Target detection method and device, radar and storage medium

Abstract

The invention discloses a target detection method and device, a radar and a storage medium. The method comprises the following steps: acquiring radar original data collected at the current moment, and performing distance processing on the original radar data to generate distance dimension radar data; obtaining target detection information which is detected at the last moment and meets a preset condition, and generating a target set based on a target corresponding to the target detection information; for each target in the target set, determining a long-time accumulation parameter according to the target detection information of the target, and performing long-time accumulation processing on the distance dimension radar data based on the long-time accumulation parameter to generate distance Doppler dimension data; and performing target detection based on the distance Doppler dimension data. According to the scheme, the long-time accumulation parameters are adaptively adjusted according to the target detection information meeting the preset conditions, the target signal-to-noise ratio is improved, meanwhile, the conditions of range migration and Doppler migration can be effectively avoided, and the target detection precision is further improved.

Description

Technical Field

[0001] The present invention relates to the field of radar detection technology, and in particular to a target detection method, device, radar and storage medium. Background Art

[0002] As the number of cars in my country continues to rise, the pressure on road traffic is increasing. While it brings convenience to residents' daily travel, it also poses huge challenges to modern traffic management. The rapid development of Internet of Things technology and artificial intelligence technology can provide potential solutions to traffic challenges, but no matter how intelligent the solution is, it requires front-end perception technology to provide massive traffic data to support advanced intelligent algorithms. Millimeter-wave radar, as a vehicle perception device that is available all day, all weather, with high detection distance and high sensitivity, can be used to detect the real-time movement trajectory of vehicles within hundreds of meters, thereby laying a solid data foundation for traffic management algorithms.

[0003] In the process of radar tracking vehicle trajectories, the signal-to-noise ratio of the echo signal received by the radar is the key to target detection. The higher the signal-to-noise ratio, the easier it is to detect the target, and the higher the accuracy of the target detection information obtained. Therefore, improving the signal-to-noise ratio of the radar echo signal can effectively improve the accuracy of target trajectory determination.

[0004] In the related technology, the signal-to-noise ratio of the radar echo signal can be improved by hardware or software. Among them, the hardware method will increase the cost, and the software method is mainly based on a long-term accumulation algorithm to achieve the coherent accumulation of the target, thereby maximizing the signal-to-noise ratio of the echo signal. However, in actual engineering applications, when the target moves at a fast speed or accelerates quickly, there are problems of range migration and Doppler migration, which causes the energy of the target echo signal to be dispersed, and effective accumulation cannot be achieved, which may also lead to an increase in the target distance and speed estimation errors. Summary of the invention

[0005] The present invention provides a target detection method, device, radar and storage medium, which can effectively avoid range migration and Doppler migration while improving the target signal-to-noise ratio, and further improve the target detection accuracy.

[0006] According to one aspect of the present invention, a target detection method is provided, the method comprising:

[0007] Acquire the radar raw data collected at the current moment, and perform distance processing on the raw radar data to generate distance-dimensional radar data;

[0008] Acquire target detection information that meets preset conditions detected at the previous moment, and generate a target set based on the targets corresponding to the target detection information;

[0009] For each target in the target set, determining a long-term accumulation parameter according to target detection information of the target, and performing long-term accumulation processing on the range-dimensional radar data based on the long-term accumulation parameter to generate range-Doppler-dimensional data;

[0010] Target detection is performed based on the range-Doppler data.

[0011] According to another aspect of the present invention, there is provided a target detection device, the device comprising:

[0012] A distance dimension radar data generation module is used to obtain the radar raw data collected at the current moment, and perform distance processing on the raw radar data to generate distance dimension radar data;

[0013] A target set determination module, used to obtain target detection information that meets preset conditions detected at the previous moment, and generate a target set based on the target corresponding to the target detection information;

[0014] a long-term accumulation parameter determination module, configured to determine a long-term accumulation parameter for each target in the target set according to target detection information of the target, and perform long-term accumulation processing on the range-dimensional radar data based on the long-term accumulation parameter to generate range-Doppler-dimensional data;

[0015] A target detection module is used to perform target detection based on the range Doppler dimension data.

[0016] According to another aspect of the present invention, there is provided a radar, the radar comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the target detection method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the target detection method described in any embodiment of the present invention when executed.

[0021] The target detection scheme provided by the embodiment of the present invention includes: obtaining the radar raw data collected at the current moment, and performing distance processing on the raw radar data to generate distance dimension radar data; obtaining the target detection information that meets the preset conditions detected at the previous moment, and generating a target set based on the target corresponding to the target detection information; for each target in the target set, determining a long-term accumulation parameter according to the target detection information of the target, and performing long-term accumulation processing on the distance dimension radar data based on the long-term accumulation parameter to generate distance Doppler dimension data; and performing target detection based on the distance Doppler dimension data. Through the technical scheme provided by the embodiment of the present invention, by adaptively adjusting the long-term accumulation parameter according to the target detection information that meets the preset conditions, while achieving the improvement of the target signal-to-noise ratio, it can not only effectively avoid the occurrence of distance migration and Doppler migration, but also further improve the target detection accuracy.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a target detection method provided according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a schematic diagram of a radar network provided by an embodiment of the present invention;

[0026] Figure 3 is a flow chart of a target detection method provided according to Embodiment 2 of the present invention;

[0027] Figure 4 is a schematic diagram of a target detection process provided by Embodiment 2 of the present invention;

[0028] Figure 5 is a schematic diagram of the structure of a target detection device provided according to Embodiment 3 of the present invention;

[0029] Figure 6 It is a schematic diagram of the structure of a radar for implementing the target detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1 A flow chart of a target detection method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of performing target detection based on radar. The method can be executed by a target detection device. The target detection device can be implemented in the form of hardware and / or software. The target detection device can be configured in a radar. Figure 1 As shown, the method includes:

[0034] S110, acquiring raw radar data collected at the current moment, and performing distance processing on the raw radar data to generate distance-dimensional radar data.

[0035] In an embodiment of the present invention, the radar obtains the radar raw data collected at the current moment. For example, the radar raw data collected at the current moment may be the kth frame data, and the radar raw data may be the analog-to-digital converter (ADC) data. Among them, the radar raw data can be understood as the echo signal after the radar transmits an electromagnetic wave signal to the target scene and the target in the target scene reflects the electromagnetic wave signal. For example, the radar raw data is the echo signal after the vehicle in the traffic scene reflects the electromagnetic wave signal after the radar transmits an electromagnetic wave signal to the traffic scene. The original radar data is processed for distance to generate distance-dimensional radar data. Among them, the distance processing of the original radar data can be understood as performing a fast Fourier transform (Fast Fourier Transform, FFT) of the distance dimension on the original radar data, that is, performing fast time dimension processing (that is, Range-FFT) on the original radar to generate distance-dimensional radar data.

[0036] S120: Acquire target detection information that meets preset conditions and is detected at the previous moment, and generate a target set based on targets corresponding to the target detection information.

[0037] Among them, the previous moment is the moment before the current moment. For example, the radar raw data collected at the current moment is the k-th frame echo information, then the target detection information that meets the preset conditions detected at the previous moment is the target detection based on the acquired k-1-th frame echo signal, and the target detection information that meets the preset conditions is obtained.

[0038] Exemplarily, the radar raw data acquired by the radar at the last moment is obtained, and the target detection is performed based on the radar raw data acquired at the last moment to obtain the first detection information, and the first detection information that meets the preset conditions is used as the target detection information. Wherein, the target detection based on the radar raw data acquired at the last moment may include: performing distance processing and speed processing on the radar raw data acquired at the last moment in sequence to obtain the range Doppler dimension data, and then generating a range Doppler dimension map (Range Doppler Map, RDM) based on the range Doppler dimension data, and performing constant false alarm rate detection (Constant False Alarm Rate, CFAR) based on the RDM, so as to obtain the first detection information. Wherein, the first detection information may include relevant information such as distance information, speed information, acceleration information and signal-to-noise ratio. Wherein, the signal-to-noise ratio is the ratio between the relative strength or power of the echo signal and the noise. Exemplarily, when the distance information in the first detection information is greater than the preset distance threshold, or the signal-to-noise ratio is less than the preset threshold, the first detection information is used as the target detection information that meets the preset conditions. The target corresponding to the target detection information constitutes a target set.

[0039] Optionally, the method is applied to a local radar, and the target detection information satisfying preset conditions detected at the previous moment is obtained, and a target set is generated based on the targets corresponding to the target detection information, including: obtaining the first target information detected by the local radar at the previous moment and the second target information detected by the networked radar of the local radar at the previous moment; determining the first target far away from the local radar according to the first target information, and determining the second target close to the local radar according to the second target information; and taking the third target in the first target whose signal-to-noise ratio is within the detection threshold range of the constant false alarm rate detection CFAR and the fourth target in the second target that is in the detection overlapping area of ​​the local radar and the networked radar as targets in the target set.

[0040] For example, in a traffic radar scenario, radar networking can be used to continuously track the trajectory of the target. Figure 2 A schematic diagram of a radar network provided by an embodiment of the present invention. Figure 2 As shown, the networking method is to install two radars at a certain distance along the road to detect the two directions of the hanging poles respectively. For the road between the two hanging poles, the networked radars can be used to implement full road tracking by using a relay tracking method.

[0041] Among them, a networked radar system can be formed based on various radars that perform trajectory tracking in a networking manner. The radar to be studied in the networked radar system is used as a local radar, and other radars in the networked radar system that perform target trajectory tracking in a networking manner with the local radar are used as networked radars. The local radar can be any radar in the networked radar system.

[0042] In an embodiment of the present invention, the first target information detected by the local radar at the last moment and the second target information detected by the networked radar of the local radar at the last moment are obtained. Exemplarily, the local radar obtains the first radar raw data collected at the last moment, and performs distance processing, speed processing and CFAR detection on the first radar raw data in sequence to obtain the first target information. At the same time, the networked radar of the local radar obtains the second radar raw data collected at the last moment, and performs distance processing, speed processing and CFAR detection on the second radar raw data in sequence to obtain the second target information. Then the networked radar transmits the second target information to the local radar, so that the local radar obtains the second target information collected by the networked radar at the last moment. Among them, the first target information and the second target information can both include relevant information such as distance information, speed information and acceleration information.

[0043] Determine the first target far from the local radar according to the first target information, for example, determine whether the corresponding target is a target far from the local radar according to the speed information in the first target information, wherein the direction of the speed information in the first target information is not directed to the local radar, determine the target corresponding to the first target information is a target far from the local radar. For another example, determine whether the corresponding target is a target far from the local radar according to the distance information in the first target information, wherein the distance in the first target information is greater than the distance corresponding to the moment before the last moment, determine the target corresponding to the first target information is a target far from the local radar. Determine the second target close to the local radar according to the second target information, for example, determine whether the corresponding target is a target close to the local radar according to the speed information in the second target information, wherein the direction of the speed information in the second target information points to the local radar, determine the target corresponding to the second target information is a target close to the local radar. For another example, determine whether the corresponding target is a target close to the local radar according to the distance information in the second target information, wherein the distance in the second target information is smaller than the distance corresponding to the moment before the last moment relative to the local radar, determine the target corresponding to the second target information is a target close to the local radar.

[0044] It is determined whether the signal-to-noise ratio of the first target is within the CFAR detection threshold range. If so, it means that the first target is far away from the local radar. When the local radar performs target detection on the raw radar data collected at the current moment, the first target may not be detected. Therefore, the third target in the first target whose signal-to-noise ratio is within the CFAR detection threshold range is taken as the target in the target set. Wherein, the threshold value of CFAR detection is P. When the signal-to-noise ratio is lower than the CFAR detection threshold value, the target is basically undetectable. Then the CFAR detection threshold range can be [P, P+δ], δ is the buffer signal-to-noise ratio, wherein the buffer signal-to-noise ratio can be any value between 6 and 8 dB. Since the second target is a target close to the local radar determined according to the second target information detected by the networked radar, when the second target is in the detection overlap area of ​​the local radar and the networked radar, it means that the second target is still far away from the local radar. When the local radar performs target detection on the raw radar data collected at the current moment, the second target may not be detected. Therefore, the fourth target in the second target that is in the detection overlap area of ​​the local radar and the networked radar is taken as the target in the target set.

[0045] S130. For each target in the target set, determine a long-term accumulation parameter according to the target detection information of the target, and perform long-term accumulation processing on the range-dimensional radar data based on the long-term accumulation parameter to generate range-Doppler-dimensional data.

[0046] Among them, the fundamental reason for the target to generate range migration is that when the target's moving speed is too large, the target's moving distance exceeds one distance unit during the accumulation time, resulting in the target's energy after accumulation being distributed in two or more distance units. The fundamental reason for the target to generate Doppler migration is that when the target's acceleration is too large, the target's speed change exceeds one Doppler unit during the accumulation time, resulting in the target's energy being distributed in multiple Doppler units. According to the above analysis, it can be seen that if it is assumed that the bandwidth of the electromagnetic wave signal emitted by the radar remains unchanged, the fundamental reason for the migration is that the target's speed is too large relative to the distance unit, or the target's acceleration is too long relative to the accumulation time. If the prior information of the target's speed or acceleration can be obtained, the accumulation time can be adaptively adjusted according to the prior information to avoid the occurrence of migration.

[0047] In an embodiment of the present invention, for each target in the target set, a long-term accumulation parameter is determined according to the target detection information of the target, and the distance dimension radar data corresponding to the current moment is subjected to long-term accumulation processing based on the long-term accumulation parameter to generate distance Doppler dimension data, that is, the distance dimension radar data corresponding to the current moment is subjected to fast Fourier transformation based on the long-term accumulation parameter, so as to convert the distance dimension radar data in the fast time domain into distance Doppler dimension data. It can be understood that the target in the target set is a target that is far away from the local radar and may not be detected by the local radar when performing target detection on the original radar data collected at the current moment. Therefore, the distance dimension radar data corresponding to the current moment can be subjected to long-term accumulation processing, which can not only effectively avoid the occurrence of migration phenomenon, but also ensure that each target in the target set can be detected at the current moment.

[0048] Optionally, the long-term accumulation parameter includes a target accumulation time; the target detection information includes target speed information and target acceleration information; for each target in the target set, the long-term accumulation parameter is determined according to the target detection information of the target, including: for each target in the target set, the target accumulation time is determined according to the target speed information and the target acceleration information. Exemplarily, for each target in the target set, the target accumulation time is determined according to the target speed information and the target acceleration information corresponding to the target, wherein the corresponding target accumulation time determined for different targets in the target set is different. It is understandable that the target accumulation time corresponding to each target in the target set can be adaptively determined according to the target speed information and the target acceleration information corresponding to each target in the target set. For example, if the target set includes target A, target B and target C, the target accumulation time corresponding to target A is determined according to the target speed information and the target acceleration information corresponding to target A; the target accumulation time corresponding to target B is determined according to the target speed information and the target acceleration information corresponding to target B; the target accumulation time corresponding to target C is determined according to the target speed information and the target acceleration information corresponding to target C.

[0049] Optionally, determining the target accumulation time according to the target speed information and the target acceleration information includes: determining a first accumulation time according to the target speed information and the radar distance resolution; determining a second accumulation time according to the target acceleration information and the wavelength of the electromagnetic wave signal; and using the minimum accumulation time between the first accumulation time and the second accumulation time as the target accumulation time. Optionally, determining the first accumulation time according to the target speed information and the radar distance resolution includes: determining the first accumulation time according to the following formula: Determining the second accumulation time according to the target acceleration information and the wavelength of the electromagnetic wave signal includes: determining the second accumulation time according to the following formula: Among them, T v represents the first accumulation time, R res represents the radar range resolution, v i represents the target speed information, T a represents the second accumulation time, a i represents the target acceleration information, and λ represents the wavelength of the electromagnetic wave signal. v With T a The minimum value of is taken as the target accumulation time. Among them, the radar range resolution R res It is used to characterize the radar's ability to resolve targets at different distances. The larger the radar distance resolution, the stronger the radar's ability to detect targets at longer distances.

[0050] Optionally, the long-term accumulation parameter also includes a distance unit range; the target detection information also includes target distance information; before determining the target accumulation time according to the target speed information and the target acceleration information, it also includes: determining the distance unit range according to the target distance information and the target speed information; performing long-term accumulation processing on the range-dimensional radar data based on the long-term accumulation parameter to generate range Doppler-dimensional data, including: performing long-term accumulation processing on the range-dimensional radar data within the range of the range unit based on the target accumulation time, and performing Doppler processing on the range-dimensional radar data outside the range of the range unit to generate range Doppler-dimensional data.

[0051] In the embodiment of the present invention, long-term accumulation will lead to an increase in the amount of calculation and memory, thereby causing a shortage of radar resources. In order to reduce the amount of calculation and the memory occupied, long-term accumulation processing can be performed only on targets that are far away from the local radar. Specifically, the range of the distance unit is determined according to the target distance information and the target speed information. Optionally, the range of the distance unit is determined according to the target distance information and the target speed information, including: determining the range of the distance unit according to the following formula: [ceil(r i +v i T-Δr),floor(r i +viT+Δr)]; where ceil means rounding down, floor means rounding up, and r i Represents the target distance information, v i represents the target speed information, Δr represents a fixed parameter, T represents the interval between the previous moment and the current moment, and the position change of the target within the interval is considered. Then, the first range dimension radar data within the range unit range and the second range dimension radar data outside the range unit range are determined from the range dimension radar data. The first range dimension radar data is subjected to long-term accumulation processing based on the target accumulation time, and the second range dimension radar data is subjected to Doppler processing to generate range Doppler dimension data.

[0052] S140: Perform target detection based on the range Doppler data.

[0053] Exemplarily, RDM can be generated based on range-Doppler dimension data, and then CFAR detection and direction of arrival (DOA) estimation are performed on the range-Doppler dimension data in the RDM, so as to obtain target detection information corresponding to the target point cloud detected at the current moment. Among them, CFAR detection can include range-dimensional CFAR detection and Doppler-dimensional CFAR detection, and the target detection information can include distance information, speed information, acceleration information, angle information, SNR information, bit mask and other information. Then, based on the target detection information corresponding to the target point cloud in the radar raw data detected at the current moment, the target point cloud can be clustered to determine the target corresponding to the current moment, so as to achieve target trajectory tracking.

[0054] Optionally, the target detection information detected at the current moment is analyzed. When it is determined that the signal-to-noise ratio of the target far away from the radar is gradually reduced to less than P+δ (where P is the CFAR detection threshold and δ is the buffer signal-to-noise ratio) based on the target detection information detected at the current moment, the target detection information such as the distance information, speed information and acceleration information of the target is transmitted to the distance processing output end of the radar raw data collected by the local radar at the next moment, so that when the local radar performs target detection on the radar data obtained at the next moment, the tracking time can be extended through long-term accumulation. Optionally, at the same time, it is also possible to judge whether the target distance of the target far away from the local radar is in the detection overlap area of ​​the local radar and the networked radar based on the target detection information detected at the current time. If so, the target detection information detected at the current moment can be transmitted to the networked radar through the communication system, so that the networked radar can more easily detect and track the target.

[0055] The target detection method provided by the embodiment of the present invention includes: obtaining the radar raw data collected at the current moment, and performing distance processing on the raw radar data to generate distance dimension radar data; obtaining the target detection information that meets the preset conditions detected at the previous moment, and generating a target set based on the target corresponding to the target detection information; for each target in the target set, determining a long-term accumulation parameter according to the target detection information of the target, and performing long-term accumulation processing on the distance dimension radar data based on the long-term accumulation parameter to generate distance Doppler dimension data; and performing target detection based on the distance Doppler dimension data. Through the technical solution provided by the embodiment of the present invention, by adaptively adjusting the long-term accumulation parameter according to the target detection information that meets the preset conditions, while achieving the improvement of the target signal-to-noise ratio, it not only effectively avoids the occurrence of distance migration and Doppler migration, but also further improves the target detection accuracy.

[0056] Embodiment 2

[0057] Figure 3A flowchart of a target detection method provided in Embodiment 2 of the present invention is provided. Figure 4 A schematic diagram of a target detection process provided in the second embodiment of the present invention can be based on Figure 4 right Figure 3 Understand the various steps in Figure 3 As shown, the method includes:

[0058] S310: Acquire the radar raw data collected at the current moment, and perform distance processing on the raw radar data to generate distance-dimensional radar data.

[0059] S320: Obtain first target information detected by the local radar at the last moment and second target information detected by the networking radar of the local radar at the last moment.

[0060] S330. Determine a first target far away from the local radar according to the first target information, and determine a second target close to the local radar according to the second target information.

[0061] S340, taking the third target in the first target whose signal-to-noise ratio is within the constant false alarm rate detection CFAR detection threshold range and the fourth target in the second target that is in the detection overlap area of ​​the local radar and the networked radar as targets in the target set.

[0062] S350: For each target in the target set, determine a distance unit range according to the target distance information and the target speed information.

[0063] Optionally, determining the distance unit range according to the target distance information and the target speed information includes: determining the distance unit range according to the following formula: [ceil(r i +v i T-Δr),floor(r i +v i T+Δr)]; where ceil means rounding down, floor means rounding up, and r i Indicates the target distance information, v i represents the target speed information, Δr represents a fixed parameter, and T represents the interval between the previous moment and the current moment.

[0064] S360: Determine a target accumulation time according to the target speed information and the target acceleration information.

[0065] Optionally, the target accumulation time is determined according to the target speed information and the target acceleration information, including: determining a first accumulation time according to the target speed information and the radar distance resolution; determining a second accumulation time according to the target acceleration information and the wavelength of the electromagnetic wave signal; and taking the minimum accumulation time between the first accumulation time and the second accumulation time as the target accumulation time.

[0066] Optionally, determining the first integration time according to the target speed information and the radar range resolution includes: determining the first integration time according to the following formula: Determining the second accumulation time according to the target acceleration information and the wavelength of the electromagnetic wave signal includes: determining the second accumulation time according to the following formula: Among them, T v represents the first accumulation time, R res represents the radar range resolution, v i Indicates the target speed information, T a represents the second accumulation time, a i represents the target acceleration information, and λ represents the wavelength of the electromagnetic wave signal.

[0067] S370: Perform long-term accumulation processing on the range-dimension radar data within the range unit range based on the target accumulation time, and perform Doppler processing on the range-dimension radar data outside the range unit range to generate range-Doppler-dimension data.

[0068] S380. Perform target detection based on range Doppler dimension data.

[0069] like Figure 4 As shown, in the process of tracking the target trajectory, when the signal-to-noise ratio of the detected target is less than the preset signal-to-noise ratio threshold, the range unit range is determined according to the target distance information and target speed information corresponding to the target; the range dimension radar data within the range unit range is accumulated for a long time based on the target accumulation time, and the range dimension radar data outside the range unit range is processed by conventional Doppler processing to generate range Doppler dimension data.

[0070] The target detection method provided by the embodiment of the present invention adaptively determines the range unit range and the target accumulation time according to the target detection information that meets the preset conditions, thereby performing long-term accumulation processing on the range-dimensional radar data within the range unit range based on the target accumulation time. While achieving the improvement of the target signal-to-noise ratio, it can not only effectively avoid the occurrence of range migration and Doppler migration, but also further improve the target detection accuracy, and at the same time can also reduce the radar calculation amount and the memory occupied.

[0071] Embodiment 3

[0072] Figure 5 This is a schematic diagram of the structure of a target detection device provided in Embodiment 3 of the present invention. Figure 5 As shown, the device comprises:

[0073] The range-dimensional radar data generating module 510 is used to obtain the radar raw data collected at the current moment, and perform distance processing on the raw radar data to generate range-dimensional radar data;

[0074] The target set determination module 520 is used to obtain target detection information that meets preset conditions detected at the previous moment, and generate a target set based on the target corresponding to the target detection information;

[0075] a long-term accumulation parameter determination module 530, configured to determine a long-term accumulation parameter for each target in the target set according to target detection information of the target, and perform long-term accumulation processing on the range-dimensional radar data based on the long-term accumulation parameter to generate range-Doppler-dimensional data;

[0076] The target detection module 540 is configured to perform target detection based on the range Doppler dimension data.

[0077] Optionally, the long-term accumulation parameter includes target accumulation time; the target detection information includes target speed information and target acceleration information;

[0078] The long-term accumulation parameter determination module comprises:

[0079] The long-time accumulation parameter determination unit is used to determine a target accumulation time for each target in the target set according to the target speed information and the target acceleration information.

[0080] The long-term accumulation parameter determination unit comprises:

[0081] A first accumulation time determination subunit, used to determine a first accumulation time according to the target speed information and the radar range resolution;

[0082] a second accumulation time determination subunit, configured to determine a second accumulation time according to the target acceleration information and the wavelength of the electromagnetic wave signal;

[0083] The target accumulation time determination subunit is configured to use the minimum accumulation time between the first accumulation time and the second accumulation time as the target accumulation time.

[0084] Optionally, the first accumulation time determination subunit is used to:

[0085] The first accumulation time is determined according to the following formula:

[0086]

[0087] The second accumulation time determination subunit is used for:

[0088] The second accumulation time is determined according to the following formula:

[0089]

[0090] Among them, T vrepresents the first accumulation time, R res represents the radar range resolution, v i represents the target speed information, T a represents the second accumulation time, a i represents the target acceleration information, and λ represents the wavelength of the electromagnetic wave signal.

[0091] Optionally, the long-term accumulation parameter further includes a distance unit range; the target detection information further includes target distance information;

[0092] The device also includes:

[0093] a distance unit range determination module, configured to determine a distance unit range according to the target distance information and the target speed information before determining a target accumulation time according to the target speed information and the target acceleration information;

[0094] The long-term accumulation parameter determination module is used to:

[0095] Based on the target accumulation time, long-term accumulation processing is performed on the range-dimension radar data within the range unit range, and Doppler processing is performed on the range-dimension radar data outside the range unit range to generate range-Doppler dimension data.

[0096] Optionally, the distance unit range determination module is used to:

[0097] The distance unit range is determined according to the following formula:

[0098] [ceil(r i +v i T-Δr),floor(r i +v i T+Δr)];

[0099] Among them, ceil means rounding down, floor means rounding up, and r i Represents the target distance information, v i represents the target speed information, Δr represents a fixed parameter, and T represents the interval between the previous moment and the current moment.

[0100] Optionally, the device is applied to a local radar, and the target set determination module is used to:

[0101] Acquire the first target information detected by the local radar at the last moment and the second target information detected by the networking radar of the local radar at the last moment;

[0102] Determine a first target far from the local radar according to the first target information, and determine a second target close to the local radar according to the second target information;

[0103] The third target in the first target whose signal-to-noise ratio is within the constant false alarm rate detection CFAR detection threshold range and the fourth target in the second target that is in the detection overlapping area of ​​the local radar and the networked radar are taken as targets in the target set.

[0104] The target detection device provided in the embodiment of the present invention can execute the target detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0105] Embodiment 4

[0106] Figure 6 A schematic diagram of a radar 10 that can be used to implement an embodiment of the present invention is shown. The radar is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The radar can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] like Figure 6 As shown, the radar 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the radar 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] A number of components in the radar 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the radar 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0109] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a target detection method.

[0110] In some embodiments, the target detection method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the radar 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the target detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the target detection method in any other appropriate manner (e.g., by means of firmware).

[0111] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0113] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0114] To provide interaction with a user, the systems and techniques described herein may be implemented on a radar having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which a user can provide input to the radar. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0115] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0116] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0117] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0118] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A target detection method, characterized in that: The method comprises: Acquire the radar raw data collected at the current moment, and perform distance processing on the raw radar data to generate distance-dimensional radar data; Acquire target detection information that meets preset conditions detected at the previous moment, and generate a target set based on the targets corresponding to the target detection information; For each target in the target set, determining a long-term accumulation parameter according to target detection information of the target, and performing long-term accumulation processing on the range-dimensional radar data based on the long-term accumulation parameter to generate range-Doppler-dimensional data; Target detection is performed based on the range-Doppler data.

2. The method according to claim 1, characterized in that The long-term accumulation parameter includes target accumulation time; the target detection information includes target speed information and target acceleration information; For each target in the target set, determining a long-term accumulation parameter according to target detection information of the target, including: For each target in the target set, a target accumulation time is determined according to the target speed information and the target acceleration information.

3. The method according to claim 2, characterized in that Determining a target accumulation time according to the target speed information and the target acceleration information includes: Determining a first accumulation time according to the target speed information and the radar range resolution; determining a second accumulation time according to the target acceleration information and the wavelength of the electromagnetic wave signal; The minimum accumulation time between the first accumulation time and the second accumulation time is used as the target accumulation time.

4. The method according to claim 3, characterized in that Determining a first accumulation time according to the target speed information and the radar range resolution includes: The first accumulation time is determined according to the following formula: Determining a second accumulation time according to the target acceleration information and the wavelength of the electromagnetic wave signal includes: The second accumulation time is determined according to the following formula: Among them, T v represents the first accumulation time, R res represents the radar range resolution, v i represents the target speed information, T a represents the second accumulation time, a i represents the target acceleration information, and λ represents the wavelength of the electromagnetic wave signal.

5. The method according to claim 2, characterized in that: The long-term accumulation parameters also include distance unit range; the target detection information also includes target distance information; Before determining the target accumulation time according to the target speed information and the target acceleration information, the method further includes: Determining a range unit range according to the target distance information and the target speed information; The method further comprises: performing a long-term accumulation process on the range-dimensional radar data based on the long-term accumulation parameter to generate range-Doppler-dimensional data, including: Based on the target accumulation time, long-term accumulation processing is performed on the range-dimension radar data within the range unit range, and Doppler processing is performed on the range-dimension radar data outside the range unit range to generate range-Doppler dimension data.

6. The method according to claim 5, characterized in that Determining a range unit range according to the target distance information and the target speed information includes: The distance unit range is determined according to the following formula: [ceil(r i +v i T-Δr),floor(r i +v i T+Δr)]; Among them, ceil means rounding down, floor means rounding up, and r i Represents the target distance information, v i represents the target speed information, Δr represents a fixed parameter, and T represents the interval between the previous moment and the current moment.

7. The method according to claim 1, characterized in that Applied to a local radar, the step of acquiring target detection information that meets a preset condition detected at a previous moment and generating a target set based on the target corresponding to the target detection information includes: Acquire the first target information detected by the local radar at the last moment and the second target information detected by the networking radar of the local radar at the last moment; Determine a first target far from the local radar according to the first target information, and determine a second target close to the local radar according to the second target information; The third target in the first target whose signal-to-noise ratio is within the constant false alarm rate detection CFAR detection threshold range and the fourth target in the second target that is in the detection overlapping area of ​​the local radar and the networked radar are taken as targets in the target set.

8. A target detection device, characterized in that: The device comprises: A distance dimension radar data generation module is used to obtain the radar raw data collected at the current moment, and perform distance processing on the raw radar data to generate distance dimension radar data; A target set determination module, used to obtain target detection information that meets preset conditions detected at the previous moment, and generate a target set based on the target corresponding to the target detection information; a long-term accumulation parameter determination module, configured to determine a long-term accumulation parameter for each target in the target set according to target detection information of the target, and perform long-term accumulation processing on the range-dimensional radar data based on the long-term accumulation parameter to generate range-Doppler-dimensional data; A target detection module is used to perform target detection based on the range Doppler dimension data.

9. A radar, characterized in that: The radar comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the target detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the target detection method according to any one of claims 1 to 7 when executed.

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