WO2021196165A1 - 频率分析方法、装置及雷达 - Google Patents
频率分析方法、装置及雷达 Download PDFInfo
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- WO2021196165A1 WO2021196165A1 PCT/CN2020/083237 CN2020083237W WO2021196165A1 WO 2021196165 A1 WO2021196165 A1 WO 2021196165A1 CN 2020083237 W CN2020083237 W CN 2020083237W WO 2021196165 A1 WO2021196165 A1 WO 2021196165A1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- This application relates to the field of signal processing technology, and in particular to a frequency analysis method, device and radar.
- DBF digital beam forming
- Fourier analysis are commonly used for spectrum analysis of radar detection targets, so as to estimate the range, speed and angle spectrum.
- the estimated resolution of the spectrum is limited by the number of spectrum sampling points, that is, the length of the spectrum array.
- the embodiments of the present application provide a frequency analysis method, device, and radar, so as to improve the resolution of a signal with a small amount of calculation.
- an embodiment of the present application provides a frequency analysis method, including: acquiring at least one set of reflected signal groups received by M receivers, and each set of reflected signal groups includes M reflected signals corresponding to the M receivers, M reflected signals are signals reflected by at least one object on the same emission signal, M is an integer greater than or equal to 2; Y extended signals are obtained from M reflected signals, and Y is greater than M; frequency analysis and calculation are performed on Y extended signals .
- the M receivers can be receivers in various types of equipment.
- At least one set of reflected signals is obtained, and then the at least one set of reflected signals is reflected.
- the signal group is expanded, and the Y expanded signals obtained increase the length of the spectrum array, can achieve super-resolution, improve the resolution of the signal, and do not need to perform covariance calculation, reduce the amount of calculation, and can achieve rapid speed in the system. deal with.
- N sub-array extension vectors can be obtained according to the M reflection signals, and each sub-array extension vector includes Y vector elements; then, according to the N sub-array extension vectors, Y extension signals are obtained.
- a way to obtain Y extended signals based on the reflected signal group is provided.
- the reflected signal group is extended to obtain N sub-array extension vectors, and then the N sub-array extension vectors are averaged to obtain Y Expanding the signal realizes the increase of the length of the spectrum array under the premise of a small amount of calculation, thereby increasing the resolution of the signal.
- the i-th extended signal among the Y extended signals is:
- the i-th extended signal of r i , b im is the i-th vector element in the m-th sub-array extension vector, i is a positive integer less than or equal to Y, and m is a positive integer less than or equal to N.
- a method for specifically obtaining Y extension signals from N subarray extension vectors is provided, and an extension signal can be obtained by averaging vector elements at the same position in each N subarray extension vectors.
- frequency analysis is performed based on the Y extended signals, which can improve the resolution of the signal.
- N sub-array expansion vectors can be obtained in the following manner:
- the N sub-array expansion vectors are obtained.
- the vector elements in the i-th group of processing vectors are:
- Ai( ⁇ ) [a i ( ⁇ ),a i+1 ( ⁇ ),a i+2 ( ⁇ ),...,a M-N+i ( ⁇ )],
- Ai( ⁇ ) is the i-th group of processing vectors
- a i ( ⁇ ) is the i-th reflected signal in the M reflection signals
- a M-N+i ( ⁇ ) is the M reflection signals The M-N+i-th reflected signal in the signal, where i is a positive integer less than or equal to N.
- a processing method of obtaining N sub-array extension vectors from M reflection signals in the reflection signal group is provided, and N groups of processing are obtained by smoothing the M reflection signals.
- Vector, and then N sub-array extension vectors are obtained, and the number of vector elements included in each sub-array extension vector is greater than M, which realizes the extension of the spectrum array, thereby improving the resolution of the signal.
- the i-th sub-array extension vector in the N sub-array extension vectors is:
- bi( ⁇ ) [bi M * ( ⁇ ),bi M-1 * ( ⁇ ),...,bi 1 ( ⁇ ),...,bi M-1 ( ⁇ ),bi M ( ⁇ )] ,
- bi ( ⁇ ) is the i-th array extended vector
- bi m ( ⁇ ) ai m ( ⁇ ) * ai n * ( ⁇ )
- ai m ( ⁇ ) is the i-th group treated vector in the m-th vector element
- ai n ( ⁇ ) is the i-th group treated vector n-th vector elements
- ai n * ( ⁇ ) is ai n ( ⁇ ) conjugate
- i is a positive integer less than or equal to N
- m is less than Or a positive integer equal to (M-N+1)
- n is a constant
- n is greater than or equal to 1 and less than M-N+1.
- a method is provided to obtain the sub-array expansion vector by processing the vector. After the reflected signal group is expanded, the data averaging method is used to eliminate the cross terms, and the expression of the sub-array expansion vector can be obtained. According to the sub-array expansion vector, Y expansion signals are further obtained to improve the resolution of the signal.
- M and N satisfy:
- the case where the number of reflected signal groups is one group that is, the case of single snapshot data is aimed at.
- N sub-array extension vectors can be obtained in the following manner:
- N sub-array expansion vectors are obtained.
- a processing method of obtaining N sub-array extension vectors from N*M reflection signals in N groups of reflection signal groups is provided.
- each sub-array extension vector The number of vector elements included in the array expansion vector is greater than M, which realizes the expansion of the spectrum array, thereby improving the resolution of the signal.
- performing frequency analysis and calculation on Y extended signals includes:
- an embodiment of the present application provides a frequency analysis device, including:
- the acquiring module is used to acquire at least one set of reflection signal groups received by M receivers, each group of reflection signal groups includes M reflection signals corresponding to the M receivers, and the M reflection signals are the same for at least one object pair The reflected signal of the transmitted signal, where M is an integer greater than or equal to 2;
- a processing module configured to obtain Y extended signals according to the M reflected signals, where the Y is greater than the M;
- the analysis module is used to perform frequency analysis and calculation on the Y extended signals.
- the processing module is specifically configured to:
- each sub-array expansion vector includes Y vector elements
- the Y extension signals are obtained.
- the i-th extended signal in the Y extended signals is:
- the i-th extended signal of r i , b im is the i-th vector element in the m-th sub-array extension vector, i is a positive integer less than or equal to Y, and m is a positive integer less than or equal to N.
- the number of the reflected signal groups is one group; the processing module is specifically configured to:
- the N sub-array expansion vectors are obtained.
- the vector elements in the i-th group of processing vectors are:
- Ai( ⁇ ) [a i ( ⁇ ),a i+1 ( ⁇ ),a i+2 ( ⁇ ),...,a M-N+i ( ⁇ )],
- Ai( ⁇ ) is the i-th group of processing vectors
- a i ( ⁇ ) is the i-th reflected signal in the M reflection signals
- a M-N+i ( ⁇ ) is the M reflection signals The M-N+i-th reflected signal in the signal, where i is a positive integer less than or equal to N.
- the i-th sub-array extension vector in the N sub-array extension vectors is:
- bi( ⁇ ) [bi M * ( ⁇ ),bi M-1 * ( ⁇ ),...,bi 1 ( ⁇ ),...,bi M-1 ( ⁇ ),bi M ( ⁇ )] ,
- bi ( ⁇ ) is the i-th array extended vector
- bi m ( ⁇ ) ai m ( ⁇ ) * ai n * ( ⁇ )
- ai m ( ⁇ ) is the i-th group treated vector in the m-th vector element
- ai n ( ⁇ ) is the i-th group treated vector n-th vector elements
- ai n * ( ⁇ ) is ai n ( ⁇ ) conjugate
- i is a positive integer less than or equal to N
- m is less than Or a positive integer equal to (M-N+1)
- n is a constant
- n is greater than or equal to 1 and less than M-N+1.
- the M and the N satisfy:
- the number of the reflected signal groups is K groups, and the K is an integer greater than 1; the processing module is specifically configured to:
- the N sub-array expansion vectors are obtained.
- the analysis module is specifically configured to:
- an embodiment of the present application provides a frequency analysis device, including: a processor and a memory;
- the memory stores computer execution instructions
- the processor executes the computer-executable instructions stored in the memory, so that the processor executes the frequency analysis method according to any one of the first aspect.
- an embodiment of the present application provides a computer-readable storage medium having computer-executable instructions stored in the computer-readable storage medium, and when the processor executes the computer-executable instructions, any item as in the first aspect is implemented The described frequency analysis method.
- an embodiment of the present application provides a radar, including a transmitter, a receiving array, and a frequency analysis device, the receiving array includes M receivers, and M is an integer greater than or equal to 2;
- the transmitter is used to transmit a detection signal
- the receiving array is used to receive reflected signals
- the frequency analysis device is configured to perform frequency analysis on the received signal of the receiving array according to the frequency analysis method provided in any one of the first aspect, and the received signal of the receiving array includes the reflected signal.
- an embodiment of the present application provides a program product, which when a computer reads and executes the computer program product, causes the computer to execute the frequency analysis method provided in any one of the above.
- Figure 1 is a schematic diagram of an application scenario provided by an embodiment of the application
- Figure 2 is a schematic diagram of an array provided by an embodiment of the application.
- Figure 3 is a schematic diagram of an ESPRIT array provided by an embodiment of the application.
- FIG. 4 is a schematic diagram of another frequency analysis provided by an embodiment of the application.
- Figure 5 is a schematic diagram of a linear array provided by an embodiment of the application.
- Figure 6 is a schematic diagram of a square matrix provided by an embodiment of the application.
- FIG. 7 is a schematic flowchart of a frequency analysis method provided by an embodiment of the application.
- FIG. 8 is a first schematic diagram of reflected signal processing provided by an embodiment of this application.
- FIG. 9 is a schematic diagram of a smooth operation provided by an embodiment of the application.
- FIG. 10 is a second schematic diagram of reflected signal processing provided by an embodiment of this application.
- FIG. 11 is a schematic diagram of simulation results provided by an embodiment of the application.
- FIG. 12 is a schematic structural diagram of a frequency analysis device provided by an embodiment of the application.
- FIG. 13 is a schematic diagram of the hardware structure of a frequency analysis device provided by an embodiment of the application.
- Radar It is an electronic device that uses electromagnetic waves to measure objects.
- the measurement of the radar object may include: the speed of the measurement object, the distance between the measurement object and the radar, the position of the measurement object, and so on.
- the objects can be people, vehicles, airplanes, etc.
- the radar can be set on roads, industrial scenes, etc., and the radar can be set according to actual needs.
- DOA direction of arrival, the direction of arrival.
- receiving arrays are used in many electronic devices.
- the receiving array includes multiple receivers arranged in an array, and each receiver can receive external signals, that is, the received signal of the receiving array includes signals received by the multiple receivers.
- the receiving array Since different receivers in the receiving array receive different signals, it is necessary to perform frequency analysis on the received signals of the receiving array. For example, due to the different positions of the receivers in the receiving array, the phases of the target signals received by different receivers are also different for the same target signal. Through frequency analysis of the received signal of the receiving array, the direction of arrival of the target signal can be estimated based on the relationship between the position of each receiver and the phase of the target signal received by each receiver.
- the embodiments of the present application are applicable to multiple types of receivers.
- the type of signal received by the receiver is also different.
- the echo signal received by the antenna in the millimeter wave radar is a millimeter wave signal
- the echo signal received by the photodetector in the laser radar is a laser signal.
- the embodiments of the present application are described by taking a radar as an example in the following.
- the radar may be a millimeter wave radar, a lidar, or an infrared radar. The embodiments of the present application do not limit this.
- FIG. 1 is a schematic diagram of an application scenario provided by an embodiment of the application. Please refer to FIG. 1, which includes a radar system 10 and a vehicle (obstacle) 20.
- the radar system 10 may include a transmitting component 101, a receiving component 102, and a controller 103.
- the transmitting component 101 can perform signal transmission.
- the signal transmitted by the transmitting component 101 is referred to as a transmitted signal below.
- the transmitting component 101 can transmit signals in multiple directions.
- the controller 103 can control the transmitting component to transmit signals in different directions. After the transmitted signal reaches the obstacle 20, the obstacle 20 can reflect the transmitted signal, and the signal reflected by the obstacle can be called an echo signal.
- the transmitting component 101 may periodically transmit a signal, the period of transmitting the signal may be referred to as a transmitting period or a scanning period, and the transmitting period may be the duration of one transmitting signal.
- the receiving component 102 can perform signal reception.
- the receiving component 102 can receive echo signals and interference signals.
- interference signals may include environmental noise signals, hacking signals, signals reflected by obstacles from other radar systems, and the like.
- the radar system 10 may include one or more receiving components 102. When the radar system 10 includes multiple receiving components 102, the multiple receiving components 102 can be arranged in different positions, so that the receiving component 102 can receive more obstacles.
- the echo signal of the object may be referred to as a transmitting period or a scanning period, and the transmitting period may be the duration of one transmitting signal.
- the receiving component 102 can perform signal reception.
- the receiving component 102 can receive echo signals and interference signals.
- interference signals may include environmental noise signals, hacking signals, signals reflected by obstacles from other radar systems, and the like
- the controller 103 may obtain the signal received by the receiving component 102, and determine the echo signal from the signal received by the receiving component 102.
- the controller 103 can also obtain the signal transmitted by the transmitting component 101, and measure the object (obstacle) according to the transmitted signal and the echo signal.
- the measurement of the object may include: measuring the speed of the object (speed measurement), measuring the distance between the object and the radar (ranging), measuring the position of the object (positioning), and so on.
- the objects can be people, vehicles, airplanes, etc.
- the vehicle may reflect the transmission signal.
- the receiving component 102 can receive the echo signal reflected by the vehicle on the transmitted signal. Since there are still environmental noise signals, hacker attack signals, etc., the receiving component 102 may also receive environmental noise signals, hacker attack signals, and the like.
- the controller 103 can determine the echo signal in the receiving component 102, and measure the vehicle (speed measurement, distance measurement, positioning, etc.) based on the echo signal and the transmitted signal.
- FIG. 1 merely illustrates the application scenarios applicable to this application in the form of an example, and is not a limitation on the application scenarios.
- FIG. 1 merely illustrates the components included in the radar system 10 by way of example, and does not limit the radar system 10.
- DBF and Fourier analysis are commonly used for spectrum analysis of radar detection targets, so as to estimate the range, speed and angle spectrum.
- the estimated resolution of the spectrum is limited by the number of spectrum sampling points, that is, the length of the spectrum array.
- the MUSIC algorithm In order to obtain a higher target resolution, it is necessary to carry out research on the super-resolution method, currently mainly the MUSIC algorithm and the ESPRIT algorithm.
- FIG. 2 is a schematic diagram of an array provided by an embodiment of the application. As shown in FIG. 2, it includes M array elements, and M array elements are linearly arranged.
- the k-th array element is any one of the M array elements, and the output of the k-th array element in the M arrays can be expressed as:
- J is the number of detected objects, J is a positive integer greater than or equal to 1, f ki is the gain of the k-th element relative to the i-th reflected signal, and n k (t) represents the k-th element channel
- the output noise, ⁇ ki is the time delay for the i-th signal to reach the k-th array element relative to the reference array element.
- the reference array element may be any one of M array elements, and i is a positive integer less than or equal to M.
- Equation (1) shows the output of the k-th array element.
- the M array elements include M outputs. Arrange the output of the M array elements in the array into a vector form. There are:
- equation (2) can be simplified as:
- the array output vector X(t) [x 1 (t),...,x M (t)] T
- the signal vector S(t) [s 1 (t),...,s J ( t)] T
- the noise vector N(t) [n 1 (t),...,n M (t)] T.
- the MUSIC algorithm uses eigenvectors to construct two orthogonal subspaces, namely the signal subspace and the noise subspace.
- the space composed of the eigenvectors corresponding to some eigenvalues is the signal subspace, and the other part of the eigenvalues is the signal subspace.
- the space formed by the eigenvectors corresponding to the values is the noise subspace.
- the noise feature vector is used as the column vector to form the noise subspace matrix U Noise . Since the signal subspace and the noise subspace orthogonal, the signal subspace matrix U S is referred to as the noise subspace are orthogonal. As mentioned above, the following spatial spectrum function can be constructed:
- the subspace formed by the eigenvector group u J+1 , u J+2 ,..., u M is called the noise subspace, so the matrix U Noise is called the noise subspace matrix.
- U S (u 1, u 2, ..., u J) is referred to as the signal subspace matrix.
- This scheme has multi-signal direction finding capabilities, and has high resolution and estimation accuracy under certain conditions to achieve super-resolution of signals.
- this scheme requires eigenvalue decomposition of the covariance matrix, which is relatively complex and difficult to calculate. Implemented in the processing system.
- Fig. 3 is a schematic diagram of the ESPRIT array provided by an embodiment of the application. As shown in Fig. 3, it includes M array elements. The numbers of the M array elements in Fig. 3 are 0, 1, 2,... , M-1.
- the incoming wave signal matrix received by sub-array 1 is:
- the incoming wave signal matrix received by sub-array 2 is:
- ⁇ is the delay phase between the two arrays, which can be expressed as:
- auto-correlation matrix and cross-correlation matrix can be constructed, as shown in the following formula:
- R XX is the auto-correlation matrix
- R XY is the cross-correlation matrix
- This scheme also needs to perform eigenvalue decomposition on the covariance matrix, which has high computational complexity and is difficult to implement in a processing system.
- Fig. 4 is a schematic diagram of another frequency analysis provided by an embodiment of the application.
- the covariance matrix Rxx of the receiving array is obtained according to the solution illustrated in Fig. 2 or Fig. 3.
- the covariance matrix is an N*N matrix as shown in the figure below
- perform signal averaging as shown by the dotted line in the figure below to obtain the first row and first column data of the covariance matrix
- perform the first row and first column data Expand to obtain data with a length of 2*N-1, and use this data for spectrum estimation.
- This scheme can expand the signal, so the measurement accuracy of the target can be improved when detecting a single target.
- the disadvantage of this scheme is that it only has a better effect for the detection of a single target.
- the echo of the target is the sampling envelope of the signal, the covariance matrix is diagonal to the dotted line in the example in Figure 4.
- the amplitudes sampled at the positions are inconsistent. If the signal is forced to be averaged, it will affect the accuracy of spectrum estimation during multi-target detection.
- this application proposes a spectrum analysis solution, which obtains a longer virtual array sampling signal by data structure of the sampled data, thereby improving the spectrum resolution under the premise of lower computational complexity.
- the solution of the present application will be introduced below in conjunction with the drawings.
- Fig. 5 is a schematic diagram of a linear array provided by an embodiment of the application.
- a plurality of array elements are arranged linearly.
- the linear array illustrated in FIG. 5 is a uniform linear array, that is, the distance between two adjacent array elements is equal, for example, both are d.
- the array can be a uniform linear array or a non-uniform linear array, that is, the distance between the array elements can be equal or unequal.
- Fig. 6 is a schematic diagram of a square matrix provided by an embodiment of the application.
- the square matrix illustrated in Figure 6 is composed of multiple linear matrixes. When the square array is composed of multiple linear arrays, the square array is treated as multiple linear arrays, and for each linear array, the solution provided in this application can be adopted. In the subsequent embodiments, a linear array is used as an example for description.
- FIG. 7 is a schematic flowchart of a frequency analysis method provided by an embodiment of the application. As shown in FIG. 7, the method may include:
- each set of reflected signal groups includes M reflected signals corresponding to M receivers, and the M reflected signals are at least one object performing the same transmission signal.
- the M is an integer greater than or equal to 2.
- the M receivers are arranged linearly, and the M receivers may be receivers in various types of equipment, for example, receivers in millimeter wave radars, receivers in lidars, and so on.
- the radar emits a signal to detect an object, and then the object reflects the radar's emitted signal to obtain a reflected signal, which can also be called an echo signal.
- the reflected signal is received by M receivers, and at least one set of reflected signal groups received by M receivers is obtained.
- Each reflected signal group includes M reflected signals corresponding to M receivers, and each reflected signal group represents one group. Snapshot signal.
- the number of received reflection signal groups is one group, the received single snapshot signal with length M is received.
- the data of the received reflection signal group is greater than one group, more than one group is received. Snapshot signal, the length of each snapshot is M.
- M is an integer greater than or equal to 2, that is, the number of receivers in the radar (such as the number of antennas included in the antenna array) is greater than or equal to 2.
- Y extended signals are obtained according to the received at least one set of reflected signal groups, and Y is an integer greater than M.
- the frequency analysis and calculation of the Y extended signals can achieve super resolution and improve spectrum resolution. Rate.
- the FFT calculation can be performed on the Y extended signals to obtain the FFT spatial spectrum, or the DBF calculation can be performed on the Y extended signals to obtain the DBF Spatial spectrum, etc., which are not limited in the embodiment of the present application.
- the frequency analysis method provided by the embodiment of the present application first obtains at least one set of reflected signal groups received by M receivers, and each set of reflected signal groups includes M reflected signals corresponding to the M receivers, and the M reflected signals are at least An object reflects the same transmitted signal, and then obtains Y extended signals according to the M reflected signal groups, and Y is greater than M.
- the obtained Y extended signals increase the length of the spectrum array, can achieve super-resolution, improve the resolution of the signal, and do not need to perform covariance calculation, reduce the amount of calculation, and can achieve Fast processing in the system.
- the M reflected signals in a set of reflected signals received by M receivers can be expressed as:
- M represents the number of receivers and also the number of sampling points.
- ⁇ represents the initial phase of measurement
- d m is the length of the array relative to the reference receiving array element
- the available effective sampling number is M
- ⁇ represents the wavelength of the observed signal carrier frequency.
- any one of the M receivers can be selected as the reference receiving array element, then d m is the length of the array relative to the reference receiving array element.
- d m is the length of the array relative to the reference receiving array element.
- the reference receiving array element can also be the second, third, etc. from the left.
- the receiving phase of any receiver can be extracted, for example, by extracting the receiving phase of the nth receiver, we can get:
- b m ( ⁇ ) a m ( ⁇ )*a n * ( ⁇ ), where a m ( ⁇ ) represents the reflected signal received by the m-th receiver, that is, the m-th in a( ⁇ ) element.
- n is a constant, i.e., before extracting the selected n-th receivers receiving phase, a n * ( ⁇ ) indicates a n ( ⁇ ) conjugate.
- the reflection signal received by the m-th receiver can be expressed as:
- ⁇ 1 and ⁇ 2 respectively represent the amplitudes of the two objects
- ⁇ 1 and ⁇ 2 respectively represent the DOA angles of the two objects
- ⁇ 1 and ⁇ 2 respectively represent the initial phases of the sampling of the two objects.
- the third term And item 4 For the expressions of the initial phases ⁇ 1 and ⁇ 2, the third and fourth terms in equation (14) can be eliminated on average by using multiple snapshot data, that is, the target cross term is eliminated. Then construct the construction matrix as shown in equation (10) for spectrum analysis.
- Fig. 8 is a schematic diagram 1 of reflected signal processing provided by an embodiment of the application. As shown in Fig. 8, the original signals received initially are M reflected signals.
- FIG. 9 is a schematic diagram of the smoothing operation provided by an embodiment of the application. As shown in FIG. 9, taking the smoothing operation from the first reflected signal in the M reflected signals as an example, according to the first to M- in the M reflected signals, N+1 reflection signals are used to obtain the first group of processing vectors. According to the second to M-N+2 reflection signals of the M reflection signals, the second group of processing vectors is obtained, and so on, to obtain N groups of processing vectors.
- the M reflected signals are numbered in turn, from 1-20, and each box represents a reflected signal.
- Figure 9 illustrates the smoothing operation starting from the first reflected signal as an example.
- the smoothing operation can also be started from the second, third, or i-th. The steps of the smoothing operation are similar. I won't repeat it here.
- Ai( ⁇ ) [a i ( ⁇ ),a i+1 ( ⁇ ),a i+2 ( ⁇ ),...,a M-N+i ( ⁇ )],
- Ai( ⁇ ) is the i-th group of processing vectors
- a i ( ⁇ ) is the i-th reflected signal in the M reflected signals
- a M-N+i ( ⁇ ) is the M-th in the M reflected signals.
- N+i reflection signals, i is a positive integer less than or equal to N.
- the M reflected signals are:
- N sub-array expansion vectors can be obtained according to the N sets of processing vectors.
- N processing vectors including (M-N+1) vector elements are respectively multiplied by the conjugate of each n-th sampled signal to obtain N processing data with a length of (M-N+1), and then According to N processing data of length (M-N+1), N subarray expansion vectors are obtained.
- N processing vectors including (M-N+1) vector elements are respectively multiplied by the conjugate of the first sampled signal to obtain N processing data as follows:
- N sub-array expansion vectors according to A( ⁇ ), and construct N data with a length of 2*(M-N+1) for arbitrary processing data.
- N [1:M-N+1] Data take the conjugate of [2:M-N+1] data, and arrange them in reverse order before the first data of each of the N data.
- b 1 ( ⁇ ) is the expansion vector of the first sub-array
- b 2 ( ⁇ ) is the expansion vector of the second sub-array
- b N ( ⁇ ) is the expansion vector of the N-th sub-array.
- the expansion vector of the i-th subarray is:
- the i th sub-array extended vector b i ( ⁇ ) comprises 2 * (M-N + 1 ) -1 vectors elements.
- the N pieces of data with a length of 2*(M-N+1)-1 are averaged, and one piece of data with a length of 2*(M-N+1)-1 is obtained.
- the i-th extended signal in the Y extended signals is:
- the i-th extended signal of r i , b im is the i-th vector element in the m-th sub-array extension vector
- i is a positive integer less than or equal to Y
- m is a positive integer less than or equal to N
- Y 2*(M-N+1)-1. Since Y>M, there is 2*(M-N+1)-1>M, that is, M>2N-1.
- the first vector element in b 1 ( ⁇ ), b 2 ( ⁇ ),..., b N ( ⁇ ) is averaged to obtain the first
- the extended signals are:
- the solution method for other extended signals is similar to the solution method for the first extended signal, and will not be repeated here.
- the above solution is described with reference to the number of reflected signal groups received by M receivers as one group.
- M receivers can receive multiple groups of reflected signal groups.
- the obtained data is how fast the data is, that is, the reflection signal whose array length is K*M, and K is a positive integer greater than 1.
- the corresponding N sets of processing vectors can be obtained according to the K sets of reflected signal groups, and then N sub-array expansion vectors can be obtained according to the N sets of processing vectors.
- N groups of processing vectors are obtained according to the K groups of reflected signals.
- the reflection signal of K*M can be obtained through the reflection signal group of K group, and the reflection signal group of N group can be selected in the reflection signal group of K group to obtain the reflection signal of N*M, and then according to N*M
- the corresponding N sets of processing vectors are obtained, and then N sub-array expansion vectors are obtained according to the N sets of processing vectors.
- N*M reflection signals can be obtained through K groups of reflection signals. At this time, there is no need to smooth the reflection signals. According to the N*M reflection signals, the corresponding N groups of processing vectors can be obtained, and then according to N sets of processing vectors are used to obtain N sub-array expansion vectors.
- the reflection signals included in each reflection signal group in the 4 reflection signal groups can be smoothed, and each reflection signal group is smoothed to obtain 2 sets of processing vectors, and 4 groups After the reflection signal group is smoothed, 8 groups of processing vectors can be obtained, and then 6 groups are selected from the 8 groups of processing vectors to obtain the corresponding 6 groups of processing vectors.
- FIG. 10 is a second schematic diagram of reflected signal processing provided by an embodiment of the application.
- the original signal received initially is K groups of reflected signal groups, and each group of reflected signal groups includes M reflected signals.
- a group of processing vectors can be obtained according to each group of reflected signal groups, so that K groups of reflected signal groups can correspondingly obtain K groups of processing vectors.
- the vector elements in any i-th group of processing vectors are:
- N sub-array extension vectors can be obtained, where the i-th sub-array extension vector is:
- bi( ⁇ ) [bi M * ( ⁇ ),bi M-1 * ( ⁇ ),...,bi 1 ( ⁇ ),...,bi M-1 ( ⁇ ),bi M ( ⁇ )] ,
- bi ( ⁇ ) is the i-th array extended vector
- bi m ( ⁇ ) ai m ( ⁇ ) * ai n * ( ⁇ )
- ai m ( ⁇ ) is the i-th group treated vector in the m-th vector element
- ai n ( ⁇ ) is the i-th group treated vector n-th vector elements
- ai n * ( ⁇ ) is ai n ( ⁇ ) conjugate
- i is a positive integer less than or equal to N
- m is less than Or a positive integer equal to M
- n is a constant.
- N processing vectors including (M-N+1) vector elements are respectively multiplied by the conjugate of each n-th sampled signal to obtain N processing data with a length of (M-N+1), and then According to N processing data with a length of (M-N+1), N sub-array expansion vectors are obtained.
- N processing vectors including (M-N+1) vector elements are respectively multiplied by the conjugate of the first sampled signal to obtain N processing data as follows:
- N sub-array expansion vectors according to A( ⁇ ), specifically, according to bi m ( ⁇ ) ai m ( ⁇ )*ai 1 * ( ⁇ ) to construct the vector elements in the sub-array expansion vector, for arbitrary processing data , Construct N pieces of data with a length of 2*(M-N+1), specifically, for N pieces of [1:M-N+1] data, take the conjugate of [2:M-N+1] pieces of data , And sort the N data before the first data in reverse order.
- Y expansion signals After obtaining the N sub-array expansion vectors and averaging the data, Y expansion signals can be obtained. Among them, the i-th expansion signal of the Y expansion signals is:
- r i is the i-th extended signal
- b im is the i-th vector element in the m-th subarray extension vector b m ( ⁇ )
- i is a positive integer less than or equal to Y
- m is a positive integer less than or equal to N Integer.
- Y extended signals After performing the above processing on the reflection signal groups received by the M receivers, Y extended signals can be obtained, and then, the Y extended signals obtained by the above scheme are analyzed and calculated with various frequencies to obtain the target angle spectrum, for example, Perform FFT or DBF estimation to obtain FFT space spectrum or DBF space spectrum.
- FIG. 11 is a schematic diagram of the simulation results provided by an embodiment of the application.
- the abscissa represents the angle
- the ordinate represents the signal strength (amplitude).
- the solid line in Figure 11 represents the space of the MUSIC algorithm.
- Spectrum, two different types of dashed lines respectively represent the spatial spectrum of the solution of the application and the FFT analysis method. It can be seen from Figure 11 that both the MUSIC algorithm and the solution of this application can identify two objects, while the FFT analysis method cannot distinguish the two objects better.
- the solution of this application does not require covariance decomposition, Therefore, compared with the MUSIC algorithm, its computational complexity is smaller.
- FIG. 12 is a schematic structural diagram of a frequency analysis device provided by an embodiment of the application, as shown in FIG. 12, including:
- the acquiring module 121 is configured to acquire at least one set of reflection signal groups received by M receivers, each group of reflection signal groups includes M reflection signals corresponding to the M receivers, and the M reflection signals are the same for at least one object pair.
- the reflected signal of the transmitted signal where M is an integer greater than or equal to 2;
- the processing module 122 is configured to obtain Y extended signals according to the M reflected signals, where the Y is greater than the M;
- the analysis module 123 is configured to perform frequency analysis and calculation on the Y extended signals.
- processing module 122 is specifically configured to:
- each sub-array extension vector includes Y vector elements, where N is a positive integer greater than 1;
- the Y extension signals are obtained.
- the i-th extended signal in the Y extended signals is:
- the i-th extended signal of r i , b im is the i-th vector element in the m-th sub-array extension vector, i is a positive integer less than or equal to Y, and m is a positive integer less than or equal to N.
- the number of the reflected signal groups is one group; the processing module 122 is specifically configured to:
- the N sub-array expansion vectors are obtained.
- the vector elements in the i-th group of processing vectors are:
- Ai( ⁇ ) [a i ( ⁇ ),a i+1 ( ⁇ ),a i+2 ( ⁇ ),...,a M-N+i ( ⁇ )],
- Ai( ⁇ ) is the i-th group of processing vectors
- a i ( ⁇ ) is the i-th reflected signal in the M reflection signals
- a M-N+i ( ⁇ ) is the M reflection signals The M-N+i-th reflected signal in the signal, where i is a positive integer less than or equal to N.
- the i-th sub-array extension vector in the N sub-array extension vectors is:
- bi( ⁇ ) [bi M * ( ⁇ ),bi M-1 * ( ⁇ ),...,bi 1 ( ⁇ ),...,bi M-1 ( ⁇ ),bi M ( ⁇ )] ,
- bi ( ⁇ ) is the i-th array extended vector
- bi m ( ⁇ ) ai m ( ⁇ ) * ai n * ( ⁇ )
- ai m ( ⁇ ) is the i-th group treated vector in the m-th vector element
- ai n ( ⁇ ) is the i-th group treated vector n-th vector elements
- ai n * ( ⁇ ) is ai n ( ⁇ ) conjugate
- i is a positive integer less than or equal to N
- m is less than Or a positive integer equal to (M-N+1)
- n is a constant
- n is greater than or equal to 1 and less than M-N+1.
- the M and the N satisfy:
- the number of the reflected signal groups is K groups, and the K is an integer greater than 1; the processing module 122 is specifically configured to:
- the N sub-array expansion vectors are obtained.
- the analysis module 123 is specifically configured to:
- the frequency analysis device provided in the embodiment of the present application can execute the technical solutions shown in the foregoing method embodiments, and its implementation principles and beneficial effects are similar, and details are not described herein again.
- FIG. 13 is a schematic diagram of the hardware structure of a frequency analysis device provided by an embodiment of the application.
- the frequency analysis device includes a processor 131 and a memory 132.
- the processor 131 and the memory 132 are connected through a bus 133.
- the processor 131 executes the computer-executable instructions stored in the memory 132, so that the processor 131 executes the above frequency analysis method.
- the processor may be a central processing unit (English: Central Processing Unit, abbreviated as: CPU), or other general-purpose processors or digital signal processors (English: Digital Signal Processor, referred to as DSP), application specific integrated circuit (English: Application Specific Integrated Circuit, referred to as ASIC), etc.
- the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like. The steps of the method disclosed in combination with the application can be directly embodied as being executed and completed by a hardware processor, or executed and completed by a combination of hardware and software modules in the processor.
- the memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory.
- NVM non-volatile storage
- the bus can be an Industry Standard Architecture (ISA) bus, Peripheral Component (PCI) bus, or Extended Industry Standard Architecture (EISA) bus, etc.
- ISA Industry Standard Architecture
- PCI Peripheral Component
- EISA Extended Industry Standard Architecture
- the bus can be divided into address bus, data bus, control bus and so on.
- the buses in the drawings of this application are not limited to only one bus or one type of bus.
- the embodiment of the present invention also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the frequency analysis method as described above is implemented.
- the above-mentioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable and removable Programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
- SRAM static random access memory
- EEPROM electrically erasable and removable Programmable read-only memory
- EPROM erasable programmable read-only memory
- PROM programmable read-only memory
- ROM read-only memory
- magnetic memory flash memory
- flash memory magnetic disk or optical disk.
- a readable storage medium may be any available medium that can be accessed by a general purpose or special purpose computer.
- An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and can write information to the readable storage medium.
- the readable storage medium may also be an integral part of the processor.
- the processor and the readable storage medium may be located in Application Specific Integrated Circuits (ASIC for short).
- ASIC Application Specific Integrated Circuits
- the processor and the readable storage medium may also exist in the device as discrete components.
- An embodiment of the present application also provides a radar, which includes a transmitter, a receiving array, and a frequency analysis device, the receiving array includes M receivers, and M is an integer greater than 1.
- Transmitter used to transmit detection signals
- the frequency analysis device is used to perform frequency analysis on the received signal of the receiving array according to the frequency analysis method provided in the above-mentioned embodiment, and the received signal of the receiving array includes the reflected signal.
- the embodiments of the present application may also provide a computer program product, which can be executed by a processor, and when the computer program product is executed, it can implement the frequency analysis method executed by any of the frequency analysis devices shown above.
- the disclosed system, device, and method can be implemented in other ways.
- the device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another system, or some features can be ignored or not implemented.
- the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
- the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
- the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
- the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
- the aforementioned computer program can be stored in a computer readable storage medium.
- the computer program When the computer program is executed by the processor, it realizes the steps including the foregoing method embodiments; and the foregoing storage medium includes: ROM, RAM, magnetic disk, or optical disk and other media that can store program codes.
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Abstract
一种频率分析方法、装置及雷达，频率分析方法包括：获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，M个反射信号为至少一个对象对相同发射信号进行反射的信号，M为大于或等于2的整数（S71）；根据M个反射信号得到Y个扩展信号，Y大于M（S72）；对Y个扩展信号进行频率分析计算（S73）。能够在较小的计算量下提高信号的分辨率。
Description
本申请涉及信号处理技术领域，尤其涉及一种频率分析方法、装置及雷达。
目前常用数字波束合成(digital beam forming，DBF)和傅里叶分析进行雷达探测目标的频谱分析，从而进行距离、速度和角度谱的估计。频谱的估计分辨率受限于频谱采样点数，即频谱阵列长度。
在频谱阵列长度一定的情况下，为了获得更高的目标分辨率，需要进行超分辨的方法的研究，目前主要为多重信号分类算法(Multiple Signal Classification，MUSIC)和借助旋转不变技术估计信号参数方法(Estimating Signal Parameters via Rotational Invariance techniques，ESPRIT)。
通过MUSIC算法和ESPRIT算法进行频谱分析时，需要进行特征值分解操作，计算复杂度高，较难在实时处理系统中实现。
发明内容
本申请实施例提供一种频率分析方法、装置及雷达，以实现在较小的计算量下提高信号的分辨率。
第一方面，本申请实施例提供一种频率分析方法，包括：获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，M个反射信号为至少一个对象对相同发射信号进行反射的信号，M为大于或等于2的整数；根据M个反射信号得到Y个扩展信号，Y大于M；对Y个扩展信号进行频率分析计算。
在上述过程中，M个接收器可以为各种类型的设备中的接收器，通过接收至少一个对象对相同发射信号进行反射的信号，得到至少一组反射信号组，然后对该至少一组反射信号组进行扩展处理，得到的Y个扩展信号提高了频谱阵列的长度，能够实现超分辨，提高信号的分辨率，且无需进行协方差计算，减小了计算量，能够实现在系统中的快速处理。
在一种可能的实现方式中，可以根据M个反射信号，得到N个子阵扩展向量，每个子阵扩展向量中包括Y个向量元素；然后，根据N个子阵扩展向量，得到Y个扩展信号。
在上述过程中，提供了一种根据反射信号组得到Y个扩展信号的方式，首先对反射信号组的扩展，获取N个子阵扩展向量，然后对N个子阵扩展向量进行平均处理，得到Y个扩展信号，实现了在较小的计算量的前提下提高频谱阵列的长度，进而提高信号的分辨率。
在一种可能的实现方式中，Y个扩展信号中的第i个扩展信号为：
其中，r
i所述第i个扩展信号，b
im为第m个子阵扩展向量中的第i个向量元素，i为小于或等于Y的正整数，m为小于或等于N的正整数。
在上述过程中，提供了一种根据N个子阵扩展向量具体得到Y个扩展信号的方式，可以通过对每个N个子阵扩展向量中相同位置的向量元素进行平均，得到一个扩展信号。在得到Y个扩展信号后，根据Y个扩展信号进行频率分析，能够提高信号的分辨率。
在一种可能的实现方式中，当反射信号组的数量为一组时，可以通过如下方式得到N个子阵扩展向量：
对所述M个反射信号进行平滑处理，得到N组处理向量，每组处理向量中包括(M-N+1)个向量元素；
根据所述N组处理向量，得到所述N个子阵扩展向量。
在一种可能的实现方式中，第i组处理向量中向量元素为：
Ai(θ)＝[a
i(θ),a
i+1(θ),a
i+2(θ),...,a
M-N+i(θ)]，
其中，Ai(θ)为所述第i组处理向量，a
i(θ)为所述M个反射信号中的第i个反射信号，a
M-N+i(θ)为所述M个反射信号中的第M-N+i个反射信号，所述i为小于或等于N的正整数。
在上述过程中，针对单快拍数据，提供了一种通过反射信号组中的M个反射信号得到N个子阵扩展向量的处理方式，通过对M个反射信号进行平滑处理，得到了N组处理向量，进而得到N个子阵扩展向量，且每个子阵扩展向量中包括的向量元素的数目大于M，实现了对频谱阵列的扩展，从而能够提高信号的分辨率。
在一种可能的实现方式中，所述N个子阵扩展向量中的第i个子阵扩展向量为：
bi(θ)＝[bi
M
*(θ),bi
M-1
*(θ),...,bi
1(θ),...,bi
M-1(θ),bi
M(θ)]，
其中，bi(θ)为第i个子阵扩展向量，bi
m(θ)＝ai
m(θ)*ai
n
*(θ)，ai
m(θ)为第i组处理向量中的第m个向量元素，ai
n(θ)为第i组处理向量中的第n个向量元素，ai
n
*(θ)为ai
n(θ)的共轭，i为小于或等于N的正整数，m为小于或等于(M-N+1)的正整数，n为常数，n大于或等于1且小于M-N+1。
在上述过程中，提供了一种通过处理向量得到子阵扩展向量的方式，在针对反射信号组进行扩展后，采用数据平均的方法进行交叉项消除，得到子阵扩展向量的表达式，从而能够根据子阵扩展向量进一步得到Y个扩展信号，提高信号的分辨率。
在一种可能的实现方式中，M和N满足：
M>2N-1。
在上述过程中，针对的是反射信号组的数量为一组的情况，即单快拍数据的情况。在反射信号组的数量为一组时，参数Y的取值为Y＝2*(M-N+1)-1，因此，通过限定M>2N-1，能够保证Y>M，从而才能够保证对频谱阵列的扩展，提高信号的分辨率。
在一种可能的实现方式中，当反射信号组的数量为K组时，可以通过如下方式得到N个子阵扩展向量：
根据K组反射信号组，得到对应的N组处理向量；
根据N组处理向量，得到N个子阵扩展向量。
在上述过程中，针对多快拍数据，提供了一种通过N组反射信号组中的N*M个反射信号得到N个子阵扩展向量的处理方式，得到的N个子阵扩展向量中，每个子阵扩展向量中包括的向量元素的数目大于M，实现了对频谱阵列的扩展，从而能够提高信号的分辨率。
在一种可能的实现方式中，对Y个扩展信号进行频率分析计算，包括：
对Y个扩展信号进行快速傅里叶变换FFT计算，得到FFT空间谱；或者，
对Y个扩展信号进行数字波束成型DBF计算，得到DBF空间谱。
在上述过程中，通过M个接收器接收到的至少一组反射信号组得到Y个扩展信号后，实现了频谱阵列的扩展，在此基础上，可以采用例如FFT计算或DBF计算等频谱分析方法来对Y个扩展信号进行分析，得到对应的信号空间谱，实现了在较小的计算量的前提下提高信号的分辨率的目的。
第二方面，本申请实施例提供一种频率分析装置，包括：
获取模块，用于获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，所述M个反射信号为至少一个对象对相同发射信号进行反射的信号，所述M为大于或等于2的整数；
处理模块，用于根据所述M个反射信号得到Y个扩展信号，所述Y大于所述M；
分析模块，用于对所述Y个扩展信号进行频率分析计算。
在一种可能的实现方式中，所述处理模块具体用于：
根据所述M个反射信号，得到N个子阵扩展向量，每个子阵扩展向量中包括Y个向量元素；
根据所述N个子阵扩展向量，得到所述Y个扩展信号。
在一种可能的实现方式中，所述Y个扩展信号中的第i个扩展信号为：
其中，r
i所述第i个扩展信号，b
im为第m个子阵扩展向量中的第i个向量元素，i为小于或等于Y的正整数，m为小于或等于N的正整数。
在一种可能的实现方式中，所述反射信号组的数量为一组；所述处理模块具体用于：
对所述M个反射信号进行平滑处理，得到N组处理向量，每组处理向量中包括(M-N+1)个向量元素；
根据所述N组处理向量，得到所述N个子阵扩展向量。
在一种可能的实现方式中，第i组处理向量中向量元素为：
Ai(θ)＝[a
i(θ),a
i+1(θ),a
i+2(θ),...,a
M-N+i(θ)]，
其中，Ai(θ)为所述第i组处理向量，a
i(θ)为所述M个反射信号中的第i个反射信号，a
M-N+i(θ)为所述M个反射信号中的第M-N+i个反射信号，所述i为小于或等于N的正整数。
在一种可能的实现方式中，所述N个子阵扩展向量中的第i个子阵扩展向量为：
bi(θ)＝[bi
M
*(θ),bi
M-1
*(θ),...,bi
1(θ),...,bi
M-1(θ),bi
M(θ)]，
其中，bi(θ)为第i个子阵扩展向量，bi
m(θ)＝ai
m(θ)*ai
n
*(θ)，ai
m(θ)为第i组处理向量中的第m个向量元素，ai
n(θ)为第i组处理向量中的第n个向量元素，ai
n
*(θ)为ai
n(θ)的共轭，i为小于或等于N的正整数，m为小于或等于(M-N+1)的正整数，n为常数，n大于或等于1且小于M-N+1。
在一种可能的实现方式中，所述M和所述N满足：
M>2N-1。
在一种可能的实现方式中，所述反射信号组的数量为K组，所述K为大于1的整数；所述处理模块具体用于：
根据所述K组反射信号组，得到对应的N组处理向量；
根据所述N组处理向量，得到所述N个子阵扩展向量。
在一种可能的实现方式中，所述分析模块具体用于：
对所述Y个扩展信号进行快速傅里叶变换FFT计算，得到FFT空间谱；或者，
对所述Y个扩展信号进行数字波束成型DBF计算，得到DBF空间谱。
第三方面，本申请实施例提供一种频率分析设备，包括：处理器和存储器；
所述存储器存储计算机执行指令；
所述处理器执行所述存储器存储的计算机执行指令，使得所述处理器执行如第一方面任一项所述的频率分析方法。
第四方面，本申请实施例提供一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机执行指令，当处理器执行所述计算机执行指令时，实现如第一方面任一项所述的频率分析方法。
第五方面，本申请实施例提供一种雷达，包括发射器、接收阵列和频率分析设备，所述接收阵列包括M个接收器，M为大于或等于2的整数；
所述发射器，用于发射探测信号；
所述接收阵列，用于接收反射信号；
所述频率分析设备，用于根据如第一方面中任一项所提供的频率分析方法，对所述接收阵列的接收信号进行频率分析，所述接收阵列的接收信号包括所述反射信号。
第六方面，本申请实施例提供一种程序产品，当计算机读取并执行所述计算机程序产品时，使得计算机执行上述中任一项所提供的频率分析方法。
图1为本申请实施例提供的应用场景示意图；
图2为本申请实施例提供的阵列示意图；
图3为本申请实施例提供的ESPRIT阵列示意图；
图4为本申请实施例提供的另一频率分析的示意图；
图5为本申请实施例提供的线阵示意图；
图6为本申请实施例提供的方阵示意图；
图7为本申请实施例提供的频率分析方法的流程示意图；
图8为本申请实施例提供的反射信号处理示意图一；
图9为本申请实施例提供的平滑操作示意图；
图10为本申请实施例提供的反射信号处理示意图二；
图11为本申请实施例提供的仿真结果示意图；
图12为本申请实施例提供的频率分析装置的结构示意图；
图13为本申请实施例提供的频率分析设备的硬件结构示意图。
为了便于理解，首先对本申请所涉及的概念进行说明。
雷达：是一种利用电磁波对对象进行测量的电子设备。雷达对象的测量可以包括：测量对象的速度、测量对象与雷达之间的距离、测量对象的位置等。对象可以为人、车辆、飞机等。雷达可以设置在道路上、工业场景等，可以根据实际需要对雷达进行设置。
DOA：direction of arrival，波达方向。
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。
目前，接收阵列在诸多电子设备中都有应用。接收阵列中包括阵列式排列的多个接收器，每个接收器皆可以接收外界信号，也就是说，接收阵列的接收信号中包括了多个接收器分别接收到的信号。
由于接收阵列中不同的接收器接收到的信号有所不同，因此需要对接收阵列的接收信号进行频率分析。例如，由于接收阵列中接收器的位置有所不同，导致针对同一个目标信号，不同的接收器接收到的目标信号的相位也有所不同。通过对接收阵列的接收信号进行频率分析，可以根据每个接收器的位置与每个接收器接收到的目标信号的相位之间的关系，估算出目标信号的来波方向。
应理解，本申请实施例适用于多种类型的接收器。例如，毫米波雷达中的接收器—天线，又例如激光雷达中的接收器—光电探测器，又例如，声呐、医学设备中的各种类型的传感器。根据接收器类型的不同，接收器接收到的信号类型也有所不同。例如，毫米波雷达中的天线接收的回波信号为毫米波信号，而激光雷达中光电探测器接收到的回波信号为激光信号。为了便于理解，本申请实施例接下来以雷达为例进行说明，该雷达可以是毫米波雷达，也可以是激光雷达，也可以是红外雷达，本申请实施例对此并不多作限制。
图1为本申请实施例提供的应用场景示意图。请参见图1，包括雷达系统10和车辆(障碍物)20。雷达系统10可以包括发射组件101、接收组件102和控制器103。
发射组件101可以进行信号发射。为了便于描述，下文将发射组件101发射的信号称为发射信号。发射组件101可以向多个方向发射信号，例如，控制器103可以控制发射组件向不同的方向发射信号。在发射信号到达障碍物20之后，障碍物20可以对发射信号进行反射，障碍物对发射信号进行反射的信号可以称为回波信号。
可选的，发射组件101可以周期性的发射信号，发射信号的周期可以称为发射周期或者扫描周期，发射周期可以为一个发射信号的时长。接收组件102可以进行信号接收。接收组件102可以接收回波信号和干扰信号。例如，干扰信号可以包括环境噪声信号、黑客攻击信号、障碍物对其它雷达系统的发射信号进行反射的信号等。雷达系统10中可以包括一个或多个接收组件102，当雷达系统10中包括多个接收组件102时，该多个接收组件102可以设置在不同位置，进而使得接收组件102可以接收到更多障碍物的回波信号。
控制器103可以获取接收组件102接收到的信号，并在接收组件102接收到的信号中确定回波信号。控制器103还可以获取发射组件101发射的信号，并根据发射信号和回波信号对对象(障碍物)进行测量。对对象的测量可以包括：测量对象的速度(测速)、测量对象与雷达之间的距离(测距)、测量对象的位置(定位)等。对象可以为人、车辆、飞机等。
例如，请参见图1，在发射组件101发射的发射信号到达车辆以后，车辆可以对该发射信号进行反射。接收组件102可以接收车辆对发射信号进行反射的回波信号，由于还存 在环境噪声信号、黑客攻击信号等，因此，接收组件102还可能接收到环境噪声信号、黑客攻击信号等。控制器103可以在接收组件102中确定回波信号，并根据该回波信号和发射信号对车辆进行测量(测速、测距、定位等)。
需要说明的是，图1只是以示例的形式示意本申请所适用的应用场景，并非对应用场景进行的限定。图1只是以示例的形式示意雷达系统10中所包括的部件，并非对雷达系统10进行的限定。
目前常用DBF和傅里叶分析进行雷达探测目标的频谱分析，从而进行距离、速度和角度谱的估计。频谱的估计分辨率受限于频谱采样点数，即频谱阵列长度。在频谱阵列长度一定的情况下，为了获得更高的目标分辨率，需要进行超分辨的方法的研究，目前主要为MUSIC算法和ESPRIT算法。下面将结合附图对这几种方法进行简要介绍。
图2为本申请实施例提供的阵列示意图，如图2所示，包括M个阵元，M个阵元线性排列。第k个阵元为M个阵元中的任意一个，M个阵列中第k个阵元的输出可表示为：
其中，J为探测的对象的数目，J为大于或等于1的正整数，f
ki为第k个阵元相对于第i个反射信号的增益，n
k(t)表示第k个阵元通道输出的噪声，τ
ki为相对于参考阵元第i个信号到达第k个阵元的时延。本申请实施例中，参考阵元可以为M个阵元中的任意一个，i为小于或等于M的正整数。
式(1)示出了第k个阵元的输出，M个阵元中包括M个输出，将阵列中的M个阵元的输出排列成向量形式，有：
假设不考虑阵列中阵元的方向图特性，即相对所有信号，天线有相同的增益，且不存在阵元互耦、通道相位误差等非理想因素，那么式(2)可化简为：
将(3)写作向量形式，即：
X(t)＝AS(t)+N(t) (4)
其中，阵列输出矢量X(t)＝[x
1(t),...,x
M(t)]
T，信号矢量S(t)＝[s
1(t),...,s
J(t)]
T，噪声矢量N(t)＝[n
1(t),...,n
M(t)]
T。
对阵列输出矢量X(t)的协方差矩阵进行特征值分解，得到：
其中λ
i表示R
X的第i个特征值，按照λ
1≥λ
2≥…≥λ
J+1＝…＝λ
M的方式排列，
表示高斯白噪声的功率，u
i表示与λ
i对应的特征向量，I为单位矩阵。
由式(5)可得：
通过特征值分解，MUSIC算法利用特征向量构建了两个正交的子空间，分别是信号子空间和噪声子空间，其中，部分特征值对应的特征向量组成的空间为信号子空间，另一部分特征值对应的特征向量组成的空间为噪声子空间。
将噪声特征向量作为列向量，组成噪声子空间矩阵U
Noise。由于信号子空间和噪声子空间正交，因此信号子空间矩阵U
S被称为与噪声子空间也是正交的。如上所述，可构造如下的空间谱函数：
其中U
Noise＝(u
J+1,u
J+2,...,u
M)。
特征向量组u
J+1,u
J+2,...,u
M所张成的子空间被称作噪声子空间，因此矩阵U
Noise称为噪声子空间矩阵。对应的，U
S＝(u
1,u
2,...,u
J)被称为信号子空间矩阵。
在空间谱域求取谱函数最大值，其谱峰对应的角度即是来波方向角的估计值。
该方案具备多信号测向能力，在特定的条件下具有较高的分辨度和估计精度实现信号的超分辨，但是该方案需要对协方差矩阵进行特征值分解，计算复杂度较高，较难在处理系统中实现。
除了MUSIC算法外，还有一种常用的方案，即ESPRIT方案，下面将进行介绍。
图3为本申请实施例提供的ESPRIT阵列示意图，如图3所示，包括M个阵元，M个阵元在图3中的编号从左到右依次为0、1、2、...、M-1。
对子阵1所接收的来波信号矩阵为：
X＝AS+n
1，
对子阵2所接收的来波信号矩阵为：
Y＝AΦS+n
2，
其中，Φ为两个阵列之间的延迟相位，可以表示为：
其中，
根据上述来波信号矩阵，可以构造自相关矩阵和互相关矩阵，如下式所示：
R
XX＝E{X(n)X
H(n)}＝AR
SSA
H+σ
2I，
R
XY＝E{X(n)Y
H(n)}＝AR
SSΦ
HA
H+σ
2Z，
其中：
然后，对R
XX进行特征分解得出σ
2，则协方差矩阵有：
C
XX＝R
XX-σ
2I＝AR
SSA
H，
C
XY＝R
XY-σ
2Z＝AR
SSΦ
HA
H。
最后，对矩阵束{C
XX,C
XY}进行广义特征值分解，从而得到位于单位圆上的p个广义特征值
i＝1,2,...,p，这些特征值的相位即为信号的频率估计。
该方案同样需要对协方差矩阵进行特征值分解，计算复杂度较高，较难在处理系统中实现。
图4为本申请实施例提供的另一频率分析的示意图，如图4所示，首先根据图2或图3示例的方案中得到接收阵列的协方差矩阵Rxx。假设协方差矩阵如下图所示为N*N的矩阵，按照下图虚线所示进行信号平均，获得协方差矩阵的第一行和第一列数据，并把第一行和第一列数据进行展开获得长度为2*N-1的数据，利用该数据进行频谱估计。
该方案能够对信号进行扩展，因此在对单目标进行探测时能够提高目标的测量精度。但是，该方案的缺点是，仅针对单目标的探测有较好的效果，对于多目标的探测，由于目标的回波为信号的采样包络，导致协方差矩阵在图4示例的虚线对角位置处采样的幅度不一致。如果强制对信号进行平均处理，会影响多目标探测时频谱估计的精度。
针对上述技术问题，本申请提出一种频谱分析方案，通过对采样数据进行数据构造来获得更长的虚拟阵列采样信号，从而在较低的计算复杂度的前提下提高频谱分辨率。下面将结合附图对本申请的方案进行介绍。
图5为本申请实施例提供的线阵示意图。在图5中，多个阵元线性排列，图5中示意的线阵为均匀线阵，即两个相邻的阵元之间的距离相等，例如均为d，本申请实施例中的线阵可以为均匀线阵，也可以为非均匀线阵，即阵元之间的距离可以相等，也可以不相等。图6为本申请实施例提供的方阵示意图。图6中示例的方阵是由多个线阵组成的。当方阵是由多个线阵组成时，将该方阵作为多个线阵进行处理，针对每个线阵，均可采用本申请提供的方案。在后续实施例中，均以线阵为例进行说明。
图7为本申请实施例提供的频率分析方法的流程示意图，如图7所示，该方法可以包括：
S71，获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，所述M个反射信号为至少一个对象对相同发射信号进行反射的信号，所述M为大于或等于2的整数。
本申请实施例中，M个接收器线性排列，M个接收器可以为各种类型的设备中的接收器，例如可以为毫米波雷达中的接收器、激光雷达中的接收器、等等。以雷达为例，雷达发射信号，来探测对象，然后对象对雷达的发射信号进行反射，得到反射信号，亦可称为回波信号。
反射信号被M个接收器接收，得到M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，每组反射信号组代表一组快拍信号，当接收到的反射信号组的数量为一组时，接收到的是长度为M的单快拍信号，当接收到的反射信号组的数据大于一组时，接收到的是多快拍信号，每个快拍的长度为M。其中M为大于或等于2的整数，即雷达中的接收器(如天线阵列中包括的天线数)大于或等于2个。
S72，根据所述M个反射信号得到Y个扩展信号，所述Y大于所述M。
在获取M个接收器接收到的至少一组反射信号组后，根据接收到的至少一组反射信号 组得到Y个扩展信号，Y为大于M的整数。
S73，对所述Y个扩展信号进行频率分析计算。
由于Y大于M，因此相对于原始的M个反射信号，其信号采样点数有了扩展和增加，频谱阵列长度变长，因此对Y个扩展信号进行频率分析计算，能够实现超分辨，提高频谱分辨率。在得到Y个扩展信号后，对Y个扩展信号进行频率分析计算，其中，可以是对Y个扩展信号进行FFT计算，得到FFT空间谱，也可以是对Y个扩展信号进行DBF计算，得到DBF空间谱，等等，本申请实施例对此不作限定。
本申请实施例提供的频率分析方法，首先获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，M个反射信号为至少一个对象对相同发射信号进行反射的信号，然后，根据M个反射信号组得到Y个扩展信号，且Y大于M。通过对M个反射信号进行扩展处理，得到的Y个扩展信号提高了频谱阵列的长度，能够实现超分辨，提高信号的分辨率，且无需进行协方差计算，减小了计算量，能够实现在系统中的快速处理。
下面将举例说明本申请中获取扩展信号的方案。
以图2所示的线阵为例，M个接收器接收到的一组反射信号组中的M个反射信号可以表示为：
其中M表示接收器的个数，同时也是采样点数。
表示M个反射信号中的第i个反射信号，i大于或等于1且小于或等于M。φ表示测量初相，
d
m为相对参考接收阵元的阵列长度，其可用的有效采样数为M，λ表示观测信号载频波长。
对于参考接收阵元的选取，可以选取M个接收器中的任意一个接收器作为参考接收阵元，则d
m为相对该参考接收阵元的阵列长度。例如，M个接收器从左至右顺序排列时，若选择左边第一个接收器作为参考接收阵元，则d
m为相对左边第一个接收器的阵列长度，若选择右边第一个接收器作为参考接收阵元，则d
m为相对右边第一个接收器的阵列长度，当然，参考接收阵元还可以选左边第二个、第三个等等。在选定参考接收阵元后，任意接收器的d
m即为相对选定的参考接收阵元的阵列长度。
在得到M个反射信号后，可以提取任意一个接收器的接收相位，例如，提取第n个接收器的接收相位，可得：
以n＝1，即提取第一个接收器的接收相位为例，式(8)提取第一个接收器的接收相位可以表示为：
然后，构造向量b(θ)如下：
b(θ)＝[b
M
*(θ),b
M-1
*(θ),...,b
1(θ),...,b
M-1(θ),b
M(θ)] (10)
其中，b
m(θ)＝a
m(θ)*a
n
*(θ)，其中，a
m(θ)表示第m个接收器接收到的反射信号，即a(θ) 中的第m个元素。n为常数，即之前选定的提取第n个接收器的接收相位，a
n
*(θ)表示a
n(θ)的共轭。
仍以n＝1为例，则：
b
m(θ)＝a
m(θ)*a
1
*(θ) (11)
从而可得构造的向量b(θ)如下：
对于多对象(以下以两目标观测为例)，第m个接收器接收到的反射信号可以表示为：
其中α
1和α
2分别表示两个对象的幅度，θ
1和θ
2分别表示两个对象的DOA角度，φ
1和φ
2分别表示对两个对象的采样初相。
根据式(11)构造如下向量元素：
根据欧拉公式e
ix＝cos x+i sin x对上式进行转换，有：
在式(14)中，第3项
和第4项
为初相φ1和φ2的表达式，利用多快拍数据可以平均消除式(14)中的第3项和第4项，即消除目标交叉项。然后构造如式(10)所示的构造矩阵进行频谱分析。
下面将结合附图对本申请的方案进行详细说明。
首先针对M个接收器接收到的反射信号组的数量为一组来进行说明。当M个接收器接收到的反射信号组的数量为一组时，一组反射信号组对应的M个反射信号为一个长度为M的单快拍信号。图8为本申请实施例提供的反射信号处理示意图一，如图8所示，其中 初始接收到的原始信号即为M个反射信号。
在接收到M个反射信号后，需要对其进行平滑操作，将其划分为N组处理向量，每组处理向量中包括M-N+1个向量元素。图9为本申请实施例提供的平滑操作示意图，如图9所示，以M个反射信号中从第一个反射信号开始进行平滑操作为例，根据M个反射信号中的第1至M-N+1个反射信号，得到第1组处理向量，根据M个反射信号中的第2至M-N+2个反射信号，得到第2组处理向量，以此类推，得到N组处理向量。
如图9中所示，M为20，N为6，则共得到6组处理向量，每组处理向量中包括M-N+1＝15个向量元素。图9中对M个反射信号依次进行了编号，从1-20，每个方框代表一个反射信号。则，根据反射信号1至反射信号15，得到第一组处理向量；根据反射信号2至反射信号16，得到第二组处理向量；根据反射信号3至反射信号17，得到第三组处理向量；根据反射信号4至反射信号18，得到第四组处理向量；根据反射信号5至反射信号19，得到第五组处理向量；根据反射信号6至反射信号20，得到第六组处理向量。图9示例的是从第1个反射信号开始进行平滑操作为例，可选的，也可以从第2个、第3个或第i个来开始进行平滑操作，其平滑操作的步骤类似，此处不再赘述。当从第i个反射信号开始进行平滑操作时，任意第i组处理向量中的向量元素依次为：
Ai(θ)＝[a
i(θ),a
i+1(θ),a
i+2(θ),...,a
M-N+i(θ)]，
其中，Ai(θ)为第i组处理向量，a
i(θ)为M个反射信号中的第i个反射信号，a
M-N+i(θ)为M个反射信号中的第M-N+i个反射信号，i为小于或等于N的正整数。
例如，M个反射信号为：
其中，
表示第i个反射信号。对其进行平滑操作，可得到N组处理向量，任意第i组处理向量中的向量元素为：
将N组处理向量写成矩阵形式如下：
在得到N组处理向量后，根据N组处理向量，可以得到N个子阵扩展向量。
具体的，将N个包括(M-N+1)个向量元素的处理向量分别乘以各自第n个采样信号的共轭，得到N个长度为(M-N+1)的处理数据，然后根据N个长度为(M-N+1)的处理数据，得到N个子阵扩展向量。
例如，对A(θ)进行处理，将N个包括(M-N+1)个向量元素的处理向量分别乘以各自第一个采样信号的共轭，得到N个处理数据如下：
然后根据A(θ)得到N个子阵扩展向量，针对任意处理数据，构造N个长度为2*(M-N+1)的数据，具体的，对于N个[1：M-N+1]数据，取[2:M-N+1]个数据的共轭，并倒序排列到N个数据各自第一个数据之前。
其中，
b
1(θ)为第1个子阵扩展向量，b
2(θ)为第2个子阵扩展向量，b
N(θ)为第N个子阵扩展向量。第i个子阵扩展向量为：
其中，第i个子阵扩展向量b
i(θ)中包括2*(M-N+1)-1个向量元素。
将N个长度为2*(M-N+1)-1的数据进行平均，获得1个长度为2*(M-N+1)-1的数据。
其中，Y个扩展信号中的第i个扩展信号为：
其中，r
i所述第i个扩展信号，b
im为第m个子阵扩展向量中的第i个向量元素，i为小于或等于Y的正整数，m为小于或等于N的正整数，其中，Y＝2*(M-N+1)-1。由于Y>M，因此有2*(M-N+1)-1>M，即M>2N-1。
例如，针对Y个扩展信号中的第1个扩展信号，对b
1(θ)，b
2(θ)，...，b
N(θ)中的第一个向量元素求均值，得到第1个扩展信号为：
对于其他的扩展信号的求解方式与第1个扩展信号的求解方式类似，此处不再赘述。
上述方案是针对M个接收器接收到的反射信号组的数量为一组来进行说明的，实际上M个接收器可以接收到多组反射信号组。当反射信号组的数量为K组时，得到的为多快拍数据，即阵列长度为K*M的反射信号，K为大于1的正整数。
可选的，此时，可根据K组反射信号组，得到对应的N组处理向量，然后根据N组处理向量，得到N个子阵扩展向量。
根据K组反射信号组得到N组处理向量，其中，K与N的关系存在3种可能的情形，分别是K>N、K＝N和K<N。
当K>N时，通过K组反射信号组可得到K*M的反射信号，在K组反射信号组中可任取N组反射信号组，得到N*M的反射信号，然后根据N*M的反射信号，得到对应的N组处理向量，进而根据N组处理向量来得到N个子阵扩展向量。
当K＝N时，通过K组反射信号组可得到N*M的反射信号，此时无需对反射信号进行平滑操作，根据N*M的反射信号，可得到对应的N组处理向量，进而根据N组处理向量 来得到N个子阵扩展向量。
当K<N时，需要对K组反射信号组中的部分或全部反射信号组进行平滑处理，来得到N组反射信号组，然后根据N组反射信号组，得到对应的N组处理向量，进而根据N组处理向量来得到N个子阵扩展向量。
例如，当K＝4，N＝6时，可以对4组反射信号组中的每个反射信号组包括的反射信号进行平滑处理，每个反射信号组平滑处理后得到2组处理向量，4组反射信号组在经过平滑处理后可以得到8组处理向量，然后在8组处理向量中选择6组，得到对应的6组处理向量。例如，也可以在4组反射信号组中任选3组反射信号组，然后将选择的3组反射信号组中的每个反射信号组包括的反射信号进行平滑处理，每个反射信号组平滑处理后得到2组处理向量，3组反射信号组在经过平滑处理后可以得到6组处理向量。
下面将结合图10对多组反射信号组的情形时的反射信号处理进行说明，图10中以K＝N的情形为例，K>N和K<N时的处理方式类似。
图10为本申请实施例提供的反射信号处理示意图二，如图10所示，其中初始接收到的原始信号为K组反射信号组，每组反射信号组中包括M个反射信号。根据每组反射信号组即可得到一组处理向量，从而K组反射信号组对应得到K组处理向量。图10所示的方案与图8所示的方案的主要区别在于，图8中由于获取的是单快拍数据，因此要进行平滑处理，得到N组处理向量，而图10中获取的是多快拍数据，且K＝N，因此无需进行平滑处理即可得到N组处理向量。针对K>N的情形，也无需进行平滑处理，之间在K组处理向量中选择N组处理向量即可。针对K<N的情形，需要对部分或全部反射信号组中的反射信号进行平滑处理，得到N组处理向量。
与上述实施例类似，任意第i组处理向量中的向量元素为：
将N组处理向量写成矩阵形式如下：
在得到N组处理向量后，可得到N个子阵扩展向量，其中，第i个子阵扩展向量为：
bi(θ)＝[bi
M
*(θ),bi
M-1
*(θ),...,bi
1(θ),...,bi
M-1(θ),bi
M(θ)]，
其中，bi(θ)为第i个子阵扩展向量，bi
m(θ)＝ai
m(θ)*ai
n
*(θ)，ai
m(θ)为第i组处理向量中的第m个向量元素，ai
n(θ)为第i组处理向量中的第n个向量元素，ai
n
*(θ)为ai
n(θ)的共轭，i为小于或等于N的正整数，m为小于或等于M的正整数，n为常数。
具体的，将N个包括(M-N+1)个向量元素的处理向量分别乘以各自第n个采样信号的共轭，得到N个长度为(M-N+1)的处理数据，然后根据N个长度为(M-N+1)的处理数据，得到N个子阵扩展向量，本申请实施例中，以n＝1为例进行说明。
例如，对A(θ)进行处理，将N个包括(M-N+1)个向量元素的处理向量分别乘以各自第一个采样信号的共轭，得到N个处理数据如下：
然后根据A(θ)得到N个子阵扩展向量，具体的，根据bi
m(θ)＝ai
m(θ)*ai
1
*(θ)来构造子阵扩展向量中的向量元素，针对任意处理数据，构造N个长度为2*(M-N+1)的数据，具体的，对于N个[1：M-N+1]数据，取[2:M-N+1]个数据的共轭，并倒序排列到N个数据各自第一个数据之前。
其中，
在得到N个子阵扩展向量，将其进行数据平均，即可得到Y个扩展信号，其中，Y个扩展信号中的第i个扩展信号为：
其中，r
i第i个扩展信号，b
im为第m个子阵扩展向量b
m(θ)中的第i个向量元素，i为小于或等于Y的正整数，m为小于或等于N的正整数。
在对M个接收器接收到的反射信号组进行如上的处理后，可得到Y个扩展信号，然后，对上述方案获得的Y个扩展信号进行多种频率分析计算，获得目标角度谱，例如可以进行FFT或DBF估计，得到FFT空间谱或DBF空间谱。
为了验证本申请的方案的效果，针对几种频率分析的方案进行了仿真，仿真探测的对象为2个，采用的方案包括MUSIC算法、FFT分析以及本申请的方案。图11为本申请实施例提供的仿真结果示意图，如图11所示的空间谱中，横坐标表示角度，纵坐标表示信号强度(振幅)，其中，图11中的实线表示MUSIC算法的空间谱，两种不同类型的虚线分别表示本申请的方案和FFT分析方法的空间谱。从图11中可知，MUSIC算法与本申请的方案，均能识别出两个对象，而FFT分析方法无法对两个对象进行较好的分辨，同时，由于本申请的方案无需进行协方差分解，因此与MUSIC算法相比，其计算复杂度更小。
图12为本申请实施例提供的频率分析装置的结构示意图，如图12所示，包括：
获取模块121用于获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，所述M个反射信号为至少一个对象对相同发射信号进行反射的信号，所述M为大于或等于2的整数；
处理模块122用于根据所述M个反射信号得到Y个扩展信号，所述Y大于所述M；
分析模块123用于对所述Y个扩展信号进行频率分析计算。
在一种可能的实现方式中，所述处理模块122具体用于：
根据所述至少一组反射信号组，得到N个子阵扩展向量，每个子阵扩展向量中包括Y个向量元素，所述N为大于1的正整数；
根据所述N个子阵扩展向量，得到所述Y个扩展信号。
在一种可能的实现方式中，所述Y个扩展信号中的第i个扩展信号为：
其中，r
i所述第i个扩展信号，b
im为第m个子阵扩展向量中的第i个向量元素，i为小于或等于Y的正整数，m为小于或等于N的正整数。
在一种可能的实现方式中，所述反射信号组的数量为一组；所述处理模块122具体用于：
对所述M个反射信号进行平滑处理，得到N组处理向量，每组处理向量中包括(M-N+1)个向量元素；
根据所述N组处理向量，得到所述N个子阵扩展向量。
在一种可能的实现方式中，第i组处理向量中向量元素为：
Ai(θ)＝[a
i(θ),a
i+1(θ),a
i+2(θ),...,a
M-N+i(θ)]，
其中，Ai(θ)为所述第i组处理向量，a
i(θ)为所述M个反射信号中的第i个反射信号，a
M-N+i(θ)为所述M个反射信号中的第M-N+i个反射信号，所述i为小于或等于N的正整数。
在一种可能的实现方式中，所述N个子阵扩展向量中的第i个子阵扩展向量为：
bi(θ)＝[bi
M
*(θ),bi
M-1
*(θ),...,bi
1(θ),...,bi
M-1(θ),bi
M(θ)]，
其中，bi(θ)为第i个子阵扩展向量，bi
m(θ)＝ai
m(θ)*ai
n
*(θ)，ai
m(θ)为第i组处理向量中的第m个向量元素，ai
n(θ)为第i组处理向量中的第n个向量元素，ai
n
*(θ)为ai
n(θ)的共轭，i为小于或等于N的正整数，m为小于或等于(M-N+1)的正整数，n为常数，n大于或等于1且小于M-N+1。
在一种可能的实现方式中，所述M和所述N满足：
M>2N-1。
在一种可能的实现方式中，所述反射信号组的数量为K组，所述K为大于1的整数；所述处理模块122具体用于：
根据所述K组反射信号组，得到对应的N组处理向量；
根据所述N组处理向量，得到所述N个子阵扩展向量。
在一种可能的实现方式中，所述分析模块123具体用于：
对所述Y个扩展信号进行快速傅里叶变换FFT计算，得到FFT空间谱；或者，
对所述Y个扩展信号进行数字波束成型DBF计算，得到DBF空间谱。
本申请实施例提供的频率分析装置可以执行上述方法实施例所示的技术方案，其实现原理以及有益效果类似，此处不再进行赘述。
图13为本申请实施例提供的频率分析设备的硬件结构示意图，如图13所示，该频率分析设备包括：处理器131和存储器132。其中，处理器131和存储器132通过总线133连接。
在具体实现过程中，处理器131执行所述存储器132存储的计算机执行指令，使得处理器131执行如上的频率分析方法。
处理器131的具体实现过程可参见上述方法实施例，其实现原理和技术效果类似， 本实施例此处不再赘述。
在上述图13所示的实施例中，应理解，处理器可以是中央处理单元(英文：Central Processing Unit，简称：CPU)，还可以是其他通用处理器、数字信号处理器(英文：Digital Signal Processor，简称：DSP)、专用集成电路(英文：Application Specific Integrated Circuit，简称：ASIC)等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合申请所公开的方法的步骤可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。
存储器可能包含高速RAM存储器，也可能还包括非易失性存储NVM，例如至少一个磁盘存储器。
总线可以是工业标准体系结构(Industry Standard Architecture，ISA)总线、外部设备互连(Peripheral Component，PCI)总线或扩展工业标准体系结构(Extended Industry Standard Architecture，EISA)总线等。总线可以分为地址总线、数据总线、控制总线等。为便于表示，本申请附图中的总线并不限定仅有一根总线或一种类型的总线。
本发明实施例还提供一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机执行指令，当处理器执行所述计算机执行指令时，实现如上所述的频率分析方法。
上述的计算机可读存储介质，上述可读存储介质可以是由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。可读存储介质可以是通用或专用计算机能够存取的任何可用介质。
一种示例性的可读存储介质耦合至处理器，从而使处理器能够从该可读存储介质读取信息，且可向该可读存储介质写入信息。当然，可读存储介质也可以是处理器的组成部分。处理器和可读存储介质可以位于专用集成电路(Application Specific Integrated Circuits，简称：ASIC)中。当然，处理器和可读存储介质也可以作为分立组件存在于设备中。
本申请实施例还提供一种雷达，该雷达包括发射器、接收阵列和频率分析设备，接收阵列包括M个接收器，M为大于1的整数；
发射器，用于发射探测信号；
接收阵列，用于接收反射信号；
频率分析设备，用于根据如上述实施例中所提供的频率分析方法，对接收阵列的接收信号进行频率分析，接收阵列的接收信号包括反射信号。
本申请实施例还可提供一种计算机程序产品，该计算机程序产品可以由处理器执行，在计算机程序产品被执行时，可实现上述任一所示的频率分析设备执行的频率分析方法。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执 行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
本领域普通技术人员可以理解：实现上述各方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成。前述的计算机程序可以存储于一计算机可读取存储介质中。该计算机程序在被处理器执行时，实现包括上述各方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述各实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。
Claims (21)
- 一种频率分析方法，其特征在于，包括：获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，所述M个反射信号为至少一个对象对相同发射信号进行反射的信号，所述M为大于或等于2的整数；根据所述M个反射信号得到Y个扩展信号，所述Y大于所述M；对所述Y个扩展信号进行频率分析计算。
- 根据权利要求1所述的方法，其特征在于，根据所述M个反射信号得到Y个扩展信号，包括：根据所述M个反射信号，得到N个子阵扩展向量，每个子阵扩展向量中包括Y个向量元素，所述N为大于1的正整数；根据所述N个子阵扩展向量，得到所述Y个扩展信号。
- 根据权利要求2所述的方法，其特征在于，所述Y个扩展信号中的第i个扩展信号为：
其中，r i所述第i个扩展信号，b im为第m个子阵扩展向量中的第i个向量元素，i为小于或等于Y的正整数，m为小于或等于N的正整数。 - 根据权利要求2或3所述的方法，其特征在于，所述反射信号组的数量为一组；根据所述M个反射信号，得到N个子阵扩展向量，包括：对所述M个反射信号进行平滑处理，得到N组处理向量，每组处理向量中包括(M-N+1)个向量元素；根据所述N组处理向量，得到所述N个子阵扩展向量。
- 根据权利要求4所述的方法，其特征在于，第i组处理向量中向量元素为：Ai(θ)＝[a i(θ),a i+1(θ),a i+2(θ),...,a M-N+i(θ)]，其中，Ai(θ)为所述第i组处理向量，a i(θ)为所述M个反射信号中的第i个反射信号，a M-N+i(θ)为所述M个反射信号中的第M-N+i个反射信号，所述i为小于或等于N的正整数。
- 根据权利要求5所述的方法，其特征在于，所述N个子阵扩展向量中的第i个子阵扩展向量为：bi(θ)＝[bi M *(θ),bi M-1 *(θ),...,bi 1(θ),...,bi M-1(θ),bi M(θ)]，其中，bi(θ)为第i个子阵扩展向量，bi m(θ)＝ai m(θ)*ai n *(θ)，ai m(θ)为第i组处理向量中的第m个向量元素，ai n(θ)为第i组处理向量中的第n个向量元素，ai n *(θ)为ai n(θ)的共轭，i为小于或等于N的正整数，m为小于或等于(M-N+1)的正整数，n为常数，n大于或等于1且小于M-N+1。
- 根据权利要求4-6任一项所述的方法，其特征在于，所述M和所述N满足：M>2N-1。
- 根据权利要求2或3所述的方法，其特征在于，所述反射信号组的数量为K组，所述K为大于1的整数；根据所述M个反射信号，得到N个子阵扩展向量，包括：根据所述K组反射信号组，得到对应的N组处理向量；根据所述N组处理向量，得到所述N个子阵扩展向量。
- 根据权利要求1-8任一项所述的方法，其特征在于，对所述Y个扩展信号进行频率分析计算，包括：对所述Y个扩展信号进行快速傅里叶变换FFT计算，得到FFT空间谱；或者，对所述Y个扩展信号进行数字波束成型DBF计算，得到DBF空间谱。
- 一种频率分析装置，其特征在于，包括：获取模块，用于获取M个接收器接收到的至少一组反射信号组，每组反射信号组中包括M个接收器对应的M个反射信号，所述M个反射信号为至少一个对象对相同发射信号进行反射的信号，所述M为大于或等于2的整数；处理模块，用于根据所述M个反射信号得到Y个扩展信号，所述Y大于所述M；分析模块，用于对所述Y个扩展信号进行频率分析计算。
- 根据权利要求10所述的装置，其特征在于，所述处理模块具体用于：根据所述M个反射信号，得到N个子阵扩展向量，每个子阵扩展向量中包括Y个向量元素，所述N为大于1的正整数；根据所述N个子阵扩展向量，得到所述Y个扩展信号。
- 根据权利要求11所述的装置，其特征在于，所述Y个扩展信号中的第i个扩展信号为：
其中，r i所述第i个扩展信号，b im为第m个子阵扩展向量中的第i个向量元素，i为小于或等于Y的正整数，m为小于或等于N的正整数。 - 根据权利要求11或12所述的装置，其特征在于，所述反射信号组的数量为一组；所述处理模块具体用于：对所述M个反射信号进行平滑处理，得到N组处理向量，每组处理向量中包括(M-N+1)个向量元素；根据所述N组处理向量，得到所述N个子阵扩展向量。
- 根据权利要求13所述的装置，其特征在于，第i组处理向量中向量元素为：Ai(θ)＝[a i(θ),a i+1(θ),a i+2(θ),...,a M-N+i(θ)]，其中，Ai(θ)为所述第i组处理向量，a i(θ)为所述M个反射信号中的第i个反射信号，a M-N+i(θ)为所述M个反射信号中的第M-N+i个反射信号，所述i为小于或等于N的正整数。
- 根据权利要求14所述的装置，其特征在于，所述N个子阵扩展向量中的第i个子阵扩展向量为：bi(θ)＝[bi M *(θ),bi M-1 *(θ),...,bi 1(θ),...,bi M-1(θ),bi M(θ)]，其中，bi(θ)为第i个子阵扩展向量，bi m(θ)＝ai m(θ)*ai n *(θ)，ai m(θ)为第i组处理向量中的第m个向量元素，ai n(θ)为第i组处理向量中的第n个向量元素，ai n *(θ)为ai n(θ)的共轭，i为小于或等于N的正整数，m为小于或等于(M-N+1)的正整数，n为常数，n大于或等于1且小于M-N+1。
- 根据权利要求13-15任一项所述的装置，其特征在于，所述M和所述N满足：M>2N-1。
- 根据权利要求11或12所述的装置，其特征在于，所述反射信号组的数量为K组，所述K为大于1的整数；所述处理模块具体用于：根据所述K组反射信号组，得到对应的N组处理向量；根据所述N组处理向量，得到所述N个子阵扩展向量。
- 根据权利要求10-17任一项所述的装置，其特征在于，所述分析模块具体用于：对所述Y个扩展信号进行快速傅里叶变换FFT计算，得到FFT空间谱；或者，对所述Y个扩展信号进行数字波束成型DBF计算，得到DBF空间谱。
- 一种频率分析设备，其特征在于，包括：处理器和存储器；所述存储器存储计算机执行指令；所述处理器执行所述存储器存储的计算机执行指令，使得所述处理器执行如权利要求1至9任一项所述的频率分析方法。
- 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储有计算机执行指令，当处理器执行所述计算机执行指令时，实现如权利要求1至9任一项所述的频率分析方法。
- 一种雷达，其特征在于，包括发射器、接收阵列和频率分析设备，所述接收阵列包括M个接收器，M为大于或等于2的整数；所述发射器，用于发射探测信号；所述接收阵列，用于接收反射信号；所述频率分析设备，用于根据如权利要求1至9中任一项所提供的频率分析方法，对所述接收阵列的接收信号进行频率分析，所述接收阵列的接收信号包括所述反射信号。
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