Signal direction finding method based on frequency domain, program product and equipment

Abstract

The invention discloses a signal direction finding method based on a frequency domain, a program product and equipment, and the method comprises the steps: receiving a to-be-detected time domain signal, and obtaining the signal parameter data of the to-be-detected time domain signal; obtaining an intercepted signal based on the signal parameter data and the to-be-measured time domain signal; determining noise vector data based on the intercepted signal; and based on the noise vector data, determining an orientation parameter value corresponding to the to-be-measured time domain signal.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a signal direction finding method based on frequency domain, a computer program product and a signal direction finding device based on frequency domain. Background Art

[0002] In recent years, the technology of signal source equipment (such as drones) has developed rapidly, and the threats it brings have also penetrated into various fields: aviation safety threats, public safety threats, infrastructure safety threats, network security threats, etc. Therefore, it is urgent to respond to the challenges brought by signal source equipment. In addition to accurately discovering and identifying signal source equipment in complex electromagnetic environments, it is also necessary to measure the angle of the signal source equipment, including pitch and azimuth.

[0003] There are various direction finding algorithms, such as amplitude ratio method, interferometer method, correlation interferometer method, array digital beam forming (DBF), fast Fourier transform (FFT) beam forming, multiple signal classification (MUSIC), etc. Among them, the MUSIC algorithm has super-resolution characteristics and breaks through the Rayleigh limit of conventional array direction finding, but its disadvantages are also obvious. It has high computational complexity and is significantly affected by the signal-to-noise ratio. When the signal-to-noise ratio is low, the accuracy of the source number estimation is low, which is easy to cause estimation errors. Summary of the invention

[0004] In order to solve the existing technical problems, the present invention provides a signal direction finding method based on frequency domain, a computer program product and a signal direction finding device based on frequency domain, which can accurately estimate the azimuth parameters of a signal source.

[0005] In a first aspect, a frequency domain-based signal direction finding method is provided, comprising: receiving a time domain signal to be measured, and obtaining signal parameter data of the time domain signal to be measured; obtaining an intercepted signal based on the signal parameter data and the time domain signal to be measured; determining noise vector data based on the intercepted signal; and determining an azimuth parameter value corresponding to the time domain signal to be measured based on the noise vector data.

[0006] In a second aspect, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the signal direction finding method based on the frequency domain as described in any one of the first aspects of the present application is implemented.

[0007] In a third aspect, a frequency domain-based signal direction finding device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes a frequency domain-based signal direction finding method as described in any one of the first aspects of the present application.

[0008] The present application receives a time domain signal to be measured and obtains signal parameter data of the time domain signal to be measured, obtains an intercepted signal based on the signal parameter data and the time domain signal to be measured, and obtains the intercepted signal from a frequency domain signal corresponding to the time domain signal to be measured based on the signal parameter data. This can effectively reduce the noise signal, enhance the characteristics of the useful signal part, and improve the distinction between the signal characteristics and the noise characteristics. Based on the intercepted signal, the noise vector data is determined, and based on the noise vector data, the azimuth parameter value corresponding to the time domain signal to be measured is determined. Since the intercepted signal contains more characteristics of the useful signal and the signal-to-noise ratio is improved, the azimuth parameter value determined based on the intercepted signal is more accurate, thereby improving the overall estimation capability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A diagram of an application environment of a frequency-domain-based signal direction finding method in an embodiment;

[0010] Figure 2 is a flow chart of a signal direction finding method based on frequency domain in one embodiment;

[0011] Figure 3 This is a schematic diagram of bandwidth and center frequency range in one embodiment;

[0012] Figure 4 A schematic diagram of a process of obtaining an intercepted signal based on signal parameter data and a time domain signal to be measured in an embodiment;

[0013] Figure 5 A schematic diagram showing a comparison between a frequency domain signal after down-conversion and a captured signal in one embodiment;

[0014] Figure 6 A schematic diagram of a process for determining noise vector data based on an intercepted signal in an embodiment;

[0015] Figure 7 An example diagram of a comparison between a frequency domain signal obtained by directly converting a time domain signal into a frequency domain and a characteristic value obtained by intercepting a signal in one embodiment;

[0016] Figure 8 A schematic diagram of a search based on a spectrum peak search function in one embodiment;

[0017] Fig. 9 is a schematic diagram of a signal direction finding device based on frequency domain in one embodiment;

[0018] Fig.10 Schematic diagram of a frequency domain-based signal direction finding device in an embodiment. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the technical field of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0021] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it should be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0022] See also Figure 1 , is an application environment diagram of a frequency-domain-based signal direction finding method in one embodiment. The application environment diagram includes a frequency-domain-based signal direction finding device 10 and a signal source device 20, and the signal source device 20 can transmit a communication signal, for example, the signal source device 20 is a drone device. The frequency-domain-based signal direction finding device 10 is used to receive the signal transmitted by the signal source device 20 when working, and process the received signal to be tested, sense the azimuth parameter value of the signal to be tested, so as to determine the azimuth of the signal source device 20, wherein the azimuth parameter value includes but is not limited to the elevation angle and azimuth angle of the incident time-domain signal to be tested.

[0023] In recent years, drone technology has developed rapidly, and the threats it brings have also penetrated into various fields: aviation safety threats, public safety threats, infrastructure security threats, network security threats, etc. Therefore, it is urgent to deal with the challenges brought by drones. In addition to accurately discovering and identifying drones in complex electromagnetic environments, it is also necessary to measure the angle of drones, including pitch and azimuth. The purpose of direction finding is to obtain angle information to provide input for subsequent interference strikes, and on the other hand, it can also assist in estimating the number of drone targets.

[0024] There are many different direction-finding algorithms, such as amplitude ratio method, interferometer method, correlation interferometer method, array DBF, FFT beamforming, MUSI C (Multiple Signal Classification) method, etc. Among them, the MUSI C algorithm has super-resolution characteristics, breaking through the Rayleigh limit of conventional array direction-finding, but its shortcomings are also obvious, with high computational complexity and significant influence by the signal-to-noise ratio. When the signal-to-noise ratio is low, the accuracy of the source number estimation is low, which is prone to estimation errors. For applications related to MUSI C direction-finding, Renzixing Network Technology Co., Ltd. has proposed a patent for "LTE uplink signal DOA estimation method and system based on MUSI C algorithm". The patent proposes to use uplink prior known information to extract part of the signal for MUSI C direction-finding, but it does not solve the problems of low signal-to-noise ratio and large amount of data calculation.

[0025] See also Figure 2 , is a flow chart of a frequency domain-based signal direction finding method provided by an embodiment of the present application. A frequency domain-based signal direction finding method is applied to a frequency domain-based signal direction finding device, and the frequency domain-based signal direction finding method includes the following steps:

[0026] S11. Receive a time domain signal to be measured, and obtain signal parameter data of the time domain signal to be measured.

[0027] In this embodiment, the signal to be measured is a signal received by a frequency domain-based signal direction finding device 10. For example, the signal source device 20 is a drone. When the drone flies into the search range of a frequency domain-based signal direction finding device 10, the signal of the drone can be received by a frequency domain-based signal direction finding device 10.

[0028] In this embodiment, the time domain signal may be preprocessed, and the signal parameter data may be obtained based on the preprocessed signal, wherein the preprocessing includes but is not limited to: extracting useful signals, noise smoothing, and the like.

[0029] S12. Obtain an intercepted signal based on the signal parameter data and the time domain signal to be measured.

[0030] In this embodiment, the intercepted signal indicates the frequency domain signal data after filtering part of the noise. The intercepted signal is a partial signal intercepted from a section of the frequency domain signal corresponding to the time domain signal to be measured. By obtaining the intercepted signal from a section of the frequency domain signal corresponding to the time domain signal to be measured based on the signal parameter data, the noise signal can be effectively reduced, the characteristics of the useful signal part can be enhanced, and the distinction between the signal characteristics and the noise characteristics can be improved. Moreover, compared with the time domain signal to be measured, there are fewer data points in the intercepted signal, which can reduce the subsequent calculation amount.

[0031] S13. Determine noise vector data based on the intercepted signal.

[0032] In this embodiment, although the intercepted signal has filtered out some noise relative to the time domain signal to be measured, there is still some noise signal in the intercepted signal, and the noise feature vector needs to be further extracted so that the azimuth parameter value of the signal source device 20 can be estimated more accurately later. The noise vector data indicates the feature data of the noise part.

[0033] S14. Determine the azimuth parameter value corresponding to the time domain signal to be measured based on the noise vector data.

[0034] In this embodiment, the noise feature vector may be one or more, a noise subspace is formed based on the noise vector data, a spectrum peak search function is constructed based on the noise subspace, and the azimuth parameter corresponding to the time domain signal to be measured is searched based on the spectrum peak search function. The azimuth parameter value includes but is not limited to the elevation angle and azimuth angle of incidence of the time domain signal to be measured.

[0035] In the above embodiment, a time domain signal to be measured is received, and signal parameter data of the time domain signal to be measured is obtained. An intercepted signal is obtained based on the signal parameter data and the time domain signal to be measured. By obtaining the intercepted signal from a frequency domain signal corresponding to the time domain signal to be measured based on the signal parameter data, the noise signal can be effectively reduced, the characteristics of the useful signal part can be enhanced, and the distinction between the signal characteristics and the noise characteristics can be improved. Then, based on the intercepted signal, the noise vector data is determined. Based on the noise vector data, the azimuth parameter value corresponding to the time domain signal to be measured is determined. Since the intercepted signal contains more characteristics of the useful signal, the signal-to-noise ratio is improved. Therefore, the azimuth parameter value determined based on the intercepted signal is more accurate, thereby improving the overall estimation capability of the system.

[0036] In some embodiments, the signal parameter data includes at least one of the following: signal carrier frequency, center frequency, and bandwidth.

[0037] The signal carrier frequency is also called carrier frequency, which refers to the basic frequency used to transmit information in a communication system. In an optional implementation, the signal carrier frequency can be obtained based on the time domain signal to be measured using the centroid method. In the spectrum, the signal power is higher than the noise power. The centroid of the signal spectrum can be counted, and the position of the carrier frequency in the signal spectrum can be determined according to the position of the centroid of the signal spectrum, thereby obtaining the signal carrier frequency. In other embodiments, the signal carrier frequency can also be obtained using an iterative method.

[0038] The center frequency refers to the center frequency of the signal or spectrum, that is, the frequency point where the signal energy is most concentrated. In many cases, it is the main frequency component of the signal, or the location of the peak of the signal spectrum. The bandwidth refers to the frequency range occupied by the signal, that is, the difference between the lowest frequency and the highest frequency of the signal. Bandwidth is an indicator to measure the width of the signal spectrum, which determines the frequency band resources required for signal transmission. In an optional embodiment, the time domain signal to be measured can be transformed into the frequency domain to obtain the frequency domain signal to be measured, and the frequency domain signal to be measured is subjected to noise smoothing processing to obtain a processed spectrum signal, and an amplitude-frequency diagram is obtained based on the processed spectrum signal, and the bandwidth and center frequency are obtained based on the amplitude-frequency diagram.

[0039] The signal parameter data represents the characteristic parameters of the signal part. Based on these parameters, the time domain signal to be measured is subsequently processed to obtain the useful signal part, thereby improving the distinction between signal characteristics and noise characteristics. Figure 3 As shown, Figure 3 Schematic diagram of bandwidth and center frequency range in one embodiment. The bandwidth is approximately between f1 and f2, and the center frequency is approximately near (f1+f2) / 2. Therefore, these signal parameter data can represent the characteristic parameters of the useful signal part.

[0040] In the above embodiment, the signal parameter data at least includes the signal carrier frequency, the center frequency, and the bandwidth. By using the signal parameter data, an intercepted signal is obtained from a frequency domain signal corresponding to the time domain signal to be measured, which can effectively reduce the noise signal, enhance the characteristics of the useful signal part, and improve the distinction between the signal characteristics and the noise characteristics.

[0041] In some embodiments, Figure 4 As shown, Figure 4 1 is a flow chart of obtaining an intercepted signal based on signal parameter data and a time domain signal to be measured in an embodiment, wherein S12 specifically includes:

[0042] S121. Perform a down-conversion operation on the time domain signal to be measured based on the signal carrier frequency to obtain a baseband signal.

[0043] In this embodiment, the baseband signal refers to a signal whose spectrum center is located in the low frequency band. The baseband signal usually refers to the original signal before modulation or the signal after demodulation in the communication system, and its frequency range is suitable for direct transmission in the channel without further frequency conversion. By down-converting the time domain signal to be measured to the baseband signal through the down-conversion operation, the spectrum resources can be used more efficiently. In the baseband, the signal can be filtered and processed to reduce the bandwidth requirement. Baseband signals are usually more easily protected and less susceptible to high-frequency noise and interference because these noise and interference are often concentrated in the higher frequency range.

[0044] In this embodiment, the time domain signal to be measured x(n) is first subjected to a down-conversion operation to obtain a baseband signal Where fs is the sampling rate, f0 is the signal carrier frequency, and n is the index of discrete time, which is an integer used to represent different time points corresponding to the signal.

[0045] S122. Perform a transformation operation on the baseband signal to obtain a frequency domain signal.

[0046] In this embodiment, the transform operation is a Fourier transform operation, and the Fourier transform operation is performed on the baseband signal to transform the baseband signal into the frequency domain to obtain a frequency domain signal. y(n) is transformed into the frequency domain z(n) = FFT[y(n)].

[0047] S123. Obtain an intercepted signal from the frequency domain signal based on the center frequency point and the bandwidth.

[0048] In this embodiment, the center frequency refers to the center frequency of a signal or spectrum, that is, the frequency point where the signal energy is most concentrated. In many cases, it is the main frequency component of the signal, or the location of the peak of the signal spectrum. The bandwidth refers to the frequency range occupied by the signal, so the center frequency and bandwidth can indicate the characteristics of the signal, and a useful signal can be obtained based on the center frequency and bandwidth.

[0049] Optionally, the intercepting the intercepted signal from the frequency domain signal based on the center frequency and the bandwidth includes:

[0050] The difference between the center frequency point and half of the bandwidth is used as the first frequency point, and the sum of the center frequency point and half of the bandwidth is used as the second frequency point;

[0051] Finding a first signal point corresponding to the first frequency point and a second signal point corresponding to the second frequency point in the frequency domain signal;

[0052] A portion of the signal between the first signal point and the second signal point is used as the intercepted signal.

[0053] In this embodiment, according to the input center frequency f mid and bandwidth B, and obtain the corresponding intercept signal Where f represents the frequency. Figure 5 As shown, Figure 5 It is a schematic diagram comparing the baseband signal and the intercepted signal in an embodiment. Compared with the frequency domain signal after down-conversion, the noise part is eliminated, while the useful signal is retained, the data points are also reduced, and the signal-to-noise ratio of the intercepted signal is significantly improved.

[0054] In the above embodiment, based on the signal carrier frequency, a down-conversion operation is performed on the time domain signal to be measured to obtain a baseband signal, and a transformation operation is performed on the baseband signal to obtain a frequency domain signal. Based on the center frequency point and bandwidth, an intercepted signal is obtained from the down-converted frequency domain signal corresponding to the time domain signal to be measured, which can effectively reduce the noise signal, enhance the characteristics of the useful signal part, and improve the distinction between the signal characteristics and the noise characteristics.

[0055] In some embodiments, Figure 6 As shown, Figure 6 This is a flow chart of determining noise vector data based on an intercepted signal in an embodiment, where S13 further includes:

[0056] S131. Determine a covariance matrix based on the intercepted signal.

[0057] In this embodiment, the covariance matrix is ​​obtained based on the intercepted signal and the conjugate of the intercepted signal. s =s(n)·s H (n) / N, the superscript H indicates the conjugate transpose, s H (n) represents the conjugate transpose of the intercepted signal s(n). N represents the number of points of s(n). Since the covariance matrix is ​​obtained based on the intercepted signal, compared with the covariance matrix estimated by directly converting the time domain signal to be measured, since the intercepted signal is intercepted from the frequency domain signal after down-conversion corresponding to the time domain signal to be measured, the number of sampling points of the intercepted signal is obviously less than the frequency domain signal obtained by directly converting the time domain signal to be measured. Therefore, the covariance matrix obtained based on the intercepted signal has a smaller amount of calculation.

[0058] S132. Perform an eigenvalue decomposition operation based on the covariance matrix to obtain multiple eigenvalues.

[0059] In this embodiment, the covariance matrix R s Perform eigenvalue decomposition, [V,D] = eig(R s ), where V represents the eigenvector matrix, D represents the eigenvalue matrix, and R s Can be decomposed into Among them U s represents the signal feature vector, U n represents the noise feature vector, Λ s represents the signal characteristic value, σ 2 I represents the noise characteristic value; σ 2 represents the noise frequency value, I is the unit matrix, A is the decomposition matrix, A H is the conjugate transpose of the decomposition matrix, P is a diagonal matrix, and the superscript H represents the conjugate transpose.

[0060] like Figure 7 As shown, Figure 7 FIG. 1 is a comparison example of a frequency domain signal obtained by directly converting a time domain signal to a frequency domain and an eigenvalue obtained by intercepting a signal in an embodiment. In the prior art, a frequency domain signal is obtained by directly converting a time domain signal to a frequency domain, that is, a frequency domain signal is obtained by directly performing a Fourier transform operation on the time domain signal without performing a down-conversion operation or an interception operation. The eigenvalues ​​obtained by decomposing the covariance matrix estimated based on the frequency domain signal are shown in FIG. Figure 7 From the eigenvalues ​​before truncation shown above, it can be seen that the differences between the eigenvalues ​​before truncation are not obvious, indicating that the distinction between noise and useful signals is not high. Figure 7 From the truncation eigenvalues ​​shown below, it can be seen that eigenvalues ​​less than 3 are noise values, and eigenvalues ​​greater than 100 are signal eigenvalues. The signal eigenvalues ​​in the eigenvalues ​​after truncation are significantly larger than those in the eigenvalues ​​before truncation, and the distinction between noise and signal is more obvious, so that more accurate orientation parameter values ​​can be extracted later based on the intercepted signals.

[0061] S133. Determine a dividing point among multiple eigenvalues.

[0062] Optionally, the determining of a demarcation point among the plurality of characteristic values ​​comprises:

[0063] Sorting the plurality of eigenvalues, calculating adjacent differences between two adjacent eigenvalues, and obtaining a plurality of adjacent differences;

[0064] Determine, from the plurality of adjacent differences, a target adjacent difference whose adjacent difference is greater than a preset difference threshold;

[0065] At least one of the two eigenvalues ​​used to calculate the target adjacent difference is used as the demarcation point.

[0066] S134. Determine the signal characteristic value and the noise characteristic value according to the demarcation point, and determine the signal vector data according to the signal characteristic value and determine the noise vector data according to the noise characteristic value.

[0067] Optionally, determining the signal characteristic value and the noise characteristic value according to the demarcation point includes:

[0068] The smaller eigenvalue of the two eigenvalues ​​used to calculate the target adjacent difference is used as the first target eigenvalue and the larger eigenvalue of the two eigenvalues ​​used to calculate the target adjacent difference is used as the second target eigenvalue;

[0069] An eigenvalue that is less than or equal to the first target eigenvalue among the plurality of eigenvalues ​​is used as a noise eigenvalue, and an eigenvalue that is greater than or equal to the second target eigenvalue among the plurality of eigenvalues ​​is used as a signal eigenvalue.

[0070] In this embodiment, we find Theoretically, when the signal-to-noise ratio is reasonable, Λ s +σ 2 I>>σ 2 I; then perform noise subspace extraction, that is, filter the noise feature vector, sort the feature matrix D, that is, D = sort (D), the signal feature vector D signal =D(K:End), noise feature vector U n =V(1:K-1); where K represents the serial number corresponding to the second target eigenvalue.

[0071] For example Figure 7 The truncated eigenvalue shown below, if the preset difference threshold is 40, and the adjacent difference between the eigenvalue of 2.2 and the eigenvalue of 110.2 is greater than 40, then the eigenvalue of 2.2 and / or the eigenvalue of 110.2 is the dividing point. Then the eigenvalue of 2.2 is the first target eigenvalue, and the eigenvalue of 110.2 is the second target eigenvalue. Among the values ​​on the diagonal of the eigenvalue matrix, those less than or equal to 2.2 are noise eigenvalues, and those greater than or equal to 110.2 are signal eigenvalues. The eigenvector corresponding to the noise eigenvalue less than or equal to 2.2 is the noise vector data, and the eigenvector corresponding to the signal eigenvalue greater than or equal to 110.2 is the signal vector data. Therefore, the MUSIC algorithm in the embodiment of the present application, relative to the MUSIC algorithm in the prior art, can improve the signal-to-noise ratio, improve the accuracy of the estimation of the number of signal sources, and thereby improve the overall estimation capability of the system.

[0072] In the above embodiment, the covariance matrix is ​​determined based on the intercepted signal, which reduces the computational complexity of the covariance matrix estimation and improves the signal-to-noise ratio. Based on the covariance matrix, an eigenvalue decomposition operation is performed to obtain multiple eigenvalues; a dividing point among the multiple eigenvalues ​​is determined. Since the signal-to-noise ratio of the intercepted signal is higher, the degree of distinction between the signal eigenvalue and the noise eigenvalue in the obtained multiple eigenvalues ​​is higher. Then, based on the intercepted signal, noise vector data is determined. Based on the noise vector data, the azimuth parameter value corresponding to the time domain signal to be measured is determined. Since the intercepted signal contains more features of the useful signal, the signal-to-noise ratio is improved. Therefore, the azimuth parameter value determined based on the intercepted signal is more accurate, thereby improving the overall estimation capability of the system.

[0073] In some embodiments, determining the azimuth parameter value corresponding to the time domain signal to be measured based on the noise vector data includes:

[0074] Acquiring array structure data for detecting signals;

[0075] Based on the array structure data and the noise vector data, constructing a spectrum peak search function with a direction parameter as a variable;

[0076] Based on the spectrum peak search function, an azimuth parameter value corresponding to the time domain signal to be measured is determined.

[0077] In this embodiment, the spectrum peak search function generally refers to a function used to identify peaks in the signal spectrum in signal processing. These peaks represent the main frequency components in the signal. The array structure data is used to indicate the geometric layout of each sensor (array element) in the frequency domain-based signal direction finding device 10 array. In array signal processing, common array structures include uniform linear array (ULA), uniform circular array (URA), etc. Different array structures will affect the form of the steering vector. For example, in a uniform linear array, the steering vector usually has a Vandermonde structure, while in arrays of other geometric shapes, the structure of the steering vector will be different. The steering vector is a very important concept in array signal processing, which describes the spatial phase difference when the signal arrives at each sensor in the array. Specifically, the steering vector is a column vector that contains the phase information when the signal arrives at each sensor in the array from a specific direction (determined by angles θ and φ). The steering vector is related to the carrier frequency of the signal, the spacing between array elements, the speed of signal propagation, the number of sensors in the array, and the azimuth parameters, wherein the azimuth parameters are the angles at which the time domain signal to be measured arrives at the array, such as the elevation angle and the azimuth angle.

[0078] The noise subspace U is formed based on the noise vector data n Then, construct the peak search function Where A is the steering vector, which is related to the array structure, and θ is the elevation angle. is the azimuth.

[0079] Optionally, determining the azimuth parameter value corresponding to the time domain signal to be measured based on the spectrum peak search function includes:

[0080] The direction parameters are traversed to determine the maximum value of the spectrum peak search function and obtain the target azimuth parameter value corresponding to the maximum value of the spectrum peak search function, and the target azimuth parameter value is used as the azimuth parameter value corresponding to the time domain signal to be measured.

[0081] In this embodiment, after constructing the spectrum peak search function, θ and Then iterate through θ and Until the iteration termination condition is met, the maximum value of the spectrum peak search function can be found, where the iteration termination condition includes but is not limited to the number of iterations, etc. Figure 8 As shown, Figure 8 is a schematic diagram of a search based on a spectrum peak search function in one embodiment, Figure 8There are three sharp peaks in (d), which are the maximum values ​​of the spectrum peak search function. For each maximum value of the spectrum peak search function, there is a corresponding set of azimuth parameter values. The target angle θ0 and When , the spectrum peak search function P will have a sharp peak, such as Figure 8 In (d), three targets are set during simulation.

[0082] Optionally, the method further includes:

[0083] The number of maximum values ​​of the spectrum peak search function is calculated, and the number of maximum values ​​of the spectrum peak search function is used as the number of signal source devices.

[0084] As shown in (d) in the figure, three sharp peaks appear, indicating that three signal source devices, such as three drones, are detected. A peak corresponds to a set of azimuth parameter values, and a signal source device corresponds to a set of azimuth parameter values.

[0085] In the above embodiment, an intercepted signal is obtained from the down-converted frequency domain signal corresponding to the time domain signal to be measured, which can effectively reduce the noise signal, enhance the characteristics of the useful signal part, and improve the distinction between the signal characteristics and the noise characteristics. Then, based on the intercepted signal, the noise vector data is determined. Based on the noise vector data and the array structure data, a spectrum peak search function with the direction parameter as a variable is constructed. The peak value is searched based on the spectrum peak search function to determine the azimuth parameter value corresponding to the time domain signal to be measured. The azimuth parameter value determined based on the intercepted signal is more accurate, which improves the accuracy of the estimation of the number of signal sources, thereby improving the overall estimation capability of the system. Moreover, the method of searching the peak value through the spectrum peak search function can also estimate the number of signal source devices and provide more data.

[0086] In some embodiments, the method further comprises:

[0087] According to the azimuth parameter value corresponding to each signal source device, the position of each signal source device is displayed in different display modes on the user interface.

[0088] In this embodiment, on the user interface, the position of each signal source device can be displayed in different display modes with the frequency domain-based signal direction finding device 10 as the reference position point, for example, the position coordinates of each signal source can be displayed in different color coordinates, so that the user can directly observe the position of the signal source device 20.

[0089] In the above embodiment, the position of each signal source device can be displayed through the interface, so that the user can observe the position of each signal source device more intuitively.

[0090] On the other hand, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the frequency domain-based signal direction finding method described in any embodiment of the present application.

[0091] Among them, in the computer program product, an optional implementation form of the program module architecture of the computer program that implements each step of the signal direction finding method based on the frequency domain may be a signal direction finding device based on the frequency domain.

[0092] See also Fig. 9 An embodiment of the present application provides a frequency domain-based signal direction finding device, including: an acquisition module 90, used to receive a time domain signal to be measured and obtain signal parameter data of the time domain signal to be measured; an interception module 91, used to obtain an intercepted signal based on the signal parameter data and the time domain signal to be measured; a calculation module 92, used to determine noise vector data based on the intercepted signal; the calculation module 92 is also used to determine the azimuth parameter value corresponding to the time domain signal to be measured based on the noise vector data.

[0093] Optionally, the signal parameter data includes at least one of the following: signal carrier frequency, center frequency, and bandwidth.

[0094] Optionally, the interception module 91 is further used for:

[0095] Based on the signal carrier frequency, performing a down-conversion operation on the time domain signal to be measured to obtain a baseband signal;

[0096] Performing a transformation operation on the baseband signal to obtain a frequency domain signal;

[0097] The intercepted signal is obtained by intercepting the frequency domain signal based on the central frequency point and the bandwidth.

[0098] Optionally, the interception module 91 is further used for:

[0099] The difference between the center frequency point and half of the bandwidth is used as the first frequency point, and the sum of the center frequency point and half of the bandwidth is used as the second frequency point;

[0100] Finding a first signal point corresponding to the first frequency point and a second signal point corresponding to the second frequency point in the frequency domain signal;

[0101] A portion of the signal between the first signal point and the second signal point is used as the intercepted signal.

[0102] Optionally, the calculation module 92 is further used for:

[0103] determining a covariance matrix based on the intercepted signal;

[0104] Based on the covariance matrix, perform an eigenvalue decomposition operation to obtain a plurality of eigenvalues;

[0105] determining a cutoff point among a plurality of said characteristic values;

[0106] According to the demarcation point, a signal characteristic value and a noise characteristic value are determined, and signal vector data is determined according to the signal characteristic value and noise vector data is determined according to the noise characteristic value.

[0107] Optionally, the calculation module 92 is further used for:

[0108] Sorting the plurality of eigenvalues, calculating adjacent differences between two adjacent eigenvalues, and obtaining a plurality of adjacent differences;

[0109] Determine, from the plurality of adjacent differences, a target adjacent difference whose adjacent difference is greater than a preset difference threshold;

[0110] At least one of the two eigenvalues ​​used to calculate the target adjacent difference is used as the demarcation point.

[0111] Optionally, the calculation module 92 is further used for:

[0112] The smaller eigenvalue of the two eigenvalues ​​used to calculate the target adjacent difference is used as the first target eigenvalue and the larger eigenvalue of the two eigenvalues ​​used to calculate the target adjacent difference is used as the second target eigenvalue;

[0113] An eigenvalue that is less than or equal to the first target eigenvalue among the plurality of eigenvalues ​​is used as a noise eigenvalue, and an eigenvalue that is greater than or equal to the second target eigenvalue among the plurality of eigenvalues ​​is used as a signal eigenvalue.

[0114] Optionally, the calculation module 92 is further used for:

[0115] Acquiring array structure data for detecting signals;

[0116] Based on the array structure data and the noise vector data, constructing a spectrum peak search function with a direction parameter as a variable;

[0117] Based on the spectrum peak search function, an azimuth parameter value corresponding to the time domain signal to be measured is determined.

[0118] Optionally, the calculation module 92 is further used for:

[0119] The direction parameters are traversed to determine the maximum value of the spectrum peak search function and obtain the target azimuth parameter value corresponding to the maximum value of the spectrum peak search function, and the target azimuth parameter value is used as the azimuth parameter value corresponding to the time domain signal to be measured.

[0120] Optionally, the calculation module 92 is further used for:

[0121] The number of maximum values ​​of the spectrum peak search function is calculated, and the number of maximum values ​​of the spectrum peak search function is used as the number of signal source devices.

[0122] See also Fig.10 On the other hand, an embodiment of the present application further provides a frequency domain-based signal direction finding device 10, including a processor 13 and a memory 14, wherein the memory 14 stores a computer program. When the computer program is executed by the processor, the processor 13 executes the steps of a frequency domain-based signal direction finding method provided in any of the above embodiments of the present application.

[0123] The processor 13 is a control center, which uses various interfaces and lines to connect the various parts of the entire frequency-domain-based signal direction-finding device, and executes various functions and processes data of the frequency-domain-based signal direction-finding device by running or executing software programs and / or modules stored in the memory 14, and calling data stored in the memory 14. Optionally, the processor 13 may include one or more processing cores; preferably, the processor 13 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user pages, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 13.

[0124] The memory 14 can be used to store software programs and modules. The processor 13 executes various functional applications and data processing by running the software programs and modules stored in the memory 14. The memory 14 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of a signal direction finding device based on the frequency domain, etc. In addition, the memory 14 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 14 can also include a memory processor to provide the processor 13 with access to the memory 14.

[0125] On the other hand, an embodiment of the present application further provides a storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a frequency domain-based signal direction finding method provided in any of the above embodiments of the present application.

[0126] In another aspect of an embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, a frequency domain-based signal direction finding method as described in any embodiment of the present application is implemented.

[0127] Those skilled in the art can understand that all or part of the processes in the methods provided in the above embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A signal direction finding method based on frequency domain, characterized in that: include: Receiving a time domain signal to be measured, and acquiring signal parameter data of the time domain signal to be measured; Based on the signal parameter data and the time domain signal to be measured, obtaining an intercepted signal; determining noise vector data based on the intercepted signal; Based on the noise vector data, an azimuth parameter value corresponding to the time domain signal to be measured is determined.

2. The signal direction finding method based on frequency domain according to claim 1, characterized in that: The signal parameter data includes at least one of the following: signal carrier frequency, center frequency, and bandwidth.

3. The signal direction finding method based on frequency domain as claimed in claim 2, characterized in that: The obtaining of the intercepted signal based on the signal parameter data and the time domain signal to be measured comprises: Based on the signal carrier frequency, performing a down-conversion operation on the time domain signal to be measured to obtain a baseband signal; Performing a transformation operation on the baseband signal to obtain a frequency domain signal; The intercepted signal is obtained by intercepting the frequency domain signal based on the central frequency point and the bandwidth.

4. The signal direction finding method based on frequency domain as claimed in claim 3, characterized in that: The step of obtaining the intercepted signal from the frequency domain signal based on the center frequency point and the bandwidth includes: The difference between the center frequency point and half of the bandwidth is used as the first frequency point, and the sum of the center frequency point and half of the bandwidth is used as the second frequency point; Finding a first signal point corresponding to the first frequency point and a second signal point corresponding to the second frequency point in the frequency domain signal; A portion of the signal between the first signal point and the second signal point is used as the intercepted signal.

5. The signal direction finding method based on frequency domain according to claim 1, characterized in that: The determining of noise vector data based on the intercepted signal comprises: determining a covariance matrix based on the intercepted signal; Based on the covariance matrix, an eigenvalue decomposition operation is performed to obtain a plurality of eigenvalues; determining a cutoff point among a plurality of said characteristic values; According to the demarcation point, a signal characteristic value and a noise characteristic value are determined, and signal vector data is determined according to the signal characteristic value and noise vector data is determined according to the noise characteristic value.

6. The signal direction finding method based on frequency domain as claimed in claim 5, characterized in that: Determining the demarcation points among the plurality of characteristic values ​​comprises: Sorting the plurality of eigenvalues, calculating adjacent differences between two adjacent eigenvalues, and obtaining a plurality of adjacent differences; Determine, from the plurality of adjacent differences, a target adjacent difference whose adjacent difference is greater than a preset difference threshold; At least one of the two eigenvalues ​​used to calculate the target adjacent difference is used as the demarcation point.

7. The signal direction finding method based on frequency domain according to claim 6, characterized in that: Determining the signal characteristic value and the noise characteristic value according to the demarcation point includes: The smaller eigenvalue of the two eigenvalues ​​used to calculate the target adjacent difference is used as the first target eigenvalue and the larger eigenvalue of the two eigenvalues ​​used to calculate the target adjacent difference is used as the second target eigenvalue; An eigenvalue that is less than or equal to the first target eigenvalue among the plurality of eigenvalues ​​is used as a noise eigenvalue, and an eigenvalue that is greater than or equal to the second target eigenvalue among the plurality of eigenvalues ​​is used as a signal eigenvalue.

8. The signal direction finding method based on frequency domain according to claim 1, characterized in that: Determining the azimuth parameter value corresponding to the time domain signal to be measured based on the noise vector data includes: Acquiring array structure data for detecting signals; Based on the array structure data and the noise vector data, constructing a spectrum peak search function with a direction parameter as a variable; Based on the spectrum peak search function, an azimuth parameter value corresponding to the time domain signal to be measured is determined.

9. The signal direction finding method based on frequency domain as claimed in claim 8, characterized in that: The determining, based on the spectrum peak search function, the azimuth parameter value corresponding to the time domain signal to be measured comprises: The direction parameters are traversed to determine the maximum value of the spectrum peak search function and obtain the target azimuth parameter value corresponding to the maximum value of the spectrum peak search function, and the target azimuth parameter value is used as the azimuth parameter value corresponding to the time domain signal to be measured.

10. The signal direction finding method based on frequency domain according to claim 9, characterized in that: The method further comprises: The number of maximum values ​​of the spectrum peak search function is calculated, and the number of maximum values ​​of the spectrum peak search function is used as the number of signal source devices.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the frequency domain-based signal direction finding method according to any one of claims 1 to 10 is implemented.

12. A signal direction finding device based on frequency domain, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the signal direction finding method based on the frequency domain as claimed in any one of claims 1 to 10.

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