技术领域Technical Field
本发明属于信息处理技术领域，更进一步涉及雷达信号处理技术领域中的一种利用对照表估计多分辨率海杂波模型类型和参数的方法，本发明可用于对海面目标的多分辨率海杂波的模型类型和参数进行估计。The invention belongs to the field of information processing technology, and further relates to a method for estimating multi-resolution sea clutter model types and parameters by using a comparison table in the field of radar signal processing technology. The invention can be used to estimate the model types and parameters of multi-resolution sea clutter of sea surface targets.
背景技术Background Art
为了检测海上目标，必须深入研究海杂波，对海杂波特性进行精确的分析描述。海杂波幅度分布是海杂波的一个重要统计特性，近年来，有关各种既定海杂波幅度模型下的研究已颇具规模，每种海杂波模型均有对应的目标检测算法，为使这些算法达到有效实现的效果，首先要估计海杂波的模型类型，这是目标检测前不可或缺的预处理步骤。在海杂波幅度分布模型中，形状参数和尺度参数是反映海杂波特性的重要指标参数，尺度参数表征海杂波的功率，形状参数可表征海杂波的非高斯特性，也是后续海面目标检测所需的重要特性参数。In order to detect targets at sea, it is necessary to conduct an in-depth study of sea clutter and accurately analyze and describe the characteristics of sea clutter. The amplitude distribution of sea clutter is an important statistical characteristic of sea clutter. In recent years, research on various established sea clutter amplitude models has been quite large. Each sea clutter model has a corresponding target detection algorithm. In order to make these algorithms achieve effective results, the model type of sea clutter must be estimated first, which is an indispensable preprocessing step before target detection. In the sea clutter amplitude distribution model, the shape parameter and the scale parameter are important indicator parameters reflecting the characteristics of sea clutter. The scale parameter characterizes the power of sea clutter, and the shape parameter can characterize the non-Gaussian characteristics of sea clutter. It is also an important characteristic parameter required for subsequent sea surface target detection.
海面目标的多分辨分层检测技术，是将海面目标根据一定的尺寸标准进行划分，对于不同尺寸大小的海面目标(航母、舰船、浮标、潜望镜等)用一组多分辨(诸如3m、9m、15m等)的波束序列进行遍历扫描，实现较大分辨单元检测海面大目标、较小分辨单元检测海面小目标的动态分层检测，避免了小目标丢失、大目标不完整等问题。在进行海面目标的多分辨分层检测前，多分辨率海杂波数据幅度分布的模型类型和参数。The multi-resolution layered detection technology for sea surface targets is to divide sea surface targets according to certain size standards, and use a set of multi-resolution (such as 3m, 9m, 15m, etc.) beam sequences to traverse and scan sea surface targets of different sizes (aircraft carriers, ships, buoys, periscopes, etc.), so as to achieve dynamic layered detection of large sea surface targets with larger resolution units and small sea surface targets with smaller resolution units, thus avoiding problems such as small target loss and large target incompleteness. Before performing multi-resolution layered detection of sea surface targets, the model type and parameters of the amplitude distribution of multi-resolution sea clutter data.
Xu S W,Wang L,Shui P L等人在其发表的论文“Iterative maximum likelihoodand zlogz estimation of parameters of compound-Gaussian clutter with inverseGamma texture”(2018IEEE ICSPCC,Qingdao,China,2018:1-6)中公开了一种利用迭代最大似然估计海杂波幅度模型参数的方法。该方法首先通过矩估计方法求得参数的迭代初始值，再利用对数似然函数的参数偏导方程组通过迭代的方式得到参数估计值。该方法不仅可以保证与最大似然估计法近似的估计精度，且大大降低最大似然估计法的计算量。但是，该方法仍然存在的不足之处是，参数估计值的精度易受海杂波数据中的异常值影响。Xu S W, Wang L, Shui P L et al. published a method for estimating sea clutter amplitude model parameters using iterative maximum likelihood in their paper “Iterative maximum likelihood and zlogz estimation of parameters of compound-Gaussian clutter with inverse Gamma texture” (2018 IEEE ICSPCC, Qingdao, China, 2018: 1-6). This method first obtains the iterative initial value of the parameter by the moment estimation method, and then obtains the parameter estimation value by iteratively using the parameter partial derivative equation group of the log-likelihood function. This method can not only ensure the estimation accuracy similar to the maximum likelihood estimation method, but also greatly reduce the calculation amount of the maximum likelihood estimation method. However, the disadvantage of this method is that the accuracy of the parameter estimation value is easily affected by the outliers in the sea clutter data.
石小帆在其发表的学位论文“海杂波关键特性参数的预测方法研究”(西安电子科技大学，硕士论文，2020)中提出了一种估计利用K-S距离估计海杂波模型类型的方法。该方法利用参数估计值拟合得到累积分布函数，进而计算各模型的K-S距离，确定K-S距离最小的海杂波幅度模型为海杂波数据的最优模型类型。该方法存在的不足之处是，模型类型估计准确率易受海杂波数据中的异常值影响，且估计多分辨海杂波数据的模型类型时，需要分别对不同分辨率的海杂波数据进行计算以估计模型类型，计算量大，估计速度慢，难以满足工程应用中数据实时处理的需要。Shi Xiaofan proposed a method for estimating the type of sea clutter model using the K-S distance in his published dissertation "Research on Prediction Methods of Key Characteristic Parameters of Sea Clutter" (Xidian University, Master's Thesis, 2020). This method uses parameter estimates to fit the cumulative distribution function, and then calculates the K-S distance of each model to determine the sea clutter amplitude model with the smallest K-S distance as the optimal model type for sea clutter data. The shortcomings of this method are that the accuracy of model type estimation is easily affected by outliers in sea clutter data, and when estimating the model type of multi-resolution sea clutter data, it is necessary to calculate sea clutter data of different resolutions separately to estimate the model type, which requires large amount of calculation and slow estimation speed, and it is difficult to meet the needs of real-time data processing in engineering applications.
发明内容Summary of the invention
本发明的目的是针对上述现有技术的不足，提出一种利用对照表预测多分辨率海杂波模型类型和参数的方法，用于解决参数估计值的精度易受海杂波数据中的异常值影响，以及多分辨率海杂波模型类型估计准确率易受海杂波数据中的异常值影响，且计算量大、估计速度慢，难以满足工程应用中数据实时处理的需要的问题。The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and to propose a method for predicting the type and parameters of a multi-resolution sea clutter model using a comparison table, so as to solve the problems that the accuracy of parameter estimation is easily affected by outliers in sea clutter data, the accuracy of multi-resolution sea clutter model type estimation is easily affected by outliers in sea clutter data, and the calculation amount is large and the estimation speed is slow, which makes it difficult to meet the needs of real-time data processing in engineering applications.
实现本发明目的的思路是：本发明构建广义Pareto分布强度模型和IGCG幅度分布模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表，获得无明显异常值的高分辨率海杂波数据，按照分辨率转换方法，得到多分辨率海杂波数据集，数据集中的每一组海杂波数据取模，并将模值按升序排列，得到分辨率转换后的低分辨率的海杂波幅度序列进而估计其参数，可以减少低分辨率海杂波数据中的异常值对参数估计性能的影响，以此减小参数估计的误差，解决参数估计值的精度易受海杂波数据中的异常值影响的问题。由于尺度参数易从海杂波平均功率中获得，本发明估计参数的重点在形状参数。根据高分辨率海杂波数据的模型类型和初始形状参数，以及分辨率转换倍数，通过查表获得目标分辨率的海杂波数据的模型类型和形状参数，克服现有技术估计模型类型时估计准确率易受海杂波数据中的异常值影响，且计算量大、估计速度慢的问题，实现实时估计多分辨海杂波数据的模型类型和参数，满足工程应用中数据实时处理的需求。The idea of achieving the purpose of the present invention is: the present invention constructs a comparison table of "initial shape parameter-resolution conversion multiple-model type and shape parameter" of a generalized Pareto distribution intensity model and an IGCG amplitude distribution model, obtains high-resolution sea clutter data without obvious outliers, obtains a multi-resolution sea clutter data set according to a resolution conversion method, takes a modulus of each group of sea clutter data in the data set, and arranges the modulus values in ascending order to obtain a low-resolution sea clutter amplitude sequence after resolution conversion and then estimates its parameters, which can reduce the influence of outliers in low-resolution sea clutter data on parameter estimation performance, thereby reducing the error of parameter estimation and solving the problem that the accuracy of parameter estimation values is easily affected by outliers in sea clutter data. Since scale parameters are easily obtained from the average power of sea clutter, the focus of the present invention in estimating parameters is on shape parameters. According to the model type and initial shape parameters of high-resolution sea clutter data, as well as the resolution conversion multiple, the model type and shape parameters of sea clutter data of the target resolution are obtained by looking up a table, thereby overcoming the problems that the estimation accuracy of the model type in the prior art is easily affected by the outliers in the sea clutter data, the amount of calculation is large, and the estimation speed is slow. The real-time estimation of the model type and parameters of multi-resolution sea clutter data is achieved, meeting the needs of real-time data processing in engineering applications.
为实现上述发明目的，本发明的技术方案包括如下：To achieve the above-mentioned object of the invention, the technical solution of the present invention includes the following:
步骤1，获得海杂波幅度序列：Step 1, obtain the sea clutter amplitude sequence:
雷达接收机接收雷达发射机连续发射脉冲信号的高分辨率海面回波数据，在高分辨率回波数据中随机选取至少10000个海杂波数据，对所选取的杂波数据取模后将模值按升序排列，得到海杂波幅度序列r；The radar receiver receives high-resolution sea surface echo data of pulse signals continuously transmitted by the radar transmitter, randomly selects at least 10,000 sea clutter data from the high-resolution echo data, takes the modulus of the selected clutter data and arranges the modulus values in ascending order to obtain a sea clutter amplitude sequence r;
步骤2，估计海杂波幅度序列的模型类型和参数：Step 2, estimate the model type and parameters of the sea clutter amplitude series:
步骤2.1，构造广义Pareto分布强度模型的幅度概率密度函数；Step 2.1, construct the amplitude probability density function of the generalized Pareto distribution intensity model;
步骤2.2，构造IGCG幅度分布模型的幅度概率密度函数；Step 2.2, construct the amplitude probability density function of the IGCG amplitude distribution model;
步骤2.3，对广义Pareto分布强度模型的幅度概率密度函数中的海杂波幅度序列r进行积分，生成标准累积分布函数；Step 2.3, integrating the sea clutter amplitude sequence r in the amplitude probability density function of the generalized Pareto distribution intensity model to generate a standard cumulative distribution function;
步骤2.4，对IGCG幅度分布模型的幅度概率密度函数中的海杂波幅度序列r进行积分，生成标准累积分布函数；Step 2.4, integrating the sea clutter amplitude sequence r in the amplitude probability density function of the IGCG amplitude distribution model to generate a standard cumulative distribution function;
步骤2.5，生成海杂波幅度序列的经验累积分布函数；Step 2.5, generate the empirical cumulative distribution function of the sea clutter amplitude series;
步骤2.6，计算广义Pareto分布强度模型和IGCG幅度分布模型的K-S距离；Step 2.6, calculate the K-S distance of the generalized Pareto distribution intensity model and the IGCG amplitude distribution model;
步骤2.7，计算广义Pareto分布强度模型和IGCG幅度分布模型的对数K-S距离；Step 2.7, calculate the logarithmic K-S distance of the generalized Pareto distribution intensity model and the IGCG amplitude distribution model;
步骤2.8，将广义Pareto分布强度模型和IGCG幅度分布模型中的最小K-S距离或最小对数K-S距离对应的海杂波幅度模型估计为海杂波幅度序列的模型类型；如果海杂波幅度序列的模型类型为广义Pareto分布强度模型，则海杂波幅度序列的形状参数为如果海杂波幅度序列的模型类型为IGCG幅度分布模型，则海杂波幅度序列的形状参数为其中，表示对海杂波幅度序列进行迭代最大似然估计得到的广义Pareto分布强度模型的形状参数估计值，表示对海杂波幅度序列进行迭代最大似然估计得到的IGCG幅度分布模型的形状参数估计值；Step 2.8, estimate the sea clutter amplitude model corresponding to the minimum KS distance or minimum logarithmic KS distance in the generalized Pareto distribution intensity model and the IGCG amplitude distribution model as the model type of the sea clutter amplitude sequence; if the model type of the sea clutter amplitude sequence is the generalized Pareto distribution intensity model, then the shape parameter of the sea clutter amplitude sequence is If the model type of the sea clutter amplitude sequence is the IGCG amplitude distribution model, the shape parameter of the sea clutter amplitude sequence is in, represents the estimated value of the shape parameter of the generalized Pareto distribution intensity model obtained by iterative maximum likelihood estimation of the sea clutter amplitude series, represents the estimated value of the shape parameter of the IGCG amplitude distribution model obtained by iterative maximum likelihood estimation of the sea clutter amplitude sequence;
步骤3，构建广义Pareto分布强度模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表：Step 3: Construct a comparison table of "initial shape parameter - resolution conversion factor - model type and shape parameter" for the generalized Pareto distribution intensity model:
步骤3.1，在(1.0,10.0]区间内每间隔0.5取长度为0.5的一个值，将1.05作为序列的初始值，组成一组形状参数序列，共19各元素；Step 3.1, in the interval (1.0, 10.0], take a value of length 0.5 every 0.5, take 1.05 as the initial value of the sequence, and form a set of shape parameter sequences with a total of 19 elements;
步骤3.2，从形状参数序列中依次选取一个元素作为初始形状参数，设初始尺度参数为1，初始雷达分辨率为RP0，依据复合高斯模型的理论，仿真生成50组服从广义Pareto分布强度模型的海杂波数据{cP1,cP2,...,cP50}，每组数据互为独立同分布，且每组样本量为107；Step 3.2, select one element from the shape parameter sequence as the initial shape parameter, set the initial scale parameter to 1, the initial radar resolution to RP0 , and generate 50 groups of sea clutter data {cP1 ,cP2 ,...,cP50 } that obey the generalized Pareto distribution intensity model based on the theory of the composite Gaussian model. Each group of data is independent and identically distributed, and the sample size of each group is 107 ;
步骤3.3，两组海杂波数据复数叠加便可得到2倍分辨率2RP0时的海杂波数据为cP1+cP2，3倍分辨率3RP0时的海杂波数据为cP1+cP2+cP3，按照该分辨率转换方法，得到一个多分辨率海杂波数据集{cP1,cP1+cP2,cP1+cP2+cP3,...,cP1+cP2+cP3+…+cP50}，19个初始形状参数得到19个数据集；Step 3.3, the two sets of sea clutter data are superimposed to obtain the sea clutter data at 2 times the resolution 2RP0 as cP1 +cP2 , and the sea clutter data at 3 times the resolution 3RP0 as cP1 +cP2 +cP3 . According to the resolution conversion method, a multi-resolution sea clutter data set {cP1 , cP1 +cP2 , cP1 +cP2 +cP3 , ..., cP1 +cP2 +cP3 + ... +cP50 } is obtained. 19 initial shape parameters obtain 19 data sets;
步骤3.4，将19个数据集中的每一组海杂波数据取模，并将模值按升序排列，得到分辨率转换后的海杂波幅度序列rP'；Step 3.4, take the modulus of each group of sea clutter data in the 19 data sets, and arrange the modulus values in ascending order to obtain the sea clutter amplitude sequence rP ' after resolution conversion;
步骤3.5，采用与步骤2相同的方法，估计分辨率转换后的海杂波幅度序列rP'的模型类型和形状参数；Step 3.5, using the same method as step 2, estimate the model type and shape parameters of the sea clutter amplitude series rP ' after resolution conversion;
步骤3.6，将19个初始形状参数以及其对应的分辨率转换后的海杂波幅度序列的模型类型和形状参数的估计结果，组成广义Pareto分布强度模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表；Step 3.6, the 19 initial shape parameters and the estimation results of the model type and shape parameters of the sea clutter amplitude series after the corresponding resolution conversion are used to form a comparison table of "initial shape parameters-resolution conversion multiples-model type and shape parameters" of the generalized Pareto distribution intensity model;
步骤4，构建IGCG幅度分布模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表：Step 4: Construct the comparison table of “initial shape parameters-resolution conversion factor-model type and shape parameters” of the IGCG amplitude distribution model:
步骤4.1，在[1.0,10.0]区间内每间隔0.5取长度为0.5的一个值，组成一组形状参数序列，共19各元素；Step 4.1, in the interval [1.0, 10.0], take a value of length 0.5 every 0.5 to form a set of shape parameter sequences, with a total of 19 elements;
步骤4.2，从形状参数序列中依次选取一个元素作为初始形状参数，设初始尺度参数为1，初始雷达分辨率为RI0，依据复合高斯模型的理论，仿真生成50组服从IGCG幅度分布模型的海杂波数据{cI1,cI2,...,cI50}，每组数据互为独立同分布，且每组样本量为107；Step 4.2, select one element from the shape parameter sequence as the initial shape parameter, set the initial scale parameter to 1, the initial radar resolution to RI0 , and generate 50 groups of sea clutter data {cI1 ,cI2 ,...,cI50 } that obey the IGCG amplitude distribution model based on the theory of the composite Gaussian model. Each group of data is independent and identically distributed, and the sample size of each group is 107 ;
步骤4.3，两组海杂波数据复数叠加便可得到2倍分辨率时的海杂波数据为cI1+cI2，3倍分辨率3RI0时的杂波数据为cI1+cI2+cI3，按照该分辨率转换方法，得到多分辨率杂波数据集{cI1,cI1+cI2,cI1+cI2+cI3,...,cI1+cI2+cI3+…+cI50}，19个初始形状参数得到19个数据集；Step 4.3, the two sets of sea clutter data are superimposed to obtain the sea clutter data at 2 times resolution as cI1 + cI2 , and the clutter data at 3 times resolution as 3RI0 as cI1 + cI2 + cI3 . According to the resolution conversion method, a multi-resolution clutter data set {cI1 , cI1 + cI2 , cI1 + cI2 + cI3 ,..., cI1 + cI2 + cI3 +…+cI50 } is obtained. 19 initial shape parameters result in 19 data sets.
步骤4.4，将19个数据集中的每一组海杂波数据取模，并将模值按升序排列，得到分辨率转换后的海杂波幅度序列rI'；Step 4.4, take the modulus of each group of sea clutter data in the 19 data sets, and arrange the modulus values in ascending order to obtain the sea clutter amplitude sequence rI ' after resolution conversion;
步骤4.5，采用与步骤2相同的方法，估计分辨率转换后的海杂波幅度序列rI'的模型类型和形状参数；Step 4.5, using the same method as step 2, estimate the model type and shape parameters of the sea clutter amplitude series rI ' after resolution conversion;
步骤4.6，将19个初始形状参数以及其对应的分辨率转换后的海杂波幅度序列的模型类型和形状参数的估计结果，组成IGCG幅度分布模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表；Step 4.6, the 19 initial shape parameters and the corresponding model type and shape parameter estimation results of the sea clutter amplitude sequence after resolution conversion are used to form an "initial shape parameter-resolution conversion multiple-model type and shape parameter" comparison table of the IGCG amplitude distribution model;
本发明与现有技术相比具有以下优点：Compared with the prior art, the present invention has the following advantages:
第一，由于本发明按照分辨率转换方法，得到多分辨率海杂波数据集，克服了现有技术存在的参数估计值的精度易受海杂波数据中的异常值影响的缺点，使得本发明对估计含异常值的海杂波数据的参数估计误差更小，提高了估计参数的精确性。First, since the present invention obtains a multi-resolution sea clutter data set according to the resolution conversion method, the disadvantage of the prior art that the accuracy of parameter estimation values is easily affected by outliers in the sea clutter data is overcome, so that the parameter estimation error of the present invention for estimating sea clutter data containing outliers is smaller, and the accuracy of the estimated parameters is improved.
第二，由于本发明构建“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表，通过查表获得目标分辨率的海杂波数据的模型类型和参数，克服了现有技术估计模型类型时准确率易受海杂波数据中的异常值影响，且计算量大、估计速度慢的缺陷，使得本发明可以实时估计多分辨海杂波数据的模型类型和参数，满足工程应用中数据实时处理的需求。Second, since the present invention constructs a comparison table of "initial shape parameters-resolution conversion multiples-model type and shape parameters", the model type and parameters of sea clutter data of the target resolution are obtained by looking up the table, which overcomes the defects of the prior art that the accuracy of estimating the model type is easily affected by the outliers in the sea clutter data, and the calculation amount is large and the estimation speed is slow. Therefore, the present invention can estimate the model type and parameters of multi-resolution sea clutter data in real time, meeting the demand for real-time data processing in engineering applications.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明的实现流程图；Fig. 1 is a flow chart of the implementation of the present invention;
图2为本发明的仿真图。FIG. 2 is a simulation diagram of the present invention.
具体实施方式DETAILED DESCRIPTION
下面结合附图和实施例，对本发明作进一步的描述。The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
参照附图1和实施例，对本发明的实现步骤做进一步的描述。The implementation steps of the present invention are further described with reference to FIG1 and the embodiments.
步骤1，获得海杂波幅度序列。Step 1: Obtain the sea clutter amplitude sequence.
雷达接收机接收雷达发射机连续发射脉冲信号的高分辨率海面回波数据，在高分辨率回波数据中随机选取10000个海杂波数据，对所选取的杂波数据取模并按升序排列，得到海杂波幅度序列r。The radar receiver receives the high-resolution sea surface echo data of the pulse signal continuously transmitted by the radar transmitter, randomly selects 10,000 sea clutter data from the high-resolution echo data, takes the modulus of the selected clutter data and arranges them in ascending order to obtain the sea clutter amplitude sequence r.
步骤2，估计海杂波幅度序列的模型类型和参数。Step 2: Estimate the model type and parameters of the sea clutter amplitude series.
步骤2.1，构造广义Pareto分布强度模型的幅度概率密度函数如下：Step 2.1, construct the amplitude probability density function of the generalized Pareto distribution intensity model as follows:
其中，表示广义Pareto分布强度模型的幅度概率密度函数，r表示海杂波幅度序列，表示对海杂波幅度序列进行迭代最大似然估计得到的广义Pareto分布强度模型的形状参数估计值，表示对海杂波幅度序列进行迭代最大似然估计得到的广义Pareto分布强度模型的尺度参数估计值。in, represents the amplitude probability density function of the generalized Pareto distribution intensity model, r represents the sea clutter amplitude sequence, represents the estimated value of the shape parameter of the generalized Pareto distribution intensity model obtained by iterative maximum likelihood estimation of the sea clutter amplitude series, represents the estimated value of the scale parameter of the generalized Pareto distribution intensity model obtained by iterative maximum likelihood estimation of the sea clutter amplitude series.
步骤2.2，构造IGCG幅度分布模型的幅度概率密度函数如下：Step 2.2, construct the amplitude probability density function of the IGCG amplitude distribution model as follows:
其中，表示IGCG幅度分布模型的幅度概率密度函数，r表示海杂波幅度，表示对海杂波幅度序列进行迭代最大似然估计得到的IGCG幅度分布模型的形状参数估计值，表示对海杂波幅度序列进行迭代最大似然估计得到的IGCG幅度分布模型的尺度参数估计值，exp表示以自然常数e为底的指数操作。in, represents the amplitude probability density function of the IGCG amplitude distribution model, r represents the sea clutter amplitude, represents the estimated value of the shape parameter of the IGCG amplitude distribution model obtained by iterative maximum likelihood estimation of the sea clutter amplitude sequence, represents the estimated value of the scale parameter of the IGCG amplitude distribution model obtained by iterative maximum likelihood estimation of the sea clutter amplitude sequence, and exp represents the exponential operation with the natural constant e as the base.
步骤2.3，对广义Pareto分布强度模型的幅度概率密度函数中的海杂波幅度序列r进行积分，生成标准累积分布函数如下：Step 2.3, the amplitude probability density function of the generalized Pareto distribution intensity model The sea clutter amplitude sequence r in is integrated to generate the standard cumulative distribution function as follows:
其中，表示广义Pareto分布强度模型的标准累积分布函数。in, Represents the standard cumulative distribution function of the generalized Pareto distribution intensity model.
步骤2.4，对IGCG幅度分布模型的幅度概率密度函数中的海杂波幅度序列r进行积分，生成标准累积分布函数如下：Step 2.4, the amplitude probability density function of the IGCG amplitude distribution model The sea clutter amplitude sequence r in is integrated to generate the standard cumulative distribution function as follows:
其中，表示IGCG幅度分布模型的标准累积分布函数。in, Represents the standard cumulative distribution function of the IGCG amplitude distribution model.
步骤2.5，海杂波幅度序列的单个元素的频数为1，频数除以海杂波幅度序列的长度得到元素的频率，按海杂波幅度序列顺序逐个累加元素的频率得到海杂波幅度序列的经验累积分布函数。Step 2.5: The frequency of a single element of the sea clutter amplitude sequence is 1. The frequency is divided by the length of the sea clutter amplitude sequence to obtain the frequency of the element. The frequencies of the elements are accumulated one by one in the order of the sea clutter amplitude sequence to obtain the empirical cumulative distribution function of the sea clutter amplitude sequence.
步骤2.6，按照下式，计算海杂波幅度序列的经验累积分布函数与标准累积分布函数的K-S距离：Step 2.6: Calculate the K-S distance between the empirical cumulative distribution function and the standard cumulative distribution function of the sea clutter amplitude series according to the following formula:
其中，表示海杂波幅度模型的K-S距离，mean{·}表示均值操作，|·|表示绝对值操作，Fr(r)表示海杂波幅度序列的经验累积分布函数，表示杂波幅度模型利用参数估计值得到的标准累积分布函数；取值时表示广义Pareto分布强度模型的标准累积分布函数，表示广义Pareto分布强度模型的K-S距离；取值时表示IGCG幅度分布模型的标准累积分布函数，表示IGCG幅度分布模型的K-S距离。in, represents the KS distance of the sea clutter amplitude model, mean{·} represents the mean operation, |·| represents the absolute value operation, Fr (r) represents the empirical cumulative distribution function of the sea clutter amplitude sequence, Represents the clutter amplitude model using parameter estimates The resulting standard cumulative distribution function; Value hour represents the standard cumulative distribution function of the generalized Pareto distribution intensity model, represents the KS distance of the generalized Pareto distribution intensity model; Value hour represents the standard cumulative distribution function of the IGCG amplitude distribution model, Represents the KS distance of the IGCG amplitude distribution model.
步骤2.7，按照下式，计算海杂波幅度序列的经验累积分布函数与标准累积分布函数的对数K-S距离：Step 2.7: Calculate the logarithmic K-S distance between the empirical cumulative distribution function and the standard cumulative distribution function of the sea clutter amplitude series according to the following formula:
其中，表示海杂波幅度模型的对数K-S距离，ln表示以自然常数e为底的对数操作，N表示海杂波幅度序列的长度。in, represents the logarithmic KS distance of the sea clutter amplitude model, ln represents the logarithmic operation with the natural constant e as the base, and N represents the length of the sea clutter amplitude sequence.
步骤2.8，将广义Pareto分布强度模型和IGCG幅度分布模型中的最小K-S距离或最小对数K-S距离对应的海杂波幅度模型估计为海杂波幅度序列的模型类型；如果海杂波幅度序列的模型类型为广义Pareto分布强度模型，则海杂波幅度序列的形状参数为如果海杂波幅度序列的模型类型为IGCG幅度分布模型，则海杂波幅度序列的形状参数为Step 2.8, estimate the sea clutter amplitude model corresponding to the minimum KS distance or minimum logarithmic KS distance in the generalized Pareto distribution intensity model and the IGCG amplitude distribution model as the model type of the sea clutter amplitude sequence; if the model type of the sea clutter amplitude sequence is the generalized Pareto distribution intensity model, then the shape parameter of the sea clutter amplitude sequence is If the model type of the sea clutter amplitude sequence is the IGCG amplitude distribution model, the shape parameter of the sea clutter amplitude sequence is
步骤3，构建广义Pareto分布强度模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表。Step 3, construct a comparison table of "initial shape parameter-resolution conversion factor-model type and shape parameter" for the generalized Pareto distribution intensity model.
步骤3.1，在(1.0,10.0]区间内每间隔0.5取长度为0.5的一个值，将1.05作为序列的初始值，组成一组形状参数序列，共19各元素；Step 3.1, in the interval (1.0, 10.0], take a value of length 0.5 every 0.5, take 1.05 as the initial value of the sequence, and form a set of shape parameter sequences with a total of 19 elements;
步骤3.2，从形状参数序列中依次选取一个元素作为初始形状参数，设初始尺度参数为1，初始雷达分辨率为RP0，依据复合高斯模型的理论，仿真生成50组服从广义Pareto分布强度模型的海杂波数据{cP1,cP2,...,cP50}，每组数据互为独立同分布，且每组样本量为107；Step 3.2, select one element from the shape parameter sequence as the initial shape parameter, set the initial scale parameter to 1, the initial radar resolution to RP0 , and generate 50 groups of sea clutter data {cP1 ,cP2 ,...,cP50 } that obey the generalized Pareto distribution intensity model based on the theory of the composite Gaussian model. Each group of data is independent and identically distributed, and the sample size of each group is 107 ;
步骤3.3，两组海杂波数据复数叠加便可得到2倍分辨率2RP0时的海杂波数据为cP1+cP2，3倍分辨率3RP0时的海杂波数据为cP1+cP2+cP3，按照该分辨率转换方法，得到一个多分辨率海杂波数据集{cP1,cP1+cP2,cP1+cP2+cP3,...,cP1+cP2+cP3+…+cP50}，19个初始形状参数得到19个数据集；Step 3.3, the two sets of sea clutter data are superimposed to obtain the sea clutter data at 2 times the resolution 2RP0 as cP1 +cP2 , and the sea clutter data at 3 times the resolution 3RP0 as cP1 +cP2 +cP3 . According to the resolution conversion method, a multi-resolution sea clutter data set {cP1 , cP1 +cP2 , cP1 +cP2 +cP3 , ..., cP1 +cP2 +cP3 + ... +cP50 } is obtained. 19 initial shape parameters obtain 19 data sets;
步骤3.4，将19个数据集中的每一组海杂波数据取模，并将模值按升序排列，得到分辨率转换后的海杂波幅度序列rP'；Step 3.4, take the modulus of each group of sea clutter data in the 19 data sets, and arrange the modulus values in ascending order to obtain the sea clutter amplitude sequence rP ' after resolution conversion;
步骤3.5，采用与步骤2相同的方法，估计分辨率转换后的海杂波幅度序列rP'的模型类型和形状参数；Step 3.5, using the same method as step 2, estimate the model type and shape parameters of the sea clutter amplitude series rP ' after resolution conversion;
步骤3.6，将19个初始形状参数以及其对应的分辨率转换后的海杂波幅度序列的模型类型和形状参数的估计结果，组成广义Pareto分布强度模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表；Step 3.6, the 19 initial shape parameters and the estimation results of the model type and shape parameters of the sea clutter amplitude series after the corresponding resolution conversion are used to form a comparison table of "initial shape parameters-resolution conversion multiples-model type and shape parameters" of the generalized Pareto distribution intensity model;
步骤4，构建IGCG幅度分布模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表。Step 4, construct a comparison table of "initial shape parameters-resolution conversion multiples-model type and shape parameters" for the IGCG amplitude distribution model.
步骤4.1，在[1.0,10.0]区间内每间隔0.5取长度为0.5的一个值，组成一组形状参数序列，共19各元素；Step 4.1, in the interval [1.0, 10.0], take a value of length 0.5 every 0.5 to form a set of shape parameter sequences, with a total of 19 elements;
步骤4.2，从形状参数序列中依次选取一个元素作为初始形状参数，设初始尺度参数为1，初始雷达分辨率为RI0，依据复合高斯模型的理论，仿真生成50组服从IGCG幅度分布模型的海杂波数据{cI1,cI2,...,cI50}，每组数据互为独立同分布，且每组样本量为107；Step 4.2, select one element from the shape parameter sequence as the initial shape parameter, set the initial scale parameter to 1, the initial radar resolution to RI0 , and generate 50 groups of sea clutter data {cI1 ,cI2 ,...,cI50 } that obey the IGCG amplitude distribution model based on the theory of the composite Gaussian model. Each group of data is independent and identically distributed, and the sample size of each group is 107 ;
步骤4.3，两组海杂波数据复数叠加便可得到2倍分辨率时的海杂波数据为cI1+cI2，3倍分辨率3RI0时的杂波数据为cI1+cI2+cI3，按照该分辨率转换方法，得到多分辨率杂波数据集{cI1,cI1+cI2,cI1+cI2+cI3,...,cI1+cI2+cI3+…+cI50}，19个初始形状参数得到19个数据集；Step 4.3, the two sets of sea clutter data are superimposed to obtain the sea clutter data at 2 times resolution as cI1 + cI2 , and the clutter data at 3 times resolution as 3RI0 as cI1 + cI2 + cI3 . According to the resolution conversion method, a multi-resolution clutter data set {cI1 , cI1 + cI2 , cI1 + cI2 + cI3 ,..., cI1 + cI2 + cI3 +…+cI50 } is obtained. 19 initial shape parameters result in 19 data sets.
步骤4.4，将19个数据集中的每一组海杂波数据取模，并将模值按升序排列，得到分辨率转换后的海杂波幅度序列rI'；Step 4.4, take the modulus of each group of sea clutter data in the 19 data sets, and arrange the modulus values in ascending order to obtain the sea clutter amplitude sequence rI ' after resolution conversion;
步骤4.5，采用与步骤2相同的方法，估计分辨率转换后的海杂波幅度序列rI'的模型类型和形状参数；Step 4.5, using the same method as step 2, estimate the model type and shape parameters of the sea clutter amplitude series rI ' after resolution conversion;
步骤4.6，将19个初始形状参数以及其对应的分辨率转换后的海杂波幅度序列的模型类型和形状参数的估计结果，组成IGCG幅度分布模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表；Step 4.6, the 19 initial shape parameters and the corresponding model type and shape parameter estimation results of the sea clutter amplitude sequence after resolution conversion are used to form an "initial shape parameter-resolution conversion multiple-model type and shape parameter" comparison table of the IGCG amplitude distribution model;
步骤5，计算分辨率转换倍数。Step 5: Calculate the resolution conversion factor.
计算分辨率转换倍数x＝[R/R0+0.5]，R0表示海杂波幅度序列r的分辨率，R表示模型类型和形状参数待求的海杂波幅度序列的目标分辨率。The resolution conversion factor x=[R/R0 +0.5] is calculated, where R0 represents the resolution of the sea clutter amplitude sequence r, and R represents the target resolution of the sea clutter amplitude sequence whose model type and shape parameters are to be determined.
步骤6，利用对照表查找目标分辨率的海杂波幅度序列的模型类型和形状参数。Step 6: Use the comparison table to find the model type and shape parameters of the sea clutter amplitude sequence of the target resolution.
步骤6.1，海杂波幅度序列的模型类型确定为广义Pareto分布强度模型时，初始形状参数νP0取值νP，分辨率转换倍数为x，利用广义Pareto分布强度模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表，查找分辨率转换为目标分辨率R后的海杂波幅度序列的模型类型和形状参数。Step 6.1, when the model type of the sea clutter amplitude sequence is determined to be the generalized Pareto distribution intensity model, the initial shape parameter νP0 takes the value νP , the resolution conversion factor is x, and the "initial shape parameter-resolution conversion factor-model type and shape parameter" comparison table of the generalized Pareto distribution intensity model is used to find the model type and shape parameter of the sea clutter amplitude sequence after the resolution is converted to the target resolution R.
步骤6.2，海杂波幅度序列的模型类型确定为IGCG幅度分布模型时，初始形状参数νI0取值νI，分辨率转换倍数为x，利用IGCG幅度分布模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表，查找分辨率转换为目标分辨率R后的海杂波幅度序列的模型类型和形状参数。Step 6.2, when the model type of the sea clutter amplitude sequence is determined to be the IGCG amplitude distribution model, the initial shape parameter νI0 takes the value νI , the resolution conversion factor is x, and the “initial shape parameter-resolution conversion factor-model type and shape parameter” comparison table of the IGCG amplitude distribution model is used to find the model type and shape parameter of the sea clutter amplitude sequence after the resolution is converted to the target resolution R.
本发明的效果可通过以下仿真进一步说明：The effect of the present invention can be further illustrated by the following simulation:
1.仿真条件：1. Simulation conditions:
本发明的仿真实验的硬件平台为：处理器为Intel(R)Core(TM)i7-4790 CPU，主频为3.60GHz，内存16GB。The hardware platform of the simulation experiment of the present invention is: the processor is Intel(R) Core(TM) i7-4790 CPU, the main frequency is 3.60GHz, and the memory is 16GB.
本发明的仿真实验的软件平台为：64位Windows7操作系统，MATLAB R2014b。The software platform of the simulation experiment of the present invention is: 64-bit Windows 7 operating system, MATLAB R2014b.
2.仿真内容及其结果分析：2. Simulation content and results analysis:
本发明仿真实验采用本发明与两个现有技术(K-S距离模型类型估计法、迭代最大似然参数估计法)分别估计多分辨海杂波的模型类型和形状参数。依据复合高斯模型的理论，仿真生成50组服从广义Pareto分布强度模型的杂波数据，每组样本量为10000，杂波数据是由50个距离单元和10000个脉冲构成的矩阵。初始新尺度参数取值为1，初始形状参数为ν0，取值于集合{1.05,1.5,2.0,...,9.0,9.5,10.0}，共19种选择。取50个距离单元中的第1个单元，将其中200个样本的值替换为原数据的10倍。基于分辨率转换得到多分辨海杂波数据，并进行迭代最大似然参数估计和模型选择，同时根据广义Pareto分布强度模型的“初始形状参数-分辨率转换倍数-模型类型和形状参数”对照表可得模型类型和形状参数的估计结果。将两个现有技术的计算结果与本发明估计结果对比，得到本发明的仿真实验图如图2所示。The simulation experiment of the present invention adopts the present invention and two prior arts (KS distance model type estimation method, iterative maximum likelihood parameter estimation method) to estimate the model type and shape parameters of multi-resolution sea clutter respectively. According to the theory of the composite Gaussian model, 50 groups of clutter data obeying the generalized Pareto distribution intensity model are simulated and generated, and the sample size of each group is 10000, and the clutter data is a matrix composed of 50 distance units and 10000 pulses. The initial new scale parameter is 1, and the initial shape parameter is ν0 , which is taken from the set {1.05, 1.5, 2.0, ..., 9.0, 9.5, 10.0}, with a total of 19 choices. Take the first unit among the 50 distance units, and replace the values of 200 samples therein with 10 times of the original data. Based on the resolution conversion, multi-resolution sea clutter data is obtained, and iterative maximum likelihood parameter estimation and model selection are performed. At the same time, the estimation results of the model type and shape parameters can be obtained according to the "initial shape parameter-resolution conversion multiple-model type and shape parameter" comparison table of the generalized Pareto distribution intensity model. The calculation results of the two prior arts are compared with the estimation results of the present invention, and the simulation experiment diagram of the present invention is shown in Figure 2.
在仿真实验中，采用的两个现有技术是指：In the simulation experiment, the two existing technologies used are:
K-S距离模型类型估计法是指，石小帆在其发表的学位论文“海杂波关键特性参数的预测方法研究”(西安电子科技大学，硕士论文，2020)中提出了一种估计利用K-S距离估计海杂波模型类型的方法。The K-S distance model type estimation method refers to a method for estimating the sea clutter model type using the K-S distance proposed by Shi Xiaofan in his published thesis "Research on Prediction Methods of Key Characteristic Parameters of Sea Clutter" (Xi'an University of Electronic Science and Technology, Master's Thesis, 2020).
迭代最大似然参数估计法是指，Xu S W,Wang L,Shui P L等人在其发表的论文“Iterative maximum likelihood and zlogz estimation of parameters of compound-Gaussian clutter with inverse Gamma texture”(2018IEEE ICSPCC,Qingdao,China,2018:1-6)中公开了一种利用迭代最大似然估计海杂波幅度模型参数的方法。The iterative maximum likelihood parameter estimation method refers to a method for estimating sea clutter amplitude model parameters using iterative maximum likelihood, which is disclosed in the paper “Iterative maximum likelihood and zlogz estimation of parameters of compound-Gaussian clutter with inverse Gamma texture” (2018 IEEE ICSPCC, Qingdao, China, 2018: 1-6) by Xu S W, Wang L, Shui P L et al.
图2(a)为模型类型估计准确率曲线，其中，横坐标表示初始形状参数值，纵坐标表示模型估计准确率。图2(a)中以虚线表示K-S距离模型类型估计法的模型类型估计准确率曲线，以实线表示本发明的模型类型估计准确率曲线。FIG2(a) is a model type estimation accuracy curve, wherein the horizontal axis represents the initial shape parameter value and the vertical axis represents the model estimation accuracy. FIG2(a) shows the model type estimation accuracy curve of the K-S distance model type estimation method with a dotted line, and shows the model type estimation accuracy curve of the present invention with a solid line.
图2(b)为形状参数估计的相对误差曲线，其中，横坐标表示分辨单元面积变化倍数，纵坐标表示相对误差。图2(b)中以虚线表示迭代最大似然参数估计法的相对误差曲线，以实线表示本发明的相对误差曲线。FIG2(b) is a relative error curve of shape parameter estimation, wherein the horizontal axis represents the multiple of the resolution unit area change, and the vertical axis represents the relative error. FIG2(b) shows the relative error curve of the iterative maximum likelihood parameter estimation method with a dotted line, and shows the relative error curve of the present invention with a solid line.
由图2(a)可以看出，由50个距离单元和10000个脉冲构成的矩阵海杂波数据，在含有200个异常样本的情况下，依据初始形状参数用2种方法进行模型类型的估计，K-S距离模型类型估计法的模型类型估计准确率较低，而本发明对应的模型类型估计准确率更高。As can be seen from Figure 2(a), for the matrix sea clutter data consisting of 50 distance units and 10,000 pulses, when containing 200 abnormal samples, the model type is estimated by two methods based on the initial shape parameters. The model type estimation accuracy of the K-S distance model type estimation method is low, while the model type estimation accuracy corresponding to the present invention is higher.
由图2(b)可以看出，由50个距离单元和10000个脉冲构成的矩阵海杂波数据，在含有200个异常样本的情况下，初始形状参数为2.5时，用2种方法进行形状参数的估计，迭代最大似然参数估计法估计的形状参数误差较大，而本发明对应的形状参数估计误差较小，抗异常样本能力更强。As can be seen from Figure 2(b), for the matrix sea clutter data consisting of 50 range units and 10,000 pulses, when there are 200 abnormal samples and the initial shape parameter is 2.5, the shape parameter estimation method using the iterative maximum likelihood parameter estimation method has a large error in the shape parameter estimation, while the shape parameter estimation error corresponding to the present invention is small and has a stronger ability to resist abnormal samples.
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| CN202210594127.5ACN114966570B (en) | 2022-05-27 | 2022-05-27 | A method for estimating the type and parameters of multi-resolution sea clutter models using a comparison table |
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| CN202210594127.5ACN114966570B (en) | 2022-05-27 | 2022-05-27 | A method for estimating the type and parameters of multi-resolution sea clutter models using a comparison table |
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| CN114966570A CN114966570A (en) | 2022-08-30 |
| CN114966570Btrue CN114966570B (en) | 2024-08-16 |
| Application Number | Title | Priority Date | Filing Date |
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| CN202210594127.5AActiveCN114966570B (en) | 2022-05-27 | 2022-05-27 | A method for estimating the type and parameters of multi-resolution sea clutter models using a comparison table |
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| CN (1) | CN114966570B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109143196B (en)* | 2018-09-25 | 2023-01-06 | 西安电子科技大学 | Estimation method of tertile point parameters based on K-distribution sea clutter amplitude model |
| FR3091358B1 (en)* | 2018-12-27 | 2020-12-11 | Thales Sa | Device for generating a set of simulated sea clutter data, associated method and computer program |
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| "Multiresolution hierachical target detection in high-resolution maritime surveillance redars";XiaoJun Zhang等;《IEEE transactions on aerospace and electrionic systems》;20240514;全文* |
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| CN114966570A (en) | 2022-08-30 |
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