2中求出满足Lf≥M·0.95的fx最小值,设为

Figure BDA00002785819700092
同时求出满足τl≥M·0.95的τy最小值,设为
Figure BDA00002785819700093
Step A32: Find the minimum value of f x that satisfies L f ≥ M·0.95 from Table 2, and set it as
Figure BDA00002785819700092
At the same time, find the minimum value of τ y that satisfies τ l ≥ M·0.95, set
Figure BDA00002785819700093

步骤A33:将信道参数矩阵中的第1行和第2行数分别用

Figure BDA00002785819700094
Figure BDA00002785819700095
做归一化处理,得到:Step A33: Use the numbers in the first row and the second row in the channel parameter matrix respectively by
Figure BDA00002785819700094
and
Figure BDA00002785819700095
After normalization, we get:

ChannelChannel -- ParametParamet -- Unitaryunitary == ff &sigma;&sigma; 11 // ff xx LL ,, ff &sigma;&sigma; 22 // ff xx LL ,, .. .. .. ,, ff &sigma;&sigma; Mm // ff xx LL &tau;&tau; &sigma;&sigma; 11 // &tau;&tau; ythe y LL ,, &tau;&tau; &sigma;&sigma; 22 // &tau;&tau; ythe y LL ,, .. .. .. ,, &tau;&tau; &sigma;&sigma; Mm // &tau;&tau; ythe y LL

对于步骤B,具体实现过程如图2所示,包括:For step B, the specific implementation process is shown in Figure 2, including:

首先,计算Channel-Paramet-Unitary矩阵中的每一列数据组的密度指标。First, calculate the density index of each column data group in the Channel-Paramet-Unitary matrix.

将Channel-Paramet-Unitary矩阵中的每一列数据组

Figure BDA00002785819700097
都作为聚类中心的侯选数组点。计算每列
Figure BDA00002785819700098
的密度指标:Group each column of data in the Channel-Paramet-Unitary matrix
Figure BDA00002785819700097
All of them are used as the candidate array points of the cluster centers. Calculate each column
Figure BDA00002785819700098
The density index of:

DD. ii == &Sigma;&Sigma; jj == 11 Mm expexp [[ -- (( ff &sigma;&sigma; ii // ff xx LL -- ff &sigma;&sigma; jj // ff xx LL )) 22 ++ (( &tau;&tau; &sigma;&sigma; ii // &tau;&tau; ythe y LL -- &tau;&tau; &sigma;j&sigma;j // &tau;&tau; ythe y LL )) 22 (( rr aa )) 22 ]]

其中,ra是一个正数,定义了该点的邻域半径,如果数组

Figure BDA000027858197000910
周围有多个邻近的数组点,则具有高密度值,半径以外的数组点对该点的密度指标贡献非常小。ra的取值与希望得到的聚类中心个数有关,当ra越大,得到的聚类中心个数越少;相反的,当ra越小,得到的聚类中心个数越多。所以,ra可以根据需求进行灵活设置。Among them, r a is a positive number that defines the neighborhood radius of the point, if the array
Figure BDA000027858197000910
There are many adjacent array points around, it has a high density value, and the array points outside the radius contribute very little to the density index of the point. The value of r a is related to the number of cluster centers you want to obtain. When r a is larger, the number of cluster centers obtained is less; on the contrary, when r a is smaller, the number of cluster centers obtained is more . Therefore, r a can be flexibly set according to requirements.

然后,选取最高密度指标的数组点,进行聚类中心判断,并在该数组点为聚类中心时,以该数组点的密度指标对每个数组点的密度指标进行修正,具体如下:Then, select the array point with the highest density index to judge the cluster center, and when the array point is the cluster center, correct the density index of each array point with the density index of the array point, as follows:

计算每个数组点的密度指标后,选取具有最高密度指标的数组点

Figure BDA00002785819700101
作为第一个聚类中心,
Figure BDA00002785819700102
为其密度指标。设当前选出了第k个聚类中心
Figure BDA00002785819700103
其密度指标为
Figure BDA00002785819700104
则用
Figure BDA00002785819700105
修正每个数组点的密度指标:After calculating the density index of each array point, select the array point with the highest density index
Figure BDA00002785819700101
As the first cluster center,
Figure BDA00002785819700102
is its density index. Suppose the kth cluster center is currently selected
Figure BDA00002785819700103
Its density index is
Figure BDA00002785819700104
then use
Figure BDA00002785819700105
Fix the density index for each array point:

DD. ii == DD. ii -- DD. cc kk expexp [[ -- (( ff &sigma;&sigma; ii // ff xx LL -- ff &sigma;&sigma; kk // ff xx LL )) 22 ++ (( &tau;&tau; &sigma;&sigma; ii // &tau;&tau; ythe y LL -- &tau;&tau; &sigma;&sigma; kk // &tau;&tau; ythe y LL )) 22 (( rr bb )) 22 ]]

其中rb是一个正数,定义了一个密度指标函数显著减小的邻域,通常rb>ra,以避免出现相距很近的聚类中心,本实施例中取rb=1.5ra。通过修正后显然靠近上一个聚类中心的数组点的密度指标将显著减小,使得这些点不太可能被选为下一个聚类中心。Where r b is a positive number, which defines a neighborhood where the density index function is significantly reduced, usually r b >r a to avoid cluster centers that are very close to each other, and r b =1.5r a in this embodiment . After the correction, the density index of the array points that are obviously close to the previous cluster center will be significantly reduced, making these points less likely to be selected as the next cluster center.

在修正后的各密度指标中,选取最高密度指标的数组点,计算得到其密度指标

Figure BDA00002785819700107
然后利用如下判决方式,判决该数组点是否符合聚类中心要求,并判决是否终止聚类;设判决门限的上限为
Figure BDA00002785819700108
下限为ε=0.15。Among the corrected density indexes, select the array point of the highest density index and calculate its density index
Figure BDA00002785819700107
Then use the following judgment method to judge whether the array point meets the requirements of the clustering center, and judge whether to terminate the clustering; set the upper limit of the judgment threshold as
Figure BDA00002785819700108
The lower limit is ε = 0.15.

(a)当

Figure BDA00002785819700109
认为
Figure BDA000027858197001010
为一个聚类中心,以
Figure BDA000027858197001011
对各组的密度指标进行修正后,继续下一判决过程。(a) when
Figure BDA00002785819700109
think
Figure BDA000027858197001010
as a cluster center, with
Figure BDA000027858197001011
After correcting the density index of each group, continue to the next judgment process.

(b)当

Figure BDA000027858197001012
认为
Figure BDA000027858197001013
不是聚类中心,终止聚类过程。(b) when
Figure BDA000027858197001012
think
Figure BDA000027858197001013
If it is not a cluster center, the clustering process is terminated.

(c)当

Figure BDA000027858197001014
时,判断式
Figure BDA000027858197001015
是否成立,若成立,则认为
Figure BDA000027858197001016
为一个聚类中心,以
Figure BDA000027858197001017
对各组的密度指标进行修正后,继续下一判决过程;若不成立,认为
Figure BDA000027858197001018
不是聚类中心,并将该数组点的密度指标设为0,选择余下数组点中具有最高密度指标的点为待确认的点,重新进行判决。(c) when
Figure BDA000027858197001014
time, judgment
Figure BDA000027858197001015
Whether it is established, and if it is established, it is considered that
Figure BDA000027858197001016
as a cluster center, with
Figure BDA000027858197001017
After correcting the density index of each group, continue to the next judgment process; if not established, consider
Figure BDA000027858197001018
It is not the cluster center, and the density index of the array point is set to 0, and the point with the highest density index among the remaining array points is selected as the point to be confirmed, and the judgment is made again.

对于步骤C,具体实现过程如下:For step C, the specific implementation process is as follows:

步骤C1:计算Channel-Paramet-Unitary矩阵中所有数组点

Figure BDA00002785819700111
落在每个聚类中心半径范围内的比例。落在第k个聚类中心半径范围内的数组点个数为:满足 ( f &sigma; i / f x L - f &sigma; ck / f x L ) 2 + ( &tau; &sigma; i / &tau; y L - &tau; &sigma;ck / &tau; y L ) 2 &le; ( r a ) 2 的数组点个数,记为Nck。Step C1: Calculate all array points in the Channel-Paramet-Unitary matrix
Figure BDA00002785819700111
The proportion that falls within the radius of each cluster center. The number of array points falling within the radius of the kth cluster center is: ( f &sigma; i / f x L - f &sigma; ck / f x L ) 2 + ( &tau; &sigma; i / &tau; the y L - &tau; &sigma;ck / &tau; the y L ) 2 &le; ( r a ) 2 The number of array points in , denoted as N ck .

步骤C2:检验每个聚类中心半径范围内的Nck值,当

Figure BDA00002785819700113
并且 &ForAll; N ck M &times; 100 % &GreaterEqual; 15 % 时,认为聚类中心
Figure BDA00002785819700115
能表示信道模型。Step C2: Check the N ck value within the radius of each cluster center, when
Figure BDA00002785819700113
and &ForAll; N ck m &times; 100 % &Greater Equal; 15 % When , it is considered that the cluster center
Figure BDA00002785819700115
Can represent channel models.

步骤C3:将符合要求的聚类中心去归一化得到信道模型。信道模型表示为 ( f &sigma; c 1 , &tau; &sigma; c 1 ) . . . ( f &sigma; c k , &tau; &sigma; c k ) . Step C3: Denormalize the cluster centers that meet the requirements to obtain the channel model. The channel model is expressed as ( f &sigma; c 1 , &tau; &sigma; c 1 ) . . . ( f &sigma; c k , &tau; &sigma; c k ) .

为更进一步阐述本发明达成预定目的所采取的技术手段及功效,以下给出一具体应用实例,对本发明提出的技术方案进行进一步解释说明。In order to further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, a specific application example is given below to further explain the technical solution proposed by the present invention.

如表3所示为信道参数表,该信道参数表是基于2012年1月12日至2012年1月17日由青岛发射,北京接收的测量数据提取出的信道参数。Table 3 shows the channel parameter table. The channel parameter table is based on the channel parameters extracted from the measurement data transmitted by Qingdao and received by Beijing from January 12, 2012 to January 17, 2012.

表3table 3

Figure BDA00002785819700117
Figure BDA00002785819700117

Figure BDA00002785819700121
Figure BDA00002785819700121

本发明实施例基于上述实测数据的短波信道模型建模方法,包括如下具体步骤:The embodiment of the present invention is based on the shortwave channel model modeling method of above-mentioned measured data, comprises following concrete steps:

步骤A:获取信道参数样本。从表3中获取用于信道建模的多普勒展宽和多径展宽参数样本,具体步骤如下:Step A: Obtain channel parameter samples. Obtain samples of Doppler broadening and multipath broadening parameters for channel modeling from Table 3, the specific steps are as follows:

A1:根据短波信道特性,设置信道参数提取条件;由于本次测量数据时间跨度小,认为所有测量数据提取的信道参数均可拿来使用。设置信噪比条件为Snr>5dB。由于此次测量实验是冬季,时间跨度短,太阳黑子数变化不大(最大值与最小值之差不大于25),不划分太阳黑子数区间。A1: According to the characteristics of the short-wave channel, set the channel parameter extraction conditions; because the time span of the measurement data is small, it is considered that all the channel parameters extracted from the measurement data can be used. Set the signal-to-noise ratio condition as Snr>5dB. Since this measurement experiment is in winter, the time span is short, and the number of sunspots does not change much (the difference between the maximum value and the minimum value is not greater than 25), the sunspot number interval is not divided.

A2:提取信道参数;从使用步骤A1的条件,可以得出数据样本集,数据样本集为1135个,将其对应的多普勒展宽参数fσ和多径展宽信道参数τσ分别放入信道参数矩阵中。A2: Extract channel parameters; from the conditions of using step A1, the data sample set can be obtained, and the data sample set is 1135, and the corresponding Doppler broadening parameter f σ and multipath widening channel parameter τ σ are respectively put into the channel in the parameter matrix.

ChannelChannel -- ParametParamet == ff &sigma;&sigma; 11 ,, ff &sigma;&sigma; 22 ,, .. .. .. ,, ff &sigma;&sigma; Mm &tau;&tau; &sigma;&sigma; 11 ,, &tau;&tau; &sigma;&sigma; 22 ,, .. .. .. ,, &tau;&tau; &sigma;&sigma; Mm == 2.522.52 2.412.41 3.233.23 .. .. .. 0.250.25 0.360.36 0.210.21 .. .. ..

A3:将信道参数做归一化处理;分别根据多普勒展宽和多径展宽参数特点,找出用来做归一化的数值,对两个参数分别做归一化处理,具体步骤如下:A3: Normalize the channel parameters; according to the characteristics of Doppler broadening and multipath broadening parameters, find out the values used for normalization, and normalize the two parameters respectively. The specific steps are as follows:

A31:计算满足条件

Figure BDA00002785819700123
τσy时多普勒展宽和多径展宽参数的个数Lf和τl。生成参数数值与参数个数的对应表,如表4所示;A31: Calculation satisfies the condition
Figure BDA00002785819700123
When τ σy , the number of Doppler broadening and multipath broadening parameters L f and τ l . Generate a corresponding table of parameter values and parameter numbers, as shown in Table 4;

表4Table 4

Figure BDA00002785819700124
Figure BDA00002785819700124

A32:从表4中求出满足Lf≥M·0.98的fx最小值,为0.83Hz,同时求出满足τl≥M·0.98的τy最小值,设为79ms。A32: From Table 4, obtain the minimum value of f x satisfying L f ≥ M·0.98, which is 0.83 Hz, and obtain the minimum value of τ y satisfying τ l ≥ M·0.98, and set it to 79 ms.

A33:将信道参数矩阵中的第一行和第二行数据分别用0.83和79做归一化处理,得:A33: Normalize the first row and the second row of data in the channel parameter matrix with 0.83 and 79 respectively, and get:

ChannelChannel -- ParametParamet -- Unitaryunitary == ff &sigma;&sigma; 11 // ff xx LL ,, ff &sigma;&sigma; 22 // ff xx LL ,, .. .. .. ,, ff &sigma;&sigma; Mm // ff xx LL &tau;&tau; &sigma;&sigma; 11 // &tau;&tau; ythe y LL ,, &tau;&tau; &sigma;&sigma; 22 // &tau;&tau; ythe y LL ,, .. .. .. ,, &tau;&tau; &sigma;&sigma; Mm // &tau;&tau; ythe y LL == 0.31900.3190 0.30510.3051 0.40890.4089 .. .. .. 0.30120.3012 0.43370.4337 0.25300.2530 .. .. ..

步骤B,对矩阵Channel-Paramet-Unitary按照如下流程进行二维聚类组合:Step B, perform two-dimensional clustering combination on the matrix Channel-Paramet-Unitary according to the following process:

首先,计算Channel-Paramet-Unitary矩阵中的每一列数据组的密度指标。First, calculate the density index of each column data group in the Channel-Paramet-Unitary matrix.

将Channel-Paramet-Unitary矩阵中的每一列数据组

Figure BDA00002785819700132
都作为聚类中心的侯选数组点。计算每列
Figure BDA00002785819700133
的密度指标:Group each column of data in the Channel-Paramet-Unitary matrix
Figure BDA00002785819700132
All of them are used as the candidate array points of the cluster centers. Calculate each column
Figure BDA00002785819700133
The density index of:

DD. ii == &Sigma;&Sigma; jj == 11 Mm expexp [[ -- (( ff &sigma;&sigma; ii // ff xx LL -- ff &sigma;&sigma; jj // ff xx LL )) 22 ++ (( &tau;&tau; &sigma;&sigma; ii // &tau;&tau; ythe y LL -- &tau;&tau; &sigma;j&sigma;j // &tau;&tau; ythe y LL )) 22 (( rr aa )) 22 ]]

其中ra=0.17。where r a =0.17.

然后,选取最高密度指标的数组点,并更新每个数据点的密度指标。Then, pick the array point with the highest density index and update the density index for each data point.

计算每个数组点的密度指标后,选取具有最高密度指标的数组点

Figure BDA00002785819700135
作为第一个聚类中心,为其密度指标。设当前选出了第k个聚类中心
Figure BDA00002785819700137
其密度指标为
Figure BDA00002785819700138
则用
Figure BDA00002785819700139
修正每个数组点的密度指标After calculating the density index of each array point, select the array point with the highest density index
Figure BDA00002785819700135
As the first cluster center, is its density index. Suppose the kth cluster center is currently selected
Figure BDA00002785819700137
Its density index is
Figure BDA00002785819700138
then use
Figure BDA00002785819700139
Fix density index for each array point

DD. ii == DD. ii -- DD. cc kk expexp [[ -- (( ff &sigma;&sigma; ii // ff xx LL -- ff &sigma;&sigma; kk ++ 11 // ff xx LL )) 22 ++ (( &tau;&tau; &sigma;&sigma; ii // &tau;&tau; ythe y LL -- &tau;&tau; &sigma;&sigma; kk ++ 11 // &tau;&tau; ythe y LL )) 22 (( rr bb )) 22 ]]

其中rb=1.5rawhere r b =1.5r a .

在修正后的各密度指标中,选取最高密度指标的数组点,计算得到其密度指标

Figure BDA00002785819700142
然后利用如下判决方式,判决该数组点是否符合聚类中心要求,并判决是否终止聚类;设判决门限为
Figure BDA00002785819700143
ε=0.15;Among the corrected density indexes, select the array point of the highest density index and calculate its density index
Figure BDA00002785819700142
Then use the following judgment method to judge whether the array point meets the requirements of the clustering center, and judge whether to terminate the clustering; set the judgment threshold as
Figure BDA00002785819700143
ε =0.15;

(a)当认为

Figure BDA00002785819700145
为一个聚类中心,并继续密度指标的修正过程。(a) when think
Figure BDA00002785819700145
as a cluster center, and continue the correction process of the density index.

(b)当认为

Figure BDA00002785819700147
不是聚类中心,终止聚类过程。(b) when think
Figure BDA00002785819700147
If it is not a cluster center, the clustering process is terminated.

(c)当

Figure BDA00002785819700148
时,判断式
Figure BDA00002785819700149
是否成立,不成立时,认为
Figure BDA000027858197001410
不是聚类中心,并将该数组点的密度指标设为0,选择余下数组点中具有最高密度指标的点为待确认的点,重新进行判决。(c) when
Figure BDA00002785819700148
time, judgment
Figure BDA00002785819700149
Whether it is established, if it is not established, it is considered
Figure BDA000027858197001410
It is not the cluster center, and the density index of the array point is set to 0, and the point with the highest density index among the remaining array points is selected as the point to be confirmed, and the judgment is made again.

通过上述聚类得到三个聚类中心:(0.2530,0.0759),(0.2651,0.6329),(0.2410,0.3418)。Through the above clustering, three cluster centers are obtained: (0.2530, 0.0759), (0.2651, 0.6329), (0.2410, 0.3418).

步骤C,按照如下步骤将聚类中心与信道模型对应起来。Step C, according to the following steps to correspond the cluster center with the channel model.

步骤C1:计算Channel-Paramet-Unitary矩阵中所有数组点落在每个聚类中心半径范围内的比例。按照约束公式 ( f &sigma; i / f x L - f &sigma; ck / f x L ) 2 + ( &tau; &sigma; i / &tau; y L - &tau; &sigma;ck / &tau; y L ) 2 &le; ( r a ) 2 计算出落在三个聚类中心的数组点个数分别为454、219和170。Step C1: Calculate the proportion of all array points in the Channel-Paramet-Unitary matrix falling within the radius of each cluster center. According to the constraint formula ( f &sigma; i / f x L - f &sigma; ck / f x L ) 2 + ( &tau; &sigma; i / &tau; the y L - &tau; &sigma;ck / &tau; the y L ) 2 &le; ( r a ) 2 Calculate the number of array points falling in the three cluster centers to be 454, 219 and 170 respectively.

步骤C2:经检验,所有聚类中心半径范围内的数组点个数占总数组点的比例为 &Sigma; k = 1 K N ck M &times; 100 % = 839 1135 &times; 100 % = 73.9 % &GreaterEqual; 70 % , 并且 &ForAll; N ck M &times; 100 % &GreaterEqual; 15 % , 认为聚类中心(0.2530,0.0759),(0.2651,0.6329)和(0.2410,0.3418)能表示信道模型。Step C2: After inspection, the ratio of the number of array points within the radius of all cluster centers to the total array points is &Sigma; k = 1 K N ck m &times; 100 % = 839 1135 &times; 100 % = 73.9 % &Greater Equal; 70 % , and &ForAll; N ck m &times; 100 % &Greater Equal; 15 % , It is considered that the cluster centers (0.2530, 0.0759), (0.2651, 0.6329) and (0.2410, 0.3418) can represent the channel model.

步骤C3:将符合要求的聚类中心去归一化得到信道模型。可得此链路在步骤A1设置条件下的信道模型如表5所示。Step C3: Denormalize the cluster centers that meet the requirements to obtain the channel model. The channel model of this link under the setting conditions of step A1 can be obtained as shown in Table 5.

表5table 5

信道参数channel parameters 多普勒展宽(Hz)Doppler broadening (Hz) 多径展宽(ms)Multipath spread (ms) 信道模型1Channel Model 1 0.210.21 0.60.6 信道模型2Channel Model 2 0.220.22 5.05.0 信道模型3Channel Model 3 0.20.2 2.72.7

综上所述,本发明实施例建立了一种通过对离散信道参数的有效分类,进行短波信道模型建模的方法,使用本发明实施例提供的方法,可以建立特定时域、空域和特定太阳黑子数下的短波信道模型,相比ITU-RF.1487提供的信道模型更贴近实际信道特性,并且依据此方法建立的信道模型可为短波通信及短波频率打分提供有效支撑。In summary, the embodiment of the present invention establishes a method for modeling shortwave channel models by effectively classifying discrete channel parameters. Using the method provided by the embodiment of the present invention, specific time domain, air domain and specific sun domain can be established. The HF channel model under the sunspot number is closer to the actual channel characteristics than the channel model provided by ITU-RF.1487, and the channel model established according to this method can provide effective support for HF communication and HF frequency scoring.

显然,本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样,倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内,则本发明也意图包含这些改动和变型在内。Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims (10)

1. A short wave channel model modeling method is characterized by comprising the following steps:
step 1, extracting M samples of a certain link from a database in which short wave channel parameter samples are stored, and acquiring a multipath broadening parameter and a Doppler broadening parameter in each sample;
step 2, taking the multipath broadening parameters and Doppler broadening parameters of each sample as column vectors, constructing a channel parameter matrix of 2 rows and M columns, and carrying out normalization processing on each vector in the matrix;
step 3, defining each column of the channel parameter matrix as an array point, and calculating a clustering center in each array point by using a two-dimensional clustering combination algorithm;
and 4, detecting whether the number of the array points covered in the neighborhood radius range of each clustering center meets the clustering requirement, if so, performing de-normalization processing on the clustering centers, and taking the multipath broadening parameters and the Doppler broadening parameters corresponding to the de-normalized clustering centers as short wave channel models.
2. The method of claim 1, wherein in the step 1, the short wave channel parameter samples in the database are parameter samples obtained through a short wave channel measurement experiment.
3. The method according to claim 1 or 2, wherein in the step 1, M samples meeting the conditions of a certain link are extracted from the database according to preset time conditions, signal-to-noise ratio conditions and sun blackness conditions.
4. The method according to claim 1, wherein in the step 2, normalizing the vector quantities in the matrix specifically includes:
comparing the Doppler broadening parameters with each other to obtain f when the condition 1 is metxMinimum value of (2)
Figure FDA00002785819600011
And comparing the multipath broadening parameters with each other to obtain the tau satisfying the condition 2yMinimum value of (2)
Figure FDA00002785819600012
Are respectively provided with
Figure FDA00002785819600013
Andnormalizing the corresponding parameter vector in the matrix for the normalization cardinality of the Doppler broadening parameter and the multipath broadening parameter;
wherein the condition 1 is: doppler spread parameter
Figure FDA00002785819600015
Greater than fxThe number of the parameters satisfies a preset threshold; the condition 2 is as follows: multipath broadening parameter
Figure FDA00002785819600016
Greater than τyThe number of parameters of (2) satisfies a set threshold.
5. The method according to claim 1 or 4, wherein in the step 3, calculating the cluster center in each array point by using a two-dimensional cluster combination algorithm specifically comprises:
step 31, calculating the density index of each array point, acquiring the array point corresponding to the highest density index in each density index, and judging that the array point is a first clustering center;
step 32, correcting the density index of each array point by using the density index of the kth clustering center, and acquiring the array point corresponding to the highest value in the corrected density index;
and step 33, judging the clustering centers of the acquired array points according to the set clustering judgment threshold, obtaining the (k + 1) th clustering center when the array points are judged to be the clustering centers, and enabling k = k +1, and returning to the step 32.
6. The method according to claim 5, wherein the step 33 specifically comprises:
step 331, determining the array point correspondences
Figure FDA00002785819600021
Is greater than
Figure FDA00002785819600022
If yes, judging the array point as a clustering center; otherwise, go to step 332;
step 332, determining the array point correspondences
Figure FDA00002785819600023
Is less than
Figure FDA00002785819600024
If yes, judging that the array point is not a clustering center, and terminating the clustering process; otherwise, go to step 333;
step 333, in
Figure FDA00002785819600025
Is greater thanIs less than
Figure FDA00002785819600027
Time, judge
Figure FDA00002785819600028
If yes, judging the array point as a clustering center; otherwise, it is determined that the array point is not the clustering center, the density index corresponding to the array point is set to zero, and the array point corresponding to the highest density index in the remaining array points is selected as the point to be confirmed, and the procedure returns to step 331;
wherein, εrespectively as the upper limit and the lower limit of a preset judgment threshold,
Figure FDA000027858196000210
the density index of the first clustering center is obtained; dminFor the array point currently to be confirmedDistance from the first cluster center, raIs the neighborhood radius of the set array point.
7. The method of claim 5, wherein in step 32, a formula is used
Figure FDA000027858196000211
The density index of each array point is corrected; in the formula,is an index of the density of the k-th cluster center,
Figure FDA000027858196000213
andrespectively are the Doppler broadening parameter and the multipath broadening parameter of the ith sample after normalization processing,
Figure FDA00002785819600031
and
Figure FDA00002785819600032
respectively are Doppler broadening parameter and multipath broadening parameter r of the k-th clustering center after normalization processingbIs positive and satisfies rbGreater than the neighborhood radius of the array point.
8. The method according to claim 1, wherein the step 4 specifically comprises:
obtaining the number N of array points falling in the neighborhood radius range of each cluster centerckDetectingWhether or not it is greater than or equal to a set clustering threshold, an
Figure FDA00002785819600034
Whether the average clustering threshold is larger than or equal to a set average clustering threshold or not, when both are larger than or equal to corresponding thresholds, the clustering center is subjected to normalization processing, and a multipath broadening parameter and a Doppler broadening parameter corresponding to the clustering center after the normalization processing are used as short wave channel models; wherein K is the total number of the clustering centers,any given is meant.
9. The method of claim 8, wherein the array points falling within the neighborhood radius of the cluster center are array points satisfying the following condition:
Figure FDA00002785819600036
in the formula,
Figure FDA00002785819600037
and
Figure FDA00002785819600038
respectively are Doppler broadening parameters and multipath broadening parameters of the ith array point after normalization processing,
Figure FDA00002785819600039
and
Figure FDA000027858196000310
respectively a Doppler broadening parameter and a multipath broadening parameter r of the k-th clustering center after normalization processingaIs the neighborhood radius of the set array point.
10. The method of claim 1, 8 or 9, wherein the step 4 further comprises:
when detecting that the number of the array points covered in the radius range of each cluster center does not meet the cluster requirement, ending the modeling process, or adjusting variable parameters used by the two-dimensional cluster combination algorithm, and executing the step 3 again; wherein the variable parameters include: neighborhood radius r of set array pointsaNeighborhood r with significantly reduced set density index functionbAnd the upper and lower limits of the set clustering judgment thresholdAndε
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