CN116432144B - Data fusion system based on carrier communication - Google Patents
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to the technical field of electric data processing, in particular to a data fusion system based on carrier communication. The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring communication data of a data source to the same target and the position of the target, and determining the moving distance in a corresponding time period; the first processing module is used for sequencing the moving distances of any data source acquisition target in different time periods according to the time sequence to obtain a distance sequence, and determining the fluctuation coefficient of the moving distance; dividing the distance sequence into similar subsequences; the second processing module is used for determining the target confidence of the data source in the time period; and the data fusion module is used for carrying out data fusion on the communication data acquired by each data source according to the target confidence coefficient of all the data sources in the same time period to obtain fusion data. The invention can effectively improve the anti-interference capability of the fusion data and enhance the reliability of the fusion data.
Description
Technical Field
The invention relates to the technical field of electric data processing, in particular to a data fusion system based on carrier communication.
Background
For a communication scene, a multi-source sensor is commonly used for collecting abundant original information, and the information is fused to provide more detailed characteristic data for analysis.
In the related art, the multi-source data fusion adopts a mode of distributing fusion weights according to the distance between the data sources and the target, and in the mode, the data sources which are relatively close to each other are interfered in the process of collecting data and still fused in a relatively high proportion, so that the subsequent fusion data is easily influenced, the data fusion effect is easily influenced, the reliability of the fusion data is insufficient, and the optimization is required.
Disclosure of Invention
In order to solve the technical problems that the data fusion effect is easy to be influenced by interference and the reliability of fusion data is insufficient, the invention provides a data fusion system based on carrier communication, which adopts the following technical scheme:
the invention provides a data fusion system based on carrier communication, which comprises:
the acquisition module is used for periodically acquiring communication data and target positions of at least two data sources on the same target, and determining the moving distance in a corresponding time period according to the change of the target positions acquired by the same data source at adjacent time points;
the first processing module is used for sequencing the moving distances of any data source acquisition target in different time periods according to the time sequence to obtain a distance sequence, and determining the fluctuation coefficient of each moving distance except the first moving distance in the distance sequence according to the values of the first moving distance and other moving distances in the distance sequence; dividing the distance sequence into similar subsequences according to the difference of fluctuation coefficients of adjacent moving distances;
the second processing module is used for determining the abnormal coefficient of the similar subsequence according to the number and the numerical value of the moving distance in the similar subsequence; determining initial confidence coefficient of any data source in a corresponding time period according to difference of moving distance between any data source and all other data source acquisition targets in the same time period, and determining target confidence coefficient of the data source in the time period according to the anomaly coefficient and the initial confidence coefficient;
and the data fusion module is used for carrying out data fusion on the communication data acquired by each data source according to the target confidence coefficient of all the data sources in the same time period to obtain fusion data.
Further, the determining, according to the values of the first moving distance and the other moving distances in the distance sequence, the fluctuation coefficient of each moving distance except the first moving distance in the distance sequence includes:
taking any moving distance except the first moving distance in the distance sequence as a moving distance to be measured;
calculating the absolute value of the skewness of a subsequence from the first moving distance to the moving distance to be measured in the distance sequence as a skewness coefficient to be measured;
calculating the absolute value of the difference between the first moving distance and the moving distance to be measured as a first distance difference, and calculating the absolute value of the difference between the maximum value of the first moving distance and the moving distance to be measured as a second distance difference;
determining a distance difference coefficient according to the first distance difference and the second distance difference, wherein the first distance difference and the distance difference coefficient form a negative correlation, and the second distance difference and the distance difference coefficient form a positive correlation;
and calculating a normalized value of the product of the to-be-measured skewness coefficient and the distance difference coefficient as a fluctuation coefficient of the to-be-measured moving distance.
Further, the dividing the distance sequence into similar subsequences according to differences of fluctuation coefficients of adjacent moving distances includes:
calculating the absolute value of the difference value of the adjacent fluctuation coefficients as an adjacent difference;
and combining the moving distances of which the adjacent differences are smaller than a preset difference threshold value as a similar subsequence, wherein the first moving distance is added to the similar subsequence adjacent to the first moving distance, and therefore the distance sequence is divided into at least one similar subsequence.
Further, the determining the anomaly coefficient of the similar subsequence according to the number and the numerical value of the moving distance in the similar subsequence includes:
calculating the ratio of the total number of the moving distances in all the similar subsequences to the number of the similar subsequences as a number average;
optionally selecting a certain similar subsequence as a similar subsequence to be detected, and calculating a difference value between the number average value and the number of the moving distances in the similar subsequence to be detected as a number difference value;
taking two similar subsequences closest to the similar subsequence to be detected as a first subsequence and a second subsequence, calculating the average value of all moving distances in the first subsequence as a first distance average value, calculating the average value of all moving distances in the second subsequence as a second distance average value, and calculating the absolute value of the difference between the first distance average value and the second distance average value as a distance coefficient;
and obtaining the abnormal coefficient of the similar subsequence to be detected according to the quantity difference value and the distance coefficient, wherein the quantity difference value and the abnormal coefficient are in positive correlation, the distance coefficient and the abnormal coefficient are in inverse correlation, and the value of the abnormal coefficient is a normalized value.
Further, the determining the initial confidence of the data source in the corresponding time period according to the difference of the moving distance of any data source and all other data source acquisition targets in the same time period comprises the following steps:
calculating the mean value of the absolute value of the difference value of the moving distance obtained by any data source in any time period and the moving distance obtained by each other data source in the time period respectively as the moving distance difference;
and calculating an inverse proportion normalization value of the moving distance difference as an initial confidence.
Further, the initial confidence coefficient and the target confidence coefficient are in positive correlation, the abnormal coefficient and the target confidence coefficient are in negative correlation, and the value of the target confidence coefficient is a normalized numerical value.
Further, the data fusion is performed on the communication data acquired by each data source according to the target confidence degrees of all the data sources in the same time period, so as to obtain fusion data, including:
calculating the product of the target confidence coefficient of the data source in the corresponding time period and the communication data as data to be fused;
and taking the sum value of the data to be fused of all the target data sources in the same time period as the fusion data of the time period.
Further, the determining the moving distance in the corresponding time period according to the change of the target position acquired by the same data source at the adjacent time point includes:
and calculating Euclidean distance between the target positions acquired by the same data source at adjacent time points as the moving distance.
The invention has the following beneficial effects:
according to the invention, the moving distances of different data sources in a plurality of time periods are determined, then, the fluctuation coefficient of the moving distance is determined according to the moving distance difference of the same data source in different time periods, as the target is usually in uniform motion or uniform acceleration motion in the moving process, the corresponding difference of the moving distances should present a certain rule, therefore, the fluctuation coefficient is determined according to the change of the corresponding moving distances, and the distance sequence is divided into at least one similar subsequence according to the difference of the fluctuation coefficients of adjacent moving distances, thus, the distance sequence is effectively divided according to the difference of moving distance values, the abnormal coefficients of the similar subsequences are determined according to the number of the moving distances and the values in the similar subsequences, the abnormal degree of the acquired data of a single data source in different time periods can be effectively analyzed, the initial confidence of the data source in the corresponding time period can be determined according to the difference of the moving distances of the data source in the same time period, the real moving distance values of all data sources in the same time period can be transversely analyzed, therefore, the state of high probability occurs under the condition that the difference is larger, the corresponding confidence coefficient is combined with the corresponding data source, the data can be directly fused according to the communication, the information of the acquired by combining the confidence coefficient of the data of the moving distance, and the data can be directly fused, and the information can be obtained, and the fusion of the data can be obtained according to the information of the distance of the data of the distance of the target, and the data can be effectively fused, the anti-interference capability of the fusion data is effectively improved, and the reliability of the fusion data is enhanced.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a data fusion system based on carrier communication according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of a data fusion system based on carrier communication according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of a data fusion system based on carrier communication provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a data fusion system based on carrier communication according to an embodiment of the present invention is shown, where the data fusion system 10 based on carrier communication includes:
the acquiring module 101 is configured to periodically acquire communication data and target positions of at least two data sources for a same target, and determine a movement distance in a corresponding time period according to a change of the target positions acquired by the same data source at adjacent time points.
The carrier communication scene may specifically be, for example, a scene in which the target device communicates with the base station, and in order to increase data strength, the information is focused and complemented by fusing multi-source data under a certain limitation. Therefore, the embodiment of the invention can set at least two data sources so as to acquire the communication data and the target position of the same target through a plurality of data sources.
In the embodiment of the invention, the data sources can be specifically, for example, signal receiving devices such as a communication base station and a communication radar, and the targets can be specifically, for example, devices to be communicated, such as a mobile communication device, a communication station and the like, and the targets are simultaneously collected by a plurality of data sources in a communication way so as to obtain continuous communication data and target positions in time sequence. Thus, communication data and target positions of a plurality of data sources for the same target are obtained.
It will be appreciated that the acquisition of the communication data of the target and the target position by the data source may be periodic, for example, every 0.02 seconds, without limitation.
The target position may be obtained specifically, for example: the data source is used as a center, the farther the target is away from the data source, the higher the propagation attenuation of a carrier wave transmitted by the data source to reach the target is, the distance between the target and the data source is determined according to the propagation attenuation, the data source continuously transmits a signal to scan and capture the target in the coverage area of the signal range of the data source, so that the data source can infer the position of the target in the data source through the signal scanning process, and the target position of the target is determined according to the distance and the position of the target from the data source. Of course, the present invention also supports the determination of the target location using a variety of other arbitrary possible implementations, without limitation.
Further, in some embodiments of the present invention, determining a movement distance in a corresponding time period according to a change of a target position acquired by the same data source at an adjacent time point includes: and calculating Euclidean distance between target positions acquired by the same data source at adjacent time points as a moving distance.
In the embodiment of the invention, the time interval between two adjacent time points can be used as a time period, and the data source can also acquire the target position of the target at each time point, so that the Euclidean distance between the target positions at the two adjacent time points can be used as the moving distance of the target in the time period. The calculation of the euclidean distance is a well known technique in the art, and is not further described and limited herein.
The first processing module 102 is configured to sort moving distances of the acquired target of any one data source in different time periods according to a time sequence, obtain a distance sequence, and determine a fluctuation coefficient of each moving distance except the first moving distance in the distance sequence according to values of the first moving distance and other moving distances in the distance sequence; the distance sequence is divided into similar sub-sequences according to the difference of the fluctuation coefficients of the adjacent moving distances.
In the embodiment of the present invention, the moving distances of any data source in the acquired time periods may be ordered according to a time sequence, for example, 3 moving distances in each time period may be ordered according to a sequence of the time periods, so as to obtain a distance sequence, that is, the distance sequence includes moving distances ordered according to a time sequence.
Further, in some embodiments of the present invention, determining the fluctuation coefficient of each moving distance except the first moving distance in the distance sequence according to the values of the first moving distance and other moving distances in the distance sequence includes: taking any moving distance except the first moving distance in the distance sequence as a moving distance to be measured; calculating the absolute value of the skewness of a subsequence from the first moving distance to the moving distance to be measured in the distance sequence as a skewness coefficient to be measured; calculating the absolute value of the difference between the first moving distance and the moving distance to be measured as a first distance difference, and calculating the absolute value of the difference between the maximum value of the first moving distance and the moving distance to be measured as a second distance difference; determining a distance difference coefficient according to the first distance difference and the second distance difference, wherein the first distance difference and the distance difference coefficient are in a negative correlation relationship, and the second distance difference and the distance difference coefficient are in a positive correlation relationship; and calculating a normalized value of the product of the to-be-measured skewness coefficient and the distance difference coefficient as a fluctuation coefficient of the to-be-measured moving distance.
The positive correlation relationship indicates that the dependent variable increases along with the increase of the independent variable, the dependent variable decreases along with the decrease of the independent variable, and the specific relationship can be multiplication relationship, addition relationship, idempotent of an exponential function and is determined by practical application; the negative correlation indicates that the dependent variable decreases with increasing independent variable, and the dependent variable increases with decreasing independent variable, which may be a subtraction relationship, a division relationship, or the like, and is determined by the actual application.
In the embodiment of the invention, any moving distance except the first moving distance in the distance sequence can be used as the moving distance to be measured, the first moving distance, the moving distance to be measured and all moving distances from the first moving distance to the moving distance to be measured form a subsequence, the absolute value of the deflection of the subsequence is calculated as the deflection coefficient to be measured of the moving distance to be measured, the corresponding distance difference coefficient is calculated, the fluctuation coefficient of the moving distance to be measured is determined according to the deflection coefficient to be measured and the distance difference coefficient, and the corresponding calculation formula can be specifically:
in the method, in the process of the invention,representing the distance of movement to be measured +.>I represents the index of the distance moved in the distance sequence, n+ represents a positive integer,/-a>Representing the first movement distance to the movement distance to be measured +.>Maximum value of>Representing a first distance difference,/->Representing a second distance difference,/->Representing a constant coefficient, a constant value set to prevent the denominator from being 0, < ->Representation->To->Is j represents +.>To->Index of the moving distance of>Represents the j-th movement distance,>representation->To->Mean value of the moving distance of>Representation->To->G () represents the normalization process. In one embodiment of the present invention, the normalization process may specifically be, for example, maximum and minimum normalization processes, and the normalization in the subsequent steps may be performed by using the maximum and minimum normalization processes, and in other embodiments of the present invention, other normalization methods may be selected according to a specific range of values, which will not be described herein.
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing a distance difference coefficient, representing the absolute value of the difference between the first moving distance and the moving distance to be measured through the first distance difference, and communicatingThe second distance difference represents the absolute value of the difference between the maximum value of the first moving distance to the moving distance to be measured and the first moving distance, that is, when the moving distance to be measured is the maximum value of the first moving distance to be measured, the corresponding distance difference coefficient is closer to 1, and under other conditions, the distance difference coefficient is larger than 1, so that a large amount of small fluctuation of the distance difference fluctuation generated by the abnormal value can be prevented from being covered after the sequence data quantity is increased, and the distance difference coefficient can be guaranteed to represent the moving trend in the corresponding time range, that is, the larger the distance difference coefficient is, the larger the fluctuation coefficient is.
Wherein, the liquid crystal display device comprises a liquid crystal display device,the deviation of the subsequence from the first moving distance to the moving distance to be measured in the distance sequence is represented, wherein the calculation formula of the deviation is a calculation method well known in the art, and not described in detail, the fluctuation situation presented in the subsequence can be represented by the absolute value of the deviation, and the method is characterized by the absolute value of the deviation>And after the absolute value is passed, the larger the value of the to-be-measured skewness coefficient is, the more the moving distance deviating from the mean value to a greater extent is shown in the subsequence, namely the subsequence is higher in fluctuation condition, a section of sequence with small fluctuation is more inconvenient to take, and the larger the to-be-measured skewness coefficient is, the larger the fluctuation coefficient is.
Further, in some embodiments of the present invention, dividing the distance sequence into similar subsequences according to differences in fluctuation coefficients of adjacent movement distances includes: calculating the absolute value of the difference value of the adjacent fluctuation coefficients as an adjacent difference; the moving distances with adjacent differences smaller than a preset difference threshold are combined as one similar subsequence, wherein the first moving distance is added to the similar subsequence adjacent thereto, thereby dividing the distance sequence into at least one similar subsequence.
In the embodiment of the invention, as the fluctuation coefficient represents the fluctuation condition from the first moving distance to the moving distance to be measured, the moving distance combination with similar fluctuation condition is used as a similar subsequence, and the absolute value of the difference value of the adjacent fluctuation coefficients is calculated to be used as the adjacent difference; the moving distances with adjacent differences smaller than a preset difference threshold are combined as a similar subsequence.
The preset difference threshold is a threshold of adjacent difference, optionally, the preset difference threshold may be specifically 0.5, or may be adjusted according to an actual detection requirement, which is not limited. When the adjacent difference between any moving distance and the adjacent moving distance is detected to be smaller than 0.5, the moving distance is combined with the adjacent moving distance meeting the condition, the moving distance adjacent to the combination is recalculated, and the distance sequence is divided into at least one similar subsequence by sequential processing.
A second processing module 103, configured to determine an anomaly coefficient of the similar subsequence according to the number and the value of the moving distances in the similar subsequence; and determining the initial confidence coefficient of the data source in the corresponding time period according to the difference of the moving distance between any data source and all other data sources in the same time period, and determining the target confidence coefficient of the data source in the time period according to the anomaly coefficient and the initial confidence coefficient.
Further, in some embodiments of the present invention, determining the anomaly coefficient for the similar subsequence based on the number and value of the distance traveled in the similar subsequence includes: calculating the ratio of the total number of the moving distances in all the similar subsequences to the number of the similar subsequences as a number average; optionally selecting a certain similar subsequence as a similar subsequence to be detected, and calculating a difference value between the number average value and the number of the moving distances in the similar subsequence to be detected as a number difference value; taking two similar subsequences closest to the similar subsequence to be detected as a first subsequence and a second subsequence, calculating the average value of all moving distances in the first subsequence as a first distance average value, calculating the average value of all moving distances in the second subsequence as a second distance average value, and calculating the absolute value of the difference between the first distance average value and the second distance average value as a distance coefficient; and obtaining the abnormal coefficient of the similar subsequence to be detected according to the quantity difference value and the distance coefficient, wherein the quantity difference value and the abnormal coefficient are in positive correlation, the distance coefficient and the abnormal coefficient are in inverse correlation, and the value of the abnormal coefficient is a normalized numerical value.
In the embodiment of the invention, the ratio of the total moving distance in all similar subsequences to the number of similar subsequences is calculated as a number average, which may be usedThe expression, thus, the calculation formula of the anomaly coefficient may specifically be, for example:
in the method, in the process of the invention,abnormal coefficient representing the similar subsequence to be tested, +.>Representing the analogous subsequence to be tested,/->Index representing similar subsequence,/->Representing the number of movement distances in the similarity, +.>Represents the number average value>Representing the analogous subsequence to be tested->The number of movement distances in (a), and (b)>Representing the difference in quantity>Representing the first subsequence,/->Representing a second subsequence,/->Representing a first distance mean>A second distance average value is represented and,representing distance coefficient>Representing normalization processing->A predetermined constant coefficient is represented, and a safety coefficient is set to prevent the denominator from being 0.
It can be understood that the similar subsequence is not a subsequence formed by outliers, that is, if the number of moving distances in the similar subsequence is greater when no outlier exists in the similar subsequence, when the number of moving distances in the similar subsequence is smaller than the number average value, the degree of abnormality of the similar subsequence can be characterized to be greater, that is, the greater the number difference value is, the greater the corresponding abnormality coefficient is.
The distance coefficient is the difference of the number average value of the moving distances of two similar subsequences, which are closest to each other along the time sequence, of the to-be-detected similar subsequence, the smaller the difference is, the two adjacent and closest similar subsequences of the time sequence of the to-be-detected similar subsequence are represented, the more likely to form an integral sequence after the to-be-detected similar subsequence is removed, namely, the to-be-detected similar subsequence is most likely to have abnormal data to intercept the two adjacent and closest similar subsequences, namely, the less obvious the to-be-detected similar subsequence is to the adjacent similar subsequence in the time sequence, so that the smaller the distance coefficient is, the larger the corresponding abnormal coefficient is.
Further, in some embodiments of the present invention, determining the initial confidence of the data source in the corresponding time period according to the difference between the moving distance of any one data source and all other data source acquisition targets in the same time period, includes: calculating the mean value of the absolute value of the difference value of the moving distance obtained by any data source in any time period and the moving distance obtained by each other data source in the time period respectively as the moving distance difference; an inverse proportion normalized value of the difference in moving distance is calculated as an initial confidence.
In the embodiment of the present invention, the initial confidence coefficient of any data source in any time period may be calculated, and the corresponding calculation formula may specifically be, for example:
in the method, in the process of the invention,representing the initial confidence of the data source m in the t-th time period,/for the data source m>Representing the distance traveled by data source m during the t-th time period, Y representing the total number of all data sources, Y representing the index of the data sources other than data source m,/among the data sources>Representing the distance of movement the y-th data source gets in the t-th time period, G () represents the normalization process.
In the embodiment of the invention, it can be understood that in the same time period, the moving distance of the target is a fixed value, and the abnormal condition and position judgment error of the data acquired by the data sources can cause the corresponding moving distances to be different, that is, the moving distance acquired by any one data source in any time period is calculated to be respectively compared with the average value of the absolute values of the difference values of the moving distances acquired by each other data source in the time period, the closer the average value is to 0, the higher the reliability of the moving distance acquired by the corresponding data source in the time period is represented, that is, the higher the initial confidence is.
Further, in some embodiments of the present invention, the target confidence coefficient of the data source in the time period is determined according to the anomaly coefficient and the initial confidence coefficient, the initial confidence coefficient and the target confidence coefficient have a positive correlation, the anomaly coefficient and the target confidence coefficient have a negative correlation, and the value of the target confidence coefficient is a normalized value.
In the embodiment of the present invention, since the anomaly coefficient characterizes the anomaly degree corresponding to the similar subsequence and the initial confidence coefficient characterizes the reliability of the moving distance of the data source obtained in the corresponding time period, the target confidence coefficient of the data source in the time period is determined through the anomaly coefficient and the initial confidence coefficient, and the corresponding calculation formula may specifically be, for example:
in the method, in the process of the invention,representing the target confidence of data source m in the t-th time period,/for data source m>Representing the initial confidence of the data source m in the t-th time period,/for the data source m>And the abnormal coefficient of the similar subsequence corresponding to the data source m in the t-th time period is represented.
Therefore, the initial confidence coefficient is adjusted through the anomaly coefficient, so that the target confidence coefficient can more effectively represent the credibility of communication data acquired by the corresponding data source in the corresponding time period.
And the data fusion module 104 is configured to perform data fusion on the communication data acquired by each data source according to the target confidence degrees of all the data sources in the same time period, so as to obtain fusion data.
In the embodiment of the invention, after the target confidence coefficient is obtained, the target confidence coefficient can be used as the weight of the corresponding communication data, so that data fusion is performed to obtain fusion data, and the specific fusion steps are as follows:
further, in some embodiments of the present invention, data fusion is performed on communication data acquired by each data source according to target confidence degrees of all the data sources in the same time period, so as to obtain fused data, including: calculating the product of the target confidence coefficient of the data source in the corresponding time period and the communication data as data to be fused; and taking the sum value of the data to be fused of all the target data sources in the same time period as fusion data of the time period.
The communication data may be, for example, electric digital data, and the fusion data may be obtained by multiplying and adding the target confidence coefficient with the electric digital data, or, of course, the communication data may be, for example, signal data, and the fusion data may be obtained by multiplying and adding the target confidence coefficient with a corresponding signal wave, where the target confidence coefficient may be used as a weight for corresponding data fusion, so as to perform weighted fusion on the communication data.
According to the invention, the moving distances of different data sources in a plurality of time periods are determined, then, the fluctuation coefficient of the moving distance is determined according to the moving distance difference of the same data source in different time periods, as the target is usually in uniform motion or uniform acceleration motion in the moving process, the corresponding difference of the moving distances should present a certain rule, therefore, the fluctuation coefficient is determined according to the change of the corresponding moving distances, and the distance sequence is divided into at least one similar subsequence according to the difference of the fluctuation coefficients of adjacent moving distances, thus, the distance sequence is effectively divided according to the difference of moving distance values, the abnormal coefficients of the similar subsequences are determined according to the number of the moving distances and the values in the similar subsequences, the abnormal degree of the acquired data of a single data source in different time periods can be effectively analyzed, the initial confidence of the data source in the corresponding time period can be determined according to the difference of the moving distances of the data source in the same time period, the real moving distance values of all data sources in the same time period can be transversely analyzed, therefore, the state of high probability occurs under the condition that the difference is larger, the corresponding confidence coefficient is combined with the corresponding data source, the data can be directly fused according to the communication, the information of the acquired by combining the confidence coefficient of the data of the moving distance, and the data can be directly fused, and the information can be obtained, and the fusion of the data can be obtained according to the information of the distance of the data of the distance of the target, and the data can be effectively fused, the anti-interference capability of the fusion data is effectively improved, and the reliability of the fusion data is enhanced.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (8)
1. A data fusion system based on carrier communication, the system comprising:
the acquisition module is used for periodically acquiring communication data and target positions of at least two data sources on the same target, and determining the moving distance in a corresponding time period according to the change of the target positions acquired by the same data source at adjacent time points;
the first processing module is used for sequencing the moving distances of any data source acquisition target in different time periods according to the time sequence to obtain a distance sequence, and determining the fluctuation coefficient of each moving distance except the first moving distance in the distance sequence according to the values of the first moving distance and other moving distances in the distance sequence; dividing the distance sequence into similar subsequences according to the difference of fluctuation coefficients of adjacent moving distances;
the second processing module is used for determining the abnormal coefficient of the similar subsequence according to the number and the numerical value of the moving distance in the similar subsequence; determining initial confidence coefficient of any data source in a corresponding time period according to difference of moving distance between any data source and all other data source acquisition targets in the same time period, and determining target confidence coefficient of the data source in the time period according to the anomaly coefficient and the initial confidence coefficient;
and the data fusion module is used for carrying out data fusion on the communication data acquired by each data source according to the target confidence coefficient of all the data sources in the same time period to obtain fusion data.
2. A data fusion system based on carrier communication according to claim 1, wherein the determining the fluctuation coefficient of each moving distance except the first moving distance in the distance sequence according to the values of the first moving distance and other moving distances in the distance sequence comprises:
taking any moving distance except the first moving distance in the distance sequence as a moving distance to be measured;
calculating the absolute value of the skewness of a subsequence from the first moving distance to the moving distance to be measured in the distance sequence as a skewness coefficient to be measured;
calculating the absolute value of the difference between the first moving distance and the moving distance to be measured as a first distance difference, and calculating the absolute value of the difference between the maximum value of the first moving distance and the moving distance to be measured as a second distance difference;
determining a distance difference coefficient according to the first distance difference and the second distance difference, wherein the first distance difference and the distance difference coefficient form a negative correlation, and the second distance difference and the distance difference coefficient form a positive correlation;
and calculating a normalized value of the product of the to-be-measured skewness coefficient and the distance difference coefficient as a fluctuation coefficient of the to-be-measured moving distance.
3. A data fusion system based on carrier communication according to claim 1, wherein the dividing the distance sequence into similar sub-sequences based on differences in fluctuation coefficients of adjacent moving distances comprises:
calculating the absolute value of the difference value of the adjacent fluctuation coefficients as an adjacent difference;
and combining the moving distances of which the adjacent differences are smaller than a preset difference threshold value as a similar subsequence, wherein the first moving distance is added to the similar subsequence adjacent to the first moving distance, and therefore the distance sequence is divided into at least one similar subsequence.
4. The data fusion system based on carrier communication of claim 1, wherein the determining the anomaly coefficient of the similar subsequence based on the number and value of the distance traveled in the similar subsequence comprises:
calculating the ratio of the total number of the moving distances in all the similar subsequences to the number of the similar subsequences as a number average;
optionally selecting a certain similar subsequence as a similar subsequence to be detected, and calculating a difference value between the number average value and the number of the moving distances in the similar subsequence to be detected as a number difference value;
taking two similar subsequences closest to the similar subsequence to be detected as a first subsequence and a second subsequence, calculating the average value of all moving distances in the first subsequence as a first distance average value, calculating the average value of all moving distances in the second subsequence as a second distance average value, and calculating the absolute value of the difference between the first distance average value and the second distance average value as a distance coefficient;
and obtaining the abnormal coefficient of the similar subsequence to be detected according to the quantity difference value and the distance coefficient, wherein the quantity difference value and the abnormal coefficient are in positive correlation, the distance coefficient and the abnormal coefficient are in inverse correlation, and the value of the abnormal coefficient is a normalized value.
5. The data fusion system based on carrier communication according to claim 1, wherein the determining the initial confidence of the data source in the corresponding time period according to the difference of the moving distance of any one data source and all other data source acquisition targets in the same time period comprises:
calculating the mean value of the absolute value of the difference value of the moving distance obtained by any data source in any time period and the moving distance obtained by each other data source in the time period respectively as the moving distance difference;
and calculating an inverse proportion normalization value of the moving distance difference as an initial confidence.
6. The data fusion system based on carrier communication of claim 1, wherein the initial confidence level and the target confidence level are in positive correlation, the anomaly coefficient and the target confidence level are in negative correlation, and the value of the target confidence level is a normalized value.
7. The data fusion system based on carrier communication according to claim 1, wherein the data fusion is performed on the communication data acquired by each data source according to the target confidence of all the data sources in the same time period, so as to obtain fused data, and the data fusion system comprises:
calculating the product of the target confidence coefficient of the data source in the corresponding time period and the communication data as data to be fused;
and taking the sum value of the data to be fused of all the target data sources in the same time period as the fusion data of the time period.
8. A data fusion system based on carrier communication according to claim 1, wherein said determining the distance of movement within the corresponding time period from the change in the target position acquired by the same data source at adjacent points in time comprises:
and calculating Euclidean distance between the target positions acquired by the same data source at adjacent time points as the moving distance.
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Non-Patent Citations (1)
| Title |
|---|
| 基于巷道模型的多源异构信息融合可信度评估方法的研究;胡继雄;《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》;第B021-236页 * |
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