Target state detection method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of signal processing, and provides a target state detection method and device, electronic equipment and a storage medium, and the method comprises the steps: converting distance-slow time domain-antenna domain data into distance-slow time domain-angle domain data, and carrying out the detection of a target state through a distance unit and an angle unit of a target in a suspected quasi-static state, according to the method, dimension reduction of distance-slow time domain-angle domain data can be realized, quasi-static detection of the target can be realized after continuous multiple frames are accumulated, the data volume required during quasi-static detection is reduced, and the influence of the data volume required during quasi-static detection on the data volume required during dynamic detection of the target is further reduced. According to the method, on the premise that the dynamic target detection performance is basically not affected, continuous detection of the quasi-static target can be achieved, and simultaneous detection of the dynamic target and the quasi-static target can be achieved.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a target state detection method, apparatus, electronic device, and storage medium. Background Technology

[0002] Currently, radio sensor technology has permeated numerous fields, including but not limited to target detection and tracking, in-vehicle sensing, and gesture sensing. In these fields, radio sensors collect and process large amounts of data in real time, providing valuable real-time information for each industry. However, because radio sensors typically need to operate under miniaturized and low-power conditions, their built-in task data storage memory is relatively limited. For example, radar has a fixed size of task data storage memory.

[0003] Typically, when radio sensors are used for target detection and tracking, they are used to track dynamic targets. To simultaneously track dynamic targets and detect static targets, parallel processing methods are often employed. This involves processing electromagnetic wave data of a conventional dynamic human body and electromagnetic wave data of a micro-moving human body accumulated over a long period, performing dynamic processing and micro-movement processing separately within a single frame processing cycle. The introduction of electromagnetic wave data from micro-moving human bodies significantly reduces the amount of data compared to conventional dynamic human body data, thus degrading the dynamic processing performance of the radio sensor. This includes a decrease in signal-to-noise ratio, and a reduction in the upper limit of at least one of the following performance characteristics: ranging range, unambiguous velocity, distance resolution, and velocity resolution. Summary of the Invention

[0004] This invention provides a target state detection method, apparatus, electronic device, and storage medium to address the deficiencies in the prior art.

[0005] This invention provides a target state detection method, comprising:

[0006] Acquire distance-slow time-antenna domain data of the detection area of ​​the radio sensor;

[0007] The range-slow time-antenna domain data is converted into range-slow time-angle domain data, and the target within the detection area is tracked based on the range-slow time-angle domain data to obtain the dynamic detection result of the target;

[0008] Based on the dynamic detection results, the target is determined to be a suspected quasi-static target in the current frame of the slow time domain. Using the distance unit and angle unit where the target is located in the current frame, the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame is extracted.

[0009] Based on the first slow time domain data in the consecutive frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data, the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit is obtained.

[0010] According to a target state detection method provided by the present invention, the method further includes, after acquiring the distance-slow time-antenna domain data of the detection area of ​​the radio sensor:

[0011] The range-slow time-antenna domain data is stored in the task data storage memory of the radio sensor;

[0012] The step involves using the distance and angle units where the target is located in the current frame to extract the first slow-time domain data of the distance-slow-time domain-angle domain data in the next frame of the current frame, followed by:

[0013] The first slow time domain data in the next frame is stored in a queue, and the queue is used to store the first slow time domain data in the consecutive frames.

[0014] The queue is pre-configured in the task data storage memory of the radio sensor, or in the non-task data storage memory of the radio sensor.

[0015] According to a target state detection method provided by the present invention, the method further includes tracking the target within the detection area based on the distance-slow time-domain-angle domain data to obtain the dynamic detection result of the target, and then further includes:

[0016] Based on the dynamic detection results, if it is determined that the target is not a suspected quasi-static target in the current frame of the slow time domain, and the queue is not empty, then the queue is cleared.

[0017] According to a target state detection method provided by the present invention, determining that the target is suspected to be quasi-static in the current frame in the slow time domain based on the dynamic detection result includes:

[0018] If the dynamic detection result is that the tracking trajectory of the target disappears in the current frame, then the target is determined to be a suspected quasi-static target in the current frame.

[0019] According to a target state detection method provided by the present invention, the step of tracking a target within a detection area based on the distance-slow time-domain-angle domain data to obtain a dynamic detection result of the target specifically includes:

[0020] Target detection is performed on each frame of the slow time domain in the distance-slow time domain data to obtain the distance-angle data of each detection point on the target.

[0021] Based on the distance-angle data, second slow time domain data is extracted from the distance-slow time domain-angle domain data;

[0022] Doppler processing is performed on the second slow time-domain data to determine the target's velocity;

[0023] Based on the distance-angle data and the movement speed, the target is tracked to obtain the dynamic detection result.

[0024] According to a target state detection method provided by the present invention, the step of obtaining a quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit based on the first slow time domain data in a series of consecutive frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data specifically includes:

[0025] Static clutter filtering is performed on the first slow time-domain data in the consecutive frames to obtain the filtering result;

[0026] The filtered results are subjected to target detection to determine the quasi-static detection result of the target in the unit region.

[0027] According to a target state detection method provided by the present invention, the first slow time domain data in the next frame includes the distance-slow time domain-angle domain data of the first preset number of slow time domain data in the unit region of the next frame;

[0028] The preset quantity is determined based on the number of sampling points in each frame in the slow time domain and the number of frames in the consecutive frames.

[0029] The present invention also provides a target motion state detection device, comprising:

[0030] The data acquisition module is used to acquire distance-slow time-antenna domain data of the detection area of ​​the radio sensor;

[0031] The first state detection module is used to convert the range-slow time domain-antenna domain data into range-slow time domain-angle domain data, and track the target within the detection area based on the range-slow time domain-angle domain data to obtain the dynamic detection result of the target;

[0032] The data dimensionality reduction module is used to determine, based on the dynamic detection results, whether the target is suspected to be quasi-static in the current frame of the slow time domain, and to extract the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame using the distance unit and angle unit where the target is located in the current frame.

[0033] The second state detection module is used to obtain the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit based on the first slow time domain data in the consecutive multiple frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the target state detection method as described above.

[0035] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the target state detection method as described above.

[0036] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the target state detection method as described above.

[0037] This invention provides a target state detection method, apparatus, electronic device, and storage medium. The method converts range-slow time-antenna domain data into range-slow time-angle domain data. By utilizing the range and angle units where the target is located in a quasi-static state, it achieves dimensionality reduction of the range-slow time-angle domain data. After accumulating multiple consecutive frames, it can achieve quasi-static target detection, reducing the amount of data required for quasi-static detection and thus mitigating the impact of the data required for dynamic target detection. This method can achieve continuous detection of quasi-static targets while maintaining minimal impact on dynamic target detection performance, enabling simultaneous detection of both dynamic and quasi-static targets. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on the drawings described below without creative effort.

[0039] Figure 1 This is one of the flowcharts of the target state detection method provided by the present invention;

[0040] Figure 2 This is a schematic diagram of the structure of the first slow time domain data of the distance-slow time domain-angle domain data in the next frame in the target state detection method provided by the present invention;

[0041] Figure 3This is a schematic diagram of the queue structure in the target state detection method provided by the present invention;

[0042] Figure 4 This is the second flowchart of the target state detection method provided by the present invention;

[0043] Figure 5 This is a schematic diagram of the target state detection device provided by the present invention;

[0044] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0046] The terms "first" and "second" in the specification and claims of this invention may explicitly or implicitly include one or more of those features. In the description of the invention, unless otherwise stated, "a plurality of" means two or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0047] The development of civilian millimeter-wave radar increasingly relies on mature millimeter-wave radar chips. To save costs, the internal resources of these chips are usually limited, such as the number of transceiver channels, memory, and computing power. The development of millimeter-wave radar is largely a process of fulfilling business requirements under the constraints of chip resources. Regarding memory, the largest block of memory in a millimeter-wave radar chip is used to store the radar data cube after range processing only. The size of this memory limits the overall upper limit of the millimeter-wave radar's performance in terms of signal-to-noise ratio, ranging range, unambiguous velocity, range resolution, and velocity resolution, making it a key issue to consider during radar system design.

[0048] One application of commercial millimeter-wave radar is indoor human detection and tracking, which can be expanded into various practical applications such as presence detection, headcount, and fall detection. After receiving the signal, the radar needs to process the signal to obtain a point cloud, and then process the data to obtain the tracking trajectory. Millimeter-wave radar can track moving humans very well. However, it is difficult to detect static humans, and the difficulty in detecting static humans has always been a major challenge for radar technology. To solve this problem, existing technologies often use long-term accumulation methods, that is, storing raw data for a longer period of time for processing. Therefore, in order to simultaneously track moving humans and detect static human point clouds, a common method is a parallel processing method. The task data storage memory of the millimeter-wave radar is divided into two parts: one for storing electromagnetic wave data of conventional moving humans and the other for storing electromagnetic wave data of micro-moving humans for long-term accumulation. Dynamic processing and micro-movement processing are performed separately within one frame processing cycle.

[0049] The disadvantage of the above approach is that, since the mission data storage memory of the millimeter-wave radar is divided into two, the amount of electromagnetic wave data of the conventional dynamic human body that can be stored is greatly reduced, which leads to a decrease in the dynamic processing performance of the millimeter-wave radar. This includes a decrease in the signal-to-noise ratio, and a decrease in the upper limit of at least one of the following performance characteristics: ranging range, unambiguous velocity, range resolution, and velocity resolution.

[0050] Based on this, in order to solve the above-mentioned technical problems, this embodiment of the invention provides a target state detection method.

[0051] Figure 1 This is a flowchart illustrating a target state detection method provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes:

[0052] S1, acquire distance-slow time-antenna domain data of the detection area of ​​the radio sensor;

[0053] S2, convert the range-slow time-antenna domain data into range-slow time-angle domain data, and track the target within the detection area based on the range-slow time-angle domain data to obtain the dynamic detection result of the target;

[0054] S3, based on the dynamic detection results, determine that the target is suspected to be quasi-static in the current frame of the slow time domain, and use the distance unit and angle unit where the target is located in the current frame to extract the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame;

[0055] S4. Based on the first slow time domain data in the consecutive frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data, the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit is obtained.

[0056] Specifically, the target state detection method provided in this embodiment of the invention can be executed by a radio sensor or a third-party device communicatively connected to the radio sensor, without specific limitations. The radio sensor may have a built-in radio sensor chip for executing the target state detection method. The radio sensor can be a radar or other type of radio sensor capable of receiving and processing electromagnetic wave signals reflected from the target. The radar can be millimeter-wave radar, lidar, pulse radar, etc., without specific limitations.

[0057] First, step S1 is executed to acquire the distance-slow time-antenna domain data of the detection area of ​​the radio sensor. The radio sensor can transmit electromagnetic wave signals into the detection area via a transmitting antenna and receive echo signals reflected from reflectors within the detection area via a receiving antenna. This echo signal can be fast time-slow time-antenna domain data.

[0058] A radio sensor transmits periodic pulse sequences, for example, 50 pulses per frame, forming a pulse sequence. The echo signals of each pulse sequence are stored row by row; for example, the echo signal of the first pulse is placed in the first row, the echo signal of the second pulse in the second row, and so on. The dimension along the row direction is defined as the fast time domain. Since the data sampling interval between rows is often greater than the pulse duration, the dimension along the column direction is defined as the slow time domain. The antenna domain refers to the dimension of the receiving antenna of the radio sensor.

[0059] The echo energy of a target within the detection area of ​​a radio sensor is dispersed in three domains: the fast time domain, the slow time domain, and the antenna domain. As a result, the signal-to-noise ratio is very low, making target detection impossible.

[0060] To improve the signal-to-noise ratio, the range-slow time-antenna domain data of the detection area is obtained by performing a Fast Fourier Transform (FFT) along the fast time domain on the fast time domain data-slow time-antenna domain data.

[0061] Since range-slow time-antenna domain data contains static clutter generated by reflections from fixed objects such as the ground and sea surface, static clutter in range-slow time-antenna domain data can be filtered out using static clutter filtering algorithms, zero-velocity channel nulling methods, and moving target display, thus providing higher quality data for subsequent target state detection.

[0062] Then, step S2 is executed, which involves performing FFT processing on the range-slow time-antenna domain data along the antenna domain to obtain the range-slow time-angle domain data of the detection area. This process can be implemented using a two-dimensional beamforming method, which is not specifically limited here.

[0063] In the obtained range-slow time-angle domain data, each frame in the slow time domain contains the distribution of the energy of the current frame in the three-dimensional space of range-azimuth-pitch angle.

[0064] By utilizing distance-slow time-angle domain data, targets within a detection area can be tracked, yielding dynamic target detection results. Specifically, target tracking can begin by detecting targets within the detection area using target detection methods, followed by dynamic tracking. Target detection methods employed can include the Constant False Alarm Rate (CFAR) algorithm and deep learning-based target detection methods.

[0065] Deep learning-based object detection methods can include the R-CNN series, SSD algorithm, YOLO series, etc.

[0066] The target can include human beings, animals, etc., and the dynamics can include large-scale movements such as walking, moving, and running. The dynamic detection results of the target can include information such as the target's speed, angle of movement, and distance between the target and the radio sensor.

[0067] Next, step S3 is executed. For the current frame in the slow time domain, the dynamic detection results of the target in the current frame can be used to determine whether the target is suspected to be quasi-static in the current frame. Quasi-static refers to non-dynamic states, which can include stationary states and slightly moving states. Stationary states refer to states where the target is completely still, such as sleeping or not moving at all; slightly moving states refer to states with small amplitude movements, such as raising a hand or shaking a head. Suspected quasi-static refers to the preliminary judgment result based on the target's dynamic detection results, indicating whether the target is in a stationary or slightly moving state.

[0068] For example, if the target's motion speed is 0 in a frame in the slow time domain, or within the neighborhood of 0 (i.e., the motion speed is 0 or close to 0), then the target can be determined to be quasi-static in that frame in the slow time domain, and further long-term accumulation is needed for tracking confirmation.

[0069] If the target is suspected to be quasi-static in the current frame of the slow time domain, the range cell and angle cell in which the target is located in the current frame can be used to extract the first slow time domain data of the range-slow time domain-angle domain data in the next frame of the current frame.

[0070] Each distance cell corresponds to a unit distance range in which the signal propagates between the radio sensor and the target. Each distance cell can be represented as [Rleft:Rright], where Rleft represents the lower limit of the distance value of the distance cell and Rright represents the upper limit of the distance value of the distance cell.

[0071] Each angle unit corresponds to a unit angle range in which the signal propagates between the radio sensor and the target. Each angle unit can be represented as [Bleft:Bright], where Bleft represents the lower limit of the angle value of the angle unit and Bright represents the upper limit of the angle value of the angle unit.

[0072] The distance unit of the target in the current frame can be determined by the distance information of each detection point in the target in the current frame, and the angle unit of the target in the current frame can be determined by the angle information of each detection point in the target in the current frame.

[0073] The target's location within the current frame is determined by defining a cell region comprised of range and angle cells. Within this cell region, the first slow-time domain data of the range-slow-time domain data in the next frame is extracted. This first slow-time domain data can be multiple slow-time domain data points located within the cell region in the next frame. The number of these slow-time domain data points can be set as needed and is not specifically limited here.

[0074] Since the actual targets are all located within the unit area of ​​the detection zone, the energy of the targets is concentrated in the range-angle dimension, which can greatly improve the signal-to-noise ratio.

[0075] Finally, step S4 is executed. If the first slow temporal data from multiple consecutive frames can be extracted through step S3, then this first slow temporal data can be used as quasi-static data accumulated over a long period. By performing target detection on the first slow temporal data from multiple consecutive frames, quasi-static detection results of the target in the cell region composed of the aforementioned distance and angle cells can be obtained. This target detection process can be implemented by processing the first slow temporal data from multiple consecutive frames using the CFAR detection algorithm.

[0076] If the presence of a target is determined by performing target detection on the first slow time-domain data in multiple consecutive frames, then the target can be further determined to be quasi-static in the aforementioned unit region, i.e., in a stationary or slightly moving state.

[0077] This invention provides a target state detection method that converts range-slow time-antenna domain data into range-slow time-angle domain data. By utilizing the range and angle cells where the target is located in a quasi-static state, dimensionality reduction of the range-slow time-angle domain data is achieved. After accumulating multiple consecutive frames, quasi-static target detection can be realized, reducing the amount of data required for quasi-static detection and thus mitigating the impact of the data required for quasi-static detection on the data required for dynamic target detection. This method can achieve continuous detection of quasi-static targets while maintaining minimal impact on dynamic target detection performance, enabling simultaneous detection of both dynamic and quasi-static targets.

[0078] Based on the above embodiments, the step of acquiring distance-slow time-antenna domain data of the detection area of ​​the radio sensor further includes:

[0079] The range-slow time-antenna domain data is stored in the task data storage memory of the radio sensor;

[0080] The step involves using the distance and angle units where the target is located in the current frame to extract the first slow-time domain data of the distance-slow-time domain-angle domain data in the next frame of the current frame, followed by:

[0081] The first slow time domain data in the next frame is stored in a queue, and the queue is used to store the first slow time domain data in the consecutive frames.

[0082] The queue is pre-configured in the task data storage memory of the radio sensor, or in the non-task data storage memory of the radio sensor.

[0083] Specifically, after acquiring the range-slow time-antenna domain data of the detection area of ​​the radio sensor, static clutter filtering can be performed on the range-slow time-antenna domain data to filter out static clutter generated by reflections from fixed objects such as the ground and buildings within the detection area. Static clutter filtering can be achieved through static clutter filtering algorithms, zero-velocity channel nulling methods, moving target indication, etc., which will not be elaborated upon here.

[0084] Subsequently, the range-slow time-antenna domain data can be stored in the mission data storage memory in radar data cube format. This mission data storage memory is the memory space configured in the radio sensor for storing the data required for the target state detection mission.

[0085] Furthermore, after extracting the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame, the first slow time domain data in the next frame can be stored in a queue. This queue has first-in-first-out semantics. This queue can be used to store the first slow time domain data in multiple consecutive frames in step S4. The first slow time domain data in multiple consecutive frames can be stored in the queue sequentially according to the frame order.

[0086] This queue can be pre-configured in the task data storage memory of the radio sensor, or in the non-task data storage memory of the radio sensor. The non-task data storage memory refers to the memory space used to store the parameters, configuration information, and other content of the radio sensor.

[0087] If the queue is pre-configured in the task data storage memory of the radio sensor, then all the space in the original task data storage memory can be used to store range-slow time-antenna domain data. Instead, most of the space is used to store range-slow time-antenna domain data, and a small portion of the space is used to configure the queue to store the first slow time-domain data. As a result, the amount of range-slow time-antenna domain data that can be stored in the task data storage memory will be slightly reduced, which has a minimal impact on the dynamic target detection performance of the radio sensor.

[0088] If the queue is pre-configured in the non-task data storage memory of the radio sensor, then the free space in the non-task data storage memory needs to satisfy the maximum memory required for quasi-static data, that is, the free space is greater than or equal to the maximum memory required for quasi-static data.

[0089] The maximum memory required for this quasi-static data can be determined based on the maximum number of targets tracked by the radio sensor, the number of range cells within the detection area, and the number of angle cells.

[0090] With the queue pre-configured in the non-task data storage memory of the radio sensor, the task data storage memory originally used to store range-slow time-antenna domain data will not be occupied by quasi-static data, and will not lead to a reduction in the amount of range-slow time-antenna domain data, thus having no impact on the dynamic target detection performance of the radio sensor.

[0091] Based on the above embodiments, the step of tracking the target within the detection area based on the distance-slow time-domain-angle domain data to obtain the dynamic detection result of the target further includes:

[0092] Based on the dynamic detection results, if it is determined that the target is not a suspected quasi-static target in the current frame of the slow time domain, and the queue is not empty, then the queue is cleared.

[0093] Specifically, after obtaining the dynamic detection results of the target, it is necessary to determine frame by frame whether the target is suspected to be quasi-static in each frame of the slow time domain.

[0094] If the target is suspected to be quasi-static in the current frame of the slow time domain, the first slow time domain data of the range-slow time domain-angle domain data in the next frame can be extracted by using the range cell and angle cell where the target is located in the current frame.

[0095] If the target is not suspected to be quasi-static in the current frame of the slow time domain, that is, if the target is not suspected to be quasi-static in the current frame of the slow time domain, then the target can be considered to be in motion. By tracking the target, the motion detection has been completed, and it is not necessary to extract the first slow time domain data in multiple consecutive frames as quasi-static data.

[0096] Therefore, it is unnecessary to store the first slow temporal data from multiple consecutive frames into the queue. Furthermore, if the queue is empty at this time, no processing is performed. If the queue is not empty, it indicates that the target was suspected to be quasi-static in the previous frame, and the first slow temporal data for the current frame has been calculated and stored in the queue. That is, the queue now stores the target's first slow temporal data for the current frame. Since the target is now dynamic in the current frame, the data stored in the queue cannot be used for the next quasi-static detection of the target. Therefore, the queue can be cleared, or the data stored in the current queue can be replaced with the data to be stored in the next frame. This ensures the accuracy of the data stored in the queue.

[0097] Based on the above embodiments, determining that the target is a suspected quasi-static target in the current frame in the slow time domain based on the dynamic detection results includes:

[0098] If the dynamic detection result is that the tracking trajectory of the target disappears in the current frame, then the target is determined to be a suspected quasi-static target in the current frame.

[0099] Specifically, since the dynamic detection results can also include the target's tracking trajectory, which can be determined by the distance between the target and the radio sensor and the target's motion angle in each frame, when using the dynamic detection results to determine whether the target is suspected to be quasi-static in the current frame in the slow time domain, the presence or absence of the target's tracking trajectory in the current frame can be used to determine whether the target is suspected to be quasi-static in the current frame.

[0100] If the distance between the target and the radio sensor, as well as the target's motion angle, are both empty in the current frame, or if the difference between the distance between the target and the radio sensor in the current frame and the distance in the previous frame is within a first neighborhood of 0, and the difference between the target's motion angle in the current frame and the motion angle in the previous frame is within a second neighborhood of 0, it can be concluded that the target's tracking trajectory has disappeared in the current frame, i.e., there is no tracking trajectory. Therefore, the target can be determined to be quasi-static in this current frame. The first and second neighborhood ranges are both small ranges and can be set as needed; no specific limitations are imposed here.

[0101] This method makes a more intuitive judgment by determining whether the tracking trajectory has disappeared and whether the target is a suspected quasi-static target in the current frame.

[0102] Based on the above embodiments, the step of tracking the target within the detection area based on the distance-slow time-domain-angle domain data to obtain the dynamic detection result of the target specifically includes:

[0103] Target detection is performed on each frame of the slow time domain in the distance-slow time domain data to obtain the distance-angle data of each detection point on the target.

[0104] Based on the distance-angle data, second slow time domain data is extracted from the distance-slow time domain-angle domain data;

[0105] Doppler processing is performed on the second slow time-domain data to determine the target's velocity;

[0106] Based on the distance-angle data and the movement speed, the target is tracked to obtain the dynamic detection result.

[0107] Specifically, when tracking a target within the detection area, target detection can be performed on each frame of the slow time domain in the range-slow time domain data to obtain the range-angle data of each detection point on the target. Here, each frame of the slow time domain in the range-slow time domain data is a range-angle map. Target detection algorithms such as CFAR can be used to perform target detection on each frame of the slow time domain data to obtain the range-angle data of each detection point on the target. Here, each detection point on the target refers to the detection point position formed on the target by the target detection algorithm during target detection.

[0108] Subsequently, using the distance-angle data of each detection point on the target, a second slow time domain data is extracted from the distance-slow time domain-angle domain data. This second slow time domain data is the slow time domain data corresponding to the distance-angle data of each detection point on the target within the distance-slow time domain-angle domain data.

[0109] Subsequently, Doppler processing can be performed on the second slow-time domain data to determine the target's velocity. By combining the range-angle data and the velocity, the target can be tracked, yielding dynamic detection results.

[0110] Based on the above embodiments, the step of obtaining the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit based on the first slow time domain data in the range-slow time domain-angle domain data corresponding to the distance unit and the angle unit in a series of consecutive frames specifically includes:

[0111] Static clutter filtering is performed on the first slow time-domain data in the consecutive frames to obtain the filtering result;

[0112] The filtered results are subjected to target detection to obtain the quasi-static detection results of the target in the unit region.

[0113] Specifically, in this embodiment of the invention, by performing static clutter filtering on the first slow time-domain data in multiple consecutive frames, the influence of clutter between consecutive frames can be avoided. Furthermore, by performing target detection on the filtering results, the obtained quasi-static detection results can be made more accurate.

[0114] Based on the above embodiments, the first slow time domain data in the next frame includes the distance-slow time domain-angle domain data in the unit region of the next frame, which is the first preset number of slow time domain data.

[0115] The preset quantity is determined based on the number of sampling points in each frame in the slow time domain and the number of frames in the consecutive frames.

[0116] Specifically, in this embodiment of the invention, the first slow time-domain data of the distance-slow time-domain-angle domain data in the next frame includes the first preset number of slow time-domain data in the unit region of the distance-slow time-domain-angle domain data in the next frame. Each slow time-domain data in the unit region can be the echo signal of each pulse transmitted sequentially by the radio sensor in the corresponding frame. For example, the first slow time-domain data of the distance-slow time-domain-angle domain data in the unit region of the i-th frame can be the echo signal of the first pulse transmitted by the radio sensor in the i-th frame, the second slow time-domain data can be the echo signal of the second pulse transmitted by the radio sensor in the i-th frame, and so on, thus obtaining the first preset number of slow time-domain data of the distance-slow time-domain-angle domain data in the unit region of the next frame.

[0117] This preset number can be set as needed, for example, by the number of sampling points in each frame in the slow time domain and the number of frames in multiple consecutive frames.

[0118] This preset quantity can be represented as M. Since the number of sampling points in each frame in the slow time domain is the same and can all be represented as Q, the preset quantity can be expressed as:

[0119] M = Q / K.

[0120] Where K is the number of frames in a series of consecutive frames, which can be selected based on experience. It can be greater than or equal to 2, such as 3, 4, 6, etc.

[0121] A schematic diagram of the structure of the first slow-time domain data in the next frame of the distance-slow-time domain-angle domain data can be shown as follows: Figure 2 As shown, the distance unit of the target in the current frame is represented as [Rleft:Rright], where Rleft represents the lower limit of the distance value of the distance unit, and Rright represents the upper limit of the distance value of the distance unit. The angle unit of the target in the current frame is represented as [Rleft:Rright], where Bleft represents the lower limit of the angle value of the angle unit, and Bright represents the upper limit of the angle value of the angle unit. M is the number of slow time domain data points in the first slow time domain data of the distance-slow time domain data in the next frame. Figure 2 The value of M is 4.

[0122] Queue structure as follows Figure 3 As shown, it contains the first slow time domain data of distance-slow time domain-angle domain data in K consecutive frames.

[0123] Based on the above embodiments, the step of obtaining the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit based on the first slow time domain data in the range-slow time domain-angle domain data corresponding to the distance unit and the angle unit in a series of consecutive frames, and then includes:

[0124] By fusing the dynamic detection results of the target with the quasi-static detection results, continuous detection of the target can be achieved, regardless of whether the target is dynamic or quasi-static.

[0125] Based on the above embodiments, the step of using the distance unit and angle unit where the target is located in the current frame to extract the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame specifically includes:

[0126] The distance and angle units of the target in the current frame are fed back to the data selection module, and the data selection module is called to extract the first slow time domain data in the next frame.

[0127] Specifically, in this embodiment of the invention, a data selection module is introduced to extract the first slow time domain data of the distance-slow time domain-angle domain data in the next frame.

[0128] Here, if the target is suspected to be quasi-static in the current frame in the slow time domain, the range cell and angle cell where the target is in the current frame are fed back to the data selection module.

[0129] After receiving the distance and angle units, the data selection module uses the received distance and angle units to extract the first slow time domain data of the next frame from the distance-slow time domain-angle domain data and stores it in the queue. In this way, the data selection process and the target tracking process can be implemented through different modules.

[0130] Figure 4 This is a complete flowchart of a target state detection method provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the method includes:

[0131] 1) The echo signal received by the receiving antenna of the radio sensor is processed by the receiver and converted into ADC data. The data is then processed to obtain range-slow time-antenna domain data. Static clutter is filtered out from the range-slow time-antenna domain data and stored in the task data storage memory of the radio sensor.

[0132] 2) Perform two-dimensional beamforming on the range-slow time domain-antenna domain data to convert the range-slow time domain-antenna domain data into range-slow time domain-angle domain data. At this time, each frame in the slow time domain stores the distribution of the energy of the current frame in the three-dimensional space of range-azimuth-pitch angle.

[0133] 3) Perform CFAR detection on each frame of the distance-angle map in the slow time domain of the distance-slow time domain data obtained in 2), and extract the second slow time domain data from the distance-slow time domain data based on the distance-angle data of each detection point on the target, perform Doppler processing, and estimate the target's motion speed.

[0134] 4) Track the target based on its distance-angle data and speed to obtain dynamic detection results. Simultaneously, based on these results, determine whether the target is potentially quasi-static in the current frame of the slow-time domain. For example, if the target's tracking trajectory disappears in the current frame, it can be determined that the target is potentially quasi-static in the current frame of the slow-time domain, indicating that the target may have already stopped.

[0135] 5) If the target is suspected to be quasi-static in the current frame of the slow time domain, the distance cell and angle cell where the target is in the current frame are fed back to the data selection module.

[0136] 6) After 2) is completed, check whether the data selection module has received the feedback distance and angle units. If not, the data selection module does not work. If the feedback distance and angle units are received, the data selection module uses the feedback distance and angle units to extract the first M slow time domain data in the cell region formed by the feedback distance and angle units from the distance-slow time domain-angle domain data obtained in 2) as the first slow time domain data in the current frame.

[0137] 7) Store the first slow time domain data in K consecutive frames into a queue.

[0138] 8) When the queue is full, the first slow time domain data in K consecutive frames is obtained. Static clutter filtering is performed on the first slow time domain data in K consecutive frames to obtain the filtering result.

[0139] 9) Perform CFAR detection on the filtered results to confirm whether the target is quasi-static within the cell region, and obtain the quasi-static detection results of the target.

[0140] 10) The quasi-static detection results and dynamic detection results of the target are fused. If the target is detected in the cell area, it is considered that the target is in the quasi-static state in the cell area. At this time, the distance cell and angle cell where the target is suspected to be quasi-static are fed back for continuous detection.

[0141] 11) When the number of targets to be tracked is greater than 1, memory needs to be allocated for each target to store the data.

[0142] like Figure 5 As shown, based on the above embodiments, this embodiment of the invention provides a target state detection device, including:

[0143] Data acquisition module 51 is used to acquire distance-slow time-antenna domain data of the detection area of ​​the radio sensor;

[0144] The first state detection module 52 is used to convert the range-slow time domain-antenna domain data into range-slow time domain-angle domain data, and track the target within the detection area based on the range-slow time domain-angle domain data to obtain the dynamic detection result of the target;

[0145] The data dimensionality reduction module 53 is used to determine, based on the dynamic detection results, that the target is suspected to be quasi-static in the current frame of the slow time domain, and to extract the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame using the distance unit and angle unit where the target is located in the current frame.

[0146] The second state detection module 54 is used to obtain the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit based on the first slow time domain data in the consecutive multiple frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data.

[0147] Based on the above embodiments, the target state detection device provided in this embodiment of the invention further includes a storage module, used for:

[0148] The range-slow time-antenna domain data is stored in the task data storage memory of the radio sensor;

[0149] The first slow time domain data in the next frame is stored in a queue, and the queue is used to store the first slow time domain data in the consecutive frames.

[0150] The queue is pre-configured in the task data storage memory of the radio sensor, or in the non-task data storage memory of the radio sensor.

[0151] Based on the above embodiments, the target state detection device provided in this embodiment of the invention further includes a queue clearing module, used for:

[0152] Based on the dynamic detection results, if it is determined that the target is not a suspected quasi-static target in the current frame of the slow time domain, and the queue is not empty, then the queue is cleared.

[0153] Based on the above embodiments, the target state detection device provided in this embodiment of the invention, wherein the data dimensionality reduction module is specifically used for:

[0154] If the dynamic detection result is that the tracking trajectory of the target disappears in the current frame, then the target is determined to be a suspected quasi-static target in the current frame.

[0155] Based on the above embodiments, the target state detection device provided in this embodiment of the invention, wherein the first state detection module is specifically used for:

[0156] Target detection is performed on each frame of the slow time domain in the distance-slow time domain data to obtain the distance-angle data of each detection point on the target.

[0157] Based on the distance-angle data, second slow time domain data is extracted from the distance-slow time domain-angle domain data;

[0158] Doppler processing is performed on the second slow time-domain data to determine the target's velocity;

[0159] Based on the distance-angle data and the movement speed, the target is tracked to obtain the dynamic detection result.

[0160] Based on the above embodiments, the target state detection device provided in this embodiment of the invention, the second state detection module, is specifically used for:

[0161] Static clutter filtering is performed on the first slow time-domain data in the consecutive frames to obtain the filtering result;

[0162] The filtered results are subjected to target detection to determine the quasi-static detection result of the target in the unit region.

[0163] Based on the above embodiments, the target state detection device provided in this embodiment of the invention includes the first slow time domain data in the next frame, which includes the distance-slow time domain-angle domain data in the unit region of the next frame, representing the first preset number of slow time domain data.

[0164] The preset quantity is determined based on the number of sampling points in each frame in the slow time domain and the number of frames in the consecutive frames.

[0165] Specifically, the functions of each module in the target motion state detection device provided in this embodiment of the invention correspond one-to-one with the operation flow of each step in the above method-like embodiments, and the achieved effects are also the same. For details, please refer to the above embodiments, and this will not be repeated in this embodiment of the invention.

[0166] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute the target state detection method provided in the above embodiments.

[0167] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0168] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the target state detection method provided in the above embodiments. It is understood that the computer-readable storage medium can be a non-transitory computer-readable storage medium or a transient computer-readable storage medium, and no specific limitation is made here.

[0169] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the target state detection method provided in the above embodiments.

[0170] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0171] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A target state detection method, characterized in that, include: Acquire distance-slow time-antenna domain data of the detection area of ​​the radio sensor; The range-slow time-antenna domain data is converted into range-slow time-angle domain data, and the target within the detection area is tracked based on the range-slow time-angle domain data to obtain the dynamic detection result of the target; Based on the dynamic detection results, the target is determined to be a suspected quasi-static target in the current frame of the slow time domain. Using the distance unit and angle unit where the target is located in the current frame, the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame is extracted. Based on the first slow time domain data in the consecutive frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data, the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit is obtained.

2. The target state detection method according to claim 1, characterized in that, The process of acquiring distance-slow time-antenna domain data of the detection area of ​​the radio sensor further includes: The range-slow time-antenna domain data is stored in the task data storage memory of the radio sensor; The step involves using the distance and angle units where the target is located in the current frame to extract the first slow-time domain data of the distance-slow-time domain-angle domain data in the next frame of the current frame, followed by: The first slow time domain data in the next frame is stored in a queue, and the queue is used to store the first slow time domain data in the consecutive frames. The queue is pre-configured in the task data storage memory of the radio sensor, or in the non-task data storage memory of the radio sensor.

3. The target state detection method according to claim 2, characterized in that, The step of tracking targets within the detection area based on the distance-slow time-angle domain data to obtain dynamic detection results of the targets further includes: Based on the dynamic detection results, if it is determined that the target is not a suspected quasi-static target in the current frame of the slow time domain, and the queue is not empty, then the queue is cleared.

4. The target state detection method according to claim 1, characterized in that, The step of determining that the target is a suspected quasi-static target in the current frame in the slow time domain based on the dynamic detection results includes: If the dynamic detection result is that the tracking trajectory of the target disappears in the current frame, then the target is determined to be a suspected quasi-static target in the current frame.

5. The target state detection method according to any one of claims 1-4, characterized in that, The tracking of targets within the detection area based on the distance-slow time-domain-angle domain data to obtain dynamic detection results of the targets specifically includes: Target detection is performed on each frame of the slow time domain in the distance-slow time domain data to obtain the distance-angle data of each detection point on the target. Based on the distance-angle data, second slow time domain data is extracted from the distance-slow time domain-angle domain data; Doppler processing is performed on the second slow time-domain data to determine the target's velocity; Based on the distance-angle data and the movement speed, the target is tracked to obtain the dynamic detection result.

6. The target state detection method according to any one of claims 1-4, characterized in that, The step of obtaining the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit based on the first slow time domain data in the consecutive multiple frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data specifically includes: Static clutter filtering is performed on the first slow time-domain data in the consecutive frames to obtain the filtering result; The filtered results are subjected to target detection to determine the quasi-static detection result of the target in the unit region.

7. The target state detection method according to any one of claims 1-4, characterized in that, The first slow time domain data in the next frame includes the distance-slow time domain-angle domain data in the unit region of the next frame, which is the first preset number of slow time domain data. The preset quantity is determined based on the number of sampling points in each frame in the slow time domain and the number of frames in the consecutive frames.

8. A target motion state detection device, characterized in that, include: The data acquisition module is used to acquire distance-slow time-antenna domain data of the detection area of ​​the radio sensor; The first state detection module is used to convert the range-slow time domain-antenna domain data into range-slow time domain-angle domain data, and track the target within the detection area based on the range-slow time domain-angle domain data to obtain the dynamic detection result of the target; The data dimensionality reduction module is used to determine, based on the dynamic detection results, whether the target is suspected to be quasi-static in the current frame of the slow time domain, and to extract the first slow time domain data of the distance-slow time domain-angle domain data in the next frame of the current frame using the distance unit and angle unit where the target is located in the current frame. The second state detection module is used to obtain the quasi-static detection result of the target in the unit region formed by the distance unit and the angle unit based on the first slow time domain data in the consecutive multiple frames corresponding to the distance unit and the angle unit in the distance-slow time domain-angle domain data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the target state detection method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the target state detection method as described in any one of claims 1-7.

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