Angle calculation method and device in MIMO radar

Abstract

The invention provides an angle calculation method and device in an MIMO radar, and the method comprises the steps: collecting original time domain data, and carrying out the two-dimensional compression, and obtaining the compressed two-dimensional data; constructing a conjugate product based on the two-dimensional data and performing inversion residual Doppler phase term estimation to obtain a target Doppler frequency; and performing phase compensation and angle estimation based on the Doppler frequency to obtain a target azimuth angle. A conjugate product is constructed on a distance-slow time unit after target detection so as to invert a residual Doppler phase term, decoupling of a Doppler phase and an azimuth phase is realized, so that phase consistency of a virtual array is recovered, angle estimation precision is improved, and on the premise that hardware complexity and calculation burden are not remarkably increased, the accuracy of angle estimation is improved. The angle estimation precision and the azimuth resolution capability are improved, and the angle measurement consistency and stability under different speed targets are enhanced.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to an angle calculation method and apparatus in MIMO radar. Background Technology

[0002] Multi-input Multiple-output (MIMO) radar combines multiple transmit and receive antennas to synthesize a virtual array with a larger aperture within the same physical antenna size, thereby improving angular resolution and angle estimation accuracy. This type of system has been widely used in automotive millimeter-wave radar, security monitoring radar, and industrial measurement radar. In engineering implementation, to reduce hardware complexity, avoid transmit interference, and meet transmit link isolation requirements, similar products and systems in the industry typically employ time-division multiplexing (TDM-MIMO), or on top of this, phase coding / orthogonal modulation, to achieve separable echo acquisition from multiple transmit channels.

[0003] When using a multi-channel TDM-MIMO system, the transmission times of different transmission channels inherently exhibit a time offset in the slow time dimension. For targets with non-zero radial velocities, this time offset manifests as the echoes from each transmission channel carrying an additional phase term in the Doppler dimension. When the transmission and reception channels are combined to form a virtual array, this additional phase creates a phase inconsistency related to the target velocity among the channels of the virtual array, leading to deviations or resolution degradation during angle processing. Especially in the typical processing framework of "completing range-velocity compression first, then performing angle estimation," this phase inconsistency is often difficult to completely eliminate through simple static array calibration and may vary with target velocity, frame structure, and transmission timing configuration, thus affecting angle measurement stability.

[0004] To address the aforementioned issues, existing technologies typically employ the following approaches: First, fixed phase calibration or system calibration is performed on each channel before angle processing to compensate for channel consistency errors. This approach primarily addresses static phase / amplitude inconsistencies introduced by the hardware link, but its compensation effect is limited for dynamic phase terms generated by the coupling of target velocity and transmission timing. Second, inter-channel bit interference is reduced by improving transmission orthogonality or altering transmission timing (e.g., shortening the time division interval or employing more complex coding methods). This approach may place higher demands on hardware timing, modulation methods, and system resources, and may be difficult to implement in some operating modes due to bandwidth, frame structure, or regulatory constraints. Third, more complex joint estimation methods are employed at the signal processing end, coupling and modeling velocity and angle parameters and solving them jointly. This approach theoretically improves accuracy, but typically leads to a significant increase in computational load and real-time pressure, and is susceptible to model mismatch or parameter search range issues in scenarios with multiple targets, low signal-to-noise ratios, or wide target velocity distributions.

[0005] Therefore, there is an urgent need for an angle calculation method and device in MIMO radar to improve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide an angle calculation method and apparatus for MIMO radar, which can improve the accuracy of angle estimation and azimuth resolution without significantly increasing hardware complexity and computational burden, and enhance the consistency and stability of angle measurement for targets with different speeds.

[0007] In a first aspect, the present invention provides an angle calculation method in MIMO radar, comprising the steps of: acquiring raw time-domain data and performing two-dimensional compression to obtain compressed two-dimensional data; constructing a conjugate product based on the two-dimensional data and performing inversion residual Doppler phase term estimation to obtain the target Doppler frequency; and performing phase compensation and angle estimation based on the Doppler frequency to obtain the target azimuth angle.

[0008] Optionally, acquiring the raw time-domain data includes: obtaining raw three-dimensional time-domain data with multiple receiving channels, fast-time sampling, and slow-time indexing; and / or constructing structural constraints for a virtual array before acquiring the raw time-domain data; the structural constraints of the virtual array are such that, after the virtual array is formed, there is at least one array position difference. This results in two or more sets of virtual channel pairs. satisfy:

[0009]

[0010] in: , This refers to the position index of two channels in the virtual array; the position unit can be half a wavelength corresponding to the transmission frequency, or an equivalent spatial unit that meets the field of view requirements; each virtual channel pair involves a transmitting antenna. and The corresponding launch timing difference for each group They are different from each other.

[0011] Optionally, the phase compensation and angle estimation based on the Doppler frequency to obtain the target azimuth angle includes: substituting the Doppler frequency into the compensation formula to perform phase compensation on the target cell data of all virtual channels to obtain the compensated data; and performing angle estimation based on the compensated data in combination with beamforming, MUSIC, ESPRIT and super-resolution algorithms to obtain the target azimuth angle.

[0012] Optionally, two-dimensional compression to obtain compressed two-dimensional data includes: performing distance dimension compression on the time domain data to obtain distance domain data; and performing slow time dimension compression on the distance domain data to obtain compressed two-dimensional data.

[0013] Optionally, constructing a conjugate product based on the two-dimensional data and performing an inversion residual Doppler phase term estimation to obtain the target Doppler frequency includes: filtering the two-dimensional data using the CFAR method to obtain filtered data; extracting all virtual channel pairs that satisfy the array position difference based on the filtered data, constructing a conjugate product for each virtual channel pair; and using the phase difference of the conjugate product of at least two virtual channel pairs to solve for the target Doppler frequency.

[0014] Optionally, the phase of the conjugate product consists of a coupling term between the target Doppler frequency and the corresponding transmission timing difference, and a fixed azimuth phase difference term.

[0015] Secondly, the present invention provides an angle calculation device for MIMO radar, which includes modules / units for performing any of the possible design methods described in the first aspect above. These modules / units can be implemented in hardware or by hardware executing corresponding software.

[0016] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a program executable on the processor, and when the program is executed by the processor, the electronic device implements a method for performing any of the possible designs described above.

[0017] Fourthly, the present invention provides a readable storage medium storing a program, which, when executed, implements a method of any possible design of any of the above aspects.

[0018] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0019] The beneficial effects of the method of this invention are as follows: Original time-domain data is acquired and compressed in two dimensions to obtain compressed two-dimensional data; based on the two-dimensional data, a conjugate product is constructed and the residual Doppler phase term is estimated to obtain the target Doppler frequency; based on the Doppler frequency, phase compensation and angle estimation are performed to obtain the target azimuth. Conjugate products are constructed on the range-slow time unit after target detection to invert the residual Doppler phase term, thereby decoupling the Doppler phase and azimuth phase, restoring the phase consistency of the virtual array, improving the angle estimation accuracy, and enhancing the angle estimation accuracy and azimuth resolution capability without significantly increasing hardware complexity and computational burden, thus improving the consistency and stability of angle measurement for targets with different speeds. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an angle calculation method in a MIMO radar according to an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of an angle calculation device in a MIMO radar according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed following the word and its equivalents, but do not exclude other elements or objects.

[0024] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the embodiments of the present invention, the terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to limit the present invention. The singular expressions “a,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of the present invention, “at least one” and “one or more” refer to one or more (including two). The term “and / or” is used to describe the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0025] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the invention. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," and "in still other embodiments" appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized. The term "connection" includes both direct and indirect connections, unless otherwise stated. "First" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0026] In embodiments of the present invention, "exemplarily" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design described as "exemplarily" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] like Figure 1 As shown, this invention provides a method for angle calculation in MIMO radar, including the following steps:

[0028] S101: Acquire the original time-domain data and perform two-dimensional compression to obtain compressed two-dimensional data.

[0029] In some embodiments, acquiring raw time-domain data includes: acquiring raw three-dimensional time-domain data with multiple receiver channels, fast-time sampling, and slow-time indexing.

[0030] In other embodiments, the structural constraints of the virtual array are constructed before acquiring the raw time-domain data;

[0031] The structural constraint of the virtual array is that, after the virtual array is formed, there exists at least one array position difference. This results in two or more sets of virtual channel pairs. satisfy:

[0032]

[0033] in: , This refers to the position index of two channels in the virtual array; the position unit can be half a wavelength corresponding to the transmission frequency, or an equivalent spatial unit that meets the field of view requirements; each virtual channel pair involves a transmitting antenna. and The corresponding launch timing difference for each group They are different from each other.

[0034] In some other embodiments, two-dimensional compression to obtain compressed two-dimensional data includes: performing distance dimension compression on the time domain data to obtain distance domain data; and performing slow time dimension compression on the distance domain data to obtain compressed two-dimensional data.

[0035] S102, based on the two-dimensional data, construct the conjugate product and perform inversion residual Doppler phase term estimation to obtain the target Doppler frequency.

[0036] In some embodiments, constructing a conjugate product based on the two-dimensional data and performing an inversion residual Doppler phase term estimation to obtain the target Doppler frequency includes: filtering the two-dimensional data using the CFAR method to obtain filtered data; extracting all virtual channel pairs that satisfy the array position difference based on the filtered data, constructing a conjugate product for each virtual channel pair; and solving for the target Doppler frequency using the phase difference of the conjugate product of at least two virtual channel pairs.

[0037] In some specific embodiments, the phase of the conjugate product consists of a coupling term of the target Doppler frequency and the corresponding transmission timing difference, and a fixed azimuth phase difference term.

[0038] S103, Phase compensation and angle estimation are performed based on the Doppler frequency to obtain the target azimuth angle.

[0039] In some embodiments, phase compensation and angle estimation based on the Doppler frequency to obtain the target azimuth angle includes: substituting the Doppler frequency into the compensation formula to perform phase compensation on the target cell data of all virtual channels to obtain compensated data; and performing angle estimation based on the compensated data in combination with beamforming, MUSIC, ESPRIT and super-resolution algorithms to obtain the target azimuth angle.

[0040] To facilitate understanding, this embodiment further elaborates on the specific implementation process of the above method in conjunction with a specific application scenario, which includes the following steps:

[0041] 1. Virtual array structure design constraints

[0042] At the system level, the constraints on the construction of the virtual array are as follows: after the virtual array is formed, there exists at least one array position difference. This results in two or more sets of virtual channel pairs. satisfy:

[0043]

[0044] in: , This refers to the position index of two channels in the virtual array; the position unit can be half a wavelength corresponding to the transmission frequency, or an equivalent spatial unit that meets the field of view requirements; each virtual channel pair involves a transmitting antenna. and The corresponding launch timing difference for each group They are different from each other.

[0045] This structure ensures that each group has the same array position difference. However, the corresponding launch timing difference different.

[0046] 2. Signal Model

[0047] When a distance-slow time unit is detected as a target unit, its virtual channel complex output can be expressed as:

[0048]

[0049] in: For amplitude; The target Doppler frequency; The transmission timing delay is set for the transmitting antenna corresponding to this virtual channel; This is the azimuth phase term, which is linearly related to the spatial position of the array.

[0050] 3. Processing flow

[0051] Step 1: ADC Data Acquisition

[0052] The collected data is represented as follows:

[0053]

[0054] in: : Receive channel index; : Fast sampling points; Slow-time index. Data is written to external memory via DMA.

[0055] Step 2: Distance Dimension Compression

[0056] Perform a fast Fourier transform on a fast time:

[0057]

[0058] in, This is the distance cell index.

[0059] Step 3: Slow Time Dimension Compression

[0060] Perform transformations on slow time:

[0061]

[0062] in, For slow time frequency index.

[0063] Step 4: Energy Fusion and Target Detection

[0064] Summing the energy of all channels:

[0065]

[0066] in, This represents the number of virtual channels.

[0067] Candidate target cells are selected using the CFAR method.

[0068] Step 5: Construct the conjugate product of the channel pairs with the same array position difference.

[0069] For the array position difference selected in step 1 Extract all that satisfy

[0070]

[0071] Virtual channel pairs.

[0072] For each set of constructions:

[0073]

[0074] in Slow-time compression results for the two selected channels are shown below.

[0075] Its phase is:

[0076]

[0077] in: This represents the timing difference between the transmitting antennas corresponding to this group of channels; This represents the azimuth phase difference corresponding to the array's position difference; since the array position differences are the same, The same in all groups.

[0078] Step 6: Estimation of residual Doppler terms

[0079] For the two groups ,have:

[0080]

[0081] Therefore, we can conclude that:

[0082]

[0083] If the number of groups is greater than two, least squares estimation can be used:

[0084]

[0085] in, This is a constant term.

[0086] Step 7: Phase Compensation

[0087] Compensate all virtual channels:

[0088]

[0089] After compensation:

[0090]

[0091] At this point, only the azimuth and phase information remains.

[0092] Step 8: Angle Estimation

[0093] For the compensated virtual array vector:

[0094]

[0095] Beamforming or high-resolution angle estimation is performed.

[0096] The key to the embodiments of the present invention lies in:

[0097] (1) By introducing a structural design constraint of "multiple virtual channel pairs with the same array position difference". In the virtual array design stage, it is explicitly required that there is at least one array position difference in the array. ,satisfy:

[0098]

[0099] And there are two or more virtual channel pairs This relationship is satisfied, and the corresponding transmission timing difference for each group is... Each pair is different. This structural design provides structural redundancy for subsequent Doppler residual phase inversion, ensuring that: the azimuth phase difference remains consistent across groups; and the Doppler terms introduced by the transmission timing remain different across groups.

[0100] Unlike traditional array designs that only focus on aperture continuity or minimum redundancy, this structural constraint is the first to incorporate "emission timing differences" into array design conditions.

[0101] (2) Eliminate amplitude and common phase terms by constructing a conjugate product. For virtual channel pairs that satisfy the same array position difference, construct:

[0102]

[0103] Its phase satisfies:

[0104]

[0105] By using conjugate product operations, we can: eliminate amplitude factors; eliminate absolute azimuth phase; and transform the problem into a linear Doppler parameter estimation problem.

[0106] This processing method avoids direct decoupling of the original phase, thus reducing noise sensitivity.

[0107] (3) Construct linear equations using different transmission timing differences and solve for the residual Doppler terms. For the two sets of channel pairs ,have:

[0108]

[0109] Therefore, we get:

[0110]

[0111] The key to this step is that it does not rely on the Doppler FFT raster precision; it does not require a joint angle-velocity 2D search; and it does not require velocity hypothesis traversal.

[0112] This method transforms the original two-dimensional coupling problem into a one-dimensional scalar estimation problem.

[0113] (4) Post-detection calculation strategy: The method of the present invention is only performed on the distance-slow time unit selected by CFAR detection: conjugate product construction; Doppler residual inversion; phase compensation.

[0114] Avoid performing calculations on the full-range-slow-time plane.

[0115] This strategy reduces the computational complexity from:

[0116]

[0117] Reduced to:

[0118]

[0119] Significantly reduces system computing power and memory access pressure.

[0120] (5) Perform angle estimation after compensation to achieve angle-velocity decoupling. After estimating the residual Doppler frequency, compensate for all virtual channels:

[0121]

[0122] After compensation:

[0123]

[0124] At this point, the virtual array satisfies the traditional array phase model and can be directly applied to: FFT beamforming, MUSIC, ESPRIT and super-resolution algorithms, without requiring any modifications to the existing angle algorithm.

[0125] (6) It can be extended to the joint estimation of position differences of multiple groups or arrays. When the number of channel groups that meet the conditions is greater than two, or when there are multiple array position differences that meet the conditions, an overdetermined system of equations can be constructed:

[0126]

[0127] Solve using least squares or robust regression. .

[0128] This feature improves noise resistance and estimation stability.

[0129] The advantage of this invention lies in its ability to address the angle estimation error caused by transmission timing differences in multi-channel MIMO radar. Through coordinated optimization of array structure design and signal processing algorithms, it effectively separates the Doppler phase term and azimuth phase term introduced by the transmission timing, significantly improving system performance without increasing hardware complexity. Specific effects are as follows:

[0130] I. Effectively eliminate angular deviation caused by transmission timing differences

[0131] In traditional time-division multiplexing (TIME) MIMO radars, there is an inherent difference in transmission timing between different transmission channels. When the target has a non-zero radial velocity, this timing difference introduces additional velocity-related phase between the virtual array channels, resulting in a systematic shift in angle estimation.

[0132] The method of this invention constructs multiple pairs of virtual channels with the same array position difference and utilizes the difference between different transmission timing differences to invert residual Doppler terms, completing compensation before angle processing and restoring phase consistency of the virtual array channels. Therefore, it eliminates velocity-dependent angle errors, restores the array's theoretical resolution, and improves angle measurement accuracy.

[0133] II. Achieving decoupling between angle and velocity parameters

[0134] Traditional methods typically address the coupling between angle and velocity through joint search or multi-velocity hypothesis traversal, which is computationally complex and sensitive to parameter grids.

[0135] This invention transforms the originally coupled two-dimensional problem into a one-dimensional scalar estimation problem. It directly inverts the residual Doppler phase term on the detected target cell before performing angle estimation, thus achieving analytical decoupling of angle and velocity parameters. This method has the following advantages: it does not rely on velocity search; it does not require joint angle-velocity two-dimensional traversal; and it does not depend on Doppler grid accuracy. This improves system computational efficiency and theoretical consistency.

[0136] III. Improving stability under low signal-to-noise ratio conditions

[0137] The method of this invention constructs redundant estimation conditions by using multiple channel groups with the same array position difference. When the number of channel groups is greater than two, joint estimation can be performed, thereby: enhancing noise resistance by utilizing array structure redundancy; suppressing the influence of single-channel outliers; improving estimation stability; and maintaining robustness in complex scenarios and multi-target situations.

[0138] IV. Significantly reduce computational complexity

[0139] Compared with the traditional angle-velocity joint search method, the method of this invention only performs the following on a small number of candidate units after target detection: conjugate product operation; phase difference calculation; one-dimensional parameter estimation; and phase compensation.

[0140] There is no need to process the entire distance-slow time plane, nor is there a need for large-scale matrix operations or two-dimensional searches.

[0141] Therefore: the amount of computation is significantly reduced; the computational complexity is greatly reduced; and the computing power requirement decreases.

[0142] V. Reduce storage access pressure and improve real-time processing capabilities

[0143] The computation of the method in this invention is mainly concentrated within the detection point domain, and the data scale involved is relatively small, requiring no additional large-scale caching or data rearrangement. Compared with traditional full-plane compensation or joint search methods, the method of this invention has the following advantages: fewer memory accesses; higher cache hit rate; lower storage bandwidth consumption; and more stable system latency.

[0144] Therefore, higher frame rates and lower power consumption can be achieved on computing platforms such as embedded processors, SoCs, GPUs, or FPGAs.

[0145] VI. No need to change the existing angle estimation module

[0146] The compensation process in this invention occurs before angle estimation. The compensated data satisfies the traditional array phase model, therefore existing angle estimation algorithms, including beamforming or super-resolution algorithms, can be used directly without structural modifications to the existing angle module.

[0147] Therefore: it is easy to integrate into existing radar systems; it does not affect the existing algorithm architecture; and the engineering implementation cost is low.

[0148] VII. Achieving Co-optimization of Array Design and Signal Processing

[0149] This invention not only processes data at the algorithm level but also introduces structural constraints during the virtual array design phase, giving the array structure inherent redundancy that can be used to retrieve Doppler residual terms. This structure-algorithm co-optimization approach improves overall system consistency, fully utilizes transmission timing information, and enhances scalability.

[0150] 8. Applicable to various launch scheduling structures

[0151] The method of this invention does not depend on a fixed transmission interval. It can be applied as long as the transmission timing difference can be determined and there exists an array position difference channel group that meets the conditions. Therefore, it has good versatility and scalability.

[0152] like Figure 2 As shown, based on the above method, the present invention provides an angle calculation device for MIMO radar, comprising: an acquisition unit 201, used to acquire raw time-domain data and perform two-dimensional compression to obtain compressed two-dimensional data; a processing unit 202, used to construct a conjugate product based on the two-dimensional data and perform inversion residual Doppler phase term estimation to obtain the target Doppler frequency; and a calculation unit 203, used to perform phase compensation and angle estimation based on the Doppler frequency to obtain the target azimuth angle.

[0153] It should be understood that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here. Furthermore, the use of suffixes such as "module," "component," or "unit" to represent elements is merely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "component," or "unit" can be used interchangeably. Terminals can be implemented in various forms. For example, the terminals described in this invention may include mobile terminals such as mobile phones, tablets, laptops, handheld computers, personal digital assistants (PDAs), portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, etc., as well as fixed terminals such as digital TVs and desktop computers. The following description will use mobile terminals as examples; those skilled in the art will understand that, in addition to elements specifically designed for mobile purposes, the construction according to embodiments of the present invention can also be applied to fixed-type terminals.

[0154] In other embodiments of the present invention, an electronic device 300 is disclosed, such as... Figure 3 As shown, the device may include: one or more processors 301; memory 302; display 303; one or more application programs (not shown); and one or more computer programs 304. These devices can be connected via one or more communication buses 305. The one or more computer programs 304 are stored in the memory 302 and configured to be executed by the one or more processors 301. The one or more computer programs 304 include instructions that can be used to perform actions such as... Figure 1 Each step in the corresponding embodiment.

[0155] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0156] The memory 302 can be an internal storage unit of the electronic device 300, such as a hard disk or RAM of the electronic device 300. The memory 302 can also be an external storage device of the electronic device 300, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard equipped on the electronic device 300. Furthermore, the memory 302 can include both internal and external storage units of the electronic device 300. The memory 302 is used to store computer programs and other programs and data required by the electronic device. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0157] The computer program 304 can be divided into one or more modules / units. The one or more modules / units can be a series of computer program instruction segments that can perform a specific function. The instruction segments are used to describe the execution process of the computer program 304 in the electronic device 300.

[0158] In addition to the above-described structure, those skilled in the art will understand that Figure 3 This is merely an example of electronic device 300 and does not constitute a limitation on electronic device 300. Electronic device 300 may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0159] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0160] Based on the above embodiments, the present invention also discloses a computer-readable storage medium having at least one computer program stored thereon, wherein the computer program, when executed by a processor, implements the methods described in the foregoing embodiments.

[0161] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0162] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0163] Although the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. The above descriptions are merely embodiments of the present invention and do not limit the patent scope of the present invention. However, it should be understood that such modifications and variations fall within the scope and spirit of the present invention. Moreover, the present invention described herein may have other embodiments and can be implemented or realized in various ways. All equivalent transformations made based on the description and drawings of the present invention, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for angle calculation in MIMO radar, characterized in that, Including the following steps: The original time-domain data is collected and compressed in two dimensions to obtain compressed two-dimensional data; Based on the two-dimensional data, a conjugate product is constructed and the residual Doppler phase term is estimated by inversion to obtain the target Doppler frequency. Phase compensation and angle estimation are performed based on the Doppler frequency to obtain the target azimuth angle.

2. The method according to claim 1, characterized in that, The raw time-domain data collected includes: Obtain raw three-dimensional time-domain data with multiple receiver channels, fast-time sampling, and slow-time indexing; And / or construct the structural constraints of the virtual array before acquiring the raw time-domain data; The structural constraint of the virtual array is that, after the virtual array is formed, there exists at least one array position difference. This results in two or more sets of virtual channel pairs. satisfy: in: , This refers to the position index of two channels in the virtual array; The position unit can be half a wavelength corresponding to the transmission frequency, or an equivalent spatial unit that meets the field of view requirements; Each virtual channel pair involves a transmitting antenna. and ; The corresponding transmission timing difference of each group They are different from each other.

3. The method according to claim 1, characterized in that, Based on the Doppler frequency, phase compensation and angle estimation are performed to obtain the target azimuth angle, including: Substitute the Doppler frequency into the compensation formula to perform phase compensation on the target unit data of all virtual channels to obtain the compensated data. Based on the compensated data, angle estimation is performed using beamforming, MUSIC, ESPRIT, and super-resolution algorithms to obtain the target azimuth angle.

4. The method according to claim 1, characterized in that, Two-dimensional compression yields compressed two-dimensional data, including: The time-domain data is compressed in the distance dimension to obtain distance-domain data; Based on the distance domain data, slow time dimension compression processing is performed to obtain compressed two-dimensional data.

5. The method according to any one of claims 1-4, characterized in that, Based on the two-dimensional data, a conjugate product is constructed and the residual Doppler phase term is estimated by inversion. The target Doppler frequencies are obtained as follows: Based on the two-dimensional data, the CFAR method is used to filter the data to obtain the filtered data; Based on the filtered data, extract all virtual channel pairs that satisfy the array position difference, and construct a conjugate product for each virtual channel pair. The target Doppler frequency is obtained by using the phase difference of the conjugate product of at least two sets of virtual channel pairs.

6. The method according to claim 5, characterized in that, The phase of the conjugate product consists of a coupling term between the target Doppler frequency and the corresponding transmission timing difference, and a fixed azimuth phase difference term.

7. An angle calculation device for MIMO radar, used in the method according to any one of claims 1-6, characterized in that, include: The acquisition unit is used to acquire the raw time-domain data and perform two-dimensional compression to obtain compressed two-dimensional data. The processing unit is used to construct a conjugate product based on the two-dimensional data and perform inversion residual Doppler phase term estimation to obtain the target Doppler frequency; The calculation unit is used to perform phase compensation and angle estimation based on the Doppler frequency to obtain the target azimuth angle.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a program that can run on the processor, and when the program is executed by the processor, causes the electronic device to perform the method of any one of claims 1-6.

9. A readable storage medium storing a program, characterized in that, When the program is executed, it implements the method of any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-6.

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