Signal processing method and system of antenna

Abstract

A signal processing method and system of an antenna are disclosed. According to a particular implementation example of the present technology, a phase vector of a received signal of a sparse rectangular array (SRA) antenna is converted into a covariance matrix of an SRA, the converted covariance matrix of the SRA is converted into a covariance matrix of a uniform linear array, high azimuth resolution and elevation resolution are achieved as a result of spectrum analysis of the converted covariance matrix of the uniform linear array, a sidelobe observed from a result of spectrum analysis of a beam pattern of the SRA can be suppressed to extract an accurate direction of a target, and the accurate direction of the target can be extracted by conversion of the covariance matrix of the SRA without separate additional equipment, thereby enabling an accuracy-improved direction of the target to be extracted with a lightweight device.

Description

Antenna signal processing method and system

[0001] The present invention relates to a signal processing method and system for an antenna, and more specifically, to a technology that enables high azimuth and elevation resolution by converting the covariance matrix for each received signal of a sparse rectangular array (SRA) antenna into a covariance matrix of a uniform linear array, and enables the extraction of the accurate direction of a target by effectively suppressing the sidelobe phenomenon occurring in the converted sparse rectangular array.

[0002] A typical antenna signal processing system transmits a signal and analyzes reflected or received signals to derive information such as range, velocity, and direction to the target.

[0003] Here, the distance and speed to the target are sufficient with only a single transmitting antenna (TX) and a single receiving antenna (RX).

[0004] However, azimuth and elevation, which indicate the direction of a target, require multiple transmitting and receiving antennas. Therefore, an array antenna consisting of multiple transmit (TX) and multiple receive (RX) antennas is essential for accurate target direction.

[0005] In other words, an antenna composed of multiple TX and RX analyzes the directionality of a target by utilizing the phase difference that occurs during the signal transmission and reception process and the spatial configuration of the antenna array.

[0006] However, while a large array area is required to secure high azimuth and high resolution, the number of TX / RX antennas that can actually be applied has reached a limit due to realistic constraints such as design costs, power consumption, and system complexity.

[0007] To overcome these limitations, Sparse Rectangular Array (SRA) antennas have recently been applied instead of Uniform Rectangular Array (URA) antennas to achieve high azimuth and elevation resolution with limited resources.

[0008] However, these sparse rectangular array (SPA) antennas face the problem of phase ambiguity, as they cannot estimate the phase information of a target at locations without antennas, and also have difficulty accurately estimating the direction of a target due to side lobes, where spectral peaks appear in directions without targets as a result of spectral analysis of the antenna's beam pattern.

[0009] [Prior Art Literature]

[0010] [Patent Literature]

[0011] Patent Document 1: Korean Published Patent No. 2024-0018631 (Publication Date: Feb. 13, 2024)

[0012] The technical problem to be solved by the present invention is to provide a signal processing method and system for an antenna capable of achieving high azimuth and elevation resolution by converting a phase vector derived from the phase difference of the received signal of each antenna of a sparse rectangular array (SRA) into a covariance matrix and estimating the direction of a target based on the spectral analysis results of the converted covariance matrix.

[0013] Another technical objective of the present invention is to provide a signal processing method and system for an antenna capable of estimating the accurate direction of a target by suppressing the sidelobe phenomenon observed as a result of spectrum analysis of a received signal of a sparse rectangular array (SPA).

[0014] The objectives of the present invention are not limited to those mentioned above, and other objectives and advantages of the present invention not mentioned may be understood from the following description and will become more clearly known from the embodiments of the present invention. Furthermore, it will be readily apparent that the objectives and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0015] Based on one embodiment of the present invention, a signal processing system of an antenna according to one embodiment is,

[0016] A raw data derivation unit that generates raw data including the time difference, phase change amount, and phase difference of each transmitted signal and received signal of a sparse rectangular array (SRA) antenna;

[0017] A distance calculation unit that derives the distance of a target based on the time difference between the transmitted signal and the received signal among the above raw data;

[0018] A Doppler velocity estimation unit that estimates the Doppler velocity of a target based on the phase change amount among the above raw data; and

[0019] One feature is that it includes a direction estimation unit that estimates the direction of a target based on the phase difference among the above raw data.

[0020] Preferably, the above direction estimation unit is,

[0021] The covariance matrix of a sparse rectangular array is derived from the phase vector derived from the phase difference of each received signal of a sparse rectangular array antenna, and

[0022] The covariance matrix of the above sparse rectangular array is transformed into a uniform linear array to derive an extended covariance matrix, and

[0023] It can be configured to separate the noise space and the signal space through eigenvalue decomposition of the extended covariance matrix derived above, and then suppress sidelobe phenomena based on the results of spectral analysis in the separated noise space.

[0024] Preferably, the above direction estimation unit is,

[0025] It may be configured to estimate the direction of a target, including the azimuth and elevation angles of the target, based on the spectral analysis results of an extended covariance matrix in which the above-mentioned side lobe phenomenon is suppressed.

[0026] Preferably, the above direction estimation unit is,

[0027] The spectral analysis results for the above-mentioned extended covariance array may be configured to derive the azimuth and elevation angles of the target from the coordinates of the spectral peaks.

[0028] Preferably, the above direction estimation unit is,

[0029] A phase vector derivation module that derives a phase vector from the phase difference of each received signal of a sparse rectangular array antenna;

[0030] An SRA transformation module that transforms the above-derived topological vector into a covariance matrix of a sparse rectangular array;

[0031] A ULA transformation module that derives an extended covariance matrix by transforming the covariance matrix of the above-mentioned sparse rectangular array into the covariance matrix of a uniform linear array; and

[0032] It may include a sidelobe removal module that separates the noise space and the signal space through eigenvalue resolution of the extended covariance matrix and then removes sidelobe phenomena based on the spectral analysis results of the noise space.

[0033] Preferably, the above direction estimation unit is,

[0034] It may further include a direction output module that estimates the direction of a target, including the azimuth and elevation angles of the target, using the coordinates of the spectral peaks derived from the spectral analysis results of the expanded covariance matrix from which the side lobes have been removed.

[0035] A signal processing method of an antenna according to one embodiment, based on another embodiment of the present invention, is

[0036] A step of deriving a phase vector from the phase difference of each received signal of a sparse rectangular array antenna;

[0037] A step of converting the above-derived topological vector into a covariance matrix of a sparse rectangular array;

[0038] A step of deriving an expanded covariance matrix by converting the covariance matrix of the above-mentioned sparse rectangular array into a covariance matrix of a uniform linear array; and

[0039] The method may include the step of separating the noise space and the signal space through eigenvalue resolution of the extended covariance matrix and then removing sidelobe phenomena as a result of spectral analysis of the noise space.

[0040] Preferably, the signal processing method of the antenna is,

[0041] The method may further include a step of estimating the direction of a target, including the azimuth and elevation angles of the target, using the coordinates of the spectral peaks derived from the spectral analysis results of the expanded covariance matrix from which the side lobes have been removed.

[0042] According to these features, the phase vector of each received signal of a sparse rectangular array (SRA) antenna is converted into a covariance matrix of a sparse rectangular array (SPA), and the converted covariance matrix of the sparse rectangular array is converted into a covariance matrix of a uniform linear array. High azimuth and elevation resolutions are achieved as a result of spectral analysis of the converted covariance matrix of the uniform linear array, and the sidelobe phenomenon observed as a result of spectral analysis of the beam pattern of the sparse rectangular array (SPA) is suppressed, thereby enabling the extraction of the accurate direction of the target.

[0043] In addition, based on these features, the accurate direction of the target can be extracted by transforming the covariance matrix of a sparse rectangular array without separate additional equipment, thereby enabling the extraction of the target's direction with improved accuracy using a lightweight device.

[0044] The following drawings attached to this specification illustrate preferred embodiments of the present invention and serve to further enhance understanding of the technical concept of the present invention together with the detailed description of the invention provided below; therefore, the present invention should not be interpreted as being limited only to the matters described in such drawings.

[0045] Figure 1 is a configuration diagram of a signal processing system of an antenna of one embodiment.

[0046] Figure 2 is a detailed configuration diagram of the direction estimation unit of Figure 1.

[0047] Figures 3a and 3b are example diagrams showing the results of the extended spectrum analysis of Figure 1.

[0048] Figure 4 is an overall flowchart of the signal processing process of an antenna of another embodiment.

[0049] Embodiments of the present invention are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0050] One embodiment below specifically describes a signal processing system and method for an antenna.

[0051] FIG. 1 is a configuration diagram of a signal processing system of an antenna according to one embodiment, FIG. 2 is a detailed configuration diagram of the direction estimation unit of FIG. 1, and FIG. 3a and FIG. 3b are example diagrams showing the results of spectrum analysis based on the spectrum analysis module of FIG. 2. Referring to FIG. 1 to FIG. 3b, the signal processing system of an antenna according to one embodiment derives the distance of a target based on the time delay difference between the transmitted signal of a sparse rectangular array (SRA) antenna and the received signal reflected from the target, estimates the Doppler velocity of the target based on the phase change of the received signal of the sparse rectangular array (SRA) antenna, estimates the direction of the target based on the phase difference of the received signal of the sparse rectangular array (SRA) antenna, and has a configuration to prevent collision of the target based on the derived distance, Doppler velocity, and direction of the target. Accordingly, the system may include, with reference to FIG. 1, a raw data derivation unit (100), a distance calculation unit (200), a Doppler velocity estimation unit (300), and a direction estimation unit (400).

[0052] Here, the raw data derivation unit (100) generates raw data including the distance, phase change, and phase difference of each transmitted signal and received signal of a sparse rectangular array (SRA) antenna.

[0053] At this time, the time difference between the transmission signal and the reception signal of each sparse rectangular array (SRA) antenna is provided to the distance calculation unit (200), and the distance calculation unit (100) derives the distance of the target based on the time difference (delay difference) between the transmission signal and the reception signal of the antenna.

[0054] And the Doppler velocity estimation unit (200) estimates the Doppler velocity of the target by receiving the phase change of each of the accumulated sparse rectangular array (SRA) antennas. Here, the Doppler velocity is estimated as the Doppler spectrum of the phase change derived by a two-dimensional Fast Fourier Transformer (FFT) for the received signals of multiple antennas.

[0055] Although the process of deriving the distance of a target based on the time difference (delay difference) between the transmitted and received signals of a sparse rectangular array (SRA) antenna and the process of estimating the Doppler velocity of a target based on the phase change of the accumulated antenna received signal are not specifically stated in this specification, they will be understood by those skilled in the art.

[0056] Meanwhile, the direction estimation unit (400) estimates the direction of the target by receiving the phase vector of the received signal of each antenna of the sparse rectangular array (SPA). Here, the direction of arrival (DOA) of the target includes the azimuth and elevation of the target. The azimuth represents the direction of movement in the horizontal plane of the target, and the elevation represents the direction of movement in height relative to the vertical plane of the target.

[0057] The direction estimation unit (400) derives a phase vector of a received signal of a sparse rectangular array (SRA) antenna, converts the derived phase vector into a covariance matrix of a sparse rectangular array (SRA), converts the converted covariance matrix into a covariance matrix of a uniform linear array to derive an expanded covariance matrix, and estimates the direction of a target (DOA) including an azimuth and an elevation angle using the coordinates of a spectrum peak derived through spectrum analysis of the expanded covariance matrix. Referring to FIG. 2, it may include a phase vector derivation module (410), an SRA conversion module (420), a ULA conversion module (430), and a spectrum analysis module (440).

[0058] The phase vector derivation module (410) derives the phase vector x of the received signal of the sparse rectangular array (SRA) antenna, and the derived phase vector x is provided to the SRA conversion module (420).

[0059] The SRA conversion module (420) derives the covariance matrix of the sparse rectangular array using the derived phase vector x, and the sparse rectangular array (SRA) covariance matrix Rx is a matrix representing the relationship between the received signals of each antenna and can be expressed by the following Equation 1.

[0060] [Equation 1]

[0061]

[0062] Here, E(.) is the mean expected value, and x H Rx is the Hermitian transpose matrix of the topological vector x, composed of the complex transpose and conjugate of the topological vector x, and Rx is a square matrix.

[0063] Since at least one target is independent and uncorrelated, the (i, j) component of the covariance matrix of the above sparse rectangular array depends only on the distance d(ij) between the i-th and i+1=j-th antennas.

[0064] In addition, in a sparse rectangular array (SRA), the antenna spacing is the horizontal spacing d x , vertical spacing d y Assuming that, the position of the i-th antenna is (x, y)=(n i d x , m i d y )is(where n i , m i is a positive integer).

[0065] The covariance matrix R of a sparse rectangular array (SRA) is d for each row and column. x , d y It is a non-uniform linear array with non-uniform spacing.

[0066] Accordingly, one embodiment derives an extended covariance matrix Rx by converting the covariance matrix R of a sparse rectangular array (SRA) into the covariance matrix of a uniform linear array (Uniform Linear Array).

[0067] That is, the covariance matrix R of the sparse rectangular array (SRA) is provided to the ULA transformation module (430). The ULA transformation module (430) transforms the provided covariance matrix R of the sparse rectangular array (SRA) into the covariance matrix Rx of the uniform linear array.

[0068] That is, the (i, j) component of the covariance matrix R of a sparse rectangular array is d(n i -n j The distance (n) between two antennas (i, j) is a value that depends on ). i -n j Since it is determined by a combination of ), the covariance matrix R of a non-uniform linear array has different values ​​in the diagonal direction.

[0069] In this case, the covariance matrix R of the non-uniform linear array (NULA) is output as the extended covariance matrix Rx of the uniform linear array (ULA) using various methods such as the difference coarray, interpolation-based, and transform matrix methods.

[0070] Accordingly, in one embodiment, the extended covariance matrix Rx fills the mean of the component values ​​that are equal in distance from the antenna as the middle component of the covariance matrix, and sets the remaining components to 0.

[0071] For example, the covariance matrix R of a non-uniform linear array derived with antenna spacings of 0d, 1d, and 3d can be expressed by Equation 2 below, and the covariance matrix Rx of a uniform linear array for the covariance matrix R of the non-uniform linear array of Equation 2 can be expressed by Equation 3 below.

[0072] [Equation 2]

[0073]

[0074] [Equation 3]

[0075]

[0076] This extended covariance matrix Rx is provided to the sidelobe removal module (440).

[0077] The sidelobe removal module (440) can separate the signal space and the noise space through eigendecomposition of the extended covariance matrix Rx, and then remove the sidelobe phenomenon that misdetects the target as a result of spectral analysis of the separated noise space.

[0078] In other words, the extended covariance matrix Rx is separated into a signal space and a noise space through eigenvalue decomposition, and then suppresses the sidelobe phenomenon where the spectral response is reduced as a result of spectral analysis of the noise space.

[0079] Meanwhile, the extended covariance matrix Rx, removed from the sidelobe phenomenon, is provided to the direction output module (450).

[0080] The direction output module (450) derives the coordinates of the spectrum peaks from the spectrum analysis results for the expanded covariance matrix Rx and estimates the direction of the target using the derived coordinates of the spectrum peaks.

[0081] In other words, for the covariance matrix Rx of a uniform linear array, a steering vector is derived as a phase response to the phase angle, and the smaller the derived steering vector, the higher the probability that a received signal exists at a given angle.

[0082] Accordingly, the direction output module (450) derives the azimuth and elevation angles of the target using the coordinates of the peak obtained as a result of the analysis of the spectrum for the covariance matrix Rx of the uniform linear array.

[0083] For example, referring to Fig. 3a, it can be confirmed that there is a target at an azimuth of 0 degrees as a result of spectrum analysis, and referring to Fig. 3b, it can be confirmed that there are targets at azimuths of 0 degrees and 10 degrees, respectively, as a result of spectrum analysis.

[0084] Accordingly, one embodiment converts the phase vector of each received signal of a sparse rectangular array (SRA) antenna into a covariance matrix of a sparse rectangular array (SPA), converts the converted covariance matrix of the sparse rectangular array into a covariance matrix of a uniform linear array, achieves high azimuth and elevation resolution as a result of spectral analysis of the converted covariance matrix of the uniform linear array, and suppresses the sidelobe phenomenon observed as a result of spectral analysis of the beam pattern of the sparse rectangular array (SPA) to extract the accurate direction of the target.

[0085] FIG. 4 is an overall flowchart showing the operation process of the signal processing system of the antenna of FIG. 1, and a signal processing method of an antenna according to another embodiment of the present invention will be explained with reference to FIG. 4.

[0086] That is, it may further include a computer-readable recording medium characterized by having a program recorded thereon for executing a signal processing method for each sparse rectangular array (SRA) antenna on a computer, and may further include a computer program stored on the computer-readable recording medium to be combined with a computer to execute a signal processing method of the antenna on the computer. Referring to FIG. 4, the signal processing method of the antenna of the computer program may include a raw data derivation step (S100), a distance calculation step (S200), a Doppler velocity estimation step (S300), and a direction estimation step (S400).

[0087] The raw data derivation step (S100) generates raw data including the time difference, phase change, and phase difference between the transmitted signal and the received signal of each collected sparse rectangular array (SRA) antenna.

[0088] The time difference among the generated raw data is provided to the distance calculation step (S200), and the distance calculation step (S200) derives the distance of the target using the time difference among the raw data.

[0089] Meanwhile, among the generated raw data, the phase change is provided to the Doppler velocity estimation step (S300), and the Doppler velocity estimation step (S300) derives the velocity of the target based on the received phase change.

[0090] In addition, the phase difference among the generated raw data is provided to the direction estimation step (S400), and the direction estimation step (S400) derives a phase vector for the received signal of the sparse rectangular array (SRA) antenna (S410 in FIG. 4) and derives the derived phase vector as the covariance matrix of the sparse rectangular array (S420 in FIG. 4).

[0091] Next, the direction estimation step (S400) performs interpolation on the covariance matrix of a sparse rectangular array to convert it into a covariance matrix of a uniform linear array, thereby deriving an expanded covariance matrix (S430 in FIG. 4).

[0092] The direction estimation step (S400) separates the signal space and the noise space through eigendecomposition of the extended covariance matrix Rx, and then removes the sidelobe phenomenon that causes the target to be falsely detected as a result of spectral analysis of the separated noise space (S440 in FIG. 4).

[0093] Then, the direction estimation step (S400) derives the coordinates of the spectrum peaks from the spectrum analysis results for the extended covariance matrix Rx and estimates the direction of the target using the derived spectrum peak coordinates (S450 in Fig. 4).

[0094] For ease of understanding, the processor is sometimes described as being used as a single unit, but a person of ordinary skill in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processor may include multiple processors or one processor and one controller. In addition, other processing configurations, such as a parallel processor, are also possible.

[0095] Here, software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or instruct the processing unit independently or collectively.

[0096] Software and / or information, signals and data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave in order to be interpreted by a control unit or to provide commands or data to a processing unit.

[0097] Software may be distributed across networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0098] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software.

[0099] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory.

[0100] Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

[0101] The above-described hardware device may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0102] Although the embodiments have been described above with reference to limited examples and drawings, those skilled in the art can make various modifications and variations from the description above. For example, suitable results can be achieved even if the described techniques are performed in a different order than described, and / or the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0103] Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof.

Claims

1. A raw data derivation unit that generates raw data including the time difference, phase change amount, and phase difference of each transmitted signal and received signal of a sparse rectangular array (SRA) antenna; A distance calculation unit that derives the distance of a target based on the time difference between the transmitted signal and the received signal among the above raw data; A Doppler velocity estimation unit that estimates the Doppler velocity of a target based on the phase change amount among the above raw data; and An antenna signal processing system characterized by including a direction estimation unit that estimates the direction of a target based on the phase difference among the above raw data.

2. In paragraph 1, the direction estimation unit is, The phase vector derived from the phase difference of each of the received signals of the above-mentioned sparse rectangular array (SRA) antennas is converted into the covariance matrix of the sparse rectangular array, and The covariance matrix of the above sparse rectangular array is transformed into a uniform linear array to derive an extended covariance matrix, and A signal processing system for an antenna configured to separate a noise space and a signal space through eigenvalue decomposition of the extended covariance matrix derived above, and then suppress sidelobe phenomena based on the results of spectrum analysis in the separated noise space.

3. In paragraph 2, the direction estimation unit is, A signal processing system for an antenna configured to estimate the direction of a target, including the azimuth and elevation angles of the target, based on the result of spectral analysis of an extended covariance matrix in which the above-mentioned side lobe phenomenon is suppressed.

4. In paragraph 1, the direction estimation unit is, An antenna signal processing system configured to derive the azimuth and elevation angles of a target from the coordinates of the spectrum peaks as a result of spectrum analysis of the above-mentioned extended covariance array.

5. In a signal processing method for an antenna performed based on the signal processing system of the antenna of claim 1, At least one processor included in the signal processing system of the above-mentioned sparse rectangular array (SRA) antenna, A step of deriving a covariance matrix from the phase vectors of the received signals of each sparse rectangular array (SRA) antenna; A step of converting the above-derived covariance matrix into a covariance matrix of a sparse rectangular array; A step of deriving an expanded covariance matrix by converting the covariance matrix of the above-mentioned sparse rectangular array into a covariance matrix of a uniform linear array; and A signal processing method for an antenna characterized by including the step of separating a noise space and a signal space through eigenvalue resolution of the extended covariance matrix and then removing sidelobe phenomena as a result of spectral analysis of the noise space.

6. In paragraph 5, the signal processing method of the antenna is, A signal processing method for an antenna further comprising the step of estimating the direction of a target, including the azimuth and elevation angles of the target, using the coordinates of the spectral peaks derived from the result of spectral analysis of the extended covariance matrix from which the side lobes have been removed.

7. A computer-readable recording medium characterized by having a program recorded thereon for executing the signal processing method of the antenna of claim 5 or 6 on a computer.

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