Target detection method, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of radars, and discloses a target detection method, electronic equipment and a storage medium. The target detection method comprises the steps that an echo signal is acquired, and the echo signal is obtained by reflecting a transmitting signal of a radar through a target; generating a Doppler spectrum matrix according to the echo signal; and according to a preset threshold, constant false alarm detection is carried out on the Doppler spectrum matrix so as to carry out target detection, the threshold is a k * noise estimation value, and k is greater than 1. And the accuracy of indoor target detection can be improved at least.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of radar technology, and in particular to a target detection method, an electronic device, and a storage medium. Background Art

[0002] Radar is an electronic device that uses electromagnetic waves to detect targets. Specifically, the radar first emits electromagnetic waves to illuminate the target, and then receives the echo signal generated by the reflection of the emitted electromagnetic waves by the target. It can then process and analyze the echo signal to obtain information such as the distance from the target to the electromagnetic wave emission point, the rate of change of distance (radial velocity), azimuth, and altitude.

[0003] However, compared with the outdoor environment, the indoor environment is more complex due to people, obstacles, etc. Therefore, the accuracy of target detection in the indoor environment is not high, and there is an urgent need to improve the accuracy of target detection in the indoor environment. Summary of the invention

[0004] The embodiments of the present application provide a target detection method, an electronic device, and a storage medium, which are at least helpful in improving the accuracy of indoor target detection.

[0005] According to some embodiments of the present application, on the one hand, the embodiments of the present application also provide a target detection method, including: acquiring an echo signal, wherein the echo signal is a radar's transmission signal obtained by being reflected by a target, and the radar has multiple receiving channels; generating a Doppler spectrum matrix according to the echo signal; and performing constant false alarm detection on the Doppler spectrum matrix according to a preset threshold to perform target detection, wherein the threshold is k*noise estimation value, k>1.

[0006] In some embodiments, generating a Doppler spectrum matrix based on the echo signal includes: performing a range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; generating zero Doppler channel data of each frame based on the frequency domain signals corresponding to the chirps belonging to the same frame; performing a slow-time Fourier transform on the frequency domain signals corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame, and generating the Doppler spectrum matrix based on the transformed results.

[0007] In some embodiments, before performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame, the method further includes: updating the zero Doppler channel data according to the zero Doppler channel data of each frame; removing the clutter frequency domain component from the frequency domain signal according to the corresponding updated zero Doppler channel data to obtain the frequency domain signal after each chirp update; performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame includes: performing slow-time Fourier transform on the updated frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame.

[0008] In some embodiments, the method further includes: generating corresponding feature information for each detected target; and determining whether the target is a valid target based on the corresponding feature information.

[0009] In some embodiments, determining whether the target is a valid target based on the corresponding characteristic information includes: determining the relative energy distribution of signal and noise in the characteristic information of the target; and determining whether the target is a valid target based on the relative energy distribution.

[0010] In some embodiments, the characteristic information includes an angle spectrum, the relative energy distribution includes a first validity parameter, and determining the relative energy distribution of the signal and noise in the characteristic information of the target includes: determining the maximum value in the angle spectrum corresponding to the target; determining the first validity parameter of the target based on the maximum value among the corresponding maximum values ​​of the angle spectrum and the average of other maximum values ​​except the maximum value.

[0011] In some embodiments, the relative energy distribution includes a second validity parameter. After determining the maximum value in the angle spectrum corresponding to the target, the method further includes: determining the second validity parameter of the target based on the maximum value and the second largest value in the corresponding maximum values ​​of the angle spectrum; determining whether the target is a valid target based on the relative energy distribution includes: determining whether the target is an invalid target based on the corresponding second validity parameter; and determining whether the target is a valid target based on the corresponding first validity parameter when the target does not belong to the invalid target.

[0012] In some embodiments, the feature information is the angle spectrum generated based on a preset algorithm, and the preset algorithm includes any one of the following: beamforming algorithm, Capon algorithm, multiple signal classification algorithm, rotational invariant subspace algorithm, orthogonal matching pursuit algorithm.

[0013] In some embodiments, determining whether each of the targets is a valid target based on the corresponding feature information includes: determining an entropy value of the feature information corresponding to the target; and determining whether the target is a valid target based on the corresponding entropy value.

[0014] In some embodiments, determining whether the target is a valid target based on the corresponding feature information includes: performing constant false alarm detection on the feature information corresponding to the target to obtain a signal-to-noise ratio of the target; and determining whether the target is a valid target based on the corresponding signal-to-noise ratio.

[0015] According to some embodiments of the present application, on the other hand, the embodiments of the present application also provide a target detection method, including: acquiring an echo signal, wherein the echo signal is a radar's transmitting signal obtained by being reflected by a target, and the radar has multiple receiving channels; performing a distance-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; generating zero Doppler channel data of each frame based on the frequency domain signals corresponding to the chirps belonging to the same frame; performing a slow-time-dimensional Fourier transform on the frequency domain signals corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame, and generating a Doppler spectrum matrix based on the transformed results to perform target detection.

[0016] In some embodiments, before performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame, the method further includes: updating the zero Doppler channel data according to the zero Doppler channel data of each frame; removing the clutter frequency domain component from the frequency domain signal according to the corresponding updated zero Doppler channel data to obtain the frequency domain signal after each chirp update; performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame includes: performing slow-time Fourier transform on the updated frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame.

[0017] In some embodiments, the method further includes: generating corresponding feature information for each detected target; and determining whether the target is a valid target based on the corresponding feature information.

[0018] In some embodiments, determining whether the target is a valid target based on the corresponding characteristic information includes: determining the relative energy distribution of signal and noise in the characteristic information of the target; and determining whether the target is a valid target based on the relative energy distribution.

[0019] In some embodiments, the characteristic information includes an angle spectrum, the relative energy distribution includes a first validity parameter, and determining the relative energy distribution of the signal and noise in the characteristic information of the target includes: determining the maximum value in the characteristic information corresponding to the target; determining the first validity parameter of the target based on the maximum value among the corresponding maximum values ​​of the angle spectrum and the average of other maximum values ​​except the maximum value.

[0020] In some embodiments, the relative energy distribution includes a second validity parameter. After determining the maximum value in the characteristic information corresponding to the target, the method further includes: determining the second validity parameter of the target based on the maximum value and the second largest value among the corresponding maximum values ​​of the angle spectrum; determining whether the target is a valid target based on the relative energy distribution includes: determining whether the target is an invalid target based on the corresponding second validity parameter; and determining whether the target is a valid target based on the corresponding first validity parameter when the target does not belong to the invalid target.

[0021] In some embodiments, the feature information is the angle spectrum generated based on a preset algorithm, and the preset algorithm includes any one of the following: beamforming algorithm, Capon algorithm, multiple signal classification algorithm, rotational invariant subspace algorithm, orthogonal matching pursuit algorithm.

[0022] In some embodiments, determining whether the target is a valid target based on the corresponding feature information includes: determining an entropy value of the feature information corresponding to the target; and determining whether the target is a valid target based on the corresponding entropy value.

[0023] In some embodiments, determining whether the target is a valid target based on the corresponding feature information includes: performing constant false alarm detection on the feature information corresponding to the target to obtain a signal-to-noise ratio of the target; and determining whether the target is a valid target based on the corresponding signal-to-noise ratio.

[0024] According to some embodiments of the present application, on the other hand, the embodiments of the present application further provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the target detection method described in any of the above embodiments.

[0025] According to some embodiments of the present application, on the other hand, embodiments of the present application further provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the target detection method as described in any of the above embodiments is implemented.

[0026] The technical solution provided in the embodiment of the present application has at least one of the following advantages:

[0027] 1. After the target is detected by the echo signal received by the radar's multiple receiving channels, the corresponding feature information is further generated for the detected target, so that it can be determined whether the detected target is a valid target based on the feature information. In other words, on the basis of detecting the target, the validity of the target is further determined, so that the error of detecting the clutter as the target can be eliminated, which is conducive to improving the accuracy of indoor target detection.

[0028] 2. After the echo signal is received through multiple receiving channels of the radar, a Doppler matrix is ​​generated according to the echo signal and constant false alarm detection is performed to detect the target. The threshold of the constant false alarm detection is set to k*noise estimation value, k>1, that is, the threshold is allowed to be set to a smaller value, and the threshold is no longer required to be a higher value, so that a smaller threshold can be passed, so that the constant false alarm detection can detect more points, reduce or avoid mistaking valid targets for noise points for exclusion, which is conducive to improving the accuracy of indoor target detection.

[0029] 3. After the echo signals are received through multiple receiving channels of the radar, a Doppler matrix is ​​generated according to the echo signals to detect the target. The Doppler matrix is ​​obtained by processing the zero Doppler channel data of each frame determined by the echo signal, and further performing Fourier transform on the zero Doppler of each frame, that is, performing inter-frame Fourier transform. In other words, providing a processing method adapted to the static characteristics of a stationary target to obtain the Doppler matrix can improve the accuracy of the Doppler matrix, and then improve the accuracy of target detection based on the Doppler matrix, that is, it is conducive to improving the accuracy of indoor static target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0031] Figure 1 is a flow chart of a target detection method provided in an embodiment of the present application;

[0032] Figure 2 is another flow chart of a target detection method provided in an embodiment of the present application;

[0033] Figure 3 is another flow chart of a target detection method provided in an embodiment of the present application;

[0034] Figure 4 is another flow chart of a target detection method provided in an embodiment of the present application;

[0035] Figure 5 is another flow chart of a target detection method provided in an embodiment of the present application;

[0036] Figure 6 is another flow chart of a target detection method provided in an embodiment of the present application;

[0037] Figure 7 is another flow chart of a target detection method provided in an embodiment of the present application;

[0038] Figure 8 is another flow chart of a target detection method provided in an embodiment of the present application;

[0039] Fig. 9 is another flow chart of a target detection method provided in an embodiment of the present application;

[0040] Fig.10 is another flow chart of a target detection method provided in an embodiment of the present application;

[0041] Fig.11 It is a schematic diagram of the structure of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present application, many technical details are provided in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can be implemented.

[0043] The division of the following embodiments is for the convenience of description and shall not constitute any limitation on the specific implementation of the present application. The embodiments may be combined with each other and referenced to each other without contradiction.

[0044] The present application provides a target detection method, which is applied to a processing module in a radar, a host computer connected to the radar, or other modules, devices, or electronic devices with processing and computing capabilities. The radar may be a millimeter wave radar or the like.

[0045] The process is as follows Figure 1 As shown, the following steps are included:

[0046] Step 101, obtaining an echo signal, where the echo signal is obtained by reflecting the radar's transmission signal from the target.

[0047] This embodiment does not limit the transmission signal, which may be a continuous wave (CW) or a frequency modulated continuous wave (FWCW), etc., which will not be described in detail here.

[0048] This embodiment does not limit the type of target, which may be a static target, such as a display screen fixed on the wall in a conference room, or a dynamic target, such as a moving person, etc., which will not be described in detail here.

[0049] In particular, in some embodiments, the target may be a person or object in an environment, for example, the environment is an indoor environment, and the indoor environment includes but is not limited to a room, an open-air stadium, and the like.

[0050] In some embodiments, the radar may be any radar having multiple receiving channels and / or multiple transmitting channels.

[0051] Step 102: Detect targets based on the echo signals and generate corresponding feature information for the detected targets.

[0052] This embodiment does not limit the specific implementation method of target detection, which may be based on a neural network or a Doppler Fourier transform.

[0053] In this embodiment, the characteristic information may be any information that can reflect the distribution of the signal reflected by the target in the received echo signal.

[0054] In some embodiments, the characteristic information may be an angle spectrum, which is generated based on an angle spectrum generated by a preset algorithm, wherein the angle spectrum may be a range Doppler (RD) spectrum, a Capon spectrum, or the like.

[0055] Furthermore, in some embodiments, the algorithms used in generating the angle spectrum may include: beamforming algorithm, Capon algorithm, multiple signal classification algorithm, rotational invariant subspace algorithm, orthogonal matching pursuit algorithm, etc., which will not be described one by one here.

[0056] Of course, the above is only an example of characteristic information, which does not mean that the characteristic information can only be the angle spectrum. It can also be other characteristic information that can reflect the energy distribution in the echo signal, such as the eigenvalue of the covariance matrix of the echo signal, etc., which will not be elaborated here.

[0057] Step 103: Determine whether the target is a valid target based on the corresponding feature information.

[0058] It is understandable that clutter is inevitable in space, and clutter will interfere with target detection, resulting in invalid targets being detected. The feature information can reflect the distribution of the signal reflected by the target in the received echo signal. Therefore, the target can be analyzed through the feature information to determine whether the target is a valid target.

[0059] In this way, after the above embodiment detects the target through the echo signal received by the multiple receiving channels of the radar, it further generates corresponding feature information for the detected target, so that it can determine whether the detected target is a valid target based on the feature information. In other words, on the basis of detecting the target, the validity of the target is further determined, so that the situation of erroneous detection of noise points as targets can be reduced, which is conducive to improving the accuracy of indoor target detection.

[0060] In order to help those skilled in the art better understand the target detection method provided by the above embodiment, it will be described below.

[0061] like Figure 2 As shown, in some embodiments, determining whether a target is a valid target according to corresponding feature information can be achieved through the following steps:

[0062] Step 1031, determining the relative energy distribution of the signal and the noise in the characteristic information of the target.

[0063] This embodiment does not limit the description method of the relative energy distribution of the signal and the noise, which may vary according to different feature information, and may also vary according to different scenarios, requirements, etc.

[0064] For example, when the characteristic information includes energy spectrum, based on the characteristics that signals are usually concentrated and noise is usually scattered, the entropy value of the energy spectrum can be used as the relative energy distribution of signals and noise; when the characteristic information includes angle spectrum, based on the characteristics that signals usually have greater energy, which is mainly reflected in the main lobe, and noise usually has smaller energy, which is mainly reflected in the side lobes, the relative energy distribution can be determined based on the relevant information of the main lobe and side lobes in the angle spectrum.

[0065] For example, when the characteristic information includes an angular spectrum, the relative energy distribution can be global, that is, the relative energy distribution is determined based on the relevant information of the main lobe and all side lobes; the relative energy distribution can be local, that is, the relative energy distribution is determined based on the relevant information of the main lobe and the maximum side lobe, etc.

[0066] Of course, the above is only an example. In some embodiments, the relative energy distribution can be determined by other methods, which will not be described in detail here.

[0067] Step 1032, determining whether the target is a valid target based on the relative energy distribution.

[0068] It is understandable that when the feature information includes more noise-related content than signal-related content, it means that the signal described by the feature information is more likely to be a noise signal (i.e., clutter). Therefore, based on the relative energy distribution of the signal and noise in the target, it can be determined whether the feature information of the target contains more noise-related content or more signal-related content, thereby determining whether the target is a valid target.

[0069] It can also be understood that the criteria for determining effective targets may vary depending on the scenario, requirements, etc. For example, when the noise in the environment is complex and there may be multiple superpositions resulting in high noise, the difference threshold between noise and signal in the relative energy distribution can be taken as a smaller value. This can reduce the risk of misjudgment of effective targets.

[0070] In order to facilitate those skilled in the art to better understand the above embodiment, the characteristic information is taken as an angle spectrum as an example for further description.

[0071] In some embodiments, the relative energy distribution includes a first validity parameter. At this time, determining the relative energy distribution of the signal and the noise in the characteristic information of the target includes: determining the maximum value in the angle spectrum corresponding to the target; and determining the first validity parameter of the target according to the maximum value among the maximum values ​​of the angle spectrum and the average value of other maximum values ​​except the maximum value.

[0072] Among them, the first effective parameter can be the ratio of the maximum value among the local values ​​of the angle spectrum and the average of other local values ​​except the maximum value; the first effective parameter can also be an array composed of the maximum value among the local values ​​of the angle spectrum and the average of other local values ​​except the maximum value, etc.

[0073] As mentioned above, the maximum value among the maximum values ​​of the angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the first validity parameter actually reflects whether the global signal distribution is concentrated, and then can reflect the relative energy distribution. Therefore, based on the global relative energy distribution, it is possible to more comprehensively and accurately determine whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0074] It should be noted that the "mean" in the mean of other maximum values ​​except the maximum value can be the average value, median or mode, etc., which will not be elaborated here.

[0075] In some embodiments, the relative energy distribution also includes a second validity parameter. In this case, after determining the maximum value in the angle spectrum corresponding to the target, the target detection method further includes the following steps: determining the second validity parameter of the target according to the maximum value and the second maximum value in the maximum value of the angle spectrum. Accordingly, determining whether the target is a valid target according to the relative energy distribution can be achieved in the following manner: determining whether the target is an invalid target according to the corresponding second validity parameter; and determining whether the target is a valid target according to the first validity parameter when the target is not an invalid target.

[0076] As mentioned above, the maximum value among the maximum values ​​of the angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the second validity parameter actually reflects whether the local signal distribution is concentrated, and then relative to the first validity parameter, the second validity parameter can also reflect the relative energy distribution from another perspective. Therefore, based on the local relative energy distribution, combined with the overall relative energy distribution, it is determined from different dimensions whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0077] In some embodiments, Figure 3 As shown, according to the corresponding feature information, determining whether the target is a valid target can be achieved through the following steps:

[0078] Step 1033, determining the entropy value of the feature information corresponding to the target.

[0079] This embodiment does not limit the method for determining the entropy value. Any algorithm that can quantify the degree of confusion of feature information can be used to obtain the entropy value, which will not be described in detail here.

[0080] Step 1034, determining whether the target is a valid target based on the corresponding entropy value.

[0081] It can be understood that for a valid target (i.e., a real target), the corresponding echo signal should be concentrated and have a small entropy value, while for an invalid target (i.e., a false target, such as a target detected based on clutter), the corresponding echo signal is relatively unconcentrated and has a large entropy value. In other words, whether the detected target is a valid target can be determined based on the relative size of the entropy value.

[0082] It should be noted that this embodiment does not limit the entropy value range corresponding to the valid target, which may vary according to the degree of chaos in the detection environment, the requirements of detection accuracy, etc., and will not be elaborated here.

[0083] In some embodiments, Figure 4As shown, according to the corresponding feature information, determining whether the target is a valid target can be achieved through the following steps:

[0084] Step 1035: Perform constant false alarm detection on the characteristic information corresponding to the target to obtain the signal-to-noise ratio of the target.

[0085] In this embodiment, constant false alarm rate detection (CFAR) is implemented by a corresponding CFAR algorithm, which will not be described in detail here.

[0086] Step 1036, determining whether the target is a valid target based on the corresponding signal-to-noise ratio.

[0087] It can be understood that the signal-to-noise ratio is an accurate quantification of the signal part and the noise part in the characteristic information corresponding to the target. Based on the signal-to-noise ratio, the relative size of the signal part and the noise part in the characteristic information can be accurately determined, so that it can be accurately judged whether the detected target is a valid target, which is conducive to further accurate target detection.

[0088] In some embodiments, target detection based on the echo signal can be achieved in the following manner: generating a Doppler spectrum matrix based on the echo signal; performing constant false alarm detection on the Doppler spectrum matrix according to a preset threshold to perform target detection, the threshold being k*noise estimation value, k>1.

[0089] Among them, compared with the current setting of the threshold to k'*noise estimation value in order to reduce clutter interference, k' is usually taken as dozens or hundreds, and k>1 allows the threshold to be set to a smaller value, and no longer requires the threshold to be a higher value, so that a smaller threshold can be used, so that the constant false alarm detection can detect more points, reduce or avoid mistaking valid targets for clutter for exclusion, which is beneficial to improving the accuracy of indoor target detection.

[0090] In some embodiments, the value range of k is (1, 4), for example, k=2, k=2.4, k=2.5, k=3, etc. 5 In this way, the value of k is further set to a smaller range, thereby further reducing the threshold. Therefore, a smaller threshold can be used to enable the constant false alarm detection to detect more points, further reducing or avoiding the mistaking of valid targets for miscellaneous points for exclusion, which is conducive to further improving the accuracy of indoor target detection.

[0091] Of course, the above is only an example of the value range of the threshold k. The value of the threshold k can also be adjusted according to specific scenarios, which will not be described in detail here.

[0092] In order to facilitate those skilled in the art to better understand the above embodiment, different types of targets, namely static targets and dynamic targets, will be used as examples to illustrate the above embodiment.

[0093] In some embodiments, generating a Doppler spectrum matrix based on an echo signal can be achieved in the following manner: performing a range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; generating zero Doppler channel data of each frame based on the frequency domain signals corresponding to the chirps belonging to the same frame; removing clutter frequency domain components from the frequency domain signal based on the corresponding zero Doppler channel data to obtain a dynamic frequency domain signal of each chirp; performing a slow-time-dimensional Fourier transform on the data in the dynamic frequency domain signal corresponding to the same distance unit in each chirp and generating a Doppler spectrum matrix based on the transformation result.

[0094] In this way, the Doppler matrix is ​​obtained by processing the echo signal, determining the zero Doppler channel data of each frame signal, and then determining the dynamic frequency domain signal of each chirp to perform a slow time Fourier transform. In other words, providing a processing method adapted to the dynamic characteristics of a dynamic target to obtain the Doppler matrix can improve the accuracy of the Doppler matrix, and then improve the accuracy of target detection based on the Doppler matrix, that is, it is beneficial to improve the accuracy of indoor dynamic target detection.

[0095] In some embodiments, generating a Doppler spectrum matrix based on an echo signal can also be achieved in the following manner: performing a range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; generating zero Doppler channel data of each frame based on the frequency domain signals corresponding to the chirps belonging to the same frame; performing a slow-time-dimensional Fourier transform on the frequency domain signals corresponding to the zero Doppler channel data corresponding to the same range unit in each frame, and generating a Doppler spectrum matrix based on the transformed results.

[0096] In this way, the Doppler matrix is ​​obtained by processing the zero Doppler channel data of each frame determined by the echo signal, and further performing Fourier transform on the zero Doppler of each frame, that is, performing inter-frame Fourier transform. In other words, providing a processing method adapted to the static characteristics of the crystal target to obtain the Doppler matrix can improve the accuracy of the Doppler matrix, and then improve the accuracy of target detection based on the Doppler matrix, that is, it is conducive to improving the accuracy of indoor static target detection.

[0097] In some embodiments, before performing a slow-time Fourier transform on the frequency domain signal of the zero Doppler channel data corresponding to the same distance unit in each frame, the target detection further includes the following steps: updating the zero Doppler channel data according to the zero Doppler channel data of each frame; removing the clutter frequency domain component from the frequency domain signal according to the corresponding updated zero Doppler channel data to obtain the updated frequency domain signal of each chirp. Accordingly, performing a slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame can be achieved by performing a slow-time Fourier transform on the updated frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame.

[0098] That is to say, considering the influence of long-term clutter, the zero Doppler channel data is updated according to the data of multiple frames, so that the clutter removal is more thorough, so that a more accurate static frequency domain signal of the static target can be obtained, and then the static target can be detected more accurately, which is conducive to further improving the accuracy of static target detection.

[0099] On the other hand, the embodiment of the present application also provides a target detection method, which is applied to a processing module in a radar, a host computer connected to the radar, or other modules, devices, or electronic devices with processing and computing capabilities. The radar may be a millimeter wave radar, etc. The process is as follows: Figure 5 As shown, the following steps are included:

[0100] Step 501, obtaining an echo signal, where the echo signal is obtained by reflecting the radar's transmission signal from the target.

[0101] This embodiment does not limit the transmission signal, which may be a continuous wave (CW) or a frequency modulated continuous wave (FWCW), etc., which will not be described in detail here.

[0102] This embodiment does not limit the type of target, which may be a static target, such as a display screen fixed on the wall in a conference room, or a dynamic target, such as a moving person, etc., which will not be described in detail here.

[0103] In particular, in some embodiments, the target may be a person or an object in an indoor environment.

[0104] In some embodiments, the radar may be any radar having multiple receiving channels and / or multiple transmitting channels.

[0105] Step 502: Generate a Doppler spectrum matrix according to the echo signal.

[0106] This embodiment does not limit the generation method of the Doppler matrix, which may vary depending on the environment, the expected detection result, etc.

[0107] To facilitate better understanding by those skilled in the art, the following will provide examples combining detection of static targets and detection of dynamic targets.

[0108] In some embodiments, generating a Doppler spectrum matrix based on an echo signal can be achieved in the following manner: performing a range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; generating zero Doppler channel data for each frame based on the frequency domain signals corresponding to the chirps belonging to the same frame; removing clutter frequency domain components from the frequency domain signal based on the corresponding zero Doppler channel data to obtain a dynamic target frequency domain signal for each chirp; performing a slow-time-dimensional Fourier transform on the data in the dynamic frequency domain signal corresponding to the same distance unit in each chirp and generating a Doppler spectrum matrix based on the transformation result.

[0109] In this way, the Doppler matrix is ​​obtained by processing the echo signal, determining the zero Doppler channel data of each frame signal, and then determining the dynamic frequency domain signal of each chirp to perform a slow time Fourier transform. In other words, providing a processing method adapted to the dynamic characteristics of a dynamic target to obtain the Doppler matrix can improve the accuracy of the Doppler matrix, and then improve the accuracy of target detection based on the Doppler matrix, that is, it is beneficial to improve the accuracy of indoor dynamic target detection.

[0110] In some embodiments, generating a Doppler spectrum matrix based on an echo signal can also be achieved in the following manner: performing a range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; generating zero Doppler channel data of each frame based on the frequency domain signals corresponding to the chirps belonging to the same frame; performing a slow-time-dimensional Fourier transform on the frequency domain signals corresponding to the zero Doppler channel data corresponding to the same range unit in each frame, and generating a Doppler spectrum matrix based on the transformed results.

[0111] In this way, the Doppler matrix is ​​obtained by processing the zero Doppler channel data of each frame determined by the echo signal, and further performing Fourier transform on the zero Doppler of each frame, that is, performing inter-frame Fourier transform. In other words, providing a processing method adapted to the static characteristics of the crystal target to obtain the Doppler matrix can improve the accuracy of the Doppler matrix, and then improve the accuracy of target detection based on the Doppler matrix, that is, it is conducive to improving the accuracy of indoor static target detection.

[0112] In some embodiments, before performing a slow-time Fourier transform on the frequency domain signal of the zero Doppler channel data corresponding to the same distance unit in each frame, the target detection further includes the following steps: updating the zero Doppler channel data according to the zero Doppler channel data of each frame; removing the clutter frequency domain component from the frequency domain signal according to the corresponding updated zero Doppler channel data to obtain the updated frequency domain signal of each chirp. Accordingly, performing a slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame can be achieved by performing a slow-time Fourier transform on the updated frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame.

[0113] That is to say, considering the influence of long-term clutter, the zero Doppler channel data is updated according to the data of multiple frames, so that the clutter removal is more thorough, so that a more accurate static frequency domain signal of the static target can be obtained, and then the static target can be detected more accurately, which is conducive to further improving the accuracy of static target detection.

[0114] Step 503: Perform constant false alarm detection on the Doppler spectrum matrix according to a preset threshold to detect the target. The threshold is k*noise estimation value, k>1.

[0115] Among them, compared with the current setting of the threshold to k'*noise estimation value in order to reduce clutter interference, k' is usually taken as dozens or hundreds, and k>1 allows the threshold to be set to a smaller value, and no longer requires the threshold to be a higher value, so that a smaller threshold can be used, so that the constant false alarm detection can detect more points, reduce or avoid mistaking valid targets for clutter for exclusion, which is beneficial to improving the accuracy of indoor target detection.

[0116] In some embodiments, the value range of k is (1, 4), for example, k=2, k=2.4, k=2.5, k=3, etc. In this way, the value of k is further set to a smaller range, thereby further reducing the threshold. Therefore, the constant false alarm detection can detect more points through a smaller threshold, further reducing or avoiding the misidentification of valid targets as clutter for exclusion, which is conducive to further improving the accuracy of indoor target detection.

[0117] Of course, the above is only an example of the value range of the threshold k. The value of the threshold k can also be adjusted according to specific scenarios, which will not be described in detail here.

[0118] In some embodiments, the target detection method further includes the following steps: generating corresponding feature information for each detected target; and determining whether the target is a valid target based on the corresponding feature information.

[0119] This embodiment does not limit the specific implementation method of target detection, which may be based on a neural network or a Doppler Fourier transform.

[0120] In this embodiment, the characteristic information may be any information that can reflect the distribution of the signal reflected by the target in the received echo signal.

[0121] In some embodiments, the characteristic information may be an angle spectrum generated based on a preset algorithm, wherein the angle spectrum may be a range Doppler (RD) spectrum, a Capon spectrum, or the like.

[0122] Furthermore, in some embodiments, the algorithms used in generating the angle spectrum may include: beamforming algorithm, Capon algorithm, multiple signal classification algorithm, rotational invariant subspace algorithm, orthogonal matching pursuit algorithm, etc., which will not be described one by one here.

[0123] Of course, the above is only an example of characteristic information, which does not mean that the characteristic information can only be the angle spectrum. It is other characteristic information that can reflect the energy distribution in the echo signal, such as the eigenvalue of the covariance matrix of the echo signal, etc., which will not be elaborated here.

[0124] In some embodiments, the feature information is an angle spectrum generated based on a preset algorithm, and the preset algorithm includes any one of the following: beamforming algorithm, Capon algorithm, multiple signal classification algorithm, rotation invariant subspace algorithm, orthogonal matching pursuit algorithm.

[0125] In addition, it is understandable that clutter is inevitable in space, and clutter will interfere with target detection, resulting in invalid targets being detected. The characteristic information can reflect the distribution of the signal reflected by the target in the received echo signal. Therefore, the target can be analyzed through the characteristic information to determine whether the target is a valid target.

[0126] In this way, after the above embodiment detects the target through the echo signal received by the multiple receiving channels of the radar, it further generates corresponding feature information for the detected target, so that it can determine whether the detected target is a valid target based on the feature information. In other words, on the basis of detecting the target, the validity of the target is further determined, so that the error of detecting the noise as the target can be eliminated, which is conducive to improving the accuracy of indoor target detection.

[0127] In order to help those skilled in the art better understand the target detection method provided by the above embodiment, it will be described below.

[0128] In some embodiments, determining whether a target is a valid target based on corresponding characteristic information can be achieved in the following manner: determining the relative energy distribution of signal and noise in the characteristic information of the target; and determining whether the target is a valid target based on the relative energy distribution.

[0129] This embodiment does not limit the description method of the relative capability distribution of signals and noises, which may vary according to different feature information, and may also vary according to different scenarios, requirements, and the like.

[0130] For example, when the characteristic information includes energy spectrum, based on the characteristics that signals are usually concentrated and noise is usually scattered, the entropy value of the energy spectrum can be used as the relative energy distribution of signals and noise; when the characteristic information includes angle spectrum, based on the characteristics that signals usually have greater energy, which is mainly reflected in the main lobe, and noise usually has smaller energy, which is mainly reflected in the side lobes, the relative energy distribution can be determined based on the relevant information of the main lobe and side lobes in the angle spectrum.

[0131] For example, when the characteristic information includes an angular spectrum, the relative energy distribution can be global, that is, the relative energy distribution is determined based on the relevant information of the main lobe and all side lobes; the relative energy distribution can be local, that is, the relative energy distribution is determined based on the relevant information of the main lobe and the maximum side lobe, etc.

[0132] Of course, the above is only an example. In some embodiments, the relative energy distribution can be determined by other methods, which will not be described in detail here.

[0133] In addition, it can be understood that when the feature information includes more noise-related content than signal-related content, it means that the signal described by the feature information is more likely to be a noise signal (i.e., clutter). Therefore, based on the relative energy distribution of the signal and noise in the target, it can be determined whether the feature information of the target contains more noise-related content or more signal-related content, thereby determining whether the target is a valid target.

[0134] It can also be understood that the criteria for determining effective targets may vary depending on the scenario, requirements, etc. For example, when the noise in the environment is complex and there may be multiple superpositions resulting in high noise, the difference threshold between noise and signal in the relative energy distribution can be taken as a smaller value. This can reduce the risk of misjudgment of effective targets.

[0135] In order to facilitate those skilled in the art to better understand the above embodiment, the characteristic information is taken as an angle spectrum as an example for further description.

[0136] In some embodiments, the relative energy distribution includes a first validity parameter. At this time, determining the relative energy distribution of the signal and the noise in the characteristic information of the target includes: determining the maximum value in the angle spectrum corresponding to the target; and determining the first validity parameter of the target according to the maximum value among the maximum values ​​of the corresponding angle spectrum and the average value of other maximum values ​​except the maximum value.

[0137] Among them, the first effective parameter can be the ratio of the maximum value among the local values ​​of the corresponding angle spectrum and the average of other local values ​​except the maximum value; the first effective parameter can also be an array composed of the maximum value among the local values ​​of the corresponding angle spectrum and the average of other local values ​​except the maximum value, etc.

[0138] As mentioned above, the maximum value among the maximum values ​​of the corresponding angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the first validity parameter actually reflects whether the global signal distribution is concentrated, and then can reflect the relative energy distribution. Therefore, based on the global relative energy distribution, it is possible to more comprehensively and accurately determine whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0139] It should be noted that the "mean" in the mean of other maximum values ​​except the maximum value can be the average value, median or mode, etc., which will not be elaborated here.

[0140] In some embodiments, the relative energy distribution also includes a second validity parameter. In this case, after determining the maximum value in the angle spectrum corresponding to the target, the target detection method further includes the following steps: determining the second validity parameter of the target according to the maximum value and the second maximum value in the corresponding angle spectrum maximum value. Accordingly, determining whether the target is a valid target according to the relative energy distribution can be achieved in the following manner: determining whether the target is an invalid target according to the corresponding second validity parameter; and determining whether the target is a valid target according to the corresponding first validity parameter when the target is not an invalid target.

[0141] As mentioned above, the maximum value among the maximum values ​​of the corresponding angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the second validity parameter actually reflects whether the local signal distribution is concentrated, and then relative to the first validity parameter, the second validity parameter can also reflect the relative energy distribution from another perspective. Therefore, based on the local relative energy distribution, combined with the overall relative energy distribution, it is determined from different dimensions whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0142] In some embodiments, determining whether a target is a valid target based on corresponding feature information may also be achieved in the following manner: determining an entropy value of the feature information corresponding to the target; and determining whether the target is a valid target based on the corresponding entropy value.

[0143] This embodiment does not limit the method for determining the entropy value. Any algorithm that can quantify the degree of confusion of feature information can be used to obtain the entropy value, which will not be described in detail here.

[0144] It can be understood that for a valid target (i.e., a real target), the corresponding echo signal should be concentrated and have a small entropy value, while for an invalid target (i.e., a false target, such as a target detected based on clutter), the corresponding echo signal is relatively unconcentrated and has a large entropy value. In other words, whether the detected target is a valid target can be determined based on the relative size of the entropy value.

[0145] It should be noted that this embodiment does not limit the entropy value range corresponding to the valid target, which may vary according to the degree of chaos in the detection environment, the requirements of detection accuracy, etc., and will not be elaborated here.

[0146] In some embodiments, determining whether a target is a valid target based on corresponding feature information can also be achieved in the following manner: performing constant false alarm detection on feature information corresponding to the target to obtain a signal-to-noise ratio of the target; and determining whether the target is a valid target based on the corresponding signal-to-noise ratio.

[0147] In this embodiment, constant false alarm rate detection (CFAR) is implemented by a corresponding CFAR algorithm, which will not be described in detail here.

[0148] It can be understood that the signal-to-noise ratio is an accurate quantification of the signal part and the noise part in the characteristic information corresponding to the target. Based on the signal-to-noise ratio, the relative size of the signal part and the noise part in the characteristic information can be accurately determined, so that it can be accurately judged whether the detected target is a valid target, which is conducive to further accurate target detection.

[0149] On the other hand, the embodiment of the present application also provides a target detection method, which is applied to a processing module in a radar, a host computer connected to the radar, or other modules, devices, or electronic devices with processing and computing capabilities. The radar may be a millimeter wave radar, etc. The process is as follows: Figure 6 As shown, the following steps are included:

[0150] Step 601, obtaining an echo signal, where the echo signal is obtained by reflecting the radar's transmission signal from the target.

[0151] This embodiment does not limit the transmission signal, which may be a continuous wave (CW) or a frequency modulated continuous wave (FWCW), etc., which will not be described in detail here.

[0152] In particular, in some embodiments, the target may be a person or a mobile object in an indoor environment, such as a sweeping robot.

[0153] In some embodiments, the radar may be any radar having multiple receiving channels and / or multiple transmitting channels.

[0154] Step 602: Perform range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps.

[0155] In this embodiment, the distance dimension Fourier transform may be implemented by fast Fourier transform (FFT) or the like, which will not be described in detail here.

[0156] Step 603: Generate zero Doppler channel data of each frame according to the frequency domain signal corresponding to the chirp belonging to the same frame.

[0157] This embodiment does not limit the generation of zero Doppler channel data, and it may adopt a moving target identification (MTI) algorithm, a phase mean cancellation algorithm, etc., which will not be described in detail here.

[0158] Step 604: remove the clutter frequency domain component from the frequency domain signal according to the corresponding zero Doppler channel data to obtain a dynamic frequency domain signal of each chirp.

[0159] In this embodiment, the removal of the clutter frequency domain component can be performed by directly subtracting the zero Doppler channel data from the frequency domain signal, or by adjusting the zero Doppler channel data with reference to each chirp signal and then subtracting the adjusted zero Doppler channel data from the frequency domain signal.

[0160] Step 605: Perform slow-time Fourier transform on the data in the dynamic frequency domain signal corresponding to the same range unit in each chirp and generate a Doppler spectrum matrix according to the transform result, so as to perform target detection according to the Doppler matrix.

[0161] It can be understood that, for dynamic targets, since they are in motion, through slow-time Fourier transform, information reflecting the characteristics of the dynamic targets can be more accurately obtained, which is conducive to more accurate detection of the targets.

[0162] In this way, the Doppler matrix is ​​obtained by processing the echo signal, determining the zero Doppler channel data of each frame signal, and then determining the dynamic frequency domain signal of each chirp to perform a slow time Fourier transform. In other words, providing a processing method adapted to the dynamic characteristics of a dynamic target to obtain the Doppler matrix can improve the accuracy of the Doppler matrix, and then improve the accuracy of target detection based on the Doppler matrix, that is, it is beneficial to improve the accuracy of indoor dynamic target detection.

[0163] In some embodiments, the target detection method further includes the following steps: generating corresponding feature information for each detected target; and determining whether the target is a valid target based on the corresponding feature information.

[0164] This embodiment does not limit the specific implementation method of target detection, which may be based on a neural network or a Doppler Fourier transform.

[0165] In this embodiment, the characteristic information may be any information that can reflect the distribution of the signal reflected by the target in the received echo signal.

[0166] In some embodiments, the characteristic information may be an angle spectrum generated based on a preset algorithm, wherein the angle spectrum may be a range Doppler (RD) spectrum, a Capon spectrum, or the like.

[0167] Furthermore, in some embodiments, the algorithms used in generating the angle spectrum may include: beamforming algorithm, Capon algorithm, multiple signal classification algorithm, rotational invariant subspace algorithm, orthogonal matching pursuit algorithm, etc., which will not be described one by one here.

[0168] Of course, the above is only an example of characteristic information, and does not mean that the characteristic information can only be an angle spectrum, it can also be an energy spectrum, etc., which will not be described one by one here.

[0169] In some embodiments, the feature information is an angle spectrum generated based on a preset algorithm, and the preset algorithm includes any one of the following: a beamforming algorithm, a Capon algorithm, a multiple signal classification algorithm, a rotationally invariant subspace algorithm, and an orthogonal matching pursuit algorithm.

[0170] In addition, it is understandable that clutter is inevitable in space, and clutter will interfere with target detection, resulting in invalid targets being detected. The characteristic information can reflect the distribution of the signal reflected by the target in the received echo signal. Therefore, the target can be analyzed through the characteristic information to determine whether the target is a valid target.

[0171] In this way, after the above embodiment detects the target through the echo signal received by the multiple receiving channels of the radar, it further generates corresponding feature information for the detected target, so that it can determine whether the detected target is a valid target based on the feature information. In other words, on the basis of detecting the target, the validity of the target is further determined, so that the error of detecting the noise as the target can be eliminated, which is conducive to improving the accuracy of indoor target detection.

[0172] In order to help those skilled in the art better understand the target detection method provided by the above embodiment, it will be described below.

[0173] In some embodiments, determining whether a target is a valid target based on corresponding characteristic information can be achieved in the following manner: determining the relative energy distribution of signal and noise in the characteristic information of the target; and determining whether the target is a valid target based on the relative energy distribution.

[0174] This embodiment does not limit the description method of the relative capability distribution of signals and noises, which may vary according to different feature information, and may also vary according to different scenarios, requirements, and the like.

[0175] For example, when the characteristic information includes energy spectrum, based on the characteristics that signals are usually concentrated and noise is usually scattered, the entropy value of the energy spectrum can be used as the relative energy distribution of signals and noise; when the characteristic information includes angle spectrum, based on the characteristics that signals usually have greater energy, which is mainly reflected in the main lobe, and noise usually has smaller energy, which is mainly reflected in the side lobes, the relative energy distribution can be determined based on the relevant information of the main lobe and side lobes in the angle spectrum.

[0176] For example, when the characteristic information includes an angular spectrum, the relative energy distribution can be global, that is, the relative energy distribution is determined based on the relevant information of the main lobe and all side lobes; the relative energy distribution can be local, that is, the relative energy distribution is determined based on the relevant information of the main lobe and the maximum side lobe, etc.

[0177] Of course, the above is only an example. In some embodiments, the relative energy distribution can be determined by other methods, which will not be described in detail here.

[0178] In addition, it can be understood that when the feature information includes more noise-related content than signal-related content, it means that the signal described by the feature information is more likely to be a noise signal (i.e., clutter). Therefore, based on the relative energy distribution of the signal and noise in the target, it can be determined whether the feature information of the target contains more noise-related content or more signal-related content, thereby determining whether the target is a valid target.

[0179] It can also be understood that the criteria for determining effective targets may vary depending on the scenario, requirements, etc. For example, when the noise in the environment is complex and there may be multiple superpositions resulting in high noise, the difference threshold between noise and signal in the relative energy distribution can be taken as a smaller value. This can reduce the risk of misjudgment of effective targets.

[0180] In order to facilitate those skilled in the art to better understand the above embodiment, the characteristic information is taken as an angle spectrum as an example for further description.

[0181] In some embodiments, the relative energy distribution includes a first validity parameter. At this time, determining the relative energy distribution of the signal and the noise in the characteristic information of the target includes: determining the maximum value in the angle spectrum corresponding to the target; and determining the first validity parameter of the target according to the maximum value among the maximum values ​​of the corresponding angle spectrum and the average value of other maximum values ​​except the maximum value.

[0182] Among them, the first effective parameter can be the ratio of the maximum value among the local values ​​of the corresponding angle spectrum and the average of other local values ​​except the maximum value; the first effective parameter can also be an array composed of the maximum value among the local values ​​of the corresponding angle spectrum and the average of other local values ​​except the maximum value, etc.

[0183] As mentioned above, the maximum value among the maximum values ​​of the corresponding angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the first validity parameter actually reflects whether the global signal distribution is concentrated, and then can reflect the relative energy distribution. Therefore, based on the global relative energy distribution, it is possible to more comprehensively and accurately determine whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0184] It should be noted that the "mean" in the mean of other maximum values ​​except the maximum value can be the average value, median or mode, etc., which will not be elaborated here.

[0185] In some embodiments, the relative energy distribution also includes a second validity parameter. In this case, after determining the maximum value in the angle spectrum corresponding to the target, the target detection method further includes the following steps: determining the second validity parameter of the target according to the maximum value and the second maximum value in the corresponding angle spectrum maximum value. Accordingly, determining whether the target is a valid target according to the relative energy distribution can be achieved in the following manner: determining whether the target is an invalid target according to the corresponding second validity parameter; and determining whether the target is a valid target according to the corresponding first validity parameter when the target is not an invalid target.

[0186] As mentioned above, the maximum value among the maximum values ​​of the corresponding angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the second validity parameter actually reflects whether the local signal distribution is concentrated, and then relative to the first validity parameter, the second validity parameter can also reflect the relative energy distribution from another perspective. Therefore, based on the local relative energy distribution, combined with the overall relative energy distribution, it is determined from different dimensions whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0187] In some embodiments, determining whether a target is a valid target based on corresponding feature information may also be achieved in the following manner: determining an entropy value of the feature information corresponding to the target; and determining whether the target is a valid target based on the corresponding entropy value.

[0188] This embodiment does not limit the method for determining the entropy value. Any algorithm that can quantify the degree of confusion of feature information can be used to obtain the entropy value, which will not be described in detail here.

[0189] It can be understood that for a valid target (i.e., a real target), the corresponding echo signal should be concentrated and have a small entropy value, while for an invalid target (i.e., a false target, such as a target detected based on clutter), the corresponding echo signal is relatively unconcentrated and has a large entropy value. In other words, whether the detected target is a valid target can be determined based on the relative size of the entropy value.

[0190] It should be noted that this embodiment does not limit the entropy value range corresponding to the valid target, which may vary according to the degree of chaos in the detection environment, the requirements of detection accuracy, etc., and will not be elaborated here.

[0191] In some embodiments, determining whether a target is a valid target based on corresponding feature information can also be achieved in the following manner: performing constant false alarm detection on feature information corresponding to the target to obtain a signal-to-noise ratio of the target; and determining whether the target is a valid target based on the corresponding signal-to-noise ratio.

[0192] In this embodiment, constant false alarm rate detection (CFAR) is implemented by a corresponding CFAR algorithm, which will not be described in detail here.

[0193] It can be understood that the signal-to-noise ratio is an accurate quantification of the signal part and the noise part in the characteristic information corresponding to the target. Based on the signal-to-noise ratio, the relative size of the signal part and the noise part in the characteristic information can be accurately determined, so that it can be accurately judged whether the detected target is a valid target, which is conducive to further accurate target detection.

[0194] In some embodiments, target detection based on the echo signal can be achieved in the following manner: generating a Doppler spectrum matrix based on the echo signal; performing constant false alarm detection on the Doppler spectrum matrix according to a preset threshold to perform target detection, the threshold being k*noise estimation value, k>1.

[0195] Among them, compared with the current setting of the threshold to k'*noise estimation value in order to reduce clutter interference, k' is usually taken as dozens or hundreds, and k>1 allows the threshold to be set to a smaller value, and no longer requires the threshold to be a higher value, so that a smaller threshold can be used, so that the constant false alarm detection can detect more points, reduce or avoid mistaking valid targets for clutter for exclusion, which is beneficial to improving the accuracy of indoor target detection.

[0196] In some embodiments, the value range of k is (1, 4), for example, k=2, k=2.4, k=2.5, k=3, etc. In this way, the value of k is further set to a smaller range, thereby further reducing the threshold. Therefore, the constant false alarm detection can detect more points through a smaller threshold, further reducing or avoiding the misidentification of valid targets as clutter for exclusion, which is conducive to further improving the accuracy of indoor target detection.

[0197] Of course, the above is only an example of the value range of the threshold k. The value of the threshold k can also be adjusted according to specific scenarios, which will not be described in detail here.

[0198] On the other hand, the embodiment of the present application also provides a target detection method, which is applied to a processing module in a radar, a host computer connected to the radar, or other modules, devices, or electronic devices with processing and computing capabilities. The radar may be a millimeter wave radar, etc. The process is as follows: Figure 7 As shown, the following steps are included:

[0199] Step 701, obtaining an echo signal, where the echo signal is obtained by reflecting the radar's transmission signal from the target.

[0200] This embodiment does not limit the transmission signal, which may be a continuous wave (CW) or a frequency modulated continuous wave (FWCW), etc., which will not be described in detail here.

[0201] In particular, in some embodiments, the target may be a relatively fixed object in an indoor environment, such as a display screen on a wall, a table, and the like.

[0202] In some embodiments, the radar may be any radar having multiple receiving channels and / or multiple transmitting channels.

[0203] Step 702: Perform range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps.

[0204] In this embodiment, the distance dimension Fourier transform may be implemented by fast Fourier transform (FFT) or the like, which will not be described in detail here.

[0205] Step 703: Generate zero Doppler channel data of each frame according to the frequency domain signal corresponding to the chirp belonging to the same frame.

[0206] This embodiment does not limit the generation of zero Doppler channel data, and it may adopt a moving target identification (MTI) algorithm, a phase mean cancellation algorithm, etc., which will not be described in detail here.

[0207] Step 704, performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame, and generating a Doppler spectrum matrix according to the transformed result to perform target detection.

[0208] In this way, the Doppler matrix is ​​obtained by processing the zero Doppler channel data of each frame determined by the echo signal, and further performing Fourier transform on the zero Doppler of each frame, that is, performing inter-frame Fourier transform. In other words, providing a processing method adapted to the static characteristics of the crystal target to obtain the Doppler matrix can improve the accuracy of the Doppler matrix, and then improve the accuracy of target detection based on the Doppler matrix, that is, it is conducive to improving the accuracy of indoor static target detection.

[0209] In some embodiments, Figure 8 As shown, before performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame, the target detection method further includes the following steps:

[0210] Step 705: Update the zero Doppler channel data according to the zero Doppler channel data of each frame.

[0211] That is to say, considering the influence of long-term clutter, updating the zero Doppler channel data according to the data of multiple frames is conducive to obtaining more accurate zero Doppler channel data, which in turn is conducive to more accurate target detection.

[0212] In some embodiments, the zero-Doppler channel data may be updated as an average value of multiple frames of zero-Doppler channel data.

[0213] In some embodiments, multiple frames of zero Doppler channel data can also be compared to remove abnormal data, and then the zero Doppler channel data can be updated to the average of the retained multiple frames of zero Doppler channel data, which is helpful to avoid sudden noise interference and further improve the accuracy of the zero Doppler channel.

[0214] Step 706: remove the clutter frequency domain component from the frequency domain signal according to the corresponding updated zero Doppler channel data to obtain an updated frequency domain signal for each chirp.

[0215] Accordingly, a slow-time Fourier transform is performed on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame, which is achieved by performing a slow-time Fourier transform on the updated frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame.

[0216] In this way, considering the influence of long-term clutter, the zero Doppler channel data is updated according to the data of multiple frames, so that the clutter removal is more thorough, so that a more accurate static frequency domain signal of the static target can be obtained, and then the static target can be detected more accurately, which is conducive to further improving the accuracy of static target detection.

[0217] In some embodiments, the target detection method further includes the following steps: generating corresponding feature information for each detected target; and determining whether the target is a valid target based on the corresponding feature information.

[0218] This embodiment does not limit the specific implementation method of target detection, which may be based on a neural network or a Doppler Fourier transform.

[0219] In this embodiment, the characteristic information may be any information that can reflect the distribution of the signal reflected by the target in the received echo signal.

[0220] In some embodiments, the characteristic information may be an angle spectrum generated based on a preset algorithm, wherein the angle spectrum may be a range Doppler (RD) spectrum, a Capon spectrum, or the like.

[0221] Furthermore, in some embodiments, the algorithms used in generating the angle spectrum may include: beamforming algorithm, Capon algorithm, multiple signal classification algorithm, rotational invariant subspace algorithm, orthogonal matching pursuit algorithm, etc., which will not be described one by one here.

[0222] Of course, the above is only an example of characteristic information, and does not mean that the characteristic information can only be an angle spectrum, it can also be an energy spectrum, etc., which will not be described one by one here.

[0223] In some embodiments, the feature information is an angle spectrum generated based on a preset algorithm, and the preset algorithm includes any one of the following: a beamforming algorithm, a Capon algorithm, a multiple signal classification algorithm, a rotationally invariant subspace algorithm, and an orthogonal matching pursuit algorithm.

[0224] In addition, it is understandable that clutter is inevitable in space, and clutter will interfere with target detection, resulting in invalid targets being detected. The characteristic information can reflect the distribution of the signal reflected by the target in the received echo signal. Therefore, the target can be analyzed through the characteristic information to determine whether the target is a valid target.

[0225] In this way, after the above embodiment detects the target through the echo signal received by the multiple receiving channels of the radar, it further generates corresponding feature information for the detected target, so that it can determine whether the detected target is a valid target based on the feature information. In other words, on the basis of detecting the target, the validity of the target is further determined, so that the error of detecting the noise as the target can be eliminated, which is conducive to improving the accuracy of indoor target detection.

[0226] In order to help those skilled in the art better understand the target detection method provided by the above embodiment, it will be described below.

[0227] In some embodiments, determining whether a target is a valid target based on corresponding characteristic information can be achieved in the following manner: determining the relative energy distribution of signal and noise in the characteristic information of the target; and determining whether the target is a valid target based on the relative energy distribution.

[0228] This embodiment does not limit the description method of the relative capability distribution of signals and noises, which may vary according to different feature information, and may also vary according to different scenarios, requirements, and the like.

[0229] For example, when the characteristic information includes energy spectrum, based on the characteristics that signals are usually concentrated and noise is usually scattered, the entropy value of the energy spectrum can be used as the relative energy distribution of signals and noise; when the characteristic information includes angle spectrum, based on the characteristics that signals usually have greater energy, which is mainly reflected in the main lobe, and noise usually has smaller energy, which is mainly reflected in the side lobes, the relative energy distribution can be determined based on the relevant information of the main lobe and side lobes in the angle spectrum.

[0230] For example, when the characteristic information includes an angular spectrum, the relative energy distribution can be global, that is, the relative energy distribution is determined based on the relevant information of the main lobe and all side lobes; the relative energy distribution can be local, that is, the relative energy distribution is determined based on the relevant information of the main lobe and the maximum side lobe, etc.

[0231] Of course, the above is only an example. In some embodiments, the relative energy distribution can be determined by other methods, which will not be described in detail here.

[0232] In addition, it can be understood that when the feature information includes more noise-related content than signal-related content, it means that the signal described by the feature information is more likely to be a noise signal (i.e., clutter). Therefore, based on the relative energy distribution of the signal and noise in the target, it can be determined whether the feature information of the target contains more noise-related content or more signal-related content, thereby determining whether the target is a valid target.

[0233] It can also be understood that the criteria for determining effective targets may vary depending on the scenario, requirements, etc. For example, when the noise in the environment is complex and there may be multiple superpositions resulting in high noise, the difference threshold between noise and signal in the relative energy distribution can be taken as a smaller value. This can reduce the risk of misjudgment of effective targets.

[0234] In order to facilitate those skilled in the art to better understand the above embodiment, the characteristic information is taken as an angle spectrum as an example for further description.

[0235] In some embodiments, the relative energy distribution includes a first validity parameter. At this time, determining the relative energy distribution of the signal and the noise in the characteristic information of the target includes: determining the maximum value in the angle spectrum corresponding to the target; and determining the first validity parameter of the target according to the maximum value among the maximum values ​​of the corresponding angle spectrum and the average value of other maximum values ​​except the maximum value.

[0236] Among them, the first effective parameter can be the ratio of the maximum value among the local values ​​of the corresponding angle spectrum and the average of other local values ​​except the maximum value; the first effective parameter can also be an array composed of the maximum value among the local values ​​of the corresponding angle spectrum and the average of other local values ​​except the maximum value, etc.

[0237] As mentioned above, the maximum value among the maximum values ​​of the corresponding angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the first validity parameter actually reflects whether the global signal distribution is concentrated, and then can reflect the relative energy distribution. Therefore, based on the global relative energy distribution, it is possible to more comprehensively and accurately determine whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0238] It should be noted that the "mean" in the mean of other maximum values ​​except the maximum value can be the average value, median or mode, etc., which will not be elaborated here.

[0239] In some embodiments, the relative energy distribution also includes a second validity parameter. In this case, after determining the maximum value in the angle spectrum corresponding to the target, the target detection method further includes the following steps: determining the second validity parameter of the target according to the maximum value and the second maximum value in the corresponding angle spectrum maximum value. Accordingly, determining whether the target is a valid target according to the relative energy distribution can be achieved in the following manner: determining whether the target is an invalid target according to the corresponding second validity parameter; and determining whether the target is a valid target according to the corresponding first validity parameter when the target is not an invalid target.

[0240] As mentioned above, the maximum value among the maximum values ​​of the corresponding angle spectrum can correspond to signal-related content, and other maximum values ​​except the maximum value can correspond to noise-related content. Therefore, the second validity parameter actually reflects whether the local signal distribution is concentrated, and then relative to the first validity parameter, the second validity parameter can also reflect the relative energy distribution from another perspective. Therefore, based on the local relative energy distribution, combined with the overall relative energy distribution, it is determined from different dimensions whether the target is a valid target, which is conducive to further improving the accuracy of target detection.

[0241] In some embodiments, determining whether a target is a valid target based on corresponding feature information may also be achieved in the following manner: determining an entropy value of the feature information corresponding to the target; and determining whether the target is a valid target based on the corresponding entropy value.

[0242] This embodiment does not limit the method for determining the entropy value. Any algorithm that can quantify the degree of confusion of feature information can be used to obtain the entropy value, which will not be described in detail here.

[0243] It can be understood that for a valid target (i.e., a real target), the corresponding echo signal should be concentrated and have a small entropy value, while for an invalid target (i.e., a false target, such as a target detected based on clutter), the corresponding echo signal is relatively unconcentrated and has a large entropy value. In other words, whether the detected target is a valid target can be determined based on the relative size of the entropy value.

[0244] It should be noted that this embodiment does not limit the entropy value range corresponding to the valid target, which may vary according to the degree of chaos in the detection environment, the requirements of detection accuracy, etc., and will not be elaborated here.

[0245] In some embodiments, determining whether a target is a valid target based on corresponding feature information can also be achieved in the following manner: performing constant false alarm detection on feature information corresponding to the target to obtain a signal-to-noise ratio of the target; and determining whether the target is a valid target based on the corresponding signal-to-noise ratio.

[0246] In this embodiment, constant false alarm rate detection (CFAR) is implemented by a corresponding CFAR algorithm, which will not be described in detail here.

[0247] It can be understood that the signal-to-noise ratio is an accurate quantification of the signal part and the noise part in the characteristic information corresponding to the target. Based on the signal-to-noise ratio, the relative size of the signal part and the noise part in the characteristic information can be accurately determined, so that it can be accurately judged whether the detected target is a valid target, which is conducive to further accurate target detection.

[0248] In some embodiments, target detection based on the echo signal can be achieved in the following manner: generating a Doppler spectrum matrix based on the echo signal; performing constant false alarm detection on the Doppler spectrum matrix according to a preset threshold to perform target detection, the threshold being k*noise estimation value, k>1.

[0249] Among them, compared with the current setting of the threshold to k'*noise estimation value in order to reduce clutter interference, k' is usually taken as dozens or hundreds, and k>1 allows the threshold to be set to a smaller value, and no longer requires the threshold to be a higher value, so that a smaller threshold can be used, so that the constant false alarm detection can detect more points, reduce or avoid mistaking valid targets for clutter for exclusion, which is beneficial to improving the accuracy of indoor target detection.

[0250] In some embodiments, the value range of k is (1, 4), for example, k=2, k=2.4, k=2.5, k=3, etc. In this way, the value of k is further set to a smaller range, thereby further reducing the threshold. Therefore, the constant false alarm detection can detect more points through a smaller threshold, further reducing or avoiding the misidentification of valid targets as clutter for exclusion, which is conducive to further improving the accuracy of indoor target detection.

[0251] Of course, the above is only an example of the value range of the threshold k. The value of the threshold k can also be adjusted according to specific scenarios, which will not be described in detail here.

[0252] In order to facilitate those skilled in the art to better understand the above embodiments, examples are given below.

[0253] In the case of dynamic target detection, such as Fig. 9 As shown, the target detection method may include the following steps:

[0254] Step 901: Acquire an echo signal, add a window to each chirp in the echo signal, and perform a range-dimensional FFT based on the windowed signal.

[0255] Step 902: determine the mean of each range unit (bin) obtained by performing range-dimensional FFT on each chirp in the same frame as the zero Doppler channel data of the corresponding frame, and subtract the zero Doppler channel data from the signal of each chirp to remove clutter.

[0256] Step 903: Window the range bin portion of the data after removing clutter from each chirp signal to perform a slow time dimension FFT.

[0257] Step 904, modulus the result of the slow time dimension FFT, and accumulate the results to obtain a Doppler matrix.

[0258] Step 905: Use the CFAR algorithm to perform target detection on the Doppler matrix.

[0259] Step 906 , extract the range bin and Doppler bin of the detected target, perform digital beam forming (DBF) processing and find the modulus, and obtain the DBF spectrum of the target.

[0260] Step 907, find the maximum value of the DBF spectrum of the target, and calculate the azimuth corresponding to the maximum value as the azimuth of the target, to obtain the angle measurement result of the target.

[0261] Step 908: Calculate all maximum values ​​of the target in the DBF spectrum.

[0262] Step 909, sorting the maximum values ​​calculated for the same target from large to small.

[0263] Step 910, calculating the ratio of the maximum value to the second largest value in the sorting result as the first validity parameter of the target.

[0264] Step 911, calculate the mean of all maximum values ​​of the target except the maximum value, and calculate the ratio of the maximum value to the mean as the second validity parameter.

[0265] Step 912, the targets whose first validity parameter is greater than the first preset threshold are classified as valid targets in point trace pool 1, the targets whose second validity parameter is greater than the second preset threshold among the remaining target points are classified as potential valid targets in point trace pool 2, and the remaining targets are classified as invalid targets in point trace pool 3.

[0266] Step 913, during the cluster tracking of the target, if the target in point trace pool 1 meets the tracking requirements, the target in point trace pool 1 is used; if the target in point trace pool 1 does not meet the tracking requirements, the targets in point trace pool 1 and point trace pool 2 are used.

[0267] In the case of static target detection, such as Fig.10 As shown, the target detection method may include the following steps:

[0268] Step 1001: Acquire an echo signal, add a window to each chirp in the echo signal, and perform a range-dimensional FFT based on the windowed signal.

[0269] Step 1002: Determine the mean of each range bin obtained by performing range-dimensional FFT on each chirp in the same frame as zero Doppler channel data of the corresponding frame.

[0270] Step 1003, continue to acquire zero Doppler channel data corresponding to at least one frame of echo signal.

[0271] Step 1004: Perform FFT on each range bin of the acquired zero Doppler channel data.

[0272] Step 1005, modulus the FFT result, and sum the results between channels to form a Doppler matrix.

[0273] Step 1006: Use k*noise estimation value as a threshold to detect the Doppler matrix and obtain the distance bin and Doppler bin of the candidate point, k>1.

[0274] Step 1007: extract the range bins and Doppler bins of the detected targets corresponding to all azimuth channels, perform DBF processing and find the modulus, and obtain the DBF spectrum of the target.

[0275] Step 1008, find the maximum value of the DBF spectrum of the target, and calculate the azimuth corresponding to the maximum value as the azimuth of the target, to obtain the angle measurement result of the target.

[0276] Step 1009, calculate all maximum values ​​of the target in the DBF spectrum.

[0277] Step 1010, sorting the maximum values ​​calculated for the same target from large to small.

[0278] Step 1011, calculate the ratio of the maximum value to the second largest value in the sorting result as the first validity parameter of the target.

[0279] Step 1012, calculate the mean of all maximum values ​​of the target except the maximum value, and calculate the ratio of the maximum value to the mean as the second validity parameter.

[0280] Step 1013, the targets whose first validity parameter is greater than the first preset threshold are classified as valid targets and are included in point trace pool 1, the targets whose second validity parameter is greater than the second preset threshold among the remaining target points are classified as potential valid targets and are included in point trace pool 2, and the remaining targets are classified as invalid targets and are included in point trace pool 3.

[0281] Step 1014, during the cluster tracking of the target, if the target in point trace pool 1 meets the tracking requirements, the target in point trace pool 1 is used; if the target in point trace pool 1 does not meet the tracking requirements, the targets in point trace pool 1 and point trace pool 2 are used.

[0282] It can be seen that the target detection achieved through the above process can obtain more target data through a lower threshold, reduce or avoid the possibility of removing valid targets as noise points, and help improve the distance of target detection. At the same time, there is no need to perform complex calculations on the matrix, which reduces the computational complexity of target detection and helps improve the efficiency of target detection. And the requirements for amplitude and phase consistency between channels are relatively low.

[0283] It should be noted that Fig. 9 and Fig.10 The target detection method shown takes the application in cluster tracking as an example, but it does not mean that it can only be applied to cluster tracking scenarios. It can also be applied to target detection, environmental imaging, etc., which will not be described one by one here.

[0284] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0285] Another aspect of the present application embodiment further provides an electronic device, such as Fig.11 As shown, it includes: at least one processor 1101; and a memory 1102 that is communicatively connected to the at least one processor 1101; wherein the memory 1102 stores instructions that can be executed by the at least one processor 1101, and the instructions are executed by the at least one processor 1101 so that the at least one processor 1101 can execute the target detection method described in any of the above method embodiments.

[0286] Among them, the memory 1102 and the processor 1101 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 1101 and the memory 1102 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art, and therefore, are not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 1101 is transmitted on a wireless medium through an antenna, and further, the antenna also receives data and transmits the data to the processor 1101.

[0287] The processor 1101 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 1102 can be used to store data used by the processor 1101 when performing operations.

[0288] Another aspect of the present application embodiment further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the target detection method provided by any of the above method embodiments is implemented.

[0289] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0290] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A target detection method, characterized in that: include: Acquire an echo signal, where the echo signal is obtained by reflecting the radar's transmission signal from the target; generating a Doppler spectrum matrix according to the echo signal; According to a preset threshold, constant false alarm detection is performed on the Doppler spectrum matrix to perform target detection, wherein the threshold is k*noise estimation value, k>1.

2. The target detection method according to claim 1, characterized in that: Generating a Doppler spectrum matrix according to the echo signal includes: Performing a range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; Generate zero Doppler channel data of each frame according to the frequency domain signal corresponding to the chirp belonging to the same frame; The frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame is subjected to slow time dimension Fourier transform, and the Doppler spectrum matrix is ​​generated according to the transformed result.

3. The target detection method according to claim 2, characterized in that: Before performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame, the method further includes: According to the zero Doppler channel data of each frame, the zero Doppler channel data is updated; According to the corresponding updated zero Doppler channel data, the clutter frequency domain component is removed from the frequency domain signal to obtain the frequency domain signal after each chirp update; The method of performing a slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame includes: The updated frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame is subjected to a slow time dimension Fourier transform.

4. The target detection method according to claim 2 or 3, characterized in that: The method further comprises: Generate corresponding feature information for each detected target; Determine whether the target is a valid target based on the corresponding feature information.

5. The target detection method according to claim 3, characterized in that: The determining, based on the corresponding feature information, whether the target is a valid target includes: Determining relative energy distribution of signal and noise in the characteristic information of the target; According to the relative energy distribution, it is determined whether the target is a valid target.

6. The target detection method according to claim 5, characterized in that: The characteristic information includes an angle spectrum, the relative energy distribution includes a first validity parameter, and the determining the relative energy distribution of the signal and the noise in the characteristic information of the target includes: Determine a maximum value in the angle spectrum corresponding to the target; The first validity parameter of the target is determined according to the maximum value among the corresponding maximum values ​​of the angle spectrum and the average value of other maximum values ​​except the maximum value.

7. The target detection method according to claim 6, characterized in that: The relative energy distribution includes a second validity parameter. After determining the maximum value in the angle spectrum corresponding to the target, the method further includes: Determining the second validity parameter of the target according to the maximum value and the second maximum value among the corresponding maximum values ​​of the angle spectrum; The determining, according to the relative energy distribution, whether the target is a valid target comprises: determining, according to the corresponding second validity parameter, whether the target is an invalid target; In the case that the target does not belong to the invalid target, whether the target is a valid target is determined according to the corresponding first validity parameter.

8. The target detection method according to claim 6 or 7, characterized in that: The feature information is an angle spectrum generated based on a preset algorithm, and the preset algorithm includes any one of the following: a beamforming algorithm, a Capon algorithm, a multiple signal classification algorithm, a rotationally invariant subspace algorithm, and an orthogonal matching pursuit algorithm.

9. The target detection method according to claim 4, characterized in that: The determining, based on the corresponding feature information, whether each of the targets is a valid target includes: Determine an entropy value of the feature information corresponding to the target; Determine whether the target is a valid target based on the corresponding entropy value.

10. The target detection method according to claim 4, characterized in that: The determining, based on the corresponding feature information, whether the target is a valid target includes: Performing constant false alarm detection on the characteristic information corresponding to the target to obtain a signal-to-noise ratio of the target; According to the corresponding signal-to-noise ratio, it is determined whether the target is a valid target.

11. A target detection method, characterized in that: include: Acquire an echo signal, where the echo signal is obtained by reflecting the radar's transmission signal from the target; Performing a range-dimensional Fourier transform on the echo signal to obtain frequency domain signals corresponding to at least two chirps; Generate zero Doppler channel data of each frame according to the frequency domain signal corresponding to the chirp belonging to the same frame; The frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame is subjected to slow-time Fourier transform, and a Doppler spectrum matrix is ​​generated according to the transformed result for target detection.

12. The target detection method according to claim 11, characterized in that: Before performing slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame, the method further includes: According to the zero Doppler channel data of each frame, the zero Doppler channel data is updated; According to the corresponding updated zero Doppler channel data, the clutter frequency domain component is removed from the frequency domain signal to obtain the frequency domain signal after each chirp update; The method of performing a slow-time Fourier transform on the frequency domain signal corresponding to the zero Doppler channel data corresponding to the same distance unit in each frame includes: The updated frequency domain signal corresponding to the zero Doppler channel data corresponding to the same range unit in each frame is subjected to a slow time dimension Fourier transform.

13. The target detection method according to claim 11 or 12, characterized in that: The method further comprises: Generate corresponding feature information for each detected target; Determine whether the target is a valid target based on the corresponding feature information.

14. The target detection method according to claim 13, characterized in that: The determining, based on the corresponding feature information, whether the target is a valid target includes: Determining relative energy distribution of signal and noise in the characteristic information of the target; According to the relative energy distribution, it is determined whether the target is a valid target.

15. The target detection method according to claim 14, characterized in that: The characteristic information includes an angle spectrum, the relative energy distribution includes a first validity parameter, and the determining the relative energy distribution of the signal and the noise in the characteristic information of the target includes: Determine a maximum value in the feature information corresponding to the target; The first validity parameter of the target is determined according to the maximum value among the corresponding maximum values ​​of the angle spectrum and the average value of other maximum values ​​except the maximum value.

16. The target detection method according to claim 15, characterized in that: The relative energy distribution includes a second validity parameter. After determining the maximum value in the characteristic information corresponding to the target, the method further includes: Determining the second validity parameter of the target according to the maximum value and the second maximum value among the corresponding maximum values ​​of the angle spectrum; The determining, according to the relative energy distribution, whether the target is a valid target comprises: determining, according to the corresponding second validity parameter, whether the target is an invalid target; In the case that the target does not belong to the invalid target, whether the target is a valid target is determined according to the corresponding first validity parameter.

17. The target detection method according to claim 15 or 16, characterized in that: The feature information is an angle spectrum generated based on a preset algorithm, and the preset algorithm includes any one of the following: a beamforming algorithm, a Capon algorithm, a multiple signal classification algorithm, a rotationally invariant subspace algorithm, and an orthogonal matching pursuit algorithm.

18. The target detection method according to claim 14, characterized in that: The determining, based on the corresponding feature information, whether the target is a valid target includes: Determine an entropy value of the feature information corresponding to the target; Determine whether the target is a valid target based on the corresponding entropy value.

19. The target detection method according to claim 14, characterized in that: The determining, based on the corresponding feature information, whether the target is a valid target includes: Performing constant false alarm detection on the characteristic information corresponding to the target to obtain a signal-to-noise ratio of the target; According to the corresponding signal-to-noise ratio, it is determined whether the target is a valid target.

20. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the target detection method as described in any one of claims 1 to 10, or execute the target detection method as described in any one of claims 11 to 19.

21. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the computer program implements the target detection method according to any one of claims 1 to 10, or implements the target detection method according to any one of claims 11 to 19.

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