MIMO-OFDM radar target parameter estimation method, device and equipment and storage medium

Abstract

The invention relates to the technical field of radars, and discloses an MIMO-OFDM radar target parameter estimation method, device and equipment and a storage medium, and the method comprises the steps: constructing a radar orthogonal transmission signal according to the mixing proportion of range division multiplexing and Doppler division multiplexing; constructing a far-field target echo model based on the emission signal, and preprocessing an echo signal to obtain a discrete echo signal; performing two-dimensional discrete Fourier transform on the discrete echo signal to obtain a distance-speed image corresponding to a single receiving antenna; mIMO multi-channel separation is carried out on the distance-speed image of the single receiving antenna through a band-pass filter bank to obtain a distance-speed image corresponding to each transmitting and receiving channel; and based on the distance-speed images of all the transmitting and receiving channels, performing angle dimension discrete Fourier transform to obtain a target angle image, and realizing estimation of target parameters. The angle error problem of distance dependence is avoided, and the angle imaging resolution and the angle estimation precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular, to a method, device, equipment and storage medium for estimating target parameters of a MIMO-OFDM radar. Background Art

[0002] By forming a virtual array, a Multiple-Input Multiple-Output (MIMO) radar can obtain a high angular resolution with a limited number of antennas. Orthogonal Frequency Division Multiplexing (OFDM) is a common multi-carrier transmission technology in modern communication, which has advantages such as a large time-bandwidth product, flexible parameter design, and good anti-interception performance. Combining OFDM with a MIMO radar can achieve high-resolution ranging and high-precision angle measurement.

[0003] Orthogonal waveforms are the key to the implementation of a MIMO-OFDM radar. A MIMO-OFDM radar usually adopts the Equidistant Subcarrier Interleaving (ESI) method, evenly distributing the interleaved subcarriers to each transmitting antenna to achieve orthogonality in the frequency domain for different transmission numbers, and realizing three-dimensional imaging of target range-velocity-angle based on the Three-Dimensional Discrete Fourier Transform (3D DFT). However, compared with a single-input single-output OFDM radar, ESI will reduce the maximum unambiguous range. In addition, since a fixed frequency offset is introduced between adjacent transmitting array elements in ESI, a distance-dependent phase error problem is generated, reducing the angle estimation accuracy.

[0004] Therefore, there is an urgent need for a method for estimating target parameters of a MIMO-OFDM radar that can overcome the defects of existing technical solutions, avoid the distance-dependent angle error problem, and improve the angle imaging resolution and angle estimation accuracy. Summary of the Invention

[0005] In view of this, the present application provides a method, device, equipment and storage medium for estimating target parameters of a MIMO-OFDM radar, which can avoid the distance-dependent angle error problem and improve the angle imaging resolution and angle estimation accuracy. The technical solution is as follows.

[0006] In a first aspect, the present invention provides a method for estimating target parameters of a MIMO-OFDM radar. The MIMO-OFDM radar includes a plurality of transmitting antennas and receiving antennas, and each transmitting antenna and each receiving channel form a virtual transmit-receive channel. The method includes:

[0007] Perform phase allocation on the MIMO transmit antennas according to the preset hybrid ratio of range division multiplexing and Doppler division multiplexing, and construct a radar orthogonal transmit signal;

[0008] Construct a far-field target echo model based on the transmit signal, obtain the echo signals corresponding to each receiving antenna, and preprocess the echo signals to obtain the discrete echo signals corresponding to each receiving antenna;

[0009] Perform a two-dimensional discrete Fourier transform on the discrete echo signals to obtain the range-velocity images corresponding to each receiving antenna;

[0010] Construct a bandpass filter bank based on the preset hybrid ratio of range division multiplexing and Doppler division multiplexing; perform MIMO multi-channel separation through the bandpass filter bank to obtain the range-velocity images corresponding to each transmit-receive channel;

[0011] Based on the range-velocity images corresponding to all transmit-receive channels, perform an angular dimension discrete Fourier transform to obtain the target angular image and realize the estimation of target parameters.

[0012] In an alternative embodiment, the expression of the transmit signal is:

[0013]

[0014] where p = 0, 1, …, P - 1 is the transmit antenna index, P is the total number of transmit antennas, A(n, m) is the m-th modulation symbol of the n-th subcarrier frequency, T s = T + T g is the complete OFDM symbol period, T = 1 / Δf is the effective OFDM symbol period, Δf is the subcarrier frequency spacing, T g is the cyclic prefix period, rect(t / T s ) is the unit rectangular window function, f c is the carrier frequency, φ p and are the RDM and DDM phases added to the p-th transmit antenna, respectively.

[0015] In an alternative embodiment, obtaining the baseband echo signals corresponding to each receiving antenna further includes: preprocessing the baseband echo signals corresponding to each receiving antenna to obtain the discrete forms of the baseband echo signals corresponding to each receiving antenna; the preprocessing includes down-conversion, sampling, or removing the cyclic prefix.

[0016] In an alternative embodiment, the expression of the discrete form of the baseband echo signal is:

[0017]

[0018] where \(q = 0,1,\cdots,Q - 1\) is the receiving antenna index, \(Q\) is the total number of receiving antennas, \(i = 0,1,\cdots,N - 1\) and \(m = 0,1,\cdots,M - 1\) respectively represent the fast-time dimension index and the slow-time dimension index, \(\rho=\sigma\exp(-j2\pi f c \tau)\), \(\sigma\) is the target scattering coefficient, \(\tau = 2R / c\) is the time delay, \(f d =2vf c / c\) is the Doppler caused by the target motion, \(R\), \(v\) and \(\theta\) are the initial distance, velocity and angle of the target relative to the radar, and \(c\) is the speed of light.

[0019] In an alternative embodiment, performing a two-dimensional discrete Fourier transform on the discrete echo signal to obtain a range-velocity image corresponding to each receiving antenna, including:

[0020] Performing a discrete Fourier transform on the discrete echo signal along the fast-time dimension to obtain a frequency-domain signal, and performing matrix division to obtain a radar channel matrix containing target range, velocity and angle information, the expression is:

[0021]

[0022] where \(n = 0,1,\cdots,N - 1\) is the frequency dimension index;

[0023] Performing an inverse discrete Fourier transform on the radar channel matrix along the frequency dimension and a discrete Fourier transform along the slow-time dimension to obtain a range-velocity image corresponding to each receiving antenna, the expression is:

[0024] Z q (l,k)=DFT m (IDFT n (H q )(n,m)));

[0025] where \(l = 0,1,\cdots,N - 1\) is the range bin index, \(k = 0,1,\cdots,M - 1\) is the velocity bin index. Performing peak detection on \(Z q to obtain the estimated values of the target range and velocity corresponding to the \((p,q)\)th transmit-receive channel, the expression is:

[0026]

[0027] where \(l mod,p =N\varphi p / (2\pi)\),

[0028] In an alternative embodiment, imaging the target angle of the far-field target to obtain target angle information, including:

[0029] Based on the preset RDM and DDM phases, constructing a bandpass filter bank for \(Zq Perform MIMO multi-channel separation to obtain P×Q velocity images. Rearrange the P×Q velocity images, and the expression is:

[0030] G(l,k) = [Z 0,0 (l,k), Z 0,1 (l,k), …, Z 0,Q-1 (l,k), Z 1,0 (l,k) …, Z P-1,Q-1 (l,k)];

[0031] Then the expression of the three-dimensional matrix including the target distance, velocity, and angle is:

[0032]

[0033] Perform discrete Fourier transform on this three-dimensional matrix in the angle dimension to obtain the target angle image.

[0034] The MIMO-OFDM radar target parameter estimation method provided by the present invention has the following advantages.

[0035] The MIMO-OFDM radar target parameter estimation method of the present invention is applied to a MIMO-OFDM radar, which includes a plurality of transmitting antennas and receiving antennas, and each transmitting antenna and each receiving channel form a plurality of virtual transmit-receive channels. First, according to the mixing ratio of range division multiplexing and Doppler division multiplexing, phase allocation is performed on the MIMO transmitting antennas to achieve orthogonality of different transmitted signals in the range domain and the Doppler domain. Subsequently, a far-field target echo signal is constructed based on the transmitted signal, and the echo signal is preprocessed. The preprocessed echo signal is subjected to a discrete Fourier transform along the fast time dimension, and matrix division is performed to obtain a radar channel matrix containing target range, velocity, and angle information. The radar channel matrix is subjected to an inverse discrete Fourier transform along the frequency dimension to obtain a range image; the range image is subjected to a discrete Fourier transform along the slow time dimension to obtain a range-velocity image. According to the preset range division multiplexing and Doppler division multiplexing phase design, a bandpass filter bank is designed, and MIMO multi-channel separation is performed through multiple groups of bandpass filter banks to obtain the range-velocity image of a single transmit-receive channel. Based on the range-velocity images corresponding to all transmit-receive channels, a discrete Fourier transform is performed in the angle dimension to obtain a target angle image, and target parameters are estimated based on this angle image, thereby realizing joint estimation of the target range, velocity, and angle. The MIMO-OFDM radar target parameter estimation method provided by the present invention combines range division multiplexing and Doppler division multiplexing technologies to achieve orthogonality of different transmitted signals in the range domain and the Doppler domain. The processing of range, velocity, and angle is all based on discrete Fourier transform, and there is no range-related phase term in the beamforming vector of the constructed virtual array, thereby avoiding the range-dependent angle error problem and improving the angle imaging resolution and angle estimation accuracy. In addition, this method can adapt to more antenna configurations, alleviate the limitations brought by a single multiplexing technology, and achieve a performance compromise between non-ambiguous range and non-ambiguous velocity by flexibly adjusting the mixing ratio.

[0036] In a second aspect, the present invention provides a MIMO-OFDM radar target parameter estimation device. The MIMO-OFDM radar includes a plurality of transmitting antennas and receiving antennas, and each transmitting antenna and each receiving channel form a plurality of virtual transmit-receive channels. The device includes:

[0037] A transmitted signal construction module, configured to perform phase allocation on the MIMO transmitting antennas according to a preset mixing ratio of range division multiplexing and Doppler division multiplexing to construct orthogonal radar transmitted signals;

[0038] An echo signal acquisition module, configured to construct a far-field target echo model based on the transmitted signal, obtain echo signals corresponding to each receiving antenna, and preprocess the echo signals to obtain discrete echo signals corresponding to each receiving antenna;

[0039] A range-velocity image calculation module, which is used to perform two-dimensional discrete Fourier transform on the discrete echo signals to obtain range-velocity images corresponding to each receiving antenna;

[0040] A MIMO multi-channel separation module, which is used to construct a band-pass filter bank based on a preset mixing ratio of range division multiplexing and Doppler division multiplexing; perform MIMO multi-channel separation through the band-pass filter bank to obtain range-velocity images corresponding to each transmitting and receiving channel;

[0041] A parameter estimation module, which is used to perform discrete Fourier transform in the angle dimension based on the range-velocity images corresponding to all transmitting and receiving channels to obtain a target angle image, and realize the estimation of target parameters.

[0042] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the MIMO-OFDM radar target parameter estimation method according to the first aspect or any corresponding embodiment thereof.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the MIMO-OFDM radar target parameter estimation method according to the first aspect or any corresponding embodiment thereof.

[0044] In a fifth aspect, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the MIMO-OFDM radar target parameter estimation method according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a method flow chart of a MIMO-OFDM radar target parameter estimation method provided by an embodiment of the present invention.

[0047] Figure 2 It is a method flow chart of a MIMO-OFDM radar target parameter estimation method shown according to an exemplary embodiment.

[0048] Figure 3It is a schematic diagram showing two target range-velocity diagrams at a transmit antenna number of 8 and a mixing ratio of 2:4 according to an exemplary embodiment.

[0049] Figure 4 It is a three-dimensional range-angle schematic diagram of four targets at a mixing ratio of 8:1 according to an exemplary embodiment.

[0050] Figure 5 It is a schematic diagram showing a two-dimensional range-angle diagram and estimation results of four targets after being separated by a band-pass filter bank at a mixing ratio of 8:1 according to an exemplary embodiment.

[0051] Figure 6 It is a schematic diagram of a three-dimensional range-angle diagram of four targets under the ESI method according to an exemplary embodiment.

[0052] Figure 7 It is a schematic diagram showing a two-dimensional range-angle diagram and estimation results of four targets after being separated by a band-pass filter bank under the ESI method according to an exemplary embodiment.

[0053] Figure 8 It is a three-dimensional range-velocity schematic diagram of two targets at a mixing ratio of 4:2 according to an exemplary embodiment.

[0054] Figure 9 It is a schematic diagram showing range and velocity estimation results of two targets at different mixing ratios according to an exemplary embodiment.

[0055] Figure 10 It is a schematic diagram of the structure of a download process control device provided by an embodiment of the present application.

[0056] Figure 11 It is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention. Detailed implementation manners

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0058] It should be understood that the "indication" mentioned in the embodiments of the present application can be a direct indication, an indirect indication, or a representation of an associated relationship. For example, A indicates B, which can mean that A directly indicates B. For example, B can be obtained through A; it can also mean that A indirectly indicates B. For example, A indicates C and B can be obtained through C; it can also mean that there is an associated relationship between A and B.

[0059] In the description of the embodiments of the present application, the term "corresponding" can indicate a direct or indirect corresponding relationship between two things, can also indicate an associated relationship between the two, or can be relationships such as indication and being indicated, configuration and being configured, etc.

[0060] In the embodiments of the present application, "predefined" can be implemented by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in a device (for example, including terminal devices and network devices). The present application does not limit its specific implementation method.

[0061] Multiple Input Multiple Output (MIMO) radar can obtain a high angular resolution with a limited number of antennas by forming virtual array elements. Orthogonal Frequency Division Multiplexing (OFDM) is a common multi-carrier transmission technology in modern communication, which has advantages such as a large time-bandwidth product, flexible parameter design, and good anti-interception performance. Combining OFDM with MIMO radar can achieve high-resolution ranging and high-precision angle measurement, and has received extensive attention in vehicle-mounted millimeter-wave radars in recent years.

[0062] Orthogonal waveforms are the key to the implementation of MIMO-OFDM radar. MIMO-OFDM radar usually adopts the Equidistant Subcarrier Interleaving (ESI) method to evenly distribute the interleaved subcarriers to each transmitting antenna, so as to achieve orthogonality of different transmit signals in the frequency domain. And based on the Three-Dimensional Discrete Fourier Transform (3DDFT), three-dimensional imaging of target range-velocity-angle is realized. However, compared with the single-input single-output OFDM radar, ESI will reduce the maximum unambiguous range. In addition, since a fixed frequency offset is introduced between adjacent transmitting array elements in ESI, a range-dependent phase error problem is generated, reducing the angle estimation accuracy. Another type of multiplexing method is implemented by Range-division multiplexing (RDM) in the MIMO-OFDM radar communication integrated system to achieve orthogonality of different transmit signals in the range domain, but it will reduce the maximum unambiguous range. The Doppler-division multiplexing (DDM) method realizes the separation of different transmit signals in the Doppler domain, but it will reduce the maximum unambiguous velocity.

[0063] To solve the problems existing in the prior art, the embodiment of the present invention provides a method for estimating target parameters of MIMO-OFDM radar. This method combines RDM and DDM technologies to achieve orthogonality of different transmit signals in the range domain and the Doppler domain. The processing of range, velocity, and angle is all based on the discrete Fourier transform. There is no range-related phase term in the constructed virtual array beamforming vector, thus avoiding the range-dependent angle error problem and improving the angle imaging resolution and angle estimation accuracy. In addition, this method can adapt to more antenna configurations, alleviate the limitations brought by a single multiplexing technology, and achieve a performance trade-off between the unambiguous range and the unambiguous velocity by flexibly adjusting the mixing ratio.

[0064] For the method for estimating target parameters of MIMO-OFDM radar in the embodiment of the present invention, the MIMO-OFDM radar includes a plurality of transmitting antennas and receiving antennas, and each transmitting antenna and each receiving channel form a plurality of virtual transmit-receive channels. The flow of this method is as Figure 1 shown and includes the following steps.

[0065] S101: According to the preset mixing ratio of range-division multiplexing and Doppler-division multiplexing, perform phase allocation on the MIMO transmitting antennas to construct radar orthogonal transmit signals.

[0066] Optionally, in step S101, it is set that the MIMO-OFDM radar is provided with P transmitting antennas and Q receiving antennas, and both the transmitting and receiving arrays are uniform linear arrays. The interval of the receiving array is d RX = λ / 2, and the interval of the transmitting array elements is d TX = Qλ / 2, where λ is the signal wavelength. The radar performs RDM and DDM phase allocation at the transmitting end according to the mixing ratio, and modulates them on adjacent subcarriers and symbols of OFDM on different transmitting antennas respectively, so as to achieve orthogonality of different transmitted signals in the range domain and the Doppler domain. Then, the radio frequency signal of the p-th transmitting antenna of the radar can be expressed as:

[0067]

[0068] In the formula, p = 0, 1, …, P−1 is the transmitting antenna index, P is the total number of transmitting antennas, A(n,m) is the m-th modulation symbol of the n-th subcarrier, T s = T+T g is the complete OFDM symbol period, T = 1 / Δf is the effective OFDM symbol period, Δf is the subcarrier interval, T g is the cyclic prefix period, rect(t / T s ) is the unit rectangular window function, f c is the carrier frequency, φ p and are the RDM and DDM phases added to the p-th transmitting antenna respectively.

[0069] S102. Construct a far-field target echo model according to the transmitted signal, obtain the echo signals corresponding to each receiving antenna, and preprocess the echo signals to obtain the discrete echo signals corresponding to each receiving antenna.

[0070] Optionally, in step S102, construct an echo model of the far-field target reflection according to the constructed transmitted signal, and preprocess the received echo signals, including down-conversion, sampling, and removing the cyclic prefix, to obtain the discrete form of the baseband echo signal of the q-th receiving antenna, and the expression is:

[0071]

[0072] In the formula, q = 0, 1, …, Q−1 is the receiving antenna index, Q is the total number of receiving antennas, i = 0, 1, …, Ν−1 and m = 0, 1, …, Μ−1 respectively represent the fast time dimension index and the slow time dimension index, ρ = σexp(−j2πf c τ), σ is the target scattering coefficient, τ = 2R / c is the time delay, f d = 2vf c / c is the Doppler generated by the target movement, R, v, and θ are the initial distance, speed, and angle of the target relative to the radar, and c is the speed of light.

[0073] S103. Perform a two-dimensional discrete Fourier transform on the discrete echo signal to obtain the range-velocity image corresponding to each receiving antenna.

[0074] Specifically, in step S103, perform a discrete Fourier transform on the discrete echo signal along the fast time dimension to obtain a frequency-domain signal, and perform matrix division to obtain a radar channel matrix containing target range, velocity, and angle information. Perform an inverse discrete Fourier transform on the radar channel matrix along the frequency dimension and a discrete Fourier transform along the slow time dimension to obtain the range-velocity image corresponding to each receiving antenna.

[0075] Optionally, in the above steps, perform a DFT on y q (i,m) along the fast time dimension to obtain a frequency-domain signal, and perform matrix division to eliminate the influence of modulation symbols to obtain a radar channel matrix containing target range, velocity, and angle information. The expression is:

[0076]

[0077] where n = 0, 1, …, N - 1 is the frequency dimension index;

[0078] Perform an inverse discrete Fourier transform (IDFT) on H q (n,m) along the frequency dimension n and a DFT along the slow time dimension m. This process can be expressed as:

[0079] Z q (l,k) = DFT m (IDFT n (H q (n,m)));

[0080] where l = 0, 1, …, Ν - 1 is the range cell index and k = 0, 1, …, Μ - 1 is the velocity cell index. Z q represents the range-velocity image corresponding to the q-th receiving antenna. Estimate the target range and velocity corresponding to the (p,q)-th transmit-receive channel based on peak detection. The expression is:

[0081]

[0082] where l mod,p = Nφ p / (2π),

[0083] Define the mixing ratio β = N R :N D , and there is N R ×N D = P. According to the preset RDM and DDM phases, the range-velocity map Z of the q-th receiving antenna qThe distance spectrum is equally divided into N R regions, and the velocity spectrum is equally divided into N D regions, forming a total of N R ×N D = P regions. Each region corresponds to the range-velocity image of a transmit-receive channel. In addition, φ p and cause the target position in the range-velocity image corresponding to the (p,q)-th transmit-receive channel to cyclically shift by l mod,p range cells in the range dimension and by k mod,p velocity cells in the velocity dimension.

[0084] Figure 3 shows the schematic diagrams of the range-velocity images of two targets with the number of transmit antennas P = 8 and the mixing ratio β = N R :N D = 2:4. Among them, the RDM and DDM phase sequences are {0,0,0,0,π,π,π,π} and {0,π / 2,π,3π / 2,0,π / 2,π,3π / 2} respectively, and the gray circles represent the target positions. It can be seen from Figure 3 that a single target appears equally spaced 2 times in the range dimension and 4 times in the velocity dimension. This results in the unambiguous range and unambiguous velocity becoming:

[0085]

[0086] S104. Based on the preset hybrid ratio of range division multiplexing and Doppler division multiplexing, construct a bandpass filter bank; perform MIMO multi-channel separation on the range-velocity image of a single receive antenna through this bandpass filter bank to obtain the range-velocity images corresponding to each transmit-receive channel.

[0087] Specifically, in step S104, design a bandpass filter bank according to the preset RDM and DDM phases, and separate the range-velocity image of the q-th receive antenna through multiple groups of bandpass filters to obtain the range-velocity image of the (p,q)-th transmit-receive channel. With Q receive antennas, P×Q range-velocity images can be obtained.

[0088] S105. Based on the range-velocity images corresponding to each transmit-receive channel, perform DFT in the angle dimension to obtain the target angle image and realize the estimation of target parameters.

[0089] Optionally, in step S105, rearrange the P×Q range-velocity images to obtain:

[0090] G(l,k) = [Z 0,0 (l,k),Z 0,1 (l,k),…,Z 0,Q-1 (l,k),Z1,0 (l,k)…,Z P-1,Q-1 (l,k)];

[0091] According to the following known conditions:

[0092] G s (l,k) = G q+pQ (l,k) = Z p,q (l,k);

[0093] where s = q + pQ, 0 ≤ p ≤ P - 1, 0 ≤ q ≤ Q - 1.

[0094] The beamforming vector of the virtual array can be expressed as;

[0095]

[0096] where (·) T represents the transpose operation. There is no range-dependent phase term in b(θ), and the traditional beamforming method can be directly applied for angle estimation.

[0097] Therefore, the three-dimensional matrix containing the target range, velocity, and angle can be expressed as:

[0098]

[0099] Performing DFT on the variable s can obtain the target angle image, thereby realizing the joint estimation of the target range, velocity, and angle.

[0100] In summary, the MIMO-OFDM radar target parameter estimation method provided by the embodiments of the present invention is applied to a MIMO-OFDM radar, which includes a plurality of transmitting antennas and receiving antennas, and each transmitting antenna and each receiving channel form a plurality of virtual transmit-receive channels. First, according to the mixing ratio of range division multiplexing and Doppler division multiplexing, phase allocation is performed on the MIMO transmitting antennas to achieve orthogonality of different transmitted signals in the range domain and the Doppler domain. Subsequently, a far-field target echo signal is constructed based on the transmitted signal, and the echo signal is preprocessed. The preprocessed echo signal is subjected to a discrete Fourier transform along the fast time dimension, and matrix division is performed to obtain a radar channel matrix containing target range, velocity, and angle information. The radar channel matrix is subjected to an inverse discrete Fourier transform along the frequency dimension to obtain a range image; the range image is subjected to a discrete Fourier transform along the slow time dimension to obtain a range-velocity image. According to the preset range division multiplexing and Doppler division multiplexing phase design, a bandpass filter bank is designed, and MIMO multi-channel separation is performed through multiple groups of bandpass filters to obtain the range-velocity image of a single transmit-receive channel. Based on the range-velocity images corresponding to all transmit-receive channels, a discrete Fourier transform is performed in the angle dimension to obtain a target angle image, and target parameters are estimated based on the angle image, thereby realizing joint estimation of the target range, velocity, and angle. The MIMO-OFDM radar target parameter estimation method provided by the present invention combines range division multiplexing and Doppler division multiplexing technologies to achieve orthogonality of different transmitted signals in the range domain and the Doppler domain. The processing of range, velocity, and angle is all based on discrete Fourier transform, and there is no range-related phase term in the beamforming vector of the constructed virtual array, thus avoiding the problem of range-dependent angle error and improving the angle imaging resolution and angle estimation accuracy. In addition, this method can adapt to more antenna configurations, alleviate the limitations brought by a single multiplexing technology, and achieve a performance compromise between unambiguous range and unambiguous velocity by flexibly adjusting the mixing ratio.

[0101] To better illustrate the technical effects achieved by the MIMO-OFDM radar target parameter estimation method provided in this embodiment, the method of the present invention will be compared with existing methods through simulation experiments below. The method flow of the MIMO-OFDM radar target parameter estimation method adopted in this example is as Figure 2 shown, and the method flow is the same as that of the MIMO-OFDM radar target parameter estimation method provided in the above embodiment, which will not be elaborated here. Among them, the radar parameters are set as shown in Table 1.

[0102] Table 1

[0103]

[0104] The specific simulation results are as follows: Figure 4 The three-dimensional range-angle images of four targets at a mixing ratio of 8:1;Figure 5 The two-dimensional range-angle images and estimation results of four targets at a mixing ratio of 8:1 after being separated by a bandpass filter bank. Figure 6 The three-dimensional range-angle images of four targets under the ESI method; Figure 7 The two-dimensional range-angle images and estimation results of four targets under the ESI method after being separated by a bandpass filter bank. The speeds and angles of the four targets are the same, which are 0 m / s and 0°, respectively, and the target distances are 5 m, 25 m, 45 m, and 65 m in sequence. Comparison Figure 4 、 Figure 5 、 Figure 6 and Figure 7 shows that compared with ESI, the hybrid multiplexing has stronger spatial energy focusing and lower sidelobe distribution, thus achieving higher imaging resolution. Moreover, as the distance increases, the angle estimation error of ESI increases, while the hybrid multiplexing remains unchanged, and the angle estimation performance is better than that of ESI. This is because there is a distance-dependent angle error problem in ESI, while the hybrid multiplexing does not have this problem, so it can obtain higher imaging resolution and better angle estimation performance.

[0105] Figure 8 The three-dimensional range-velocity images of two targets of a single receiving antenna at a mixing ratio of 4:2. The target distances are 50 m and 100 m in sequence, and the speeds are -10 m / s and 45 m / s in sequence. It can be seen from the figure that a single target appears 4 times in the range dimension and 2 times in the velocity dimension.

[0106] Figure 9 The range and velocity estimation results of the above two targets under different mixing ratios. It can be seen that when the mixing ratio is 4:2, the target estimation results are close to the real targets. When the mixing ratio is 8:1, range ambiguity occurs for target 2. When the mixing ratio is 2:4, velocity ambiguity occurs for target 2. Therefore, by changing the mixing ratio, the unambiguous range and unambiguous velocity of the MIMO-OFDM radar can be adjusted.

[0107] In summary, the MIMO-OFDM radar target parameter estimation method provided in this example does not have the distance-dependent angle error problem compared with the existing ESI method. Moreover, it can alleviate the limitations of using RDM or DDM alone, and achieve a performance trade-off of the radar in terms of unambiguous range and unambiguous velocity by flexibly adjusting the mixing ratio.

[0108] In an embodiment of the present application, a MIMO-OFDM radar target parameter estimation device is further provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0109] An embodiment of the present application provides a MIMO-OFDM radar target parameter estimation device. Figure 10 FIG. 5 is a schematic structural diagram of a MIMO-OFDM radar target parameter estimation device provided by an embodiment of the present application. The MIMO-OFDM radar includes a plurality of transmitting antennas and receiving antennas. Each transmitting antenna and each receiving channel form a plurality of virtual transmit-receive channels. The device includes:

[0110] A transmit signal construction module 1001, configured to perform phase allocation on the MIMO transmitting antennas according to a preset mixing ratio of range division multiplexing and Doppler division multiplexing, and construct a radar orthogonal transmit signal.

[0111] An echo signal acquisition module 1002, configured to construct a far-field target echo model according to the transmit signal, obtain echo signals corresponding to each receiving antenna, and perform preprocessing on the echo signals to obtain discrete echo signals corresponding to each receiving antenna.

[0112] A range-velocity image calculation module 1003, configured to perform two-dimensional discrete Fourier transform on the discrete echo signals to obtain range-velocity images corresponding to each receiving antenna.

[0113] A MIMO multi-channel separation module 1004, configured to construct a band-pass filter bank based on a preset mixing ratio of range division multiplexing and Doppler division multiplexing; perform MIMO multi-channel separation through the band-pass filter bank to obtain range-velocity images corresponding to each transmit-receive channel.

[0114] A parameter estimation module 1005, configured to perform discrete Fourier transform in the angle dimension based on the range-velocity images corresponding to all transmit-receive channels to obtain a target angle image, and realize the estimation of target parameters.

[0115] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0116] The MIMO-OFDM radar target parameter estimation device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0117] An embodiment of the present invention further provides a computer device having the above Figure 10 shown MIMO-OFDM radar target parameter estimation device.

[0118] Please refer to Figure 11 , Figure 11 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As Figure 11 shown, the computer device includes: one or more processors 10, a memory 20, and an interface for connecting each component, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphic information in a graphical user interface on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 11 In

[0119] FIG. 14, one processor 10 is taken as an example.

[0120] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0121] The memory 20 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0122] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memories.

[0123] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 may be connected through a bus or other means, Figure 11 taking the connection through the bus as an example.

[0124] The embodiments of the present invention further provide a computer-readable storage medium. The methods according to the embodiments of the present invention may be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and to be stored in a local storage medium, so that the methods described herein may be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0125] A part of the present invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can call or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0126] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A MIMO-OFDM radar target parameter estimation method, characterized in that: The MIMO-OFDM radar includes a plurality of transmitting antennas and receiving antennas, each transmitting antenna and each receiving channel constitutes a plurality of virtual transmitting and receiving channels, and the method includes: According to the preset mixing ratio of distance division multiplexing and Doppler division multiplexing, the phase of the MIMO transmitting antenna is allocated to construct the radar orthogonal transmitting signal; Constructing a far-field target echo model according to the transmission signal to obtain an echo signal corresponding to each receiving antenna, and preprocessing the echo signal to obtain a discrete echo signal corresponding to each receiving antenna; Performing a two-dimensional discrete Fourier transform on the discrete echo signal to obtain a range-velocity image corresponding to each receiving antenna; Based on a preset mixing ratio of distance division multiplexing and Doppler division multiplexing, a bandpass filter group is constructed; MIMO multi-channel separation is performed through the bandpass filter group to obtain a range-velocity image corresponding to each transmitting and receiving channel; Based on the range-velocity images corresponding to all channels, the angle-dimensional discrete Fourier transform is performed to obtain the target angle image and realize the estimation of target parameters.

2. The method according to claim 1, characterized in that The expression of the transmission signal is: Where p = 0, 1, ..., P-1 is the transmit antenna index, P is the total number of transmit antennas, A(n, m) is the mth modulation symbol of the nth subcarrier frequency, T s =T+T g is the complete OFDM symbol period, T = 1 / Δf is the effective OFDM symbol period, Δf is the subcarrier frequency interval, T g is the cyclic prefix period, rect(t / T s ) is the unit rectangular window function, f c is the carrier frequency, φ p and are the RDM and DDM phases added to the pth transmit antenna, respectively.

3. The method according to claim 2, characterized in that The obtaining of the baseband echo signal corresponding to each receiving antenna also includes: Preprocessing the baseband echo signal corresponding to each receiving antenna to obtain a discrete form of the baseband echo signal corresponding to each receiving antenna; The pre-processing includes down-conversion, sampling or cyclic prefix removal.

4. The method according to claim 3, characterized in that The discrete form of the baseband echo signal is expressed as: Where q = 0, 1, ..., Q-1 is the index of the receiving antenna, Q is the total number of receiving antennas, i = 0, 1, ..., N-1 and m = 0, 1, ..., M-1 represent the fast time dimension and slow time dimension indexes respectively, ρ = σexp(-j2πf c τ), σ is the target scattering coefficient, τ = 2R / c is the time delay, f d =2vf c / c is the Doppler generated by the target motion, R, v and θ are the initial distance, speed and angle of the target relative to the radar, and c is the speed of light.

5. The method according to claim 4, characterized in that The performing a two-dimensional discrete Fourier transform on the discrete echo signal to obtain a distance-velocity image corresponding to each receiving antenna includes: The discrete echo signal is subjected to discrete Fourier transform along the fast time dimension to obtain the frequency domain signal, and matrix division is performed to obtain the radar channel matrix containing the target distance, speed and angle information, which is expressed as follows: Where n=0,1,…,N-1 is the frequency dimension index; The radar channel matrix is ​​subjected to inverse discrete Fourier transform along the frequency dimension and discrete Fourier transform along the slow time dimension to obtain the range-velocity image corresponding to each receiving antenna, which is expressed as follows: Z q (l,k)=DFT m (IDFT n (H q (n,m))); Where l = 0, 1, ..., N-1 is the distance unit index, k = 0, 1, ..., M-1 is the velocity unit index; Z q Perform peak detection to obtain the distance and speed estimation values ​​corresponding to the (p,q)th transmitting and receiving channel, expressed as: In the formula, l mod,p =Nφ p / (2π), 6. The method according to claim 5, characterized in that The obtaining of the distance-speed image corresponding to each transmitting and receiving channel includes: Based on the preset distance division multiplexing and Doppler division multiplexing phase, a bandpass filter group is constructed to perform MIMO multi-channel separation to obtain P×Q velocity images.

7. The method according to claim 6, characterized in that The method of performing angle-dimensional discrete Fourier transform based on the distance-velocity images corresponding to all channels to obtain the target angle image and realize the estimation of the target parameters includes: The P×Q range-velocity images are rearranged to obtain the expression: G(l,k)=[Z 0,0 (l,k),Z 0,1 (l,k),…,Z 0,Q-1 (l,k),Z 1,0 (l,k)…,Z P-1,Q-1 (l,k)]; The three-dimensional matrix containing the target distance, speed and angle is obtained, and the expression is: The three-dimensional matrix is ​​subjected to discrete Fourier transform in the angle dimension to obtain the target angle image, thereby realizing the joint estimation of the target distance, speed and angle.

8. A MIMO-OFDM radar target parameter estimation device, characterized in that: The MIMO-OFDM radar includes a plurality of transmitting antennas and receiving antennas, each transmitting antenna and each receiving channel constitutes a plurality of virtual transmitting and receiving channels, and the device includes: A transmission signal construction module is used to perform phase allocation on the MIMO transmission antenna according to a preset mixing ratio of distance division multiplexing and Doppler division multiplexing to construct a radar orthogonal transmission signal; An echo signal acquisition module is used to construct a far-field target echo model according to the transmission signal, obtain the echo signal corresponding to each receiving antenna, and pre-process the echo signal to obtain a discrete echo signal corresponding to each receiving antenna; A range-velocity image calculation module, used for performing a two-dimensional discrete Fourier transform on the discrete echo signal to obtain a range-velocity image corresponding to each receiving antenna; The MIMO multi-channel separation module is used to construct a bandpass filter group based on a preset mixing ratio of distance division multiplexing and Doppler division multiplexing; the MIMO multi-channel separation is performed through the bandpass filter group to obtain the range-velocity image corresponding to each transmitting and receiving channel; The parameter estimation module is used to perform angle-dimensional discrete Fourier transform based on the range-velocity images corresponding to all transmitting and receiving channels to obtain the target angle image and realize the estimation of target parameters.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the MIMO-OFDM radar target parameter estimation method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the MIMO-OFDM radar target parameter estimation method according to any one of claims 1 to 7.

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