JP2016165064A - Identification device, identification method and program for identification device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To easily identify an unknown radio station with high accuracy at high speed.SOLUTION: An identification device includes: a feature parameter extraction unit which extracts feature parameters indicative of the features of a synchronous signal generated from a radio signal and included in a digital signal; a characteristic data generation unit which combines the feature parameters to output as characteristic data; a database unit in which the feature parameters based on the radio signal received from a known radio station are associated with the features of the model and the individual of the known radio station, and registered in advance as reference data; and an identification processing unit which searches the reference data, registered in the database unit, for reference data corresponding to the model of an identification target, on the basis of the characteristic data based on the radio signal received from an identification target radio station, so as to identify the model of the identification target radio station and identify the identification target radio station, on the basis of the reference data on the search result.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an identification device, an identification method, and an identification device program, and more particularly, to an identification device, an identification method, and an identification device program for identifying a radio station used in digital wireless communication.

  There is known a radio station identification device having a function of identifying an unknown radio station by receiving a radio wave transmitted by an unknown radio station and analyzing characteristics of a frequency fluctuation waveform of the received signal. The frequency fluctuation waveform of the radio wave transmitted by the radio station to be identified reflects the radio wave propagation characteristics and the transient response of the radio station components.

  In relation to the present invention, Patent Literature 1 and Patent Literature 2 describe a radio station identification device having a function of extracting a frequency fluctuation waveform of a received signal when the received signal rises or falls. Patent Document 3 describes a radio station identification device that identifies a radio station by using a symbol rate shift of a received signal as a feature.

Japanese Patent No. 4543283 JP 2011-044937 A Japanese Patent No. 4367479

  The radio station identification device described in Patent Documents 1 and 2 is a frequency fluctuation waveform at the time of rising or falling of a received signal, that is, reception at the time of emission or stop of radio waves of a radio station that is a transmission source of the received signal. Extract the frequency fluctuation waveform of the signal. An unknown radio station is identified based on the similarity between the frequency fluctuation waveform of the received signal and the frequency fluctuation waveform (hereinafter referred to as “teacher data”) of the reference known radio station registered in the database. The

  The frequency fluctuation waveform of the received signal is extracted from a state in which the state of the radio wave changes transiently when the radio wave is emitted or stopped. For this reason, unlike the state in which radio waves are stably transmitted, the transient state of radio waves is easily affected by the power state of the wireless station and aging degradation. As a result, the reproducibility of the characteristics of the radio station based on the transient change of radio waves is not high. Furthermore, a digital modulation type radio station (hereinafter referred to as “digital radio station”) is a generally used analog FM (frequency modulation) modulation type radio station (hereinafter referred to as “analog radio station”). Compared to the frequency bandwidth is narrow. For this reason, radio waves emitted from digital radio stations have small frequency fluctuations, and it is difficult to identify radio stations based on reception frequency fluctuations.

  Patent Document 3 describes a radio station identification device that uses a symbol rate shift as a feature. However, in the technique described in Patent Document 3, for example, if the design value of the symbol rate is 1 Mbps (megabits per second), the variation of the symbol rate is generally only about ± 10 bps centering on 1 Mbps. As a result, a receiver with a complex and expensive measurement circuit is required to measure such small symbol rate variations. Further, assuming that the radio stations to be identified are assigned for each 1 bps symbol rate shift, the number of radio stations that can be registered in the database is about 20 at most. Cannot be identified.

  Thus, the radio station identification device described in Patent Document 3 has a problem that it is difficult to identify radio stations with high accuracy. Further, the radio station identification device described in Patent Document 3 is complicated and expensive, and there is a problem that it is difficult to construct a database of a large number of radio stations.

  In addition, if features unique to the individual are used to identify unknown radio stations rather than features specific to the model, the characteristics of the received signal for each model of the radio station are compiled into a database based on those features It is difficult to do. Therefore, it is difficult to specify the manufacturer and model of the radio station from the result of identifying the radio station by the characteristic unique to the individual. As a result, a user of a general radio station identification device must judge the validity of the identification result by comprehensively combining information such as the voice and frequency spectrum of the received signal. However, there is a problem that the burden on the user of the radio station identification device is large.

(Object of invention)
An object of the present invention is to provide a technique for easily identifying a model and an individual of a radio station with high accuracy, high speed, and ease.

  The identification device of the present invention includes a feature parameter extraction unit that extracts a feature parameter indicating a feature of a synchronization signal included in a digital signal generated from a radio signal, and a feature data generation unit that outputs the feature parameter in combination with the feature parameter And a database part pre-registered as reference data, wherein the feature parameter based on the radio signal received from a known radio station is associated with the model of the known radio station and the feature of the individual known radio station, Based on the feature data based on the radio signal received from the radio station to be identified, the reference data corresponding to the model of the radio station to be identified is retrieved from the database unit, and based on the retrieved reference data Identification process for identifying the model of the radio station to be identified and identifying the individual radio station to be identified It comprises a part, a.

  In the identification method of the present invention, a feature parameter indicating a feature of a synchronization signal included in a digital signal generated from a radio signal is extracted, and the feature parameter is combined and output as feature data, which is received from a known radio station. The feature parameter based on the radio signal is registered as reference data in association with the known radio station model and individual feature, and the identification is performed based on the feature data based on the radio signal received from the radio station to be identified. Searching the reference data corresponding to the model of the target radio station, identifying the model of the radio station to be identified based on the searched reference data and identifying the individual of the radio station to be identified It is characterized by.

  The program of the identification device of the present invention is a procedure for extracting feature parameters indicating characteristics of a synchronization signal included in a digital signal generated from a radio signal to a computer included in the identification device, and outputs the feature data by combining the feature parameters. A procedure for registering the feature parameter based on the radio signal received from a known radio station as reference data in association with a model and an individual feature of the known radio station, the procedure received from a radio station to be identified A procedure for searching the reference data corresponding to the model of the identification target radio station from the reference data based on the feature data based on the radio signal, and the identification target radio station based on the searched reference data A procedure for identifying the model and identifying the individual radio station to be identified is executed.

  The identification apparatus, identification method, and identification apparatus program of the present invention have the effect of easily identifying the model and the individual of the radio station with high accuracy, high speed and ease.

It is a block diagram which shows the structure of the radio station identification apparatus 100 of 1st Embodiment. It is a figure for demonstrating the timing which a radio station transmits a synchronizing signal. It is a block diagram which shows an example of a structure of a synchronizing signal. It is a figure which shows the example of the rise time of the unmodulated wave contained in a synchronizing signal, and the example of CW length. It is a figure which shows the example of the length of the preamble contained in a synchronizing signal. It is a figure which shows the example of the waveform pattern of the undefined area | region of a synchronizing signal. It is a flowchart which shows the example of a radio station identification process. It is a flowchart which shows the example of the production | generation process of teacher data. It is a block diagram which shows the structure of the radio station identification apparatus 200 of 2nd Embodiment. It is a flowchart which shows the example of the radio station identification process of the radio station identification apparatus 200 of 2nd Embodiment.

(First embodiment)
The radio station identification device described in the first embodiment can identify radio stations based on highly reproducible features by extracting the features of a source radio station from a synchronization signal having a stable signal waveform. To do. In the digital radio station, in order to realize the synchronization performance according to the specification of the wireless system, the synchronization signal is generated using an electronic circuit or an electronic component designed based on the specification. Therefore, from the parameters extracted from the waveform of the synchronization signal, the characteristics unique to the model of the radio station due to the circuit configuration of the digital radio station and the characteristics specific to the individual radio station due to individual differences in electronic parts Can be extracted.

  Specifically, it analyzes parameters of IQ (in-phase / quadrature-phase) signals and FM (frequency modulation) detection signals generated from the synchronization signals, and parameters such as signal length, rise time, waveform pattern, etc. It is possible to extract features unique to the model and individual of the wireless station.

  A first embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram illustrating a configuration of a radio station identification device 100 according to the first embodiment of this invention. The radio station identification device 100 includes an antenna 2, a reception unit 3, an A / D (analog to digital) conversion unit 4, a parameter extraction unit 5, a feature data generation unit 6, an identification processing unit 7, a database unit 8, and a teacher data generation unit 9 is provided.

  The antenna 2 receives a radio wave transmitted from a radio station to be identified and outputs a received signal. The receiving unit 3 is a frequency conversion circuit that generates an intermediate frequency signal by converting the frequency of the received signal from a radio frequency to an intermediate frequency. The intermediate frequency is a signal having a frequency lower than the radio frequency. The reception unit 3 outputs the intermediate frequency signal to the A / D conversion unit 4.

  The A / D conversion unit 4 is an A / D conversion circuit that converts the intermediate frequency signal input from the reception unit 3 into a digital complex envelope signal that is a digital signal. The A / D converter 4 outputs the digital complex envelope signal to the parameter extractor 5.

  The parameter extraction unit 5 analyzes the digital complex envelope signal input from the A / D conversion unit 4 and extracts characteristic parameters (hereinafter referred to as “feature parameters”) included in the synchronization signal of the digital complex envelope signal. It is an electronic circuit. The parameter extraction unit 5 outputs the feature parameter to the feature data generation unit 6. The feature parameter will be described later.

  FIG. 2 is a diagram for explaining the timing at which the transmitter transmits the synchronization signal. The transmitter is a wireless station that transmits radio waves. The synchronization signal is a signal for the receiver to synchronize with the transmitter. The synchronization signal is emitted prior to data such as voice when the transmitter starts radio wave transmission ("ON" in FIG. 2).

  FIG. 3 is a block diagram illustrating an example of the configuration of the synchronization signal. As shown in FIG. 3, the synchronization signal has a frame structure including a non-modulated wave (CW), a preamble, a synchronization word, a synchronization burst, and the like. The synchronization burst includes information on a control channel (control CH) and a call name. The synchronous burst has an area where information is not defined (undefined area). Since the configuration of the synchronization signal is generally known, detailed description thereof is omitted. Following the synchronization signal, data such as voice is transmitted.

  As the characteristic parameter, a parameter (undefined parameter) that is not defined in the transmission method used in the radio station can be used. The characteristic parameters extracted from the synchronization signal include the length of the unmodulated wave, the rise time of the unmodulated wave, the length of the preamble signal, the number of repetitions of the synchronization burst, and the waveform pattern of the undefined area in the synchronization burst. . The characteristic parameters are classified into characteristic parameters specific to the model of the radio station that is the transmission source of the received signal and characteristic parameters specific to the individual radio station. The feature data generation unit 6 combines the plurality of feature parameters input from the parameter extraction unit 5 to generate feature data used for identifying a radio station.

  The identification processing unit 7 notifies the database unit 8 of the feature parameters specific to the model among the feature data input from the feature data generation unit 6. The database unit 8 searches the database based on the feature parameter included in the feature data input from the identification processing unit 7 and searches for teacher data of a model corresponding to the input feature parameter. The identification processing unit 7 receives the teacher data searched in the database unit 8 as teacher data used for identifying a wireless station.

  The database unit 8 stores the teacher data generated by the teacher data generation unit 9. The teacher data is reference data recorded for each model including a characteristic parameter unique to the model and a characteristic parameter unique to the individual. The characteristic parameters unique to each individual radio station are stored in the database unit 8 as a part (subset) of teacher data unique to each model for each model of the radio station. In other words, the feature parameter unique to the individual is lower-order data of the feature parameter unique to the model.

  The database unit 8 receives feature parameters specific to the model extracted from the signal received from the radio station from the identification processing unit 7, and searches the database unit 8 using this as a key. The database unit 8 searches for teacher data of a model that hits the key from the teacher data stored in the database, and notifies the identification processing unit 7 of it.

  The types of characteristic parameters specific to the model include the length of the unmodulated wave, the length of the preamble signal, and the number of repetitions of the synchronization burst. All of these are numerical values obtained from the synchronization signal, and the database unit 8 uses the feature parameter notified from the identification processing unit 7 as a key, and does not perform complex operations such as correlation processing, and the search function normally provided in the database Can be used to easily extract teacher data from the database. For example, the database unit 8 searches for teacher data of a model whose feature parameter value difference is equal to or less than a predetermined threshold for each type of feature parameter input from the identification processing unit 7. Alternatively, teacher data of a model in which only a difference in values of some characteristic parameters is equal to or less than the threshold value may be searched. When there are a plurality of teacher data searched within the threshold range, the database unit 8 may notify the identification processing unit 7 of the teacher data of all the searched models.

  In general, the threshold value used for comparison between the value of the feature parameter notified from the identification processing unit 7 and the value of the teacher data is different for each type of feature parameter. Further, there may be a plurality of threshold values for each type of feature parameter. For example, teacher data may be searched for which the threshold value is a relatively large value and the difference from the values of all types of feature parameters notified from the identification processing unit 7 is equal to or less than the threshold value. Alternatively, teacher data whose difference from the values of some types of feature parameters among the feature parameters notified from the identification processing unit 7 is equal to or less than the threshold value may be searched with a relatively small threshold value.

  In this way, the identification processing unit 7 resembles the feature parameter associated with the model-specific feature included in the feature data and the feature parameter associated with the model-specific feature included in the teacher data. Search teacher data based on degree. Features unique to the model include, but are not limited to, the manufacturer name and model name of the radio station.

  If there is only one model of the teacher data searched in the database unit 8, the identification processing unit 7 sets the model as a wireless station identification result. When there are two or more types of teacher data notified from the database unit 8, the identification processing unit 7 weights a plurality of feature parameters included in the feature data for each type of feature parameter, and the feature data And the model corresponding to the teacher data closest to may be used as the identification result of the radio station. Alternatively, the identification processing unit 7 may use a plurality of models as wireless station identification results.

  Examples of types of characteristic parameters unique to each individual radio station include the rise time of an unmodulated wave and the waveform pattern of an undefined region of a synchronization burst. The teacher data includes characteristic parameters unique to the individual that are associated with the model of the teacher data. The identification processing unit 7 identifies the model of the radio station based on the search result of the database unit 8, and then identifies the characteristic parameter specific to the individual radio station included in the feature data and the individual radio station included in the teacher data. Are compared with the characteristic parameters of the radio station to identify the individual radio station and output the result.

  The identification of the individual radio station can be performed by a procedure similar to the identification of the model of the radio station in the database unit 8. For example, the identification processing unit 7 is further unique to each individual from the teacher data retrieved by the database unit 8 for each type of characteristic parameter unique to each individual included in the feature data input from the feature data generating unit 6. A feature parameter whose difference from the value of the feature parameter is equal to or less than a predetermined threshold is searched. Alternatively, a feature parameter in which only a difference in characteristic parameter values unique to some individuals is equal to or less than the threshold value may be searched. The individual associated in the teacher data with the retrieved feature parameter is identified as the wireless station that is the transmission source of the received signal.

  As described above, the identification processing unit 7 includes the feature parameter associated with the feature unique to the individual included in the searched teacher data, and the feature parameter associated with the feature unique to the individual included in the feature data. The radio station is identified based on the similarity of. A characteristic unique to an individual is a feature that allows different individuals to be distinguished by a model at the same position. For example, as a characteristic unique to an individual, there is a serial number assigned to each individual.

  When there are a plurality of individuals corresponding to the feature parameter searched within the threshold range, the identification processing unit 7 may use all the searched individuals as the identification result. Generally, the threshold value used for comparison between the value of the feature parameter included in the feature data and the feature parameter of the teacher data is different for each type of feature parameter. Further, there may be a plurality of threshold values for each type of feature parameter. For example, even if a threshold value is set to a relatively large value, an individual feature parameter that is equal to or less than the threshold value of all types of feature parameters unique to the individual included in the feature data is searched from the teacher data. Good. Alternatively, the feature parameter of an individual whose difference from the value of some kind of feature parameter is equal to or less than the threshold value among the feature parameters specific to the individual may be searched with a relatively small threshold value. Further, the identification processing unit 7 weights the degree of coincidence between the value of the characteristic parameter unique to the individual wireless station included in the characteristic data and the value of the characteristic parameter specific to the individual included in the teacher data, An individual wireless station may be identified and the result may be output.

  4 to 6 are diagrams for explaining examples of feature parameters. 4 to 6, as examples of the types of characteristic parameters, the rise time of the unmodulated wave, the length of the unmodulated wave, the length of the preamble included in the synchronization signal, and the waveform pattern of the undefined area of the synchronization signal are shown. It is. 4 to 6, the waveform of the synchronization signal is expressed as an analog signal waveform. 4 to 6, the horizontal axis represents time, and the vertical axis represents amplitude.

  FIG. 4 is a diagram illustrating an example of the rise time of an unmodulated wave included in the synchronization signal and an example of the length (CW length) of the unmodulated wave. In FIG. 4, the unmodulated wave is shown as a waveform of an IQ signal whose phases are different from each other by 90 degrees. The rise time of the unmodulated wave of model A is T1, and the CW length is T3. The rise time of the CW signal of model B is T2, and the CW length is T4. T1 to T4 are obtained from the waveform of the envelope of the unmodulated wave. Typical numerical values are 1 ms (milliseconds) to 50 ms for T1 and T2, and 10 to 100 ms for T3 and T4, but are not limited thereto.

  FIG. 5 is a diagram illustrating an example of the length of the preamble included in the synchronization signal. The preamble length of model A is T5, and the preamble length of model B is T6. Typical values for T5 and T6 are 10 ms to 50 ms, but are not limited thereto.

  FIG. 6 is a diagram illustrating an example of a waveform pattern in an undefined region of the synchronization signal. By expressing the amplitude of the waveform pattern as “0” or “1” at regular time intervals, the temporal amplitude change of the waveform pattern can be expressed by binary data. The conversion from the waveform to the binary data may be performed by comparing the amplitude of the waveform with a threshold value at a predetermined time interval. In FIG. 6, as an example, the waveform of the model A is a data string D1: 001100000010010110101111, and the waveform of the model B is a data string D2: 100000111111011101111011. By binarizing the waveform data, the characteristic parameters of the waveform pattern can be handled as a data string. When comparing the feature parameters of the waveform pattern between the teacher data and the feature data, the inter-code distance of the feature parameter data string can be used as the waveform similarity.

  The time T1 to T6 and the data string D1 to D2 extracted in this way are extracted from the synchronization signal by the parameter extraction unit 5 as characteristic parameters of the unknown radio station. The types of feature parameters are not limited to the above, and other parameters that can be extracted from the synchronization signal may be used as feature parameters.

  When the radio station that is the transmission source of the radio wave received by the radio station identification device 100 is known, the feature data generated by the feature data generation unit 6 is used as the teacher data of the known radio station. Can be saved. Feature data generated by the feature data generation unit 6 is input to the teacher data generation unit 9. The teacher data generation unit 9 may have a function of accumulating input feature data. Upon receiving an instruction to register teacher data from the outside, the teacher data generation unit 9 associates the feature parameters included in the feature data with the features unique to the known wireless station model and the features unique to the individual, and obtains teacher data. Generate. The generated teacher data is stored in the database unit 8.

  A feature unique to a known wireless station model and a feature unique to an individual may be input to the teacher data generation unit 9 by a user of the wireless station identification device 100. A user of the radio station identification apparatus 100 may instruct teacher data generation and registration. In this way, the radio station identification device 100 can register the feature parameter of the radio signal being received in the database unit 8 as teacher data.

  The teacher data generation unit 9 may generate teacher data using a value obtained by averaging a plurality of accumulated feature parameters of the same model or a plurality of feature parameters of the same individual for each feature parameter type. Good. For example, the teacher data generation unit 9 may acquire feature data from the feature data generation unit 6 a plurality of times, obtain an average value for each type of feature parameter included in the feature data, and use it as teacher data. By using the average value as the feature parameter, the teacher data can more appropriately represent known radio stations. The teacher data generation unit 9 may register the median value, maximum value, or minimum value of the accumulated feature parameters in the database unit 8 as teacher data.

(Description of operation of the first embodiment)
Next, the operation of the first exemplary embodiment of the present invention will be described in detail with reference to a flowchart. FIG. 7 is a flowchart illustrating an example of wireless station identification processing of the wireless station identification device 100.

  The receiving unit 3 receives a radio signal from an external radio station and generates an intermediate frequency signal (steps S01 and S02 in FIG. 7). The A / D conversion unit 4 converts the intermediate frequency signal generated by the reception unit 3 into a digital complex envelope signal (S03). The parameter extraction unit 5 analyzes the digital complex envelope signal and extracts feature parameters from the synchronization signal included in the digital complex envelope signal (S04). The feature data generation unit 6 generates feature data by combining the feature parameters specific to the model and the feature parameters specific to the individual (S05). The identification processing unit 7 searches the database unit 8 using the type of feature parameter specific to the model included in the feature data as a key (S06), and acquires teacher data corresponding to the key from the database unit 8 (S07). . The identification processing unit 7 compares the feature parameter included in the feature data with the feature parameter included in the teacher data to identify the model of the wireless station, identifies the individual wireless station, and outputs the result (S08). . In step S08, the identification processing unit 7 first identifies the model of the wireless station based on the teacher data notified from the database unit 8, and based on the characteristic parameters specific to the individual included in the teacher data of the identified model. Identify individual radio stations.

  FIG. 8 is a flowchart illustrating an example of a teacher data generation process. The procedures in steps S11 to S15 in FIG. 8 are the same as those in steps S01 to S05 in FIG. The receiving unit 3 receives a radio signal from an external radio station and generates an intermediate frequency signal (steps S11 and S12 in FIG. 8). The A / D converter 4 converts the intermediate frequency signal generated by the receiver 3 into a digital complex envelope signal (S13). The parameter extraction unit 5 analyzes the digital complex envelope signal and extracts feature parameters from the synchronization signal included in the digital complex envelope signal (S14). The feature data generation unit 6 generates feature data by combining a feature parameter having a property unique to a model and a feature parameter having a property unique to an individual (S15).

  The teacher data generation unit 9 acquires feature data from the feature data generation unit 6 based on instructions for generation and registration of teacher data (S16). The teacher data generation unit 9 may accumulate the acquired feature data. The teacher data generation unit 9 registers the acquired feature data in the database unit 8 in association with the model information or individual information of the wireless station (S17). Information on the model of the wireless station or individual information may be included in the instruction for generating and registering teacher data.

  The above procedure may be executed by a computer (central processing unit, CPU) included in the radio station identification device 100 executing a program. The program may be recorded on a fixed, non-temporary recording medium such as a fixed disk device or a nonvolatile semiconductor memory. The CPU is included in an arbitrary functional block in the radio station identification device 100 such as the parameter extraction unit 5. The program may be recorded not only on the recording medium included in the wireless station identification device 100 but also on a device connected to the wireless station identification device 100 via a network. In this case, the CPU of the radio station identification apparatus 100 downloads the program from the apparatus to the radio station identification apparatus 100 and executes it.

(Explanation of effect)
The first effect of the radio station identification apparatus 100 according to the first embodiment is that the radio station features can be extracted with high reproducibility, and high-precision discrimination is realized even in digital radio stations with small frequency fluctuation waveform features. It can be done. The reason is that the radio station identification apparatus 100 extracts features unique to the model and the individual from the parameter of the synchronization signal that is a stable signal section.

  The second effect is that the characteristics of the wireless station model and the individual can be extracted. This is because characteristic parameters specific to the model of the radio station and characteristic parameters specific to the individual radio station are extracted by analyzing the IQ signal or FM detection signal obtained from the synchronization signal.

  The third effect is that a radio station can be identified and identified at high speed. The reason for this is to first search for teacher data corresponding to the model using the characteristic parameters specific to the model, and then identify the individual based on the characteristic parameters specific to the individual included in the searched teacher data. Because.

  The fourth effect is that the user of the radio station identification device 100 can easily determine whether the identification result is correct. The reason is that the model name of the wireless station can be determined from the teacher data, so that the user can easily know the function and performance of the wireless station to be identified and the usage form of the wireless station. Because. The user purchases one radio station for each model in advance, extracts the characteristic parameters specific to the model, and registers them as teacher data in the database unit 8, so that the manufacturer and model of the radio station are identified from the identification result. You can also know your name.

  As described above, the radio station identification device 100 according to the first embodiment has an effect that the radio station model and the individual can be easily identified with high accuracy, high speed, and high speed.

(Second Embodiment)
FIG. 9 is a block diagram showing a configuration of the radio station identification device 200 according to the second exemplary embodiment of the present invention. The radio station identification apparatus 200 includes a parameter extraction unit 5, a feature data generation unit 6, an identification processing unit 7, and a database unit 8. In the second embodiment, the same components and the same reference numerals are assigned to the same components as those in the first embodiment, and overlapping descriptions are omitted as appropriate.

  The functions of the parameter extraction unit 5, the feature data generation unit 6, the identification processing unit 7, and the database unit 8 included in the wireless station identification device 200 are the same as those of the wireless station identification device 100 of the first embodiment. That is, the parameter extraction unit 5 receives a digital complex envelope signal. The digital complex envelope signal is a signal obtained by converting a received signal received from a wireless station into a digital signal. The parameter extraction unit 5 analyzes the input digital complex envelope signal and extracts feature parameters of the synchronization signal of the digital complex envelope signal. The parameter extraction unit 5 outputs the feature parameter to the feature data generation unit 6.

  The feature data generation unit 6 combines the plurality of feature parameters input from the parameter extraction unit 5 to generate feature data.

  The identification processing unit 7 notifies the database unit 8 of the feature parameters specific to the model among the feature data input from the feature data generation unit 6. The database unit 8 searches the database based on the feature parameter input from the identification processing unit 7 and extracts teacher data of a model corresponding to the input feature parameter. The identification processing unit 7 receives the teacher data searched in the database unit 8.

  That is, the identification processing unit 7 obtains the reference data corresponding to the model of the identification target radio station from the reference data registered in the database unit 8 based on the feature data based on the radio signal received from the identification target radio station. Search for. Then, the identification processing unit 7 identifies the model of the radio station to be identified based on the retrieved reference data and identifies the radio station to be identified.

  The database unit 8 stores teacher data. The teacher data includes, for each model, characteristic parameters specific to the model and characteristic parameters specific to the individual. The characteristic parameters unique to each individual radio station are stored in the database unit 8 as a part of teacher data unique to the model of the radio station.

  The database unit 8 stores teacher data in which characteristic parameters of known radio stations are associated with information on the model and individual of the radio station. The database unit 8 receives feature parameters specific to the model included in the feature data 6 from the identification processing unit 7, and searches teacher data stored in the database unit 8 using this as a key. The database unit 8 extracts the teacher data corresponding to the key from the teacher data as an identification target and outputs it to the identification processing unit 7. In the database unit 8, feature parameters based on radio signals received from known radio stations are associated with known radio station models and individual features and registered in advance as reference data.

  FIG. 10 is a flowchart illustrating an example of the radio station identification process of the radio station identification device 200. Steps S21 to S25 shown in FIG. 10 are the same as steps S04 to S08 in FIG.

  That is, the parameter extraction unit 5 analyzes the input digital complex envelope signal and extracts feature parameters from the synchronization signal included in the digital complex envelope signal (step S21 in FIG. 10). The feature data generation unit 6 generates feature data by combining the feature parameters specific to the model and the feature parameters specific to the individual (S22). The identification processing unit 7 searches the database unit 8 using the type of feature parameter specific to the model included in the feature data as a key (S23), and acquires teacher data corresponding to the key from the database unit 8 (S24). . The identification processing unit 7 compares the feature data with the teacher data to identify the model of the radio station, identifies the individual radio station, and outputs the result (S25). In step S25, the identification processing unit 7 first identifies the model of the wireless station based on the teacher data notified from the database unit 8, and based on the characteristic parameters specific to the individual included in the identified model teacher data. An individual wireless station may be identified.

  The radio station identification device 200 of the second embodiment having such a configuration has the following first to fourth effects for the same reason as the radio station identification device 100 of the first embodiment.

  The first effect is that feature extraction of a radio station with high reproducibility is possible, and high-precision identification can be realized even for a digital radio station having a small frequency fluctuation waveform feature.

  The second effect is that the characteristics of the wireless station model and the individual can be extracted.

  A third effect is that a radio station can be identified at high speed.

  The fourth effect is that the user of the radio station identification device 200 can easily determine whether the identification result is correct.

  That is, the wireless station identification device 200 of the second embodiment can also easily identify the model and the individual of the wireless station with high accuracy, high speed, and high speed.

  Although the present invention has been described with reference to the embodiments, the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

  For example, the database unit 8 may be arranged outside the radio station identification devices 100 and 200. By arranging the database unit 8 outside and connecting the database unit 8 and a plurality of radio station identification devices via a network, the operation of the database can be unified.

  The present invention can be widely applied to fields where a function for identifying a transmitter is required. For example, the present invention can be applied to a radio wave monitoring apparatus for monitoring radio waves emitted from illegal radio stations.

100, 200 Radio station identification device 2 Antenna 3 Receiving unit 4 A / D conversion unit 5 Parameter extraction unit 6 Feature data generation unit 7 Identification processing unit 8 Database unit 9 Teacher data generation unit

Claims (14)

A feature parameter extraction unit that extracts a feature parameter indicating a feature of a synchronization signal included in a digital signal generated from a wireless signal;
A feature data generating unit that outputs the feature data by combining the feature parameters;
The feature parameter based on the radio signal received from a known radio station is associated with a feature unique to the known radio station model and a feature unique to the individual known radio station, and is registered in advance as reference data A database section;
Based on the feature data based on the radio signal received from the radio station to be identified, the reference data corresponding to the model of the radio station to be identified is retrieved from the database unit, and based on the retrieved reference data Identifying the identification target radio station model and identifying the individual identification target radio station,
An identification device comprising:   The characteristic parameter associated with the characteristic unique to the model of the radio station to be identified includes the length of the unmodulated wave included in the synchronization signal, the length of the preamble signal included in the synchronization signal, and the synchronization signal. The identification device according to claim 1, wherein the identification device includes at least one repetition number of synchronization bursts included.   The characteristic parameter associated with the characteristic unique to the individual radio station to be identified includes the rise time of the unmodulated wave included in the synchronization signal and the waveform pattern of the undefined region of the synchronization burst included in the synchronization signal. The identification device according to claim 1, comprising at least one of them.   The identification device according to claim 3, wherein the characteristic parameter corresponding to the waveform pattern of the undefined region of the synchronization burst is a data string obtained by binarizing the waveform pattern. The identification processing unit
The reference registered in the database unit by using, as an index, the feature parameter associated with the feature unique to the model included in the feature data based on the radio signal received from the radio station to be identified. Search the reference data corresponding to a specific model from the data,
Identifying the model of the radio station to be identified based on the retrieved reference data;
In the comparison result between the feature parameter associated with the feature of the individual included in the retrieved reference data and the feature parameter associated with the feature unique to the individual of the identification target wireless station included in the feature data Identifying the radio station to be identified based on
The identification device according to claim 1.   The identification processing unit is associated with a feature parameter associated with a feature unique to the model included in the feature data, and a feature unique to the model included in the reference data registered in the database unit. The identification apparatus according to claim 1, wherein the reference data is searched based on a first similarity that is a similarity with a feature parameter.   The identification device according to claim 6, wherein the first similarity is obtained by weighting each of the feature parameters associated with the features unique to the plurality of models.   The identification processing unit is associated with a feature parameter associated with a feature unique to the individual included in the retrieved reference data, and a feature unique to the individual of the identification target wireless station included in the feature data. The identification device according to claim 1, wherein the identification target radio station is identified based on a second similarity that is a similarity with a feature parameter.   The identification device according to claim 8, wherein the second similarity is obtained by weighting each of the feature parameters associated with the features of the plurality of individuals.   The feature parameter included in the reference data registered in the database unit is a feature parameter generated by averaging a plurality of the feature parameters obtained from a radio signal received from the known radio station. The identification device according to any one of 1 to 9.   The identification device according to claim 1, further comprising a reference data generation unit that generates the reference data based on a plurality of the characteristic parameters obtained from a radio signal received from the known radio station. A receiver that receives a radio signal from a radio station, converts the radio signal into an intermediate frequency signal, and outputs the intermediate signal;
An analog-digital converter for converting the intermediate frequency signal into a digital signal;
The identification device according to any one of claims 1 to 11, connected so that the digital signal is input to a feature parameter extraction unit;
A radio station identification device comprising: Extracting feature parameters indicating the characteristics of the synchronization signal contained in the digital signal generated from the radio signal;
Combining the feature parameters and outputting as feature data,
The feature parameter based on the radio signal received from a known radio station is registered as reference data in association with a feature of the known radio station model and an individual feature of the known radio station,
Based on the feature data based on the radio signal received from the radio station to be identified, the reference data corresponding to the model of the radio station to be identified is searched from the reference data,
Identifying the identification target radio station based on the retrieved reference data and identifying the identification target radio station;
An identification method characterized by the above. In the computer with the identification device,
A procedure for extracting a feature parameter indicating a feature of a synchronization signal included in a digital signal generated from a wireless signal;
A procedure for outputting the feature parameters by combining the feature parameters,
A procedure for registering the feature parameter based on the radio signal received from a known radio station as reference data in association with a feature of the model of the known radio station and an individual feature of the known radio station;
A procedure for searching the reference data corresponding to the model to be identified from the reference data based on the feature data based on the wireless signal received from the wireless station to be identified;
A procedure for identifying a model of the radio station to be identified based on the retrieved reference data and identifying the radio station to be identified;
The program of the identification device for causing

Images