CN108227750A - A kind of ground target real-time tracking performance estimating method and system - Google Patents
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Abstract
本发明提供一种地面目标实时跟踪性能评估方法及系统，该方法包括：获取目标估计器的估计误差概率分布，所述目标估计器为待评估的地面目标实时跟踪状态估计器；分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度；根据所述相似度对所述目标估计器进行跟踪性能评估。本发明通过采用度量误差分布相对于期望参考量的相似度，即期望水平度量，实现对不同状态估计器优劣的有效评价，进而实现对地面目标跟踪状态估计技术进行客观公正的评价。
The present invention provides a method and system for evaluating ground target real-time tracking performance. The method includes: obtaining the estimation error probability distribution of a target estimator, and the target estimator is a ground target real-time tracking state estimator to be evaluated; analyzing the estimated The similarity between the error probability distribution and the preset expected error probability distribution; performing tracking performance evaluation on the target estimator according to the similarity. The present invention adopts the measurement error distribution The similarity with respect to the expected reference quantity, that is, the expected level measure, realizes the effective evaluation of the pros and cons of different state estimators, and then realizes the objective and fair evaluation of the ground target tracking state estimation technology.
Description
技术领域technical field
本发明涉及地面目标跟踪状态估计技术的性能评估技术领域，尤其涉及一种基于估计误差分布的地面目标实时跟踪性能评估方法及系统。The invention relates to the technical field of performance evaluation of ground target tracking state estimation technology, in particular to a method and system for evaluating ground target real-time tracking performance based on estimation error distribution.
背景技术Background technique
随着现代高精度传感器技术突飞猛进的发展，在地面目标实时跟踪过程中，对跟踪算法性能的验证与评估需求越来越迫切。准确的跟踪算法性能验证与评估方法，能够帮助工程人员择选符合性能要求的滤波器，提高跟踪性能。With the rapid development of modern high-precision sensor technology, in the process of real-time tracking of ground targets, the need for verification and evaluation of tracking algorithm performance is becoming more and more urgent. Accurate tracking algorithm performance verification and evaluation methods can help engineers select filters that meet performance requirements and improve tracking performance.
目前，现有的跟踪算法性能优劣的验证与评估方法，是通过计算目标真实状态和估计状态之间的估计误差均方根的大小来实现的。但是，采用估计误差均方根来进行误差度量有着严重的缺陷，易受大的误差值主导，不能满足性能评估的要求。At present, the verification and evaluation methods for the performance of existing tracking algorithms are realized by calculating the root mean square of the estimation error between the real state of the target and the estimated state. However, using the root mean square of the estimated error to measure the error has serious defects, and it is easily dominated by large error values, which cannot meet the requirements of performance evaluation.
因此，如何实现对地面目标跟踪状态估计技术进行客观公正的评价具有重要的意义。Therefore, how to achieve an objective and fair evaluation of ground target tracking state estimation technology is of great significance.
发明内容Contents of the invention
鉴于上述问题，提出了本发明以便提供一种克服上述问题或者至少部分地解决上述问题的地面目标实时跟踪性能评估方法及系统，可有效评价目标跟踪算法的优劣。In view of the above problems, the present invention is proposed to provide a ground target real-time tracking performance evaluation method and system that overcomes the above problems or at least partially solves the above problems, and can effectively evaluate the pros and cons of target tracking algorithms.
本发明的一个方面，提供了一种地面目标实时跟踪性能评估方法，包括:One aspect of the present invention provides a method for evaluating ground target real-time tracking performance, comprising:
获取目标估计器的估计误差概率分布，所述目标估计器为待评估的地面目标实时跟踪状态估计器；Obtain the estimation error probability distribution of the target estimator, and the target estimator is a ground target real-time tracking state estimator to be evaluated;
分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度；Analyzing the similarity between the estimation error probability distribution and a preset expected error probability distribution;
根据所述相似度对所述目标估计器进行跟踪性能评估。Perform tracking performance evaluation on the target estimator according to the similarity.
其中，在所述分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度之前，所述方法还包括：Wherein, before the analysis of the similarity between the estimation error probability distribution and the preset expected error probability distribution, the method further includes:
判定所述期望误差概率分布的分布类型，根据所述分布类型选取相应的相似度分析模型。Determine the distribution type of the expected error probability distribution, and select a corresponding similarity analysis model according to the distribution type.
其中，若所述期望误差概率分布为高斯分布或拉普拉斯分布时，采用第一相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第一相似度分析模型如下：Wherein, if the expected error probability distribution is a Gaussian distribution or a Laplace distribution, the first similarity analysis model is used to analyze the similarity between the estimated error probability distribution and the expected error probability distribution, and the second A similarity analysis model is as follows:
其中，ρ(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
其中，若所述期望误差概率分布为非高斯分布和拉普拉斯分布时，采用第二相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第二相似度分析模型如下：Wherein, if the expected error probability distribution is a non-Gaussian distribution and a Laplace distribution, a second similarity analysis model is used to analyze the similarity between the estimated error probability distribution and the expected error probability distribution, and the The second similarity analysis model is as follows:
其中，ρ′(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ'(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
其中，若所述估计误差概率分布为离散分布时，离散的估计误差集合为 Wherein, if the estimation error probability distribution is a discrete distribution, the discrete estimation error set is
所述分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度包括：The analyzing the similarity between the estimation error probability distribution and the preset expected error probability distribution includes:
从期望分布中随机抽取与估计误差集合相同采样点数量的期望误差集合 Randomly draw an expected error set with the same number of sampling points as the estimated error set from the expected distribution
分别对所述和进行标准化，得到和 respectively for the and to standardize, to get and
分别计算和对应的自相关矩阵R1和R2，并计算R1的特征向量R2的特征向量 Calculate separately and Corresponding autocorrelation matrices R 1 and R 2 , and calculate the eigenvector of R 1 Eigenvector of R2
分别计算两两间的相关性，公式如下：Calculate separately The correlation between the two, the formula is as follows:
根据估计误差集合与期望采样点集合中各采样点的相关性，确定所述估计误差概率分布与预设的期望误差概率分布之间的相似度。According to the correlation between the estimated error set and each sampling point in the expected sampling point set, the similarity between the estimated error probability distribution and the preset expected error probability distribution is determined.
本发明的另一个方面，提供了一种地面目标实时跟踪性能评估系统，包括：Another aspect of the present invention provides a ground target real-time tracking performance evaluation system, comprising:
估计误差分布获取模块，适于获取目标估计器的估计误差概率分布，所述目标估计器为待评估的地面目标实时跟踪状态估计器；An estimation error distribution acquisition module is adapted to obtain an estimation error probability distribution of a target estimator, and the target estimator is a real-time tracking state estimator for a ground target to be evaluated;
相似度分析模块，适于分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度；A similarity analysis module, adapted to analyze the similarity between the estimation error probability distribution and the preset expected error probability distribution;
性能评估模块，适于根据所述相似度对所述目标估计器进行跟踪性能评估。A performance evaluation module, adapted to evaluate the tracking performance of the target estimator according to the similarity.
其中，所述系统还包括：Wherein, the system also includes:
判定模块，适于在所述相似度分析模块分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度之前，判定所述期望误差概率分布的分布类型，根据所述分布类型选取相应的相似度分析模型。A determination module, adapted to determine the distribution type of the expected error probability distribution before the similarity analysis module analyzes the similarity between the estimated error probability distribution and the preset expected error probability distribution, according to the distribution type Select the corresponding similarity analysis model.
其中，所述相似度分析模块，具体适于当所述期望误差概率分布为高斯分布或拉普拉斯分布时，采用第一相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第一相似度分析模型如下：Wherein, the similarity analysis module is specifically adapted to use a first similarity analysis model to analyze the estimation error probability distribution and the expected error probability distribution when the expected error probability distribution is a Gaussian distribution or a Laplace distribution. The similarity between distributions, the first similarity analysis model is as follows:
其中，ρ(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
其中，所述相似度分析模块，具体适于当所述期望误差概率分布为非高斯分布和拉普拉斯分布时，采用第二相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第二相似度分析模型如下：Wherein, the similarity analysis module is specifically adapted to use a second similarity analysis model to analyze the estimated error probability distribution and the expected error probability distribution when the expected error probability distribution is a non-Gaussian distribution and a Laplace distribution. The similarity between probability distributions, the second similarity analysis model is as follows:
其中，ρ′(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ'(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
其中，所述相似度分析模块，具体包括：Wherein, the similarity analysis module specifically includes:
采样子模块，适于当所述估计误差概率分布为离散分布，离散的估计误差集合为时，从期望分布中随机抽取与估计误差集合相同采样点数量的期望误差集合 The sampling sub-module is suitable for when the estimation error probability distribution is a discrete distribution, and the discrete estimation error set is When , the expected error set with the same number of sampling points as the estimated error set is randomly selected from the expected distribution
标准化子模块，适于分别对所述和进行标准化，得到和 standardized submodules, adapted separately for the and to standardize, to get and
计算子模块，适于分别计算和对应的自相关矩阵R1和R2，并计算R1的特征向量R2的特征向量 Calculation sub-module, suitable for calculating separately and Corresponding autocorrelation matrices R 1 and R 2 , and calculate the eigenvector of R 1 Eigenvector of R2
所述计算子模块，还适于分别计算两两间的相关性，公式如下：The calculation sub-module is also suitable for calculating respectively The correlation between the two, the formula is as follows:
确定子模块，适于根据估计误差集合与期望采样点集合中各采样点的相关性，确定所述估计误差概率分布与预设的期望误差概率分布之间的相似度。The determining submodule is adapted to determine the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
本发明实施例提供的地面目标实时跟踪性能评估方法及系统，通过采用度量误差分布相对于某一参考量的相似度，即期望水平度量，以实现对不同状态估计器优劣的有效评价，进而实现对地面目标跟踪状态估计技术进行客观公正的评价。The method and system for evaluating the real-time tracking performance of ground targets provided by the embodiments of the present invention adopt the measurement error distribution The similarity relative to a certain reference quantity, that is, the expected level measurement, can realize the effective evaluation of the pros and cons of different state estimators, and then realize the objective and fair evaluation of the ground target tracking state estimation technology.
在实现本发明的过程中，充分考虑利用估计误差的分布信息，公平公正地对地面目标状态估计技术性能评估，改进跟踪性能。In the process of realizing the present invention, the distribution information of the estimation error is fully considered to evaluate the performance of the state estimation technology of the ground target fairly and justly, and improve the tracking performance.
上述说明仅是本发明技术方案的概述，为了能够更清楚了解本发明的技术手段，而可依照说明书的内容予以实施，并且为了让本发明的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。The above description is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable , the specific embodiments of the present invention are enumerated below.
附图说明Description of drawings
通过阅读下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本发明的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiment. The drawings are only for the purpose of illustrating a preferred embodiment and are not to be considered as limiting the invention. Also throughout the drawings, the same reference numerals are used to designate the same parts. In the attached picture:
图1为本发明实施例的一种地面目标实时跟踪性能评估方法的流程图；Fig. 1 is a flow chart of a method for evaluating ground target real-time tracking performance according to an embodiment of the present invention;
图2为本发明实施例的一种地面目标实时跟踪性能评估系统的结构示意图。FIG. 2 is a schematic structural diagram of a real-time tracking performance evaluation system for ground targets according to an embodiment of the present invention.
具体实施方式Detailed ways
下面将参照附图更详细地描述本公开的示例性实施例。虽然附图中显示了本公开的示例性实施例，然而应当理解，可以以各种形式实现本公开而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本公开，并且能够将本公开的范围完整的传达给本领域的技术人员。Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
本技术领域技术人员可以理解，除非另外定义，这里使用的所有术语(包括技术术语和科学术语)，具有与本发明所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是，诸如通用字典中定义的那些术语，应该被理解为具有与现有技术的上下文中的意义一致的意义，并且除非被特定定义，否则不会用理想化或过于正式的含义来解释。Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention belongs. It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be used in an idealized or overly formal sense unless specifically defined to explain.
为了克服现有评估指标的缺点，实现对地面目标跟踪状态估计技术进行客观公正的评价，本发明实施例提出一种通过度量误差分布相对于某一参考量的相似度，即期望水平度量，以实现不同状态估计器的性能评估方法。In order to overcome the shortcomings of the existing evaluation indicators and realize the objective and fair evaluation of the ground target tracking state estimation technology, the embodiment of the present invention proposes a The similarity relative to a certain reference quantity, that is, the expected level measure, is used to realize the performance evaluation method of different state estimators.
图1示意性示出了本发明一个实施例的地面目标实时跟踪性能评估方法的流程图。参照图1，本发明实施例的地面目标实时跟踪性能评估方法具体包括以下步骤：Fig. 1 schematically shows a flow chart of a method for evaluating ground target real-time tracking performance according to an embodiment of the present invention. With reference to Fig. 1, the ground target real-time tracking performance evaluation method of the embodiment of the present invention specifically comprises the following steps:
步骤S11、获取目标估计器的估计误差概率分布，所述目标估计器为待评估的地面目标实时跟踪状态估计器；Step S11, obtaining the estimation error probability distribution of the target estimator, which is the real-time tracking state estimator of the ground target to be evaluated;
步骤S12、分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度；其中，所述期望误差概率分布为目标估计器的标准参考值。Step S12 , analyzing the similarity between the estimation error probability distribution and a preset expected error probability distribution; wherein, the expected error probability distribution is a standard reference value of the target estimator.
步骤S13、根据所述相似度对所述目标估计器进行跟踪性能评估。Step S13 , evaluating the tracking performance of the target estimator according to the similarity.
本发明实施例中，将估计误差概率分布与预设的期望或理想的误差概率分布之间的相似度作为估计误差分布的期望水平(DL，Desirability Level)，即基于估计误差的分布信息。通过引入估计误差分布的期望水平，刻画估计误差的分布与期望或理想的误差分布之间的相关性或相似度，有效地克服了现有评估指标评估的缺陷。In the embodiment of the present invention, the similarity between the estimated error probability distribution and the preset expected or ideal error probability distribution is taken as the expected level (DL, Desirability Level) of the estimated error distribution, that is, the distribution information based on the estimated error. By introducing the expected level of the estimation error distribution and describing the correlation or similarity between the estimation error distribution and the expected or ideal error distribution, it effectively overcomes the shortcomings of the existing evaluation index evaluation.
本发明实施例提供的地面目标实时跟踪性能评估方法，将预设的期望误差概率分布作为参考量，通过采用度量误差分布相对于期望误差概率分布的相似度，即期望水平度量，以实现对不同状态估计器优劣的有效评价，进而实现对地面目标跟踪状态估计技术进行客观公正的评价。The real-time tracking performance evaluation method of the ground target provided by the embodiment of the present invention takes the preset expected error probability distribution as a reference quantity, and adopts the measurement error distribution The similarity with respect to the expected error probability distribution, that is, the expected level measure, is used to realize the effective evaluation of the pros and cons of different state estimators, and then realize the objective and fair evaluation of the ground target tracking state estimation technology.
下面对本发明实施例中提出的估计误差分布的期望水平给出具体说明。The following gives a specific description of the expected level of estimation error distribution proposed in the embodiments of the present invention.
类比两变量间的相关系数形式，定义两估计器估计误差概率分布相对期望误差概率分布的期望水平定义为：By analogy to the form of the correlation coefficient between two variables, define two estimators Estimation Error Probability Distribution relative expected error probability distribution The expected level of is defined as:
这一度量刻画了两个概率密度函数间的相关性或者相似度。This measure characterizes the correlation or similarity between two probability density functions.
考虑在离散情况下，假设两个概率质量函数满足：Consider the discrete case, assuming two probability mass functions Satisfy:
则ρ(0)的表达式为：Then the expression of ρ(0) is:
可见，ρ(0)可以看作N维的矢量之间夹角的余弦值。连续情况下，由于两概率密度函数可以看成是一个无穷维的矢量，所以可以把ρ(0)理解为两个分布函数间夹角的度量。It can be seen that ρ(0) can be regarded as an N-dimensional vector The cosine of the angle between them. In the continuous case, since the two probability density functions can be regarded as an infinite-dimensional vector, ρ(0) can be understood as the measure of the angle between the two distribution functions.
在计算中，若已知期望分布为高斯分布和拉普拉斯分布时，可给出解析结果，即若期望分布为高斯分布则有：In the calculation, if the expected distribution is known as Gaussian distribution and Laplace distribution, analytical results can be given, that is, if the expected distribution is Gaussian distribution Then there are:
若期望分布为拉普拉斯分布则有：If the expected distribution is a Laplace distribution Then there are:
进一步的，期望水平的扩展形式还包括如下内容：Furthermore, the extended form of the expected level also includes the following content:
在算式中积分部分和很难精确计算时，本发明实施例还给出其扩展形式，定义ρ'(0)为与的相关系数：Integrate the part in the equation and When it is difficult to calculate accurately, the embodiment of the present invention also provides its extended form, defining ρ'(0) as and The correlation coefficient of:
这是由于应用了对概率密度函数在全定义域上积分为1，即这样一来，大大简化了计算难度，完全避开了原定义中和两个积分算式。This is due to the application of a probability density function that integrates to 1 over the full domain, ie In this way, the calculation difficulty is greatly simplified, completely avoiding the and Two integral calculations.
此外，考虑在实际工程应用中，可能没有估计误差真实分布的相关信息。而降维后提取出的主特征有较好的性质：首先，主成分分析没有丢失原数据的主要信息，属于原数据的特征都有唯一的特征矢量与之对应；其次提取出的主特征具有稳定性，当估计误差矢量有微小变化时，其对应的主特征变化不敏感，因此，本发明实施例还提供了基于主成分分析的估计误差期望水平。In addition, considering that in practical engineering applications, there may be no relevant information about the true distribution of estimation errors. However, the main features extracted after dimensionality reduction have better properties: first, principal component analysis does not lose the main information of the original data, and the features belonging to the original data have unique feature vectors corresponding to them; secondly, the extracted main features have Stability, when the estimation error vector has a small change, its corresponding principal feature is insensitive, therefore, the embodiment of the present invention also provides an estimation error expectation level based on principal component analysis.
综上分析，对于不同分布类型的期望误差概率分布，或是不同分布类型的估计误差概率分布，对于有不同的相似度分析模型。因此，本发明实施例中，在所述分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度之前，所述方法还包括：判定所述期望误差概率分布的分布类型，根据所述分布类型选取相应的相似度分析模型，以实现根据期望误差概率分布和/或估计误差概率分布的分布类型选取合适的相似度分析模型。In summary, for the expected error probability distribution of different distribution types, or the estimated error probability distribution of different distribution types, there are different similarity analysis models. Therefore, in the embodiment of the present invention, before analyzing the similarity between the estimation error probability distribution and the preset expected error probability distribution, the method further includes: determining the distribution type of the expected error probability distribution, A corresponding similarity analysis model is selected according to the distribution type, so as to select an appropriate similarity analysis model according to the distribution type of the expected error probability distribution and/or estimated error probability distribution.
在本发明的一个可选实施例中，若所述期望误差概率分布为高斯分布或拉普拉斯分布时，采用第一相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第一相似度分析模型如下：In an optional embodiment of the present invention, if the expected error probability distribution is a Gaussian distribution or a Laplace distribution, a first similarity analysis model is used to analyze the estimation error probability distribution and the expected error probability distribution The similarity between, the first similarity analysis model is as follows:
其中，ρ(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
在本发明的一个可选实施例中，若所述期望误差概率分布为非高斯分布和拉普拉斯分布时，采用第二相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第二相似度分析模型如下：In an optional embodiment of the present invention, if the expected error probability distribution is a non-Gaussian distribution and a Laplace distribution, a second similarity analysis model is used to analyze the estimated error probability distribution and the expected error probability The similarity between distributions, the second similarity analysis model is as follows:
其中，ρ′(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ'(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
在本发明的另一个实施例中，当估计误差真实分布信息未知时，例如，估计误差概率分布为离散分别时，基于主成分分析法实现状态估计技术性能评估。In another embodiment of the present invention, when the real distribution information of the estimation error is unknown, for example, when the probability distribution of the estimation error is discrete, the performance evaluation of the state estimation technology is implemented based on the principal component analysis method.
进一步地，所述分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度，具体实现步骤如下：Further, the specific implementation steps of analyzing the similarity between the estimation error probability distribution and the preset expected error probability distribution are as follows:
给定期望分布fd～(0,Cd)，以及离散的估计误差集合 Given the desired distribution f d ～(0,C d ), and a discrete set of estimation errors
从期望分布中随机抽取与估计误差集合相同采样点数量的期望误差集合 Randomly draw an expected error set with the same number of sampling points as the estimated error set from the expected distribution
分别对所述和进行标准化，得到和其中，和满足：respectively for the and to standardize, to get and in, and Satisfy:
分别计算和对应的自相关矩阵R1和R2，其中：Calculate separately and The corresponding autocorrelation matrices R 1 and R 2 , where:
求出自相关矩阵R1,R2的特征值以及计算R1的特征向量R2的特征向量特征值按降序排序得到并对特征向量进行相应调整得 Find the eigenvalues of the autocorrelation matrix R 1 , R 2 and computing the eigenvectors of R1 Eigenvector of R2 The eigenvalues are sorted in descending order to get And adjust the eigenvectors accordingly to get
分别计算两两间的相关性，公式如下：Calculate separately The correlation between the two, the formula is as follows:
根据估计误差集合与期望采样点集合中各采样点的相关性，确定所述估计误差概率分布与预设的期望误差概率分布之间的相似度。According to the correlation between the estimated error set and each sampling point in the expected sampling point set, the similarity between the estimated error probability distribution and the preset expected error probability distribution is determined.
本发明实施例，利用主成分分析可以提取出数据中的特征且各特征间相互独立的性质，提出基于主成分分析来计算两分布间的相关性的方法。若两个分布有较强的相关性，若从每个分布上随机采点，则两个数据集之间也应有一些特征来反应这一相关性，若两个数据集来自相似性强的分布，其各主成分方向之间的夹角应该可以表征这一相关性。因此可逐一计算各自排序后主成分方向的夹角，若各夹角均很小，则考虑两分布间有很强的相关性。In the embodiment of the present invention, features in the data can be extracted by principal component analysis and the features are independent of each other, and a method for calculating the correlation between two distributions based on principal component analysis is proposed. If the two distributions have a strong correlation, if points are randomly selected from each distribution, there should also be some features between the two data sets to reflect this correlation. If the two data sets come from strongly similar Distribution, the angle between the directions of its principal components should be able to characterize this correlation. Therefore, the included angles of the principal component directions after sorting can be calculated one by one. If the included angles are small, it is considered that there is a strong correlation between the two distributions.
可理解的是，在N个数较小时，采点个数可增大；当然这仅是估计误差分布和期望分布相关的必要条件，所以计算两个特征向量的夹角时，若夹角很小，说明两个分布各自的这一主成分很相似。本发明实施例通过将求解高维误差分布的相关性问题分解成了几个一维的子问题，简单、快速的实现相似度分析。It is understandable that when the number of N is small, the number of sampling points can be increased; of course, this is only a necessary condition for estimating the error distribution and the expected distribution, so when calculating the angle between two eigenvectors, if the angle is very large Small, indicating that the principal components of the two distributions are very similar. The embodiment of the present invention decomposes the correlation problem of solving the high-dimensional error distribution into several one-dimensional sub-problems, and realizes the similarity analysis simply and quickly.
对于方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明实施例并不受所描述的动作顺序的限制，因为依据本发明实施例，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作并不一定是本发明实施例所必须的。For the method embodiment, for the sake of simple description, it is expressed as a series of action combinations, but those skilled in the art should know that the embodiment of the present invention is not limited by the described action order, because according to the embodiment of the present invention , certain steps may be performed in other order or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification belong to preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.
图2示意性示出了本发明一个实施例的地面目标实时跟踪性能评估系统的结构示意图。参照图2，本发明实施例的地面目标实时跟踪性能评估系统具体包括估计误差分布获取模块201、相似度分析模块202以及性能评估模块203，其中：Fig. 2 schematically shows the structure diagram of a real-time tracking performance evaluation system for ground targets according to an embodiment of the present invention. Referring to Fig. 2, the ground target real-time tracking performance evaluation system of the embodiment of the present invention specifically includes an estimation error distribution acquisition module 201, a similarity analysis module 202, and a performance evaluation module 203, wherein:
估计误差分布获取模块201，适于获取目标估计器的估计误差概率分布，所述目标估计器为待评估的地面目标实时跟踪状态估计器；The estimated error distribution acquisition module 201 is adapted to acquire the estimated error probability distribution of the target estimator, and the target estimator is the real-time tracking state estimator of the ground target to be evaluated;
相似度分析模块202，适于分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度；A similarity analysis module 202, adapted to analyze the similarity between the estimation error probability distribution and the preset expected error probability distribution;
性能评估模块203，适于根据所述相似度对所述目标估计器进行跟踪性能评估。The performance evaluation module 203 is adapted to evaluate the tracking performance of the target estimator according to the similarity.
本发明实施例提供的地面目标实时跟踪性能评估系统，将预设的期望误差概率分布作为参考量，通过采用度量误差分布相对于期望误差概率分布的相似度，即期望水平度量，以实现对不同状态估计器优劣的有效评价，进而实现对地面目标跟踪状态估计技术进行客观公正的评价。The ground target real-time tracking performance evaluation system provided by the embodiment of the present invention takes the preset expected error probability distribution as a reference quantity, and adopts the measurement error distribution The similarity with respect to the expected error probability distribution, that is, the expected level measure, is used to realize the effective evaluation of the pros and cons of different state estimators, and then realize the objective and fair evaluation of the ground target tracking state estimation technology.
在本法实施例中，所述系统还包括附图中未示出的判定模块，所述的判定模块，适于在所述相似度分析模块202分析所述估计误差概率分布与预设的期望误差概率分布之间的相似度之前，判定所述期望误差概率分布的分布类型，根据所述分布类型选取相应的相似度分析模型。In this embodiment of the law, the system further includes a judgment module not shown in the drawings, and the judgment module is adapted to analyze the estimation error probability distribution and the preset expectation in the similarity analysis module 202 Before the similarity between error probability distributions, the distribution type of the expected error probability distribution is determined, and a corresponding similarity analysis model is selected according to the distribution type.
在本发明的一个可选实施例中，所述相似度分析模块202，具体适于当所述期望误差概率分布为高斯分布或拉普拉斯分布时，采用第一相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第一相似度分析模型如下：In an optional embodiment of the present invention, the similarity analysis module 202 is specifically adapted to use the first similarity analysis model to analyze the Estimating the similarity between the error probability distribution and the expected error probability distribution, the first similarity analysis model is as follows:
其中，ρ(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
在本发明的一个可选实施例中，所述相似度分析模块202，具体适于当所述期望误差概率分布为非高斯分布和拉普拉斯分布时，采用第二相似度分析模型分析所述估计误差概率分布与所述期望误差概率分布之间的相似度，所述第二相似度分析模型如下：In an optional embodiment of the present invention, the similarity analysis module 202 is specifically adapted to use the second similarity analysis model to analyze the The similarity between the estimation error probability distribution and the expected error probability distribution, the second similarity analysis model is as follows:
其中，ρ′(0)为相似度，为目标估计器，为估计误差概率分布函数，为期望误差概率分布函数。Among them, ρ'(0) is the similarity, is the target estimator, To estimate the error probability distribution function, is the expected error probability distribution function.
在本发明的另一个实施例中，所述相似度分析模块202，具体包括采样子模块、标准化子模块、计算子模块以及确定子模块，其中：In another embodiment of the present invention, the similarity analysis module 202 specifically includes a sampling submodule, a standardization submodule, a calculation submodule and a determination submodule, wherein:
采样子模块，适于当所述估计误差概率分布为离散分布时，离散的估计误差集合为时，从期望分布中随机抽取与估计误差集合相同采样点数量的期望误差集合 The sampling sub-module is suitable for when the estimation error probability distribution is a discrete distribution, the discrete estimation error set is When , the expected error set with the same number of sampling points as the estimated error set is randomly selected from the expected distribution
标准化子模块，适于分别对所述和进行标准化，得到和 standardized submodules, adapted separately for the and to standardize, to get and
计算子模块，适于分别计算和对应的自相关矩阵R1和R2，并计算R1的特征向量R2的特征向量 Calculation sub-module, suitable for calculating separately and Corresponding autocorrelation matrices R 1 and R 2 , and calculate the eigenvector of R 1 Eigenvector of R2
所述计算子模块，还适于分别计算两两间的相关性，公式如下：The calculation sub-module is also suitable for calculating respectively The correlation between the two, the formula is as follows:
确定子模块，适于根据估计误差集合与期望采样点集合中各采样点的相关性，确定所述估计误差概率分布与预设的期望误差概率分布之间的相似度。The determining submodule is adapted to determine the similarity between the estimation error probability distribution and the preset expected error probability distribution according to the correlation between the estimation error set and each sampling point in the expected sampling point set.
对于系统实施例而言，由于其与方法实施例基本相似，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the related parts, please refer to the part of the description of the method embodiment.
本发明实施例提供的地面目标实时跟踪性能评估方法及系统，提供了一种基于主成分分析的状态估计技术性能评估的度量方法，提出了衡量估计误差分布期望水平的度量准则，通过采用度量误差分布相对于某一参考量的相似度，即期望水平度量，以实现对不同状态估计器优劣的有效评价，进而实现对地面目标跟踪状态估计技术进行客观公正的评价。The ground target real-time tracking performance evaluation method and system provided by the embodiments of the present invention provide a measurement method for state estimation technology performance evaluation based on principal component analysis, and propose a measurement criterion for measuring the expected level of estimation error distribution. By using the measurement error distributed The similarity relative to a certain reference quantity, that is, the expected level measurement, can realize the effective evaluation of the pros and cons of different state estimators, and then realize the objective and fair evaluation of the ground target tracking state estimation technology.
在实现本发明的过程中，充分考虑利用估计误差的分布信息，公平公正地对地面目标状态估计技术性能评估，改进跟踪性能。In the process of realizing the present invention, the distribution information of the estimation error is fully considered to evaluate the performance of the state estimation technology of the ground target fairly and justly, and improve the tracking performance.
此外，本发明实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如图1所述方法的步骤。In addition, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method as shown in FIG. 1 are implemented.
本实施例中，所述地面目标实时跟踪性能评估系统集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。In this embodiment, if the integrated modules/units of the ground target real-time tracking performance evaluation system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention realizes all or part of the processes in the methods of the above embodiments, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps in the above-mentioned various method embodiments can be realized. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, computer-readable media Excludes electrical carrier signals and telecommunication signals.
本发明实施例提供的计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述各个地面目标实时跟踪性能评估方法实施例中的步骤，例如图1所示的方法步骤。The computer equipment provided by the embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the above method for evaluating the real-time tracking performance of each ground target is realized. The steps in the embodiments are, for example, the method steps shown in FIG. 1 .
示例性的，所述计算机程序可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器中，并由所述处理器执行，以完成本发明。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序在所述地面目标实时跟踪性能评估系统中的执行过程。Exemplarily, the computer program may be divided into one or more modules/units, and the one or more modules/units are stored in the memory and executed by the processor to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program in the ground target real-time tracking performance evaluation system.
所述计算机设备可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。The computer equipment may be computing equipment such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
所述处理器可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable GateArray，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等，所述处理器是所述计算机设备的控制中心，利用各种接口和线路连接整个计算机设备的各个部分。The processor can be a central processing unit (Central Processing Unit, CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate array (Field-Programmable GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.
所述存储器可用于存储所述计算机程序和/或模块，所述处理器通过运行或执行存储在所述存储器内的计算机程序和/或模块，以及调用存储在存储器内的数据，实现所述计算机设备的各种功能。所述存储器可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据手机的使用所创建的数据(比如音频数据、电话本等)等。此外，存储器可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。The memory can be used to store the computer programs and/or modules, and the processor realizes the computer by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory various functions of the device. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required by a function (such as a sound playback function, an image playback function, etc.) and the like; the storage data area may store Data created based on the use of the mobile phone (such as audio data, phonebook, etc.), etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, internal memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card , a flash memory card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.
本领域的技术人员能够理解，尽管在此的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如，在下面的权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。Those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different examples. For example, in the following claims, any of the claimed embodiments may be used in any combination.
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.
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