Target detection method and device, storage medium

Abstract

The present application proposes a target detection method, device, and storage medium. The method includes: acquiring sampling data of multiple chirp signals; performing fast time dimension calculations on the sampling data of each chirp signal one by one in a time sequence to generate a corresponding distance spectrum, and performing target detection each time a chirp signal calculation is completed; if a target is detected, stop calculating the remaining chirp signals; if no target is detected for all chirp signals, divide the cached distance spectra into multiple sections with priorities; perform slow time dimension calculations on each section one by one in a time sequence from high to low priority, and perform target detection each time a section calculation is completed; if a target is detected, stop calculating the remaining sections; if no target is detected for all sections, perform target detection on the next section of the distance spectrum. The present application has a small amount of calculation for target detection, short time consumption, and low power consumption.

Description

Technical Field

[0001] The present application relates to the field of radar tracking technology and the field of target detection technology using radio waves, and specifically to a target detection method and device, and a storage medium. Background Art

[0002] With the development of intelligent technology, radars represented by millimeter wave radars have been widely used in target detection in the fields of smart home, smart health, smart city, etc. In these scenarios, radars are mainly used to detect the presence or absence of targets. For example, in a home lighting system based on FMCW (Frequency Modulated Continuous Wave) millimeter wave radar, once the radar detects a person, the light can be turned on for lighting. Existing target detection technology usually uses 2D-FFT (two-dimensional Fourier transform or two-dimensional discrete Fourier transform), that is, first perform a fast Fourier transform on the sampled data of the radar chirp signal to obtain a range spectrum, then perform a fast Fourier transform on the corresponding range gate in the range spectrum, and then obtain the range-velocity spectrum data, and finally analyze the range-velocity spectrum data to achieve target detection. The entire process requires two-dimensional FFT calculations, which is large in calculation amount, resulting in a long time and high power consumption. For devices that perform target detection, it will undoubtedly reduce the battery life. Summary of the invention

[0003] In view of this, the present application provides a target detection method and device, and a storage medium, which can improve the problems of existing target detection technologies such as large amount of calculation, long time consumption, and high power consumption.

[0004] The present application provides a target detection method, comprising:

[0005] Acquire sampling data of multiple consecutive chirp signals during radar detection;

[0006] The sampling data of each chirp signal are calculated in fast time dimension one by one in time sequence to generate a corresponding distance spectrum, and each time the fast time dimension calculation of the sampling data of a chirp signal is completed, the target is detected according to the obtained distance spectrum;

[0007] If the target is detected, the calculation of the sampled data of the remaining chirp signal is stopped;

[0008] If no target is detected for the sampled data of all chirp signals, each range spectrum is divided into a plurality of sections with priorities;

[0009] According to the time sequence, the slow time dimension calculation is performed on each segment of each distance spectrum in descending order of priority, and each time the slow time dimension calculation of a segment is completed, the target detection is performed according to the calculation result;

[0010] If the target is detected, the slow time dimension calculation for the remaining segments is stopped;

[0011] If no target is detected in all segments, target detection is performed according to the segment of the next range spectrum.

[0012] Optionally, each time the fast time dimension calculation of the sampling data of a chirp signal is completed and no target is detected according to the obtained range spectrum, the obtained range spectrum is cached in time sequence.

[0013] Optionally, the distance spectrum includes a plurality of distance gates, and the step of dividing each distance spectrum into a plurality of sections with priorities includes:

[0014] Get the range gate where the most recently detected target is located as the center range gate;

[0015] In the range spectrum, the central range gate and a first number of range gates symmetrically located on both sides thereof are taken as the sections with the highest priority.

[0016] Optionally, the dividing each distance spectrum into a plurality of sections with priorities further includes:

[0017] For the range gates other than the highest priority segment in the range spectrum, a second number of range gates symmetrically located on both sides of the first priority segment are used as a second priority segment, and the second priority is lower than the first priority.

[0018] Optionally, any one of the first quantity and the second quantity is calculated by the following relationship:

[0019] K n =(k*(d 0 + d 1 ) / F / (Q-1))-K n-1

[0020] d 0 = v*t

[0021] d 1 = a * F / f(S nr )

[0022] Among them, K n The integer of is rounded upward to any one of the first number and the second number, n is the ranking of the current segment according to the priority, and when n=1, K n-1 = 0, k is the number of range gates contained in the range spectrum, d 0 is the predicted value of the target moving distance, d 1is the correction value of the current segment, F is the resolution of the radar, Q represents the number of sampled data contained in a chirp signal, v is the moving speed of the target detected most recently, t is the time interval between two adjacent chirp signals, a is the correction factor corresponding to the current segment, f(S nr ) is for S nr Perform square root operation, S nr It is the signal-to-noise ratio when the radar detects the target most recently.

[0023] Optionally, the dividing each distance spectrum into a plurality of sections with priorities further includes:

[0024] Selecting a plurality of continuous range gates from each range spectrum according to a preset monitoring range of the radar;

[0025] And, only for the selected multiple consecutive range gates, the step of obtaining the range gate where the most recently detected target is located is performed.

[0026] Optionally, each of the distance spectra is divided into a plurality of equal segments.

[0027] Optionally, each of the distance spectra is divided into two sections or three sections.

[0028] Optionally, the slow time dimension calculation is to perform fast Fourier transform on the range gates with the same number in the range spectrum in the slow time dimension.

[0029] Optionally, the slow time dimension calculation includes:

[0030] determining range gates with the same number in the range spectrum;

[0031] The signal energies corresponding to the range gates with the same number are accumulated and summed.

[0032] The present application provides a target detection device, comprising a processor and a memory, wherein a target detection program is stored in the memory, and when the target detection program is executed by the processor, the steps of the target detection method as described in any one of the above items are implemented.

[0033] The present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the target detection method as described in any one of the above items.

[0034] As described above, for sampling to obtain multiple chirp signals, the present application performs target detection based on the calculation results after performing fast time dimension calculation on the sampled data of each chirp signal. If the target is detected, the calculation of the sampled data of the remaining chirp signals is stopped. Only when the target is not detected in the sampled data of all chirp signals, the slow time dimension calculation is performed, and the slow time dimension calculation is performed one by one on each segment of the distance spectrum in descending order of priority. After the slow time dimension calculation of each segment is completed, target detection is performed based on the calculation results. If the target is detected, the calculation of the remaining segments of the distance spectrum is stopped. Compared with the prior art that requires fast time dimension calculation and slow time dimension calculation to be performed on all sampled data, the present application can reduce the amount of calculation, reduce the time required for target detection and power consumption overhead, and is beneficial to extending the battery life of the device that performs target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flowchart of a target detection method provided in an embodiment of the present application;

[0036] Figure 2 is a schematic diagram of a distance spectrum provided in an embodiment of the present application;

[0037] Figure 3 Yes Figure 2 The diagram shows a schematic diagram of the division of the distance spectrum into multiple segments. DETAILED DESCRIPTION

[0038] In order to solve the above problems existing in the prior art, the present application provides a target detection method and device, and a storage medium. These protected themes are based on the same concept, and the principles of solving the problems are basically the same or similar. The implementation methods of each protected theme can refer to each other, and the repeated parts will not be repeated.

[0039] In the scheme of the present application, for multiple chirp signals obtained by sampling, after fast time dimension calculation is performed on the sampling data of each chirp signal, the corresponding distance spectrum is generated, and then the obtained distance spectrum is used for target detection. If the target is detected, the calculation of the sampling data of the remaining chirp signals is stopped. If the target is not detected, the obtained distance spectrum can be cached in chronological order; only when the target is not detected in the sampling data of all chirp signals, the cached distance spectrum data is calculated in slow time dimension, and the slow time dimension calculation is performed on each segment of the distance spectrum one by one in order from high to low priority. After the slow time dimension calculation of each segment is completed, target detection is performed according to the calculation result. Once the target is detected, the calculation of the remaining segments of the distance spectrum is stopped.

[0040] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly described below in conjunction with specific embodiments and corresponding drawings. Obviously, the embodiments described below are only part of the embodiments of the present application, not all of the embodiments. In the absence of conflict, the following embodiments and their technical features can be combined with each other and also belong to the technical solutions of the present application.

[0041] Figure 1 This is a flow chart of a target detection method provided by an embodiment of the present application. The target detection method can also be referred to as a "method", and the execution subject of each step can be an adapted target detection device, or a storage medium, processor, controller, etc. with a target detection function.

[0042] like Figure 1 As shown, the method comprises at least the following steps:

[0043] S1: Acquire sampling data of multiple consecutive chirp signals during radar detection.

[0044] Radars include but are not limited to FMCW millimeter wave radars. The radars emit chirp signals at a preset frame rate to detect the surrounding environment. The present application obtains the N chirp signals closest to the current moment and receives an echo signal of each chirp signal, wherein the echo signal of each chirp signal contains a number of sampling data, which are called sampling data of a chirp signal.

[0045] The number of chirp signals obtained (ie, the value of N) may be determined according to the adaptability of detection requirements, for example, according to the detection accuracy and / or the frame rate of the radar, and is not limited in this application.

[0046] S2: Perform fast time dimension calculation on the sampling data of each chirp signal one by one in time sequence to generate a corresponding distance spectrum, and perform target detection according to the obtained distance spectrum after completing the fast time dimension calculation of the sampling data of a chirp signal.

[0047] Taking the fast time dimension calculation as the fast Fourier transform (i.e. FFT) as an example, by performing a K-point fast Fourier transform on the sampling data of a chirp signal, we can get the representation of the chirp signal in the frequency domain, which is the distance spectrum. Then, by analyzing the distance spectrum, we can determine whether the target is detected.

[0048] In one example, the fast time dimension calculation is performed on the sampled data of a chirp signal, and the steps are as follows:

[0049] S21. Signal sampling: Perform time domain sampling on a chirp signal. For a chirp signal, Q sampling data can be obtained.

[0050] S22. Execute FFT: Perform K-point fast Fourier transform on the sampling data of a chirp signal. The process and principle can be found in the prior art. FFT converts the time domain information into the frequency domain to obtain K complex points. Each point represents the amplitude and phase of a specific frequency component. The number of points K should not be less than the number Q of sampling data contained in a chirp signal.

[0051] S23. Get the distance spectrum: The result of FFT is a frequency spectrum. For the chirp signal, this frequency spectrum is the distance spectrum.

[0052] S24. Analyze the distance spectrum: By analyzing whether there is an obvious prominent peak in the distance spectrum, or whether there is a peak corresponding to the target to be detected, it can be determined whether the target is detected.

[0053] In addition, since the frequency component corresponding to the peak is related to the echo time of the target, the present application can determine the distance of the target by analyzing the peak position in the distance spectrum.

[0054] Before target detection, the present application can perform static clutter filtering, for example, according to the result of static clutter estimation, subtract the corresponding static clutter from the sampled data of the chirp signal, thereby eliminating the influence of stationary objects on target detection and ensuring the accuracy of target detection.

[0055] In step S2, after completing the target detection of each chirp signal, it is determined whether the target is detected. If the target is detected, step S31 is executed: stop calculating the sampling data of the remaining chirp signals, and the subsequent steps S32 to S5 do not need to be executed; if the target is not detected, the obtained distance spectrum is cached in time sequence, and it is determined whether the above detection is performed on all chirp signals. If so, step S4 is executed: divide each distance spectrum into multiple sections with priority; if not, return to step S2 to detect the next chirp signal.

[0056] Taking the sampling data of three chirp signals as an example, the sampling data of the first chirp signal is first FFT calculated. If the target is detected according to the calculation result, step S31 is executed: the sampling data of the remaining chirp signals (i.e., the second chirp signal and the third chirp signal) is stopped from being calculated. At this time, it can be output that the target has been detected, and the device is instructed to perform corresponding operations for the target, such as turning on the light. If the target is not detected, the obtained distance spectrum is cached, and the sampling data of the second chirp signal is FFT calculated, and the target is detected according to the calculation result. Detection, if the target is detected, step S31 is executed, that is, the calculation of the sampling data of the remaining chirp signals (i.e., the third chirp signal) is stopped; if the target is not detected, the obtained distance spectrum is cached, and the sampling data of the third chirp signal is FFT calculated, and the target detection is performed according to the calculation result. If the target is detected, the result that the target has been detected can be output. If the target is still not detected, it means that the target is not detected for the sampling data of all chirp signals, and step S32 is executed: the distance spectrum generated for the sampling data of each chirp signal is obtained ( Figure 1 The step S32 is not shown in the figure); and, step S4: dividing each distance spectrum into a plurality of sections with priorities.

[0057] The priority indicates the order in which slow-time dimension target detection is performed. The higher the priority of a segment, the higher the priority of slow-time dimension target detection.

[0058] Combination Figure 2 As shown, the cached distance spectrum is arranged in a two-dimensional style according to time-distance, each row represents the data of a distance spectrum, there are N distance spectra in total, and each column represents the range gates with the same range gate number obtained at different times (the reception time of the 0th, 1st, ... N-1th chirp signal in the slow time dimension), where a distance spectrum has a total of K range gates, and these range gates are numbered 0, 1, 2...K-1 according to the order of the distances represented in the distance dimension.

[0059] In one example, dividing any distance spectrum into a plurality of priority segments comprises the steps of:

[0060] S41. Obtain the range gate where the most recently detected target is located as the center range gate.

[0061] S42. In the range spectrum, the central range gate and the first number of range gates symmetrically located on both sides thereof are taken as the segments with the highest priority.

[0062] Combination Figure 3 In the example shown, if the range gate of the target detected most recently (i.e. the previous time) is K i, i is a positive integer and less than or equal to K-1, then the center distance gate is K i , taking the first quantity x as an example, the center is far from the gate K i The x range gates on the left are K i-1 , K i-2 ,...K i-x (ix is ​​greater than or equal to 0) or K i-1 , K i-2 ,...K 0 (ix is ​​less than 0), the center is close to gate K i The x range gates on the right are K i+1 , K i+2 ,...K i+x (i+x is less than or equal to K-1) or K i+1 , K i+2 ,...K K-1 (i+x is greater than K-1), here, these range gates on the left and right sides (for example Figure 3 The range gate K shown i , K i-1 , K i-2 ,...K i-x , K i+1 , K i+2 ,...K i+x ), together forming the highest priority segment.

[0063] This application divides all range gates into sections according to the range gate where the target was most recently detected. According to the probability of the target being in the section, the high-probability section can be given priority for slow-time dimension calculation, and the target can be detected immediately, which can greatly reduce the amount of calculation.

[0064] Optionally, for the range gates other than the segment with the highest priority in the range spectrum, the present application may directly determine them as one segment, thereby finally obtaining two segments, where the segment with the highest priority may be called a "high possibility segment" and the other segment may be called a "low possibility segment".

[0065] Alternatively, for the range gates other than the segment with the highest priority in the range spectrum, the present application may continue to perform priority division, and thereby obtain more than two segments. That is, the step S4 may also include:

[0066] S43. Set a second number of range gates symmetrically located on both sides of the first priority segment as a second priority segment, and the second priority is lower than the first priority.

[0067] Continue reading Figure 3As shown, taking the first priority segment as the segment with the highest priority, the first quantity as x, and the second quantity as y as an example, the left side of the first priority segment refers to the distance gate K i-x The left side of the distance gate K i-x The y range gates on the left are K i-x-1 , K i-x-2 ,...K i-x-y , the right side of the first priority segment refers to the range gate K i+x The right side of the distance gate K i+x The y range gates on the right are K i+x+1 , K i+x+2 ,...K i+x+y , here, the distance from Gate K i-x-1 , K i-x-2 ,...K i-x-y , K i+x+1 , K i+x+2 ,...K i+x+y , together forming a second priority segment, which can be called a "medium possibility segment"; the remaining range gates can be a segment, for example, which can be called a "low possibility segment".

[0068] In one example, the present application may calculate any one of the first quantity and the second quantity through the following relationship:

[0069] K n =(k*(d 0 + d 1 ) / F / (Q-1))-K n-1

[0070] d 0 = v*t

[0071] d 1 = a * F / f(S nr )

[0072] Among them, K n The integer of K is rounded upward to any one of the first number and the second number. For example, when calculating the first number x, the K n The integer of is x; n is the ranking of the current segment according to the priority, and when n=1, K n-1 = 0, k is the number of range gates contained in the range spectrum, d 0 is the predicted value of the target moving distance, d 1is the correction value of the current section, F is the resolution of the radar, Q represents the number of sampled data contained in a chirp signal, v is the target moving speed detected most recently, t is the time interval between two adjacent chirp signals, a is the correction factor corresponding to the current section, when calculating the number of bilaterally symmetrical range gates in different sections, the value of the correction factor can be different, f(S nr ) is for S nr Perform square root operation, S nr It is the signal-to-noise ratio when the radar detects the target most recently.

[0073] For example, for the highest priority segment (i.e., the “high probability segment”), n=1, K n-1 = 0, then the relationship for calculating the first quantity x is K 1 = k*(d 0 + d 1 ) / F / (Q-1); For the "medium possible segment", n=2, the relationship for calculating the second quantity y is K 2 =(k*(d 0 + d 1 ) / F / (Q-1))-K 1 .

[0074] This example associates the first number x and the second number y with relevant parameters of the previous target detection, so that the current target detection can refer to the previous target detection parameters, which is conducive to quickly and more accurately determining the probability of target appearance and reducing the amount of calculation. For example, the lower the resolution of the previous radar and the smaller the signal-to-noise ratio, the larger the value of the first number x or the second number y.

[0075] exist Figure 3 In the example shown, all range gates participate in the segment division, that is, the multiple segments finally obtained include all range gates of the range spectrum. In other examples, the present application may select only a part of the range gates of the range spectrum for segment division. For example, step S4 also includes:

[0076] S40. Selecting a plurality of continuous range gates from each range spectrum according to a preset monitoring distance of the radar;

[0077] And, only for the selected continuous plurality of range gates, the aforementioned steps S41 and S42 are executed.

[0078] Combination Figure 2 As shown, the present application can select a maximum distance gate K according to the preset monitoring distance of the radar. max , K max For this distance gate Figure 2 The numbers in the distance spectrum shown are in the range gate 0~K max The area is divided into sections, where the number Kmax The maximum number of range gates in the range spectrum is not more than K-1, so that the segment division is always limited to the range gates 0~K max Inside.

[0079] S5: For each distance spectrum, the slow time dimension calculation is performed on each segment one by one according to the priority from high to low in time sequence, and each time the slow time dimension calculation of a segment is completed, the target detection is performed according to the calculation result.

[0080] In step S5, after completing target detection for each segment, determine whether a target is detected. If a target is detected, execute step S61: stop slow time dimension calculation for the remaining segments, and no subsequent steps need to be executed. If no target is detected, determine whether the above detection has been performed on all segments of the current distance spectrum. If so, execute step S62: perform target detection according to the segments of the next distance spectrum. If not, return to step S5 to detect the next segment.

[0081] Taking the three segments divided into high possibility segment, medium possibility segment and low possibility segment as an example, the high possibility segment is firstly calculated in slow time dimension. If the target is detected according to the calculation result, step S61 is executed: the slow time dimension calculation of the remaining segments (i.e., the medium possibility segment and the low possibility segment) is stopped; at this time, it can be output that the target has been detected, and the device is instructed to perform corresponding operations for the target, such as turning on the light; if the target is not detected, the slow time dimension calculation is performed on the medium possibility segment, and the target detection is performed according to the calculation result; if the target is detected, step S61 is executed, i.e., the slow time dimension calculation of the remaining segments (i.e., the low possibility segment) is stopped; if the target is not detected, the slow time dimension calculation is performed on the low possibility segment, and the target detection is performed according to the calculation result; if the target is detected, the result that the target has been detected can be output; if the target is still not detected, it means that the target has not been detected for all segments, and step S62 is executed: target detection is performed according to the segment of the next distance spectrum. For the next distance spectrum, the principle and process of performing target detection can refer to the aforementioned steps S4 to S61, which will not be repeated here. Optionally, each distance spectrum is divided into multiple equal segments. If no target is detected for all distance spectra, it means that no target is detected this time, and step S1 can be executed for the next sampling detection.

[0082] In one example, the slow time dimension is calculated as a fast Fourier transform of the slow time dimension. By performing a fast Fourier transform on the range gate signals with the same number in a segment, a representation of the segment in the frequency domain can be obtained, that is, a velocity spectrum. Then, the velocity spectrum can be analyzed to determine whether a target is detected. In other words, the slow time dimension is calculated as a fast Fourier transform of the range gates with the same number in the range spectrum in the slow time dimension.

[0083] In other examples, the slow time dimension calculation may include the following steps:

[0084] S51. Determine the range gates with the same number in the range spectrum; and,

[0085] S52. Accumulate and sum the signal energies corresponding to the range gates with the same number.

[0086] This example can be viewed as an incoherent accumulation calculation in the slow time dimension, combined with Figure 2 and Figure 3 As shown, each column represents a range gate with the same number, and the sum of the signal energies corresponding to each range gate is obtained, that is, the sum of the energies of the radar electromagnetic wave signals. When target detection is performed according to the calculation results, if the sum of the signal energies of a certain range gate reaches a preset threshold, or the sum of the signal energies is significantly greater than the sum of the signal energies of other range gates, it can be determined that the target is detected and the target appears in the said certain range gate.

[0087] Based on any of the above examples, for sampling to obtain multiple chirp signals, the present application generates a corresponding distance spectrum after performing fast time dimension calculation on the sampled data of each chirp signal, and then uses the obtained distance spectrum to detect the target. If the target is detected, the calculation of the sampled data of the remaining chirp signals is stopped. If the target is not detected, the obtained distance spectrum can be cached in chronological order; only when the target is not detected in the sampled data of all chirp signals, the cached distance spectrum data is calculated in slow time dimension, and the slow time dimension calculation is performed on each segment of the distance spectrum one by one in order from high to low priority. After each slow time dimension calculation of a segment is completed, target detection is performed according to the calculation result. If the target is detected, the calculation of the remaining segments of the distance spectrum is stopped. Compared with the prior art that requires fast time dimension calculation and slow time dimension calculation to be performed on all sampled data, the present application can reduce the amount of calculation, reduce the time and power consumption required for target detection, and for the device that performs target detection, it is beneficial to extend the battery life.

[0088] An embodiment of the present application also provides a storage medium, on which a target detection program is stored. The target detection program is essentially a computer program, and when the target detection program is executed by a processor, the steps of the target detection method as in any example are implemented.

[0089] The storage medium includes but is not limited to any one of a read-only memory (ROM), a random access memory (RAM), a magnetic disk and an optical disk.

[0090] Since the program stored in the storage medium can execute the steps in the target detection method of any embodiment provided in the present application, the beneficial effects that can be achieved by the target detection method of any of the aforementioned embodiments can be achieved. Please refer to the aforementioned embodiments for details and will not be repeated here.

[0091] An embodiment of the present application also provides a target detection device or chip, including a memory and a processor, wherein a target detection program is stored in the memory, and when the target detection program is executed by the processor, the steps of the target detection method of any of the aforementioned embodiments are implemented; and / or, the target detection device or chip is provided with a storage medium as exemplified above, and the processor loads the storage medium to execute the steps of the target detection method, thereby achieving the beneficial effects that can be achieved by the target detection method of the corresponding embodiment.

[0092] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. For ordinary technicians in this field, all equivalent structural changes made using the contents of this specification and drawings are also included in the patent protection scope of the present application.

[0093] This document uses step codes such as S1, S2, etc., the purpose of which is to express the corresponding content more clearly and concisely, and does not constitute a substantial limitation on the order. When technical personnel in this field implement the specific steps, they may execute S2 first and then S1, etc., but these should all be within the scope of protection of this application.

[0094] Although the terms "first, second", etc. are used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. In addition, the singular forms "one", "an", and "the" are intended to include plural forms as well. The terms "or" and "and / or" are interpreted as inclusive, or mean any one or any combination. Only when the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way, an exception to this definition will occur.

Claims

1. A target detection method, characterized in that: include: Acquire sampling data of multiple consecutive chirp signals during radar detection; The sampling data of each chirp signal are calculated in fast time dimension one by one in time sequence to generate a corresponding distance spectrum, and each time the fast time dimension calculation of the sampling data of a chirp signal is completed, the target is detected according to the obtained distance spectrum; If a target is detected according to the fast time dimension calculation, then the calculation of the sampled data of the remaining chirp signal is stopped; If no target is detected for the sampled data of all chirp signals, each range spectrum is divided into a plurality of sections with priorities; According to the time sequence, the slow time dimension calculation is performed on each segment of each distance spectrum in descending order of priority, and each time the slow time dimension calculation of a segment is completed, the target detection is performed according to the calculation result; If the target is detected according to the slow time dimension calculation, then the slow time dimension calculation for the remaining segments is stopped; If no target is detected in all segments, target detection is performed according to the segment of the next range spectrum.

2. The target detection method according to claim 1, characterized in that: Each time the fast time dimension calculation of the sampling data of a chirp signal is completed and no target is detected according to the obtained range spectrum, the obtained range spectrum is cached in time sequence.

3. The target detection method according to claim 1, characterized in that: The distance spectrum includes a plurality of range gates, and the step of dividing each distance spectrum into a plurality of sections with priorities includes: Get the range gate where the most recently detected target is located as the center range gate; In the range spectrum, the central range gate and a first number of range gates symmetrically located on both sides thereof are taken as the sections with the highest priority.

4. The target detection method according to claim 3, characterized in that: The step of dividing each distance spectrum into a plurality of sections with priorities further includes: For the range gates other than the segment with the highest priority in the range spectrum, a second number of range gates symmetrically located on both sides of the first priority segment are used as a second priority segment, and the second priority is lower than the first priority.

5. The target detection method according to claim 3, characterized in that: The step of dividing each distance spectrum into a plurality of sections with priorities further includes: Selecting a plurality of continuous range gates from each range spectrum according to a preset monitoring range of the radar; And, only for the selected multiple consecutive range gates, the step of obtaining the range gate where the most recently detected target is located is performed.

6. The target detection method according to any one of claims 3 to 5, characterized in that: Each of the distance spectra is divided into two sections or three sections.

7. The target detection method according to claim 1, characterized in that: The slow time dimension is calculated by performing fast Fourier transform on the range gates with the same number in the range spectrum in the slow time dimension.

8. The target detection method according to claim 1, characterized in that: The slow time dimension calculation includes: determining range gates with the same number in the range spectrum; The signal energies corresponding to the range gates with the same number are accumulated and summed.

9. A target detection device, characterized in that: The method comprises a processor and a memory, wherein a target detection program is stored in the memory, and when the target detection program is executed by the processor, the steps of the target detection method according to any one of claims 1 to 8 are implemented.

10. A storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the steps of the target detection method according to any one of claims 1 to 8 are implemented.

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