Target detection and point cloud output method, integrated circuit, sensor and terminal equipment

Abstract

The embodiment of the invention relates to the technical field of target detection, and discloses a target detection and point cloud output method, an integrated circuit, a sensor and terminal equipment. The target detection method comprises the following steps: acquiring first data; generating second data according to the plurality of first data; detecting a first target according to the first data; and detecting a second target according to the second data. And the accuracy of radar target detection can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of target detection technology, and in particular to a target detection and point cloud output method, an integrated circuit, a sensor, and a terminal device. Background Art

[0002] Radar is an electronic device that uses electromagnetic waves to detect targets. Its working principle is: the radar system first transmits electromagnetic waves through the antenna. These waves are emitted back after encountering the target. The radar system receives the echo signal generated by the reflection of the target, and determines the target's distance, distance change rate (radial velocity), azimuth, altitude and other information by processing and analyzing the echo signal.

[0003] However, the accuracy of target detection by current radar systems still needs to be improved. Summary of the invention

[0004] The embodiments of the present application provide a target detection and point cloud output method, integrated circuit, sensor, and terminal device, which are beneficial to improving the accuracy of radar target detection.

[0005] According to some embodiments of the present application, a first aspect of the embodiments of the present application provides a target detection method, including: acquiring first data; generating second data based on multiple first data; detecting a first target based on the first data; and detecting a second target based on the second data.

[0006] According to some embodiments of the present application, the second aspect of the embodiments of the present application also provides a target detection method, including: obtaining a number of targets based on the target detection results; for each detected target, obtaining corresponding feature information, the feature information includes noise features and signal features for characterizing the target; based on the feature information, determining the confidence of each target.

[0007] According to some embodiments of the present application, a third aspect of the embodiments of the present application further provides a target detection method, comprising: obtaining a first target and a second target according to the target detection method as described in the first aspect; for each of the first target and the second target detected, obtaining corresponding feature information, the feature information including noise features and signal features for characterizing the target; and determining the confidence of each of the first target and the second target based on the feature information.

[0008] According to some embodiments of the present application, the fourth aspect of the embodiments of the present application also provides a target point cloud output method, including: acquiring a first target and a second target according to the target detection method as described in the first aspect or the target detection method as described in the third aspect; and outputting the first target and the second target in a hierarchical manner.

[0009] According to some embodiments of the present application, the fifth aspect of the embodiments of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the target detection method or target point cloud output method as described in any embodiment of the present application.

[0010] According to some embodiments of the present application, the sixth aspect of the embodiments of the present application further provides an integrated circuit, comprising a radio frequency module, an analog signal processing module and a digital signal processing module connected in sequence; wherein the radio frequency module is used to generate a radio frequency transmit signal and receive a radio frequency receive signal; the analog signal processing module is used to down-convert the radio frequency receive signal to obtain an intermediate frequency signal; the digital signal processing module is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain digital signal data, and obtain first data, generate second data based on multiple first data, detect a first target based on the first data, and detect a second target based on the second data; and / or, the digital signal processing module is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain digital signal data, and obtain several targets based on the target detection results; for each detected target, obtain corresponding feature information, the feature information includes noise features and signal features used to characterize the target; determine the confidence of each target based on the feature information.

[0011] According to some embodiments of the present application, the seventh aspect of the embodiments of the present application further provides a sensor, comprising: a carrier; the integrated circuit as described in the sixth aspect, which is arranged on the carrier; an antenna, which is arranged on the carrier, or the antenna and the integrated circuit are integrated into an integral device and arranged on the carrier; wherein the integrated circuit is connected to the antenna for processing the echo signal received by the antenna.

[0012] According to some embodiments of the present application, the eighth aspect of the embodiments of the present application also provides a terminal device, including: a device body; and a sensor as described in any embodiment of the present application arranged on the device body; wherein the sensor is used for target detection and / or communication to provide reference information for the operation of the device body.

[0013] The technical solution provided by the embodiment of the present application, when detecting the second target, the second data used to detect the second target is generated by multiple first data, and the multiple first data correspond to the observation time of the multiple first data, that is, the second target has a longer observation time relative to the first target, so that it is possible to detect targets that are difficult to detect in a short time, avoiding missing the second target. At the same time, the detection of the first target can be achieved in a short time, therefore, the present application can also achieve timely and efficient detection of the first target. Therefore, the present application can realize hierarchical detection for different targets, adapt to the characteristics of different targets, and can not only improve the accuracy of detection, but also ensure the timeliness of detection.

[0014] After detecting a target, the present application can also determine the confidence level of each detected target through the target's feature information, so that the target can be further verified, which is beneficial to improving the reliability and accuracy of target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 This is the process of the target detection method provided in the embodiment of the present application Figure 1 ;

[0017] Figure 2 This is the process of the target detection method provided in the embodiment of the present application Figure 2 ;

[0018] Figure 3 This is the process of the target detection method provided in the embodiment of the present application Figure 3 ;

[0019] Figure 4 This is the process of the target detection method provided in the embodiment of the present application Figure 4 ;

[0020] Figure 5 This is the process of the target detection method provided in the embodiment of the present application Figure 5 ;

[0021] Figure 6 This is the process of the target detection method provided in the embodiment of the present application Figure 6 ;

[0022] Figure 7 This is the process of the target detection method provided in the embodiment of the present application Figure 7 ;

[0023] Figure 8 This is the process of the target detection method provided in the embodiment of the present application Figure 8 ;

[0024] Fig. 9 This is the process of the target detection method provided in the embodiment of the present application Figure 9 ;

[0025] Fig.10 is a flow chart of a target point cloud output method provided in an embodiment of the present application;

[0026] Fig.11 is a schematic diagram of the structure of an integrated circuit provided in an embodiment of the present application;

[0027] Fig.12 It is a schematic diagram of signal waveforms involved in an embodiment of the present application. DETAILED DESCRIPTION

[0028] 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.

[0029] 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.

[0030] In order to facilitate those skilled in the art to better understand the target detection and point cloud output method, medium, circuit, sensor, and device provided in the embodiments of the present application, the process of target detection by an existing radar is first described below. The details are as follows:

[0031] Taking the frequency modulated continuous wave (FMCW) radar as an example, it sends chirp signals frame by frame when detecting targets, where each frame of the transmitted signal includes multiple chirp signals. Then, digital signal processing is performed on the echo corresponding to each frame of the signal to output radar point clouds frame by frame. The processing process of each frame of the echo digital signal is as follows:

[0032] 1. Perform Fast Fourier Transform (FFT) in chirp to obtain the original channel data.

[0033] 2. Calculate the average value of the channel data within the frame to obtain the zero Doppler channel data of the frame.

[0034] 3. Subtract the zero Doppler channel data from the original channel data to obtain updated channel data.

[0035] 4. Perform intra-frame FFT on the updated channel data to obtain Range-Doppler (RD) spectrum data.

[0036] 5. Perform (in)coherent processing and constant false alarm rate (CFAR) detection on the RD spectrum data to obtain the target.

[0037] 6. For the detected targets, extract the range unit (range bin) and Doppler unit (Doppler bin) to perform digital beam forming (DBF) to obtain the DBF spectrum of each target.

[0038] 7. For each target, find the maximum value of the DBF spectrum and calculate the azimuth corresponding to the maximum value to obtain the azimuth of the target.

[0039] However, the above-mentioned method of performing target detection in frames and outputting targets in frames cannot adapt to all targets when facing a variety of targets. For example, during detection, there may be dynamic targets whose movements can be quickly detected, such as children running in the room, and there may also be micro-movement targets whose movements are difficult to detect, such as people lying in bed, who may have small movements due to breathing, etc. For dynamic targets, since their movement speed, displacement, etc. are usually large, sufficient detection information for sensing target changes will be accumulated within a frame period. At this time, the radar can output relatively accurate detection results for dynamic targets. However, for micro-movement targets, since their movement speed, displacement, etc. are usually small, it is likely that sufficient detection information for sensing target changes will not be accumulated within a frame period. At this time, it is difficult for the radar to output accurate detection results for micro-movement targets.

[0040] Specifically, taking a person as a target, when a person is turning around or moving slowly in the target area, such as breathing or unconscious shaking of the human body, the person has no obvious motion characteristics relative to the motion state. In this case, the detected echo signal is often mixed in the noise, making the detection of such micro-motion targets very difficult. At this time, if only target detection and output are performed with a frame period, it is likely that only dynamic targets with obvious changes, that is, targets with large motion amplitude or motion speed, etc., can be obtained, and there is a lack of detection results for micro-motion targets, which will result in the inability to accurately and completely detect observed targets in different motion states.

[0041] In response to the above problems, the present application further provides different observation times that are suitable for different targets, so that for micro-movement targets without obvious motion characteristics, the target can be detected through the position changes accumulated in a relatively long time interval, thereby achieving more comprehensive detection of different types of targets and more comprehensive perception and characterization of the scene. At the same time, for dynamic targets with obvious motion characteristics, the position changes accumulated in a relatively short time zone can be output efficiently and timely.

[0042] In order to achieve the above objectives, the present application provides a target detection method for detecting different types of targets. Figure 1 As shown, the target detection method includes the following steps:

[0043] Step 101, obtaining first data.

[0044] Step 102: Generate second data based on a plurality of first data.

[0045] Step 103: Detect the first target according to the first data.

[0046] Step 104: Detect the second target according to the second data.

[0047] Among them, each first data corresponds to an observation time T, and using the second data generated by multiple first data to detect the second target is equivalent to using the second data obtained within multiple observation times T to detect the second target, so that the target features sufficient to be observed are accumulated through a longer observation time, ensuring that the second target that is difficult to detect in a short time can also be detected without missing detection. At the same time, the first target with obvious motion characteristics does not need to wait until multiple first data are accumulated before detection, and it can be detected using a single first data. Therefore, this application can not only detect the first target in a timely and efficient manner, but also provide hierarchical detection for different targets, adapt to the characteristics of different targets, and ensure the accuracy and timeliness of detection of different types of targets.

[0048] It should be noted that the embodiment of the present application does not limit the specific value of T, which can be set in combination with actual needs, application scenario characteristics, target characteristics, algorithm characteristics, etc. For example, in some cases, the value of T is a frame cycle duration. In some cases, the motion characteristics of the dynamic target may be very prominent, and sufficient information to observe the target can be accumulated in less than a frame cycle. At this time, T can be taken as a smaller value, for example, 0.5 frame cycle duration, 1 / 3 frame cycle duration, etc. In some cases, since the algorithm needs to obtain more data to ensure that the accuracy of target detection meets the requirements, at this time, T can be taken as a larger value, for example, 1.5 frame cycle duration, 2 frame cycle duration, 5 frame cycle duration, etc. In some cases, it may also be that the scene requires a more real-time output, so if the target detection algorithm supports it, T can also be taken as a smaller value, for example, 0.2 frame cycle duration, 0.5 frame cycle duration, etc. Of course, the above is only an explanation of T provided with "frame" as a reference unit. In some cases, T may also refer to other cycles, such as the length of a chirp cycle. For example, T may be the length of a plurality of consecutive chirp cycles, etc., which will not be described in detail here.

[0049] Taking a relatively closed scene such as an indoor environment as an example, when detecting human activities, due to the various human states, conventional radar target detection can only obtain targets with obvious position changes, and cannot obtain point clouds of static human targets with only breathing activities or unconscious shaking of the human body. This application introduces the second data through the above embodiment to achieve point cloud output of the second target with unclear motion characteristics, such as static human targets with only breathing activities or unconscious shaking of the human body. Thus, by increasing the amount of data based on which the second target is detected relative to the first target, the observation time is lengthened, so that the second target has sufficient displacement within the observation time, so that the point cloud of the micro-movement target can be obtained.

[0050] It should also be noted that the present embodiment does not limit the division of the first target and the second target. In some cases, the first target may be a dynamic target, and the second target may be a micro-motion target, wherein the dynamic target may be a target whose speed exceeds V1, and the micro-motion target may be a target whose speed is less than V2, etc. The specific values ​​of V1 and V2 may be determined according to the application scenario, the actual situation of the target, etc., and will not be listed here one by one. In some cases, the first target may be a large target, and the second target may be a small target, etc. In some cases, based on the application scenario, combined with experience value, big data analysis or cluster analysis, combined with motion trajectory, etc., the target may be divided for different motion states of the target to obtain the first target and the second target. In some scenarios, the first target may be defined as the target detected when a person or a car moves relatively fast as a whole in the target area, and the second target may be defined as other targets with slower moving speeds than the first target. In other cases, the dynamic target may also be divided into types in combination with dimensions such as motion speed and motion amplitude, and may be specifically set based on the application scenario, actual needs, and radar monitoring performance, etc., as long as the distinction between the first target and the second target can be achieved, or hierarchical display can be achieved in the subsequent stage. The specific situations of the first target and the second target will not be described one by one here. Of course, for the convenience of explanation, when providing exemplary explanations later, the first target will be a dynamic target and the second target will be a micro-motion target.

[0051] It should also be noted that, during implementation, there may be further requirements for target detection: taking one frame of echo signal (i.e., an echo signal of one frame size) as the first echo signal as an example, if, during implementation, the detection result is expected to be more reliable, then the first echo signal can be set to an echo signal of two frames size, and each time target detection is performed, two frames of echo signals are obtained as the first echo signal. In this way, more data can be used for target detection, which is conducive to ensuring that target detection has higher accuracy and reliability; if, during implementation, the detection result is expected to be fed back to the user or other devices in real time, then the first echo signal can be set to an echo signal of 0.5 frame size, or, when the data of the preset unit includes a preset number of adjacent chirp data in a frame, the above-mentioned dynamic point cloud data can be set accordingly based on adjacent different numbers of chirp data, that is, as long as the periods of the signal processing units corresponding to different types of dynamic point cloud data are different. In this way, not only can the echo signal within a shorter time period be processed, but the target detection can also be performed more quickly on the obtained echo signal and the target detection result can be fed back, which is conducive to improving the real-time feedback of the target detection result to the user or other devices, etc., which will not be elaborated here.

[0052] In order to help those skilled in the art better understand the target detection method provided by the above embodiment, it will be explained below.

[0053] Step 101, obtaining first data. This embodiment does not limit the meaning of the first data. It is understandable that for the radar signal processing process, after different processing is completed, corresponding data will be obtained, such as a digital signal obtained after sampling the echo signal, the aforementioned original channel data, RD spectrum data, etc. The first data can be the data obtained after any of the above processing, and the above effect can be achieved. Accordingly, when the meaning of the first data is different, the acquisition method thereof is also different.

[0054] For example, in some cases, the first data may be an initial digital signal. In this case, the first data may be obtained by performing analog-to-digital conversion on the echo signal.

[0055] For another example, in some cases, the first data may be original channel data. In this case, the acquisition of the first data may be achieved through the aforementioned step of acquiring the original channel data.

[0056] For example, in some cases, the first data may also be updated channel data. In this case, the acquisition of the first data may be achieved through the aforementioned step of acquiring the updated channel data.

[0057] For example, in some cases, the first data may also be RD spectrum data. In this case, the acquisition of the first data may be achieved through the aforementioned step of acquiring the RD spectrum data.

[0058] Of course, the above are only exemplary descriptions of the first data provided for different situations, which can be configured according to user needs when applied, and will not be described in detail here.

[0059] In some embodiments, Figure 2 As shown, obtaining the first data can be achieved through the following steps:

[0060] Step 201, acquiring echo data corresponding to a detection signal having a preset transmission period, wherein the preset transmission period is not less than 50 ms (milliseconds).

[0061] Step 202: Generate first data according to the echo data.

[0062] In this embodiment, the duration of the preset transmission cycle is extended, specifically the duration of the frame cycle is increased from about 20 ms to more than 50 ms, etc., to ensure that when detection is performed based on frame data as the basic unit, the data within the frame has sufficient sensitivity to dynamic points, and because the velocity resolution is improved, more point clouds of the first target can be further output to achieve an effect of approximate coarse imaging, and the space of the range-Doppler matrix (Range-Doppler Matrix, RDM, or "RD matrix", etc.) obtained by spectrum analysis (such as 2DFFT) can be fully utilized.

[0063] It should be noted that the present embodiment does not limit the method for extending the transmission period. It is understandable that the detection signal usually includes multiple chirp signals. Therefore, the method for extending the detection signal period can be achieved by configuring the chirp signal. In some examples, extending the transmission period can be achieved by increasing the number of chirp signals included, that is, a detection signal with a preset transmission period is obtained by increasing the number of chirp signals included in each detection signal. In some examples, extending the transmission period can also be achieved by increasing the period of each chirp signal, that is, a detection signal with a preset transmission period is obtained by increasing the period of the included chirp signal, such as increasing the period of the chirp signal. Fig.12 In some examples, the transmission period can also be extended by increasing the frame idle time between the chirp signals in the detection signal, that is, the detection signal with a preset transmission period is obtained by increasing the idle time after the chirp signal in the detection signal, such as increasing Fig.12 The duration shown is B, etc. Among them, Fig.12 The chirp signal in the FMCW waveform is a triangular wave, and the rising edge appears first and then the falling edge appears as an example for explanation, which does not mean that the embodiment of the present application is only applicable to the following example. Fig.12 The FMCW waveform shown will not be described in detail here.

[0064] In step 102, second data is generated according to a plurality of first data. As mentioned above, there are many different situations for the first data, and correspondingly, there are also many different situations for implementing the generation of the second data. For ease of understanding, the following will be exemplified by taking the first data as channel data and RD spectrum data as an example.

[0065] When the first data is channel data, and the channel data is obtained by subtracting zero Doppler channel data from the original channel data, in some embodiments, the second data is generated according to a plurality of first data, which can be achieved by superimposing a plurality of first data to obtain the second data. At this time, since the first data is channel data and the first data are not subsequently processed, the second data can be obtained by superimposing, without complex calculations, and the difficulty of implementation is low.

[0066] Furthermore, considering that the second data is generated by multiple first data, the multiple data may have the same interference, that is, the multiple first data may have the same noise component. Based on this, in some embodiments, Figure 3 As shown, generating the second data according to the plurality of first data can be achieved by the following steps:

[0067] Step 302: Averaging a plurality of first data to obtain zero Doppler channel data.

[0068] Step 303: Subtract zero Doppler channel data from a plurality of first data and perform superposition to obtain second data.

[0069] Among them, step 301, step 304, step 305 are substantially the same as step 101, step 103, step 104 in the aforementioned embodiment, and are not described in detail here.

[0070] Among them, removing the zero Doppler channel data obtained by averaging is equivalent to removing the same noise components between different data. Therefore, in the above embodiment, the noise component removal is performed twice for the echo signal. In the first time, since the average value is obtained inside the first data, the noise component inside the first data is removed. In the second time, since the average value of multiple first data is obtained, that is, the average value is obtained outside the first data, the noise component between multiple first data is removed. For multiple first data, different types of noise components can be better removed, so that the signal components can be more effectively extracted. In this way, more accurate target detection can be achieved based on more effective signal components, and better target detection effect can be obtained.

[0071] For ease of understanding, we will use frames as processing units for example. The first time, the data within a frame is averaged to remove the noise component within the frame. The second time, the data of multiple frames is averaged across frames to obtain the average value between frames, and the noise component that extends over multiple frames is removed.

[0072] In some cases, multiple frames of zero Doppler channel data may be compared to eliminate abnormal data, and then the mean of the retained multiple frames of zero Doppler channel data may be used as the zero Doppler channel data in step 302, which is helpful to avoid sudden noise interference and further improve the accuracy of the zero Doppler channel data.

[0073] 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 frequency domain signal corresponding to the micro-motion target can be obtained, and then the micro-motion target can be detected more accurately, which is conducive to further improving the accuracy of micro-motion target detection.

[0074] When the first data is RD spectrum data, in some embodiments, the second data is generated according to a plurality of first data, which can be achieved in the following manner: the second data is inferred according to a plurality of first data. At this time, since the RD spectrum data is further processed relative to the channel data, the relationship between the data is complicated. Therefore, the second data cannot be obtained by simple superposition, but requires certain inferences, wherein the inference process is related to the FFT processing of the channel data, which will not be described here one by one. However, compared with the aforementioned case where the first data is channel data, when the first data is RD spectrum data, the processing data of the detection of the first target and the second target before obtaining the RD spectrum can be shared, thereby simplifying the process and improving efficiency.

[0075] Of course, the above is only an exemplary description. In some embodiments, other data may be configured as the first data, etc., which will not be described in detail here.

[0076] It can also be understood that the above embodiment divides the target into two categories: the first target and the second target. Considering that there may be various micro-motion targets in the indoor environment, these micro-motion targets may have different movement characteristics, so that different micro-motion targets need to accumulate different observation time when they are observed, wherein the amount of echo data used for target detection reflects the observation time. For example, the head micro-motion generated when reading different parts of a page, and the body micro-motion when walking slowly indoors, the amplitude of slow walking indoors is larger and the speed is higher, so the observation time required to accumulate when the latter is observed is shorter than the observation time required to accumulate when the former is observed. Therefore, the number of echo signal frames required to detect the slow walking indoors is less than the number of echo signal frames required for the head micro-motion generated when reading different parts of a page. If in the target detection process, all micro-motion targets are combined with the same frame of historical echo signals based on the first echo signal, the detection feedback of some micro-motion targets will be delayed relative to their movement process (i.e., the real-time performance is not high), and / or some micro-motion targets cannot be detected (i.e., the accuracy is not high). Therefore, the classification of the target can be further refined according to the further refined motion features. In this way, more refined target detection can be provided based on the more refined target classification, thereby achieving more accurate target detection.

[0077] Based on this, taking the further classification of the second target as an example, in some embodiments, Figure 4 As shown, generating the second data according to the plurality of first data can be achieved by the following steps:

[0078] Step 402, obtaining i types of second data according to Ni first data.

[0079] Step 403: Obtain j types of second data according to Nj first data.

[0080] Accordingly, detecting the second target according to the second data can be achieved through the following steps:

[0081] Step 405: Detect the i-th type of second target based on the i-th type of second data.

[0082] Step 406: Detect the j-th type of second target based on the j-th type of second data.

[0083] Among them, the i-type second data and the j-type second data both belong to the second data, i and j are integers greater than 1, Ni and Nj are positive integers, and Nj>Ni.

[0084] In this way, by further subdividing the target and providing different amounts of first data for detection in a targeted manner, that is, providing different observation time lengths, it is possible to process the motion information of the second target accumulated at different observation time lengths, and present target detection results of different types of second targets from more time dimensions, more comprehensively, and more in real time, which is conducive to further improving the real-time and accuracy of target detection.

[0085] It should be noted that step 401 and step 404 in this embodiment are substantially the same as step 101 and step 103 in the aforementioned embodiment, and will not be described in detail here.

[0086] It should also be noted that Nj and Ni are not limited here. They can be fixed values ​​or values ​​that vary according to the total number of targets included in the target detection result of the first echo signal detected last time, the number of the first target and / or the second target, the speed of the second target, the acceleration of the second target, and other information. In some cases, even if Nj and Ni are fixed values, the specific values ​​of Nj and Ni can still be set according to demand, such as being determined according to the real-time requirements for target detection. It is understandable that in the case of high real-time requirements, the values ​​of Nj and Ni cannot be too large, otherwise it is easy to cause a large amount of data to be processed at present, resulting in low processing efficiency and affecting the efficiency of target detection. In the case of too small movement amplitude and speed of the micro-motion target, the values ​​of Nj and Ni cannot be too small, otherwise it is easy to fail to observe the micro-motion target during the target detection process, thereby causing the problem of missing targets in target detection. Therefore, different values ​​of Nj and Ni can also be set according to the situation during application and the requirements for real-time performance during application.

[0087] Among them, the distinction between different second targets can be set according to needs. For example, the i-th type of second target is defined as the target generated by the breathing, heartbeat, pulse, etc. of a person at rest, and the j-th type of second target is the target generated by the slow movement of the body when the person is still (such as the unconscious slow movement of the body when sitting and falling asleep, or the displacement of the body when concentrating, etc.). Alternatively, different types of dynamic point clouds can be divided based on the motion speed dimension, and the second target with a speed greater than V3 is the i-th type of second target, and the second target with a speed not less than V4 is the j-th type of second target, etc. I will not go into details here.

[0088] Of course, the above is only an exemplary explanation provided for the case where the second target is further refined. In some cases, the first target can also be further refined. In this case, the first data can be divided to support the detection of different first targets, etc., which will not be described one by one here.

[0089] In step 103, the first target is detected according to the first data. As mentioned above, there are many different possible situations for the first data, so the implementation methods of target detection based on the first data in different situations are also different. For example, when the first data is channel data, the detection of the first target also needs to obtain RD spectrum data, but when the first data is RD spectrum data, it does not need to be processed to obtain RD spectrum data, etc. The different implementation methods of first target detection based on the first data in different situations will not be described here.

[0090] It should be noted that the FFT, CFAR and other processing that may be involved in the process of detecting the first target is roughly the same as the existing target detection, and will not be described in detail here.

[0091] Step 104, detect the second target according to the second data. Similar to the detection of the first target, since the second data is generated by a plurality of first data, the second data will be different with the first data, and therefore, the detection of the second target will be different, but the FFT, CFAR and other processing performed in the target detection process are roughly the same as the existing target detection, and will not be described here one by one.

[0092] It is also understandable that, since the second target is a target that requires a longer observation time, some specific digital signal processing can be used to improve the target detection result. Figure 5 As shown, according to the second data, detecting the second target can be achieved through the following steps:

[0093] Step 504: perform slow time dimension FFT according to the second data.

[0094] Step 505: Perform constant false alarm detection according to the FFT result to achieve detection of the second target.

[0095] in, Figure 5 Steps 501 to 503 in the embodiment are substantially the same as steps 101 to 103 in the aforementioned embodiment, and will not be described in detail here.

[0096] In this way, after the slow time dimension FFT, it is possible to distinguish targets with different motion states, such as stationary targets and moving targets. Therefore, the above embodiment can effectively filter out the first target through the slow time dimension FFT, extract the effective second target, and thus provide more precise detection of the second target that requires long-term observation, thereby achieving more accurate target detection results.

[0097] Of course, when detecting the first target, a slow time dimension FFT may also be used, so as to accurately extract the first target, thereby facilitating more accurate detection of the first target.

[0098] That is to say, by acquiring RD spectrum data through slow time dimension FFT, a processing method corresponding to the dynamic characteristics of the first target and / or the second target is provided to obtain the Doppler matrix, which 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 detection of the first target and / or the second target.

[0099] Among them, from the FFT result to the constant false alarm detection, coherent integration (NCI) or non-coherent integration (NCI) is usually required in the meantime, so as to improve the signal-to-noise ratio by accumulating multiple pulses. It should be noted that the embodiment of the present application does not limit the specific implementation method of coherent accumulation and non-coherent accumulation. It can selectively perform coherent accumulation or non-coherent accumulation according to application scenarios, user needs, etc., and can also select accumulation algorithms according to computing power, resources, scenarios and needs, etc., which will not be repeated here.

[0100] It can also be understood that, for a general CFAR algorithm, a higher threshold is usually used, which will result in more target points being regarded as non-targets. Based on this, in some embodiments, when performing target detection, the above embodiment can perform constant false alarm detection according to a preset threshold, where the threshold = k*noise estimation value, k>1.

[0101] It should be noted that, compared with the current situation where the threshold is set to k'*noise estimation value in order to reduce clutter interference, k' is usually set to dozens or hundreds. Here, by setting the limit adjustment of k>1, the threshold can be set to a smaller value during target detection, and the threshold is no longer required to be a higher value. The setting of a smaller threshold allows the constant false alarm detection to detect more points, reducing or avoiding the misidentification of valid targets as clutter points for exclusion, which is conducive to improving the accuracy of indoor target detection.

[0102] In some cases, the value range of k is (1, 4), such as 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 mistaking of valid targets for clutter for exclusion, which is conducive to further improving the accuracy of indoor target detection.

[0103] Of course, the above is only an example of the value range of the parameter k in the threshold. The value of the parameter k in the threshold can also be adjusted according to specific scenarios, which will not be described here one by one.

[0104] It is also understandable that for relatively closed areas such as indoors, the accuracy of target detection is not high because it is more complicated due to people, obstacles, etc. compared to relatively open environments such as outdoors, and it is urgent to improve the accuracy of target detection in such scenarios. In addition, clutter is inevitable in the space, and clutter will interfere with target detection, resulting in invalid targets being detected.

[0105] To this end, the present application also provides a target detection method, which provides confidence for the detected target, so as to avoid the problem of misjudgment of removing the target as a non-target and the problem of not being able to accurately obtain target information. In some embodiments, the process of the target detection method is as follows: Figure 6 As shown, the following steps are included:

[0106] Step 601, obtaining several targets according to the target detection results.

[0107] Step 602: For each detected target, corresponding feature information is obtained, where the feature information includes noise features and signal features for characterizing the target.

[0108] Step 603, determining the confidence level of each target based on the feature information.

[0109] In this way, since the feature information includes noise features and signal features used to characterize the target, after the target is detected, the feature information can be used to determine whether the target is a valid target and determine the confidence level of each detected target, so that the target can be further verified, which is beneficial to improving the reliability and accuracy of target detection.

[0110] In this way, after the target is detected by the echo signal, the corresponding feature information is further generated for the target in 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 noise as the target can be eliminated, which is conducive to improving the accuracy of indoor target detection.

[0111] In order to facilitate those skilled in the art to better understand the above embodiments, they will be explained below.

[0112] In step 601, a number of targets are obtained according to the target detection results. This embodiment does not limit the method for obtaining the target detection results, which can be achieved by any target detection method, and will not be described in detail here.

[0113] Step 602, for each detected target, obtain corresponding feature information, the feature information includes noise features and signal features for characterizing the target. In this embodiment, the feature information is not limited, and it can be any information that can characterize the noise features and signal features of the target. For example, the feature information can be an angle spectrum, an entropy value, a signal-to-noise ratio, etc., and the angle spectrum can be an RD spectrum, a Capon spectrum, etc. They will not be listed one by one here. Among them, the angle spectrum can be obtained from the target detection process when the target detection result is obtained, wherein the algorithm used when 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 repeated here; the entropy value can be directly obtained, and the signal-to-noise ratio can be obtained from the CFAR process when the target detection result is obtained, etc.

[0114] It should be noted that the way in which the signal features and noise features in the feature information represent the target is not limited here, and it may vary according to different feature information, and may also vary according to different scenarios, requirements, etc.

[0115] For example, when the characteristic information includes an energy spectrum, the entropy value of the energy spectrum can be used as the characteristic information, based on the characteristic that the signal is usually concentrated and the noise is usually scattered; when the characteristic information includes an angle spectrum, the signal usually has more energy, which is mainly reflected in the main lobe, and the noise usually has less energy, which is mainly reflected in the side lobe, and the relevant information of the main lobe and the side lobe in the angle spectrum can be used as the characteristic information to characterize the target, etc. For example, when the characteristic information includes an angle spectrum, the characteristic information can be related to the local noise characteristics and local signal characteristics of the target, and the characteristic information can also be related to the global noise characteristics and global signal characteristics of the target, etc., which will not be described one by one here.

[0116] 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 signal features and noise features 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.

[0117] It can also be understood that the standards for determining confidence levels 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 levels, the reference standard for confidence levels may be set to a broader condition. This can reduce the risk of misjudgment of valid targets.

[0118] Step 603, determine the confidence of each target based on the feature information. As mentioned above, there are many different possible scenarios for the feature information, and accordingly, there are many different implementation methods for the confidence. To facilitate a better understanding by those skilled in the art, the following will illustrate the acquisition of target confidence when the feature information is the angle spectrum, entropy value, and signal-to-noise ratio.

[0119] When the characteristic information is an angle spectrum, in some embodiments, such as Figure 7 As shown, obtaining the corresponding feature information can be achieved through the following steps:

[0120] Step 702, obtaining the angle spectrum generated during the target detection process.

[0121] Correspondingly, the confidence of each target can be determined based on the feature information, which can be achieved through the following steps:

[0122] Step 703: determine the maximum value in the angle spectrum corresponding to the target.

[0123] Step 704, determine a first validity parameter based on the maximum value among the maximum values ​​of the angle spectrum and the average value of other maximum values ​​except the maximum value, and the first validity parameter is used to characterize the relative size between the maximum value among the maximum values ​​of the angle spectrum and the average value of other maximum values ​​except the maximum value.

[0124] Step 705: Determine the confidence level of each target based on the first validity parameter.

[0125] It should be noted that this embodiment does not limit the specific method for determining the first effective parameter. In some examples, it may 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; in some examples, it may also be an array consisting of the maximum value among the local values ​​of the angle spectrum and the average of other local values ​​except the maximum value, etc.

[0126] 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 can then 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, that is, accurately determine the confidence of the target, which is conducive to further improving the accuracy of target detection.

[0127] 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.

[0128] In some embodiments, Figure 8 As shown, according to the first validity parameter, the confidence of each target is determined, which can be achieved by the following steps:

[0129] Step 805, determining a second validity parameter of the corresponding target according to the maximum value and the second maximum value in the maxima of the angle spectrum, the second validity parameter being used to characterize the relative size between the maximum value and the second maximum value in the maxima of the angle spectrum.

[0130] Step 806: Determine whether the corresponding target is an invalid target according to the second validity parameter.

[0131] Step 807: When the corresponding target is not an invalid target, determine the confidence level of the target according to the first validity parameter.

[0132] It should be noted that the specific method for determining the second validity parameter is limited in this embodiment, which can be the ratio of the maximum value and the second largest value among the maximum values ​​of the angle spectrum, or the difference between the maximum value and the second largest value among the maximum values ​​of the angle spectrum, etc.

[0133] 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 the confidence level, thereby improving the accuracy of target detection.

[0134] In some embodiments, obtaining the corresponding feature information can be achieved by: obtaining the entropy value of the corresponding target. Correspondingly, determining the confidence of each target based on the feature information can be achieved by: determining the confidence of each target based on the entropy value.

[0135] It should be noted that the method for determining the entropy value is not limited here. 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.

[0136] 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, thereby determining the confidence of the target.

[0137] It should be noted that the entropy value range corresponding to the target is not limited here, and it may vary according to the degree of chaos of the detection environment, the requirements of detection accuracy, etc., which will not be elaborated here.

[0138] In some embodiments, obtaining the corresponding feature information can be achieved by: obtaining the signal-to-noise ratio generated by the constant false alarm detection of the corresponding target during the target detection process. Correspondingly, determining the confidence of each target based on the feature information can be achieved by: determining the confidence of each target based on the signal-to-noise ratio.

[0139] Among them, 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 accurately determining the confidence of the target, thereby achieving accurate target detection.

[0140] Of course, the above embodiments can also be combined. For example, in some embodiments, the present application embodiment provides a target detection method, and its process is as follows: Fig. 9 As shown, the following steps are included:

[0141] Step 901, perform target detection to obtain a first target and a second target.

[0142] Step 902: For each of the detected first target and the second target, corresponding feature information is obtained, where the feature information includes noise features and signal features for characterizing the target.

[0143] Step 903: Determine the confidence level of each first target and second target based on the feature information.

[0144] It should be noted that, in this embodiment, step 901 is implemented by any of the above target detection methods that can detect the first target and the second target, and steps 902 and 903 are roughly the same as steps 602-603 in the aforementioned embodiment, and will not be described in detail here.

[0145] It can also be understood that in the aforementioned embodiment, the first target and the second target are detected for the target motion state (i.e., point cloud type). That is, in the time dimension, for the same period of time, the corresponding target object can be divided into the first target and the second target, and can even be further subdivided, for example, according to the motion state generated, it can be subdivided into a moving target, a short micro-motion target, and a long micro-motion target. Among them, in the detection of moving targets, based on the amplitude of the target motion, it can be divided into a "movement" state and a "micro-motion" state, that is, the amplitude of "movement" is greater than the amplitude of "micro-motion"; at the same time, the "micro-motion" state can be divided into at least two states of "long micro-motion" and "short micro-motion" based on the time dimension, and the time period of "long micro-motion" is greater than the time period of "short micro-motion".

[0146] However, the existing preset frame data can generally only output one type of detection target, such as dynamic point cloud or micro-motion point cloud. When the radar system needs to output two types of detection targets, it can only output alternately, that is, one preset frame data is used to output dynamic point cloud, and the other preset frame data is used to output micro-motion point cloud. At the same time, micro-motion point data is generally detected based on a single frame or multiple frames.

[0147] Therefore, the present application also provides a method for outputting a target point cloud, which is used to display the above-mentioned different targets in a hierarchical manner to improve the user experience. In some embodiments, the process is as follows: Fig.10 As shown, the following steps are included:

[0148] Step 1001, perform target detection to determine a first target and a second target.

[0149] Step 1002: Output the first target and the second target in a hierarchical manner.

[0150] Among them, the first target and the second target determined in step 1001 can be the first target and the second target determined according to the target detection method provided in the aforementioned embodiment, and the first target and the second target determined in step 1001 can also be the first target and the second target with confidence determined according to the target detection method provided in the aforementioned embodiment.

[0151] As for step 1002, in some embodiments, the above-mentioned subdivision into moving targets, short micro-motion targets, and long micro-motion targets according to the generated motion state is assumed to be based on the acquisition of data of a preset group frames, that is, the data based on the preset group frames is used as a data unit for long micro-motion detection, and at least two (such as two) frames included in the preset group frames are used as a data unit for short micro-motion detection, and the data of each frame can be used for motion detection. In addition, for example, when the data of 6 consecutive frames is used as a data unit for long micro-motion detection, the data of 3 consecutive frames can be selected as a data unit for short micro-motion detection, and the data of 2 consecutive frames can be selected as a data unit for motion detection.

[0152] For step 1002, in some embodiments, when detecting motion point cloud based on single frame data, point cloud data can be outputted for each frame corresponding to short micro-motion points by sliding window, so as to further supplement the detected dynamic (motion state) points. That is, by outputting dynamic points and short micro-motion points for each frame, the overall posture and partial movements of a human body can be more accurately and comprehensively portrayed, so as to achieve the effect of approximate imaging (imaging with relatively coarse granularity).

[0153] That is to say, through the method provided in the above embodiment, the scene can be perceived based on the echo signals at different time scales, and the corresponding confidence level can be set for the point cloud according to the preset rules, thereby realizing the hierarchical output of the point cloud data, so as to realize a more comprehensive scene display and the detection of the all-round state information of the target. At the same time, based on the echo signal of a preset unit, at least two types of detection target data can be output simultaneously, that is, at least two types of detection target results can be obtained based on the data of the same preset unit. Among them, the data of the preset unit can be a preset number of adjacent chirp echo signals in a frame (such as half a frame, 1 / 4 frame, etc.), or it can be a frame of data or at least two adjacent frames of echo signals, etc. For example, when the data of the preset unit is three adjacent frames of echo signals, three set types of dynamic point clouds are obtained based on the adjacent three frames of data as data sources. Specifically, the first type of dynamic point cloud (such as short-term micro-motion point cloud) can be output based on two adjacent frames of data, and the second type of dynamic point cloud (such as motion point cloud) can be output based on each frame of data. On the basis of output, the third type of dynamic point cloud (such as long-term micro-motion point cloud) can be output based on the adjacent three frames of data. At the same time, different colors, shapes, etc. are set in the point cloud presentation to achieve hierarchical display of the three types of point cloud data.

[0154] In order to facilitate those skilled in the art to better understand the methods and combinations provided by the above embodiments, the following will be described by example. In the description, for ease of understanding, the first target is a motion target, the second target is a micro-motion target, and the second target can be further refined into a long-term micro-motion target and a short-term micro-motion target.

[0155] For a multi-channel 4D FMCW millimeter-wave radar installed in a room (such as a roof or wall), the radar transmits electromagnetic waves to targets in the room in frames. Each frame signal may contain several chirp signals, and the frame period or the total transmission time of the chirp within the frame is longer than the traditional one. For example, the total transmission time of the chirp within the frame can be increased from 20ms to 50ms or more. At the same time, the radar receives the echo signal generated by the transmitted electromagnetic wave in frames and processes the received signal as follows:

[0156] Step S1101, performing range windowing and range FFT on each chirp signal in the frame to obtain original channel data.

[0157] Step S1102: Calculate zero Doppler channel data of the signal in the frame according to the original channel data in the frame, and save it.

[0158] Step S1103: Subtract zero Doppler channel data from each chirp in the frame according to the distance bin.

[0159] Step S1104, performing chirp windowing and FFT on the data obtained in step S1103.

[0160] Step S1105, modulo the data obtained in step S1104 of all channels, and accumulate the results.

[0161] Step S1106, using the CFAR algorithm to perform target detection on the result in step S1105.

[0162] In step S1107, the range bins and Doppler bins of all azimuth channels corresponding to the target detected in step S1106 are extracted, DBF is performed, and modulus is calculated to obtain the DBF spectrum of the target.

[0163] Step S1108 , find the maximum value of the DBF spectrum of each target, and calculate the azimuth angle corresponding to the maximum value as the azimuth angle of the target.

[0164] Step S1109, calculating the confidence of the target through the concentration degree of the DBF spectrum to obtain the output of the dynamic point.

[0165] In step S1110, the data acquired in step 1103 of nearly four frames are used to form a micro-motion frame, and steps S1104 to S1109 are performed to obtain short-term micro-motion points.

[0166] Among them, 4 frames can also be replaced by a duration of 1 second. It should be noted that the duration of 1 second can be adaptively adjusted according to actual needs, and the embodiment of the present application does not limit it, and can also be 0.8 seconds, 0.5 seconds or 0.3 seconds.

[0167] In step S1111, the data acquired in step 1103 of nearly 16 frames are used to form a micro-motion frame, and the processing of steps S1104-S1109 is performed to obtain long-term micro-motion points.

[0168] Among them, 16 frames can also be replaced by a duration greater than 1 second. The specific duration can be adaptively adjusted according to actual needs, and the embodiment of the present application does not limit it, such as 5 seconds, 3 seconds or 1 second.

[0169] Step S1112: output the dynamic points, short-time micro-motion points, long-time micro-motion points and their confidences, wherein the dynamic points, short-time micro-motion points and long-time micro-motion points are displayed in a hierarchical manner so that the user can observe more intuitively.

[0170] In some embodiments, based on the above-mentioned method embodiment of target detection, the RD matrix for long-term micro-motion points may not be recalculated, but may be obtained by accumulating and extrapolating the RD matrix of short-term micro-motion points. The RD matrix of short-term micro-motion points is obtained by sliding window, so that each frame can have output. At the same time, the RD matrix of long-term micro-motion points can also be obtained by sliding window, or by processing once every preset number of frames, or by accumulating the RD matrix of short-term micro-motion points to obtain the RD matrix of long-term micro-motion points. When the RD matrix of long-term micro-motion points is obtained based on the RD matrix of accumulated short-term micro-motion points, the RD matrix of long-term micro-motion points can be obtained by accumulating the RD matrix of each short-term micro-motion point after performing phase compensation on the frame corresponding to the RD matrix of each short-term micro-motion point.

[0171] In some embodiments, different time scales of the scene can also be perceived by using different numbers of frames. For example, the radar is set to at least two scenes such as daytime (or active or living room) scenes or nighttime (or inactive or bedroom) scenes. The above-mentioned divided states can be set based on different time scales for different scenes, that is, different scenes and different time scales for the same state. For example, corresponding to the daytime living room scene, the time scale of short micro-motion can be set to 0.8s, and the time scale of long micro-motion can be set to 3~5s. For the bedroom scene at night, the time scale of short micro-motion can be set to 1~2s, the time scale of long micro-motion can be set to 10~100s, etc., so as to achieve adaptive adjustment of the scene while effectively reducing the power consumption of the radar.

[0172] In this way, all possible potential target points on the RD detection matrix can be detected by using a very low threshold noise-based constant false alarm rate (NRCFAR), and then the possible target points can be screened out from a large number of candidate points by using the DBF spectrum. The difference from conventional RD processing is that the formation of the final point cloud depends on how to screen the point cloud rather than how to detect the RD matrix.

[0173] That is, conventional RD processing forms an RD detection matrix after completing 2DFFT, and then detects the target points from the RD detection matrix based on CFAR, and calculates its azimuth and pitch angle to form a point cloud. However, the indoor environment is relatively complex, and there are a large number of reflectors (walls, tables, chairs, furniture, etc.), which leads to a large number of multi-angle echoes at the same distance (mostly from the reflection of the scene) and the existence of multipath phenomena. That is, among the points detected by conventional RD processing, some are targets of interest, some are multipath points, and some are false points after the synthesis of multi-source reflections. Therefore, there is a phenomenon of more noise in the results of conventional RD processing. On the other hand, in order to suppress noise, conventional RD processing methods will use a higher detection threshold, which will also suppress points with relatively weak signal-to-noise ratio from real targets, resulting in fewer point clouds of real targets (put together with noise, it shows the phenomenon of scattered points without aggregation) or no points on low signal-to-noise ratio targets.

[0174] For a point from a real target of interest, since the signal component is single, its DBF spectrum shape must be close to the sinc function, that is, there is an obvious main peak. For multipath points, due to the difference between the outgoing phase and the incident phase, after the multiple-input multiple-output (MIMO) processing, the DBF spectrum of the multipath point cannot form a main peak similar to the real target, and the energy of the DBF spectrum is relatively dispersed. For the false points synthesized by multi-source reflections throughout the room, their DBF spectrum also cannot form an obvious main peak. Therefore, the DBF spectrum can be used to screen candidate points.

[0175] In indoor applications, the difference between human targets and typical radar targets is that the speeds of different parts of the human body are not the same, that is, the human body echo has a rich velocity component. At the same time, the human body has a variety of motion states, including tangential motion, slight shaking while standing, and other low radial velocity states. These motion states are a challenge for radars that rely on radial velocity for target separation. On the other hand, the radial velocity of a certain point cloud of the human body only reflects the radial velocity of a certain part of the human body, and there may be a large difference between the radial component of the actual moving speed of the human body. In this scenario, the unambiguous speed range has no substantial effect on target detection. That is, by using a long chirp period, high-resolution speed detection of indoor targets can be performed, which can effectively improve the comprehensiveness of target detection.

[0176] In addition, improving the speed detection resolution has at least the following advantages:

[0177] 1) A more detailed speed dimension characterization of the target can effectively improve the signal-to-noise ratio of each speed unit.

[0178] 2) The echoes from two targets at the same distance can be distinguished more finely in the velocity dimension, so that the two targets at the same distance can be detected simultaneously.

[0179] 3) Improve the radar's sensitivity to the scene, that is, improve the detection capability of low radial velocity states such as tangential motion, which has high practical value in indoor scenes.

[0180] 4) Make full use of the points in the speed dimension of the RD matrix to avoid wasting storage space.

[0181] At the same time, for the detection of micro-motion targets, the intra-frame zero Doppler channel data of multiple frames is used for processing. Since the above-mentioned dynamic point processing method can detect dynamic points based on the RD matrix well, a similar processing idea can also be used for micro-motion targets. The intra-frame zero Doppler channel of multiple frames is subjected to inter-frame FFT to form an inter-frame RD matrix. Based on the RD matrix, the above-mentioned dynamic point processing method is used to form the final micro-motion point cloud.

[0182] In addition, in order to perceive the scene more completely, the micro-motion processing is divided into short-term micro-motion processing and long-term micro-motion processing. Short-term micro-motion point processing can use fewer frames. Compared with in-frame processing, short-term micro-motion processing can effectively detect sitting and standing human bodies. Short-term micro-motion points are an effective supplement to dynamic points in the frame. The two complement each other in the real-time perception of the scene. The posture and movement of the human body can be portrayed by dynamic points and short-term micro-motion points. Long-term micro-motion point processing uses more frames to perceive more subtle scene changes, to assist short-term micro-motion points in detecting static human bodies.

[0183] That is, in the process of dynamic point detection, through:

[0184] a. Use signals obtained at different time scales for processing to fully perceive the dynamic and static targets in the scene, and output dynamic points, short-term micro-motion points, and long-term micro-motion points at the same time.

[0185] b. In dynamic point processing, high-resolution velocity waveforms are used to more finely characterize the scene in the velocity dimension, expanding the “pool” that accommodates the target point cloud.

[0186] c. Use low-threshold NRCFAR to detect in the RD domain of the scene echo, detect all the “bright spots” in the RD domain, and do not miss any possible target echo point.

[0187] d. Use the DBF spectrum to evaluate the detected candidate point set, and select the point cloud that meets the target features from the candidate point set as the final point cloud output.

[0188] Through the above improvements, it is possible to achieve:

[0189] 1. For human targets, it outputs rich point clouds that can depict the posture of the human body in dynamic and static states, and perform point cloud tracking (coarse imaging) of human body movements (such as waving, etc.).

[0190] 2. Filter out a large number of clutter and multipath points that are widely present in indoor environments, while retaining effective weak target points to improve the radar's weak target detection capability.

[0191] 3. Improve the point cloud cohesion of human targets, thereby improving the ability to distinguish between human targets.

[0192] 4. A large number of micro-motion point clouds can be output for both distant and near static human targets.

[0193] 5. Dynamic point clouds can still be continuously output for tangentially moving targets.

[0194] In some embodiments, the specific signal processing process can be divided into dynamic point processing and micro-motion point processing (i.e., the "static" point processing described later in the embodiments of the present application). Dynamic point processing can be intra-frame processing. It is mainly used to detect dynamic points with obvious radial velocity. Micro-motion point processing is inter-frame processing, which further improves the velocity resolution by extending the observation time to perceive the subtle movements of the human body. Micro-motion point processing is further divided into short-time micro-motion point processing and long-time micro-motion point processing.

[0195] In some optional embodiments, the processing of dynamic points may be intra-frame processing, that is, using echo data of multiple chirps in one frame to detect dynamic targets. Specifically, the following steps may be included:

[0196] Step S1201: distance windowing and distance FFT.

[0197] Specifically, the ADC data of each received chirp is windowed in the distance direction and subjected to the distance FFT to obtain a one-dimensional distance image. The above operation may or may not be windowed, and different windows may be added for processing. In addition, for pulse compression radar, each chirp echo signal is pulse compressed to obtain a one-dimensional distance image. In some cases, a stepped frequency radar may also be used in this step, that is, the embodiment of the present application does not limit the radar system, as long as it can obtain a one-dimensional distance image.

[0198] Step S1202, remove zero Doppler.

[0199] Specifically, the one-dimensional range image results of each chirp are accumulated and averaged according to the range bin to obtain the zero Doppler channel data in the frame, and then the zero Doppler channel data in the frame is subtracted from each chirp, so as to filter out the stationary targets in the frame and retain the moving targets in the frame.

[0200] Step S1203: velocity windowing and velocity FFT.

[0201] Specifically, the data after removing the zero Doppler is windowed (or not windowed) along the slow time dimension according to the range bin, and the slow time dimension FFT is performed to separate the echoes of the same distance in the scene in the velocity dimension.

[0202] Step S1204: Inter-channel RDM incoherent accumulation.

[0203] Specifically, the modulus square of the FFT result of each channel velocity is calculated, and the average is accumulated between channels to obtain the incoherent gain between channels, thereby obtaining the RDM for target detection.

[0204] In some cases, multiple RDMs can be formed through incoherent accumulation of multiple channels or coherent accumulation, and then the multiple RDMs can be tested. It is also possible to directly square the modulus of one or several channels for testing without accumulation.

[0205] Step S1205, perform NRCFAR.

[0206] Specifically, extract the first 10 distance bin data (within a range of about 1m) of the 20 Doppler channels near the zero Doppler channel from the detection RDM, average them, and calculate the noise mean. Compare each point on the detection RDM with the above noise mean. If it is greater than a certain threshold, the point is included in the candidate point. In order to detect all weak echo points, the threshold set here can be obtained by modulus or modulus square, that is, a relatively low threshold (compared to the threshold value of the traditional method) is used to detect as many candidate points as possible, for example, it can be set to 10 times by modulus square. Optionally, the noise can also be selected by selecting several distance bins at the farthest distance to calculate the noise mean, that is, it is mainly based on which range is relatively clean, with only noise but no target, so as to obtain a more accurate noise floor.

[0207] Step S1206, performing DOA.

[0208] Specifically, according to the TRX array, DBF is performed on the main sub-array and slave sub-array channel data of the candidate point to obtain the azimuth DBF spectrum. And the elevation DBF is calculated by combine mode to obtain the elevation angle.

[0209] Step S1207, calculating the point cloud confidence.

[0210] Specifically, if the energy of the candidate point comes only from the target, its signal composition is relatively simple, and the DBF spectrum is close to the ideal sinc function, there will be an obvious main peak. Therefore, the confidence based on the DBF spectrum of the candidate point orientation can be used to determine whether the candidate point is the desired target point. The confidence is divided into confidence 1 and confidence 2.

[0211] Among them, the calculation method of confidence 1 (conf1) can be:

[0212] conf1=max / peak2,

[0213] Among them, max is the maximum value of the azimuth DBF spectrum, and peak2 is the second largest extreme value of the azimuth DBF spectrum, that is, confidence 1 reflects the highest sidelobe level of the DBF spectrum. The larger the confidence 1, the lower the highest sidelobe, the more single its signal component is, and the greater the possibility that it comes from a single target.

[0214] The calculation method of confidence 2 (conf2) can be:

[0215] conf2=max / mean(peak oth ),

[0216] Among them, max is the maximum value of the azimuthal DBF spectrum, peak oth is the other extreme values ​​of the azimuthal DBF spectrum except the maximum and the second largest extreme value. Therefore, the confidence level 2 reflects the energy concentration of the DBF spectrum at the main peak.

[0217] It should be noted that in the embodiment of the present application, the confidence calculation method is not unique, as long as it can measure the energy concentration of the DBF spectrum, that is, the higher the concentration, the purer the echo is and the more likely it is to be a valid target.

[0218] Optionally, the confidence calculation in the embodiment of the present application may be based on the azimuth DBF spectrum, but may also be obtained by using the elevation DBF spectrum, or by using non-azimuth and non-elevation DBF spectra.

[0219] Step S1208, effective target extraction—single target point.

[0220] Specifically, as mentioned above, the higher the confidence 1 of the candidate point, the greater the possibility that it comes from a single target. Therefore, a certain threshold is set for the confidence 1. If it is higher than the threshold, the target is output as a single target point cloud. The current judgment condition for a single target point is:

[0221] conf1>2.

[0222] Step S1209, effective target extraction—dual target points.

[0223] Specifically, in indoor scenes, it is inevitable that two human targets will be in the same distance unit. If the echoes of the two targets fall into the same RD unit, when the two targets are sufficiently separated in azimuth, two close peaks will appear in the azimuth DBF spectrum. In order to prevent the candidate points in this situation from being screened out by the "single target point screening" module, confidence 1 and confidence 2 are used to judge the dual targets. If confidence 1 and confidence 2 meet the following conditions at the same time, the main lobe and the highest side lobe of the candidate point DBF spectrum are output as dual targets:

[0224] conf1<1.5,

[0225] conf2>2.5.

[0226] Step S1210, supplementing the point cloud.

[0227] Specifically, for point clouds that do not meet the single or dual target point judgment, if the following conditions are met, they will be output as supplementary point clouds.

[0228] conf1>1.5,

[0229] conf2>3,

[0230] SNR>10.

[0231] Step S1211, point cloud enhancement.

[0232] Specifically, for "high-quality" point clouds, i.e. point clouds with concentrated DBF spectrum energy and high signal-to-noise ratio, the left and right azimuth points of the main peak of its DBF spectrum are output at the same time, and more weight is given to the "high-quality" point clouds in terms of point cloud quantity to enhance the concentration of scene point clouds. The current judgment conditions for "high-quality" point clouds are:

[0233] conf2>4.5 and SNR>30.

[0234] In some embodiments, in the processing of micro-motion points, the micro-motion point processing can be divided into short-term micro-motion points and long-term micro-motion points. Both can be cross-frame processing operations. For example, short-term micro-motion points are processed using 4 frames, and long-term micro-motion points can be processed using 16 frames of duration data (16 frames are extracted from two, and the final number of frames used is 8 frames). Except for the difference in the number of frames used, the processing procedures of the two are basically the same. In order to avoid the interference of moving targets on the detection of micro-motion targets, both use zero Doppler channel data within the frame as input. Specifically, the following steps may be included:

[0235] The micro-motion point processing is divided into short-term micro-motion points and long-term micro-motion points. Both are cross-frame processing. Short-term micro-motion points are processed using 4 frames. Long-term micro-motion points are processed using 16 frames of duration data (16 frames are extracted from 2, and the final number of frames used is 8 frames). Except for the difference in the number of frames used, the processing procedures of the two are basically the same. In order to avoid the interference of moving targets on micro-motion target detection, both use zero Doppler channel data within the frame as input. The processing steps of micro-motion points are:

[0236] Step S1301: distance windowing and distance FFT.

[0237] Step S1302, calculating zero Doppler channel data within the frame.

[0238] Specifically, the one-dimensional range image results of each chirp in a frame are accumulated according to the range bin and averaged to obtain the zero Doppler channel data in the frame.

[0239] In some cases, the zero Doppler may be calculated by averaging all chirps in a frame, averaging some continuous chirps, or averaging some non-continuous chirps.

[0240] Step S1303: removing zero Doppler between frames.

[0241] Specifically, for each range bin, the inter-frame zero Doppler mean of several frames is taken, and the mean is subtracted from the range bin corresponding to each frame of zero Doppler data.

[0242] Step S1304: inter-frame DFT.

[0243] Specifically, an inter-frame DFT is performed on the zero Doppler channel data (after subtracting the mean) of each range bin in a sliding window manner (or a non-sliding window manner) to obtain a two-dimensional FFT matrix of each channel.

[0244] Step S1305: performing inter-channel RDM incoherent accumulation.

[0245] Step S1306, perform NRCFAR.

[0246] Step S1307, performing DOA.

[0247] Step S1308, calculating the point cloud confidence.

[0248] Step S1309, single target point screening.

[0249] Step S1310, dual target determination.

[0250] Step S1311, supplement point cloud.

[0251] Step S1312: point cloud enhancement.

[0252] It should be noted that steps S1301-S1302 and S1305-S1312 here are roughly the same as the aforementioned steps S1201-S1211, and the main difference is that step S1303-not going to S1304 is also introduced here. Steps S1301-S1302 and S1305-S1312 will not be described one by one here.

[0253] In some optional embodiments, when the point cloud is output, the output point cloud is processed hierarchically, so as to fully express the scene situation from different dimensions, so that the user can accurately understand the meaning, use and use conditions of various point clouds. Specifically:

[0254] In some embodiments, point cloud data such as dynamic points, short-time micro-motion points, long-time micro-motion points, and static points are displayed hierarchically, that is:

[0255] Since dynamic points can reflect the moving targets in the scene, they can include moving human bodies with obvious radial velocity, as well as human targets with slight shaking and most tangential motion. In general, the confidence of dynamic points is higher than that of short-term micro-motion points and long-term micro-motion points. Therefore, they can be used to start the track and maintain the track confidence.

[0256] Short-term micro-motion points are an effective supplement to dynamic points, that is, when the human body is in a non-moving state, the short-term micro-motion points can continuously maintain the perception of the human target and reflect the posture of the human target to a certain extent. Therefore, short-term micro-motion points can be used for continuous tracking of non-moving human bodies.

[0257] At the same time, since long-term micro-motion points use a relatively long number of frames to detect micro-motion in the scene, they can detect weaker micro-motion targets. Because a longer number of frames is used, there will be obvious hysteresis in long-term micro-motion points for a dynamic target, that is, after the target has walked away from position A for a period of time, the long-term micro-motion points can still be seen at position A. Therefore, long-term micro-motion points can be used to continuously maintain stationary targets and maintain tracks. For example, if a stationary track has no long-term micro-motion points and the confidence of the stationary track is not high, you can consider deleting the track.

[0258] In some embodiments, for single-target, dual-target, and supplementary point clouds:

[0259] Since dynamic points and long / short time micro-motion points are evenly divided into single target point cloud, dual target point cloud and supplementary point cloud. Based on the point cloud confidence angle, we can know that:

[0260] Single target point cloud > dual target point cloud > supplementary point cloud.

[0261] Therefore, for single-point target point clouds, the higher the confidence 1 of the point cloud and the higher the SNR, the more reliable the point cloud. For dynamic single-target point clouds, they can be used for the start of the track, while dual-target point clouds need to be used with caution for the start of the track, and supplementary point clouds are not recommended for the start of the track. At the same time, when tracking the confirmed track, when there is no single-target point cloud, the dual-target point cloud can be used conditionally. That is, if the other point of the dual-target point cloud is indeed within the gate of another confirmed track, the confidence of the dual-target point cloud is improved, and the dual-target point cloud can be used to associate the current track. In addition, when there are no dynamic points of single or dual targets around a confirmed track, the use of supplementary point clouds can be considered.

[0262] 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.

[0263] Another aspect of the present application embodiment further provides a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0264] 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.

[0265] On the other hand, the embodiment of the present application also provides an integrated circuit, such as Fig.11 As shown, it includes a radio frequency module 1101, an analog signal processing module 1102 and a digital signal processing module 1103 connected in sequence;

[0266] The RF module 1101 is used to generate RF transmission signals and receive RF reception signals;

[0267] The analog signal processing module 1102 is used to down-convert the RF received signal to obtain an intermediate frequency signal;

[0268] The digital signal processing module 1103 is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain digital signal data, and obtain first data, generate second data based on multiple first data, detect the first target based on the first data, and detect the second target based on the second data; and / or, the digital signal processing module 1103 is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain digital signal data, and obtain several targets based on the target detection results; for each detected target, obtain corresponding feature information, the feature information includes noise features and signal features used to characterize the target; determine the confidence of each target based on the feature information.

[0269] Optionally, the integrated circuit may further include a data processing module, and the data processing module is used to process digital signals to achieve target detection and / or wireless communication.

[0270] Optionally, the integrated circuit may be a millimeter wave chip.

[0271] Optionally, the radio frequency receiving signal is an echo signal formed when the radio frequency transmitting signal is emitted and / or scattered by the target, and the integrated circuit is a sensor chip.

[0272] Optionally, the integrated circuit may be a millimeter wave chip.

[0273] Optionally, the radio frequency receiving signal is an echo signal formed when the radio frequency transmitting signal is emitted and / or scattered by the target, and the integrated circuit is a sensor chip.

[0274] It is not difficult to find that this embodiment is a circuit embodiment corresponding to the method embodiment, and this embodiment can be implemented in conjunction with the method embodiment. The relevant technical details mentioned in the method embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the method embodiment.

[0275] In addition, in order to highlight the innovative part of the present application, the present embodiment does not introduce units that are not closely related to solving the technical problems proposed by the present application, but this does not mean that there are no other units in the present embodiment.

[0276] Another embodiment of the present application relates to a sensor, comprising: a carrier, an integrated circuit disposed on the carrier, and an antenna disposed on the carrier, or the antenna and the integrated circuit are integrated into a device disposed on the carrier. The integrated circuit is connected to the antenna and is used to process the echo signal received by the antenna. The integrated circuit is the integrated circuit provided in the aforementioned embodiment. The integrated circuit is the integrated circuit provided in any embodiment of the present application.

[0277] When the antenna and the integrated circuit are not integrated into one device, the integrated circuit is connected to the antenna through a first transmission line, and the first transmission line can be a printed circuit board (PCB) wiring. The carrier can be a printed circuit board PCB, such as a development board, a data acquisition board or a main board of a device, etc., which will not be described in detail here.

[0278] Since the structure and working principle of the integrated circuit included in the sensor have been described in detail in the above embodiments, they will not be described in detail here.

[0279] An embodiment of the present application provides a terminal device, which may include: a device body; and a sensor as described above, which is arranged on the device body; wherein the sensor is used for target detection and / or communication to provide reference information for the operation of the device body.

[0280] In some embodiments, the sensor may be disposed outside the device body, in other embodiments, the sensor may be disposed inside the device body, and in other embodiments, the sensor may be disposed partially inside the device body and partially outside the device body. The present application embodiment does not limit this, and it depends on the specific situation.

[0281] It should be noted that the sensor can realize functions such as target detection by transmitting and receiving radio signals to provide the device body with measurement information of the detected target, thereby assisting or even controlling the operation of the device body. Examples of the measurement information include: at least one of relative distance, relative speed, and relative angle.

[0282] In some embodiments, the device body may be a component or product used in fields such as transportation, consumer electronics, monitoring, in-cabin detection, and health care. For example, the device body may be intelligent transportation equipment (such as cars, motorcycles, ships, subways, trains, etc.), security equipment (such as cameras), liquid level / flow rate detection equipment, intelligent wearable devices (such as bracelets, glasses, etc.), smart home devices (such as sweeping robots, door locks, televisions, air conditioners, smart lights, etc.), various communication devices (such as mobile phones, tablet computers, etc.), and gates, intelligent traffic lights, intelligent signs, traffic cameras, and various industrialized robotic arms (or robots), etc. It may also be various instruments for detecting life characteristic parameters and various equipment equipped with the instruments, such as in-cabin detection of automobiles, indoor personnel monitoring, intelligent medical equipment, consumer electronic equipment, etc.

[0283] In some embodiments, when the above-mentioned device body is applied to an advanced driving assistance system (ADAS), the electromagnetic wave sensor as a vehicle-mounted sensor can provide the ADAS system with various functional safety guarantees such as automatic emergency braking (AEB), blind spot detection warning (Blind Spot Detection, BSD), lane change assist warning (Lane Changing Assist, LCA), and reversing assist warning (Rear Cross Traffic Alert, RCTA).

[0284] In addition, the examples mentioned in the above embodiments can be freely combined, and any combination can be understood as an embodiment. The "embodiment" or "example" appearing in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It can be understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0285] 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 first data; Generate second data according to the plurality of the first data; Detecting a first target according to the first data; A second target is detected according to the second data.

2. The target detection method according to claim 1, characterized in that: The second data includes i-type second data and j-type second data, where i and j are integers greater than 1; The step of generating second data according to the plurality of first data comprises: According to Ni pieces of the first data, obtain the i-type second data; Obtaining the j types of second data according to Nj pieces of the first data; Wherein, Ni and Nj are positive integers, and Nj>Ni; The detecting the second target according to the second data includes: Detecting the i-th type of second target according to the i-th type of second data; The j-th type of second target is detected according to the j-th type of second data.

3. The target detection method according to claim 1 or 2, characterized in that: The first data is channel data; The step of generating second data according to the plurality of first data comprises: The second data is obtained by superimposing a plurality of the first data.

4. The target detection method according to claim 1 or 2, characterized in that: The step of generating second data according to the plurality of first data comprises: Averaging a plurality of the first data to obtain zero Doppler channel data; The zero Doppler channel data is subtracted from a plurality of the first data and the resultant data are superimposed to obtain the second data.

5. The target detection method according to claim 1 or 2, characterized in that: The detecting the second target according to the second data includes: Performing a slow time dimension FFT according to the second data; A constant false alarm detection is performed according to the FFT result to achieve the detection of the second goal.

6. The target detection method according to claim 1 or 2, characterized in that: The first data is RD spectrum data; The step of generating second data according to the plurality of first data comprises: The second data is calculated based on the plurality of the first data.

7. The target detection method according to claim 1 or 2, characterized in that: The obtaining of the first data includes: Acquire echo data corresponding to a detection signal having a preset transmission period, wherein the preset transmission period is not less than 50 ms; The first data is generated according to the echo data.

8. The target detection method according to claim 7, characterized in that: The detection signal includes multiple chirp signals, and the detection signal with the preset transmission period is obtained by at least one of the following methods: increasing the number of chirp signals included in the detection signal, increasing the period of the chirp signal, and increasing the frame idle time between the chirp signals.

9. The target detection method according to claim 1 or 2, characterized in that: The target detection comprises: According to a preset threshold, constant false alarm detection is performed, wherein the threshold=k*noise estimation value, k>1.

10. A target detection method, characterized in that: include: According to the target detection results, several targets are obtained; For each detected target, corresponding feature information is obtained, where the feature information includes noise features and signal features used to characterize the target; The confidence level of each target is determined based on the feature information.

11. The target detection method according to claim 10, characterized in that: The obtaining of corresponding feature information includes: Obtain the angle spectrum generated during target detection; Determining the confidence level of each target according to the feature information includes: Determine a maximum value in the angle spectrum corresponding to the target; Determine a first validity parameter according to a maximum value among the maximum values ​​of the angle spectrum and an average value of other maximum values ​​except the maximum value, wherein the first validity parameter is used to characterize a relative size between the maximum value among the maximum values ​​of the angle spectrum and an average value of other maximum values ​​except the maximum value; The confidence level of each target is determined based on the first validity parameter.

12. The target detection method according to claim 11, characterized in that: Determining the confidence level of each target according to the first validity parameter includes: Determine a second validity parameter of the corresponding target according to the maximum value and the second maximum value among the maximum values ​​of the angle spectrum, wherein the second validity parameter is used to characterize the relative size between the maximum value and the second maximum value among the maximum values ​​of the angle spectrum; Determining whether the corresponding target is an invalid target according to the second validity parameter; In the case that the corresponding target is not an invalid target, the confidence of the target is determined according to the first validity parameter.

13. The target detection method according to claim 10, characterized in that: The obtaining of corresponding feature information includes: Get the entropy value of the corresponding target; Determining the confidence level of each target according to the feature information includes: According to the entropy value, the confidence of each target is determined.

14. The target detection method according to claim 10, characterized in that: The obtaining of corresponding feature information includes: Obtain the signal-to-noise ratio of the corresponding target generated by constant false alarm detection during the target detection process; Determining the confidence level of each target according to the feature information includes: According to the signal-to-noise ratio, the confidence level of each target is determined.

15. A target detection method, characterized in that: include: According to any one of claims 1 to 9, obtaining a first target and a second target; For each of the first target and the second target detected, obtain corresponding feature information, where the feature information includes a noise feature and a signal feature for characterizing the target; The confidence level of each of the first target and the second target is determined according to the feature information.

16. A target point cloud output method, characterized in that: include: According to the target detection method according to any one of claims 1 to 9 or the target detection method according to claim 15, acquiring a first target and a second target; The first target and the second target are output in a hierarchical manner.

17. An integrated circuit, characterized in that: It includes a radio frequency module, an analog signal processing module and a digital signal processing module connected in sequence; Wherein, the RF module is used to generate RF transmission signals and receive RF reception signals; The analog signal processing module is used to down-convert the RF received signal to obtain an intermediate frequency signal; The digital signal processing module is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain digital signal data, and obtain first data, generate second data based on multiple first data, detect a first target based on the first data, and detect a second target based on the second data; and / or, the digital signal processing module is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain digital signal data, and obtain several targets based on the target detection results; for each detected target, obtain corresponding feature information, the feature information includes noise features and signal features used to characterize the target; and determine the confidence of each target based on the feature information.

18. A sensor, characterized in that: include: Carrier; The integrated circuit as claimed in claim 17, disposed on a carrier; An antenna is arranged on the carrier, or the antenna and the integrated circuit are integrated into one device and arranged on the carrier; The integrated circuit is connected to the antenna and is used to process the echo signal received by the antenna.

19. A terminal device, characterized in that: include: Equipment body; and a sensor as claimed in claim 18 disposed on the device body; The sensor is used for target detection and / or communication to provide reference information for the operation of the device body.

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