A millimeter wave radar target detection method

Abstract

The present invention relates to millimeter wave radar, and specifically discloses a millimeter wave radar target detection method, comprising the following steps: S01, reflecting and receiving reflected waves according to a predetermined time threshold, from the first reflected wave to the last reflected wave, to obtain a window value set; S02, determining (x, y) of each reflected wave in the window value set, to obtain an initial curve, wherein x is time, and y is the amplitude of the reflected wave; S03, smoothing the curve by the least square method, to obtain a smoothed initial curve; S04, obtaining the y difference between two adjacent reflected waves according to the smoothed initial curve, to obtain a data set; S05, selecting data whose y difference is not less than a threshold from the data set. The invention ensures that the data points extracted from the original curve can be accurately fitted and smoothed through detailed smoothing steps, thereby obtaining a more accurate and reliable smoothed curve, which provides important significance for subsequent vehicle detection and identification.

Description

Technical Field

[0001] The present invention relates to a millimeter wave radar, and in particular to a millimeter wave radar target detection method. Background Art

[0002] Millimeter wave radar transmits electromagnetic waves and receives electromagnetic waves reflected by the target. It calculates the target's motion characteristics such as distance, speed, and direction based on parameters such as the propagation time and frequency change of the electromagnetic waves. Its detection principle is similar to the ultrasonic positioning of bats or dolphins, but the radar transmits electromagnetic waves instead of sound waves.

[0003] In the highway toll island scenario, traditional vehicle separation equipment such as ground sensor coils and infrared light curtains have the disadvantages of being susceptible to interference, long repair time, high cost, low integration, complex wiring, and difficult to shift after installation. These problems may cause frequent equipment failures and affect the traffic efficiency of the lane. If millimeter-wave radar replaces the above-mentioned traditional ground sensor coils and infrared light curtains, the above problems can be well solved, and how to set up the program to be more suitable for toll station system applications is expected to be well solved. Summary of the invention

[0004] The purpose of the present invention is to provide a millimeter wave radar target detection method to solve the above problems.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A millimeter wave radar target detection method comprises the following steps:

[0007] S01, reflecting and receiving reflected waves according to a predetermined time threshold, from the first reflected wave to the last reflected wave, to obtain a window value set;

[0008] S02, determining (x, y) of each reflected wave in the window value set to obtain an initial curve, where x is time and y is the amplitude of the reflected wave;

[0009] S03, smoothing the curve by using the least square method to obtain a smoothed initial curve;

[0010] S04, obtaining the y difference between two adjacent reflected waves according to the smoothed initial curve to obtain a data set;

[0011] S05, selecting data whose y difference is not less than a threshold from the data set to obtain a data subset consisting of two continuous reflected waves, and determining a starting point and an ending point in the data subset, and two low points in the data set adjacent to the starting point and the ending point according to the window;

[0012] S06, obtaining the fuzzy length of the current vehicle through optimal calculation according to the determined low points of the two y difference values ​​and the acquired real-time speed of the current vehicle;

[0013] S07. Determine whether it is a car based on the obtained fuzzy length and matching with the expert database. If it is a car, determine the type of car based on the fuzzy length.

[0014] Preferably, the least square method for smoothing the curve in step S03 includes:

[0015] S31. Obtain coordinates (xi, yi) according to the obtained i reflected waves, where i is the number of reflected waves in the window value set;

[0016] S32, construct a polynomial model based on the cubic polynomial, y = a0 + a1x + a2x 2 +a3x 3 , we get a polynomial curve, where x is time, y is the amplitude of the reflected wave, a is the coefficient, a0 is the intercept, which indicates the value of y when x=0, and a1 is x 1 The coefficient of the linear term represents the average change in y when x changes by one unit. a2 is x 2 The coefficient of a3 is x 3 The coefficient of

[0017] S33, minimizing the sum of squares of errors between the polynomial curve and the coordinates of the reflected waves in the initial curve to generate a smooth curve.

[0018] Preferably, the data set in step S04 is to obtain the y difference between each two reflected waves in the smoothed initial curve, and the approximate value is obtained by averaging each two adjacent y differences, and the data groups with consistent approximate values ​​are retained, while the data groups with too large approximate value errors are eliminated.

[0019] Preferably, the threshold in step S05 is an average value of y-value distances of a plurality of reflected waves of the corresponding data group.

[0020] Preferably, the threshold in step S05 is the average value of y in the data set.

[0021] Preferably, determining the two low positions in the data set adjacent to the starting point and the ending point in step S05 comprises:

[0022] S51, obtaining the first reflected wave coordinate value and the last reflected wave coordinate value in the data subset according to the time sequence of the window;

[0023] S52, searching for the reflected wave coordinate values ​​of two low points in the window value set that are adjacent to the first reflected wave coordinate value and the last reflected wave coordinate value in the data subset.

[0024] Preferably, the fuzzy length of the current vehicle obtained by optimal calculation in step S06 includes:

[0025] S61, determining in chronological order the previous low point of the starting point in the first data subset and the next low point of the ending point in the second data subset, and the interval between the two low points corresponds to the distance d of the smoothed initial curve;

[0026] S62, the interval time s between the low points of the starting point and the end point, then s=d / v, where v is the vehicle speed;

[0027] S63, subtract the obtained v from the current real-time speed of the vehicle,

[0028] If the difference is less than or equal to 2, it is determined that the acquired data is correct and v is the real data;

[0029] If the difference is greater than 2, it is determined that the acquired data is incorrect and a fault error feedback is generated.

[0030] In the above technical solution, the millimeter wave radar target detection method provided by the present invention has the following beneficial effects:

[0031] 1. When the vehicle completely passes through the radar detection area, the system will continuously collect reflected wave data and integrate these data into a window value set. A curve is drawn based on these collected data, and the least squares method is used to smooth the curve to obtain a smoother curve.

[0032] 2. According to the obtained smooth curve, the y difference of each adjacent reflected wave is obtained to identify those data whose continuous difference is less than the preset threshold. The starting point and the end point in the data subset are determined, as well as the two low points in the data set between the adjacent starting point and the end point. The number of hubs is determined according to the number of high-level segments that appear in the window period. At the same time, by measuring the distance between the two low points at both ends of each data subset, the approximate data of the hub diameter can be obtained. These data provide strong support for subsequent matching with the expert database.

[0033] 3. After obtaining the approximate data of the wheel diameter, we further verify the data by calculating the fuzzy length. If the wheel diameter data obtained through different high-order segments are similar, we match these similar values ​​with the data in the expert database. This method can not only accurately determine whether the detection target is a vehicle, but also further identify the type of vehicle.

[0034] 4. When the real-time vehicle speed is compared with the vehicle speed obtained by processing the reflected wave data, if they are consistent, it means that the data is correct. If they are inconsistent, it means that there is an error in the data and the radar is faulty and needs to be checked. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 A schematic diagram of a flow structure provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of radar detection provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of an area where a high-level segment of a reflected wave is located provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1

[0041] like Figure 1-3 As shown, a millimeter wave radar target detection method includes the following steps:

[0042] S01, reflecting and receiving reflected waves according to a predetermined time threshold, from the first reflected wave to the last reflected wave, to obtain a window value set;

[0043] S02, determining (x, y) of each reflected wave in the window value set to obtain an initial curve, where x is time and y is the amplitude of the reflected wave;

[0044] S03, smoothing the curve by using the least square method to obtain a smoothed initial curve;

[0045] S04, obtaining the y difference between two adjacent reflected waves according to the smoothed initial curve to obtain a data set;

[0046] S05, selecting data whose y difference is not less than the threshold from the data set, obtaining a data subset consisting of two continuous reflection waves, and determining the starting point and the ending point in the data subset according to the window, as well as two low points in the data set between adjacent starting points and ending points;

[0047] S06, obtaining the fuzzy length of the current vehicle through optimal calculation according to the determined low point of the y difference and the acquired real-time speed of the current vehicle;

[0048] S07. Based on the obtained fuzzy length, match it with the expert database to determine whether it is a car. If it is a car, determine the type of car based on the fuzzy length.

[0049] Specifically, the expert database is a preset cloud database, in which the vehicle type corresponding to the number of wheel hubs of the vehicle and the distance value between each wheel hub are pre-stored.

[0050] The threshold in step S05 is the average value of the y value distances of the multiple reflected waves of the corresponding data set. And the threshold in step S05 is the average value of y in the data set.

[0051] In the above technology, when the vehicle completely passes through the radar detection area, the system will continuously collect reflected wave data and integrate these data into a window value set. A curve is drawn based on the collected data, and the least squares method is used to smooth the curve to obtain a smoother curve. According to the obtained smooth curve, the y difference of each adjacent reflected wave is obtained, so as to identify those data with continuous differences less than the preset threshold, thereby blindly determining the starting point and end point in the data subset, and the two low points in the data set between the adjacent starting point and end point. The number of wheel hubs is determined according to the number of high-level segments that appear during the window period. At the same time, by measuring the distance between the two low-level points at both ends of each data subset, the approximate data of the wheel hub diameter can be obtained. These data provide strong support for subsequent matching with the expert database.

[0052] Embodiment 2

[0053] Based on the first embodiment, the present embodiment provides a method of smoothing the curve obtained in step S02 of the first embodiment, so that the least square method in step S03 for smoothing the curve includes:

[0054] S31. Obtain coordinates (xi, yi) according to the obtained i reflected waves, where i is the number of reflected waves in the window value set;

[0055] S32, construct a polynomial model based on the cubic polynomial, y = a0 + a1x + a2x 2 +a3x 3, we get a polynomial curve, where x is time, y is the amplitude of the reflected wave, a is the coefficient, a0 is the intercept, which indicates the value of y when x=0, and a1 is x 1 The coefficient of the linear term represents the average change in y when x changes by one unit. a2 is x 2 The coefficient of a3 is x 3 The coefficient of

[0056] S33, minimize the sum of squares of errors between the polynomial curve and the initial curve in the reflected wave coordinates, and generate a smooth curve.

[0057] In the above technologies, there may be discontinuities or mutations between the original data points, that is, the curve, which will affect the overall trend and characteristics of the data. Smoothing can fill these discontinuities and make the data smoother and more continuous, making it easier to observe and analyze the overall trend of the data. Smoothing can remove noise and outliers in the original data, which may be caused by measurement errors, environmental interference and other factors. Through smoothing, more accurate and reliable data can be obtained, providing a solid foundation for subsequent data analysis and processing.

[0058] By fitting with a preset polynomial model, a curve that can approximate the original data points can be obtained. Smoothing can further optimize the fitting effect, make the fitting curve closer to the actual distribution of the original data, and improve the accuracy and reliability of the fitting. The smoothed initial curve is smoother and more continuous, which is helpful for subsequent data processing and analysis. For example, in vehicle detection and recognition applications, the smoothed initial curve can more easily extract feature information, such as the vehicle's profile, y value, etc., thereby improving the accuracy and efficiency of detection and recognition. Although the smoothing process itself requires a certain amount of calculation, compared with directly processing the original data points, the smoothed initial curve can reduce the computational complexity in subsequent processing. Because the smoothed initial curve is smoother and more continuous, it can be calculated and analyzed more efficiently when performing operations such as feature extraction and pattern recognition.

[0059] Embodiment 3

[0060] Based on Example 1, the data set in step S04 of this embodiment is to obtain the y difference between each two reflected waves in the smoothed initial curve, and the approximate value is obtained by averaging each two adjacent y differences. The data group with consistent approximate values ​​is retained, and the data group with too large approximate value error is eliminated.

[0061] Further, determining the two low positions in the data set of the adjacent starting point and ending point in step S05 includes:

[0062] S51, obtaining the first reflected wave coordinate value and the last reflected wave coordinate value in the data subset according to the time sequence of the window;

[0063] S52, searching for the reflected wave coordinate values ​​of two low points in the window value set that are adjacent to the first reflected wave coordinate value and the last reflected wave coordinate value in the data subset.

[0064] The above technology, because the present invention transmits waves and receives reflected waves according to a preset time interval, the so-called elimination is to eliminate the reflected waves whose coordinate values ​​of the reflected waves are lower than the two low points, thereby optimizing the set. The number of hubs is determined according to the number of times the data subset appears. At the same time, by measuring the distance between the reflected wave coordinate values ​​of the two low points in each data subset, the approximate data of the hub diameter can be obtained. These data provide strong support for subsequent matching with the expert database.

[0065] Embodiment 4

[0066] Based on the first embodiment, this embodiment provides that the fuzzy length of the current vehicle obtained by optimal calculation includes:

[0067] S61, determining in chronological order a low bit point before the starting point in the first data subset and a low bit point after the ending point in the second data subset, wherein the interval between the two low bits corresponds to the distance d of the smoothed initial curve;

[0068] S62, the interval time s between the low points of the starting point and the ending point, then s=d / v, where v is the vehicle speed;

[0069] S63, subtract the obtained v from the current real-time speed of the vehicle,

[0070] If the difference is less than or equal to 2, it is determined that the acquired data is correct and v is the real data;

[0071] If the difference is greater than 2, it is determined that the acquired data is incorrect and a fault error feedback is generated.

[0072] In the above technology, the fuzzy length of the current vehicle is obtained by optimal calculation as the distance d of the initial curve after smoothing corresponding to the interval between the previous low points of the first reflection wave coordinate value of the two data subsets. d is the physical distance covered between the first reflection wave coordinate value and the last reflection wave coordinate value in the data subset during the actual driving process of the vehicle.

[0073] At the same time, the interval time s between two adjacent data subsets is also calculated. This time interval s is obtained by dividing the distance d by the vehicle speed v, where v represents the current speed of the vehicle. This calculation process helps establish a direct relationship between time, distance and speed.

[0074] Next, the vehicle speed v obtained through the reflected wave data processing is compared with the vehicle's real-time speed. This comparison process is intended to verify the accuracy and reliability of the data. If the difference between the two is less than or equal to 2 (here 2 is a preset threshold used to determine whether the data is consistent), it can be determined that the acquired data is accurate, that is, the vehicle speed v is real and valid data.

[0075] However, if the difference between the two is greater than 2, it indicates that the acquired data is wrong. In this case, a fault error feedback will be generated, indicating that there may be a problem with the system radar and further investigation and repair are required.

[0076] In general, the accuracy and reliability of the data can be verified by comparing the real-time vehicle speed with the vehicle speed obtained by processing the reflected wave data. If the data are consistent, it means that the radar system is working normally and the data is accurate and reliable; if the data are inconsistent, it means that there may be a fault in the radar system, which needs to be checked and repaired in time to ensure the normal operation of the system and the accuracy of the data.

[0077] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0082] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method in the above embodiments, and the electronic device specifically includes the following contents:

[0083] Processor, memory, communications interface and bus;

[0084] Wherein, the processor, memory, and communication interface communicate with each other via the bus;

[0085] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the method in the above embodiment are implemented.

[0086] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented.

[0087] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Although the embodiment of this specification provides the method operation steps described in the embodiment or flow chart, more or less operation steps can be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The present invention is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the process and / or box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment. In the description of this specification, the description of the reference term "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification.

[0089] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradicting each other. The above is only an embodiment of the embodiment of this specification and is not intended to limit the embodiment of this specification. For those skilled in the art, the embodiment of this specification may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiment of this specification shall be included in the scope of the claims of the embodiment of this specification.

Claims

1. A millimeter wave radar target detection method, characterized in that: The following steps are involved: S01, reflecting and receiving reflected waves according to a predetermined time threshold, from the first reflected wave to the last reflected wave, to obtain a window value set; S02, determining (x, y) of each reflected wave in the window value set to obtain an initial curve, where x is time and y is the amplitude of the reflected wave; S03, smoothing the curve by using the least square method to obtain a smoothed initial curve; S04, obtaining the y difference between two adjacent reflected waves according to the smoothed initial curve to obtain a data set; S05, selecting data whose y difference is not less than a threshold from the data set to obtain a data subset consisting of two continuous reflected waves, and determining a starting point and an ending point in the data subset, and two low points in the data set adjacent to the starting point and the ending point according to the window; S06, obtaining the fuzzy length of the current vehicle through optimal calculation according to the determined low points of the two y difference values ​​and the acquired real-time speed of the current vehicle; S07, judging whether it is a car based on the obtained fuzzy length and the expert database, and if it is a car, judging the type of the car based on the fuzzy length; The data set in step S04 is to obtain the y difference between each two reflected waves in the smoothed initial curve, and the approximate value is obtained by averaging each two adjacent y differences. The data sets with consistent approximate values ​​are retained, and the data sets with too large approximate value errors are eliminated.

2. A millimeter wave radar target detection method according to claim 1, characterized in that: In step S03, the least square method is used to smooth the curve, including: S31. Obtain coordinates (xi, yi) according to the obtained i reflected waves, where i is the number of reflected waves in the window value set; S32. Construct a polynomial model based on the cubic polynomial, y = a0 + a1x + a2x² + a3x³, and obtain a polynomial curve, where x is time, y is the amplitude of the reflected wave, a is the coefficient, a0 is the intercept, which indicates the value of y when x=0, and a1 is x 1 The coefficient of the linear term represents the average change in y when x changes by one unit. a2 is the average change in x. 2 The coefficient of a3 is x 3 The coefficient of S33, minimizing the sum of squares of errors between the polynomial curve and the coordinates of the reflected waves in the initial curve to generate a smooth curve.

3. The millimeter wave radar target detection method according to claim 1, characterized in that: The threshold in step S05 is the average value of the y-value distances of the multiple reflected waves of the corresponding data group.

4. The millimeter wave radar target detection method according to claim 1, characterized in that: The threshold in step S05 is the average value of y in the data set.

5. The millimeter wave radar target detection method according to claim 1, characterized in that: Determining the two low positions in the data set adjacent to the starting point and the ending point in step S05 includes: S51, obtaining the first reflected wave coordinate value and the last reflected wave coordinate value in the data subset according to the time sequence of the window; S52, searching for the reflected wave coordinate values ​​of two low points in the window value set that are adjacent to the first reflected wave coordinate value and the last reflected wave coordinate value in the data subset.

6. The millimeter wave radar target detection method according to claim 1, characterized in that: The fuzzy length of the current vehicle obtained by optimal calculation in step S06 includes: S61, determining in chronological order the previous low point of the starting point in the first data subset and the next low point of the ending point in the second data subset, and the interval between the two low points corresponds to the distance d of the smoothed initial curve; S62, the interval time s between the low points of the starting point and the end point, then s=d / v, where v is the vehicle speed; S63, subtract the obtained v from the current real-time speed of the vehicle, If the difference is less than or equal to 2, it is determined that the acquired data is correct and v is the real data; If the difference is greater than 2, it is determined that the acquired data is incorrect and a fault error feedback is generated.

7. The millimeter wave radar target detection method according to claim 1, characterized in that: The expert database is a preset cloud database.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the millimeter wave radar target detection method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the millimeter wave radar target detection method according to any one of claims 1 to 7 are implemented.

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