Target detection false alarm identification method and system, and storage medium

Abstract

The invention provides a target detection false alarm identification method and system, and a storage medium. The method comprises the steps of obtaining a maximum frequency value of each target distance dimension in a detection distance range in a judgment frequency range; comparing the maximum frequency value with a preset respiratory frequency range to obtain a living body target in a detection distance range; obtaining a final detection target value in the detection distance range based on the living body target; and comparing the final detection target value with a preset distance detection value, and when the final detection target value is smaller than the preset distance detection value, determining a target detection false alarm. The method provided by the invention effectively solves the problem of false alarm caused by the interference of a swinging object or the abnormal change of a radar signal caused by rainwater and noise when the in-vehicle radar equipment detects the living body target. The false alarm probability is effectively reduced, and the adaptability and robustness of the system to the environment are enhanced.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and specifically relates to a method, system, and storage medium for detecting and identifying false alarms in object detection. Background Art

[0002] Nowadays, traveling increasingly depends on cars. During the use of cars, news often reports safety accidents caused by children being accidentally locked in cars, such as sedans or school buses. Especially after entering the hot summer, the temperature inside the car rises rapidly, and the probability of children being accidentally locked in the car and suffering personal health damage also increases significantly. With the development of UWB (Ultra-Wideband) technology for civilian use and its explosive growth in 2022, UWB radars have also begun to be used for in-vehicle living body detection to reduce safety accidents caused by children being left in the car.

[0003] The UWB radar emits UWB pulse signals and receives the echoes after the pulse signals are reflected by obstacles. By analyzing the disturbance of the echoes, it is determined whether there are objects (or people) near the UWB radar. Usually, a pulse signal needs to be sampled multiple times in the time domain. The UWB radar living body detection mainly determines whether there is someone in the car based on the breathing characteristics of people in a static state or the movement amplitude of people in a moving state. When detecting breathing characteristics, the general algorithms on the market are to accumulate UWB radar data for a period of time, perform band-pass filtering, then perform Fourier transform to obtain the RD spectrum data in the time-frequency domain, and then judge the frequency and the existence of targets based on the RD spectrum.

[0004] The slight vibrations of objects such as fan blades and hanging decorations in the vehicle interior may generate radar reflection signals similar to human movement after the signal passes through band-pass filtering, resulting in the system misjudging the existence of targets. The scattering effect of raindrops on radar waves during rainfall and the random noise generated by other non-target sources in the environment can both cause abnormal changes in radar signals and increase the false alarm rate. Summary of the Invention

[0005] To solve the above technical problems, this application proposes a method, system, and storage medium for detecting and identifying false alarms in object detection that can effectively reduce the false alarm rate.

[0006] Specifically, this application proposes a method for detecting and identifying false alarms in object detection, including:

[0007] Obtain the maximum frequency value of each target distance dimension within the detection distance range in the judgment frequency range.

[0008] Compare the maximum frequency value with a preset breathing frequency range to obtain living targets within the detection distance range.

[0009] Obtain the final detected target value within the detected distance range based on the living target.

[0010] Moreover, compare the final detected target value with a preset distance detection value, and when the final detected target value is less than the preset distance detection value, determine that the target detection is a false alarm.

[0011] In the above technical solution, by obtaining the maximum probability value of each distance dimension within the detected distance range in the judgment frequency range and comparing it with the preset breathing frequency range, a living target can be effectively identified, and the accuracy of target detection is effectively improved. By obtaining the final detected target value within the detected distance range based on the living target and comparing the final detected target value with the preset distance detection value, when the final target value is less than the preset distance detection value, it is determined as a false alarm of target detection. In this way, the occurrence of false alarms in target detection can be effectively reduced, and wrong judgments caused by environmental noise or interference can be avoided. Through the frequency analysis of each distance dimension, the system can adapt to the target detection requirements in complex environments, improving the adaptability and reliability of the system. By comparing the maximum frequency value with the preset breathing frequency range, the process of target detection is optimized, and the efficiency and accuracy of target detection are improved.

[0012] As an implementation manner, the obtaining the maximum frequency value of each target distance dimension within the detected distance range in the judgment frequency range includes:

[0013] Obtain the first energy sum of each distance dimension within the detected distance range in the judgment frequency range and the second energy sum in the total detection frequency range; obtain the energy ratio of each distance dimension within the judgment frequency range based on the first energy sum and the second energy sum; obtain the value whose energy ratio is greater than the preset energy threshold, and when the value is greater than the preset value threshold, obtain the maximum frequency value of each target distance dimension within the judgment frequency range; where the target distance dimension is the distance dimension corresponding to the energy ratio greater than the preset energy threshold.

[0014] By calculating the first energy sum of each distance dimension in the detection distance range within the judgment frequency range and the second energy sum in the total detection frequency range, and calculating the energy ratio based on the two, it is possible to effectively screen out the frequency range with concentrated energy, effectively avoiding the influence of noise or interference that may exist in directly extracting frequency values, and improving the accuracy of obtaining frequency values. By presetting an energy threshold to screen the energy ratio, the robustness of target detection is enhanced, and the possibility of misjudgment is reduced. Through the calculation and screening of the energy ratio, noise and interference can be effectively identified, and the adaptability of the system is improved. By calculating the maximum frequency value of the target distance dimension, it is possible to effectively focus on the distance range with concentrated energy, thereby reducing the interference of invalid distance dimensions and improving the accuracy and efficiency of target detection.

[0015] Further, the first energy sum and the second energy sum are obtained at least through a distance-frequency domain spectrum, and the distance-frequency domain spectrum is obtained at least through Fourier transform of a radar signal.

[0016] By performing Fourier transform on the radar signal to convert it from the time domain to the frequency domain and generating a distance-frequency domain spectrum, the signal energy of different distance dimensions and frequency ranges can be clearly separated, which can significantly improve the accuracy of energy calculation and avoid the negative impact of noise and interference in the time-domain signal on energy calculation. It effectively improves the calculation efficiency of energy and the accuracy of target detection.

[0017] Further, obtaining the living target in the detection distance range includes:

[0018] Comparing the maximum frequency value with a preset breathing frequency range. When the maximum frequency value is within the preset breathing frequency range, it is determined that there is a living target in the current detection distance range; otherwise, it is determined that there is no living target in the current detection distance range.

[0019] By comparing the maximum frequency value with a preset breathing frequency range, the breathing characteristics of the living target can be effectively identified. It makes full use of the physiological characteristics of the living target and significantly improves the accuracy of living target detection. Only when the maximum frequency value is within the breathing frequency range, it will be determined that there is a living target in the current detection distance range, which can effectively filter out non-living targets, thereby effectively reducing the misjudgment rate of target detection. It effectively excludes the interference signals of non-living targets, enhances the robustness of target detection, and avoids complex signal processing steps, improving the efficiency of target detection.

[0020] Further, obtaining the final detection target value in the detection distance range based on the living target includes:

[0021] When there is a living target in the detection distance range, obtain the variance array of the detection frequencies within a preset time range for each target distance dimension; compare based on a preset variance threshold and the variance array, and when there is a variance in the variance array that is greater than the preset variance threshold for a preset judgment value, obtain the detection target value.

[0022] By calculating the variance array of the detection frequencies for each target distance within a preset time range and making a comparison in combination with the preset variance threshold, it is possible to effectively verify the detected living target, improve the reliability of target detection, and avoid misjudgment caused by a single judgment mechanism. Through further judgment with the preset variance threshold, the robustness of target detection can be effectively enhanced, enabling target detection to meet the target detection requirements in a variety of complex environments. Only when there is a variance in the variance array that is greater than the preset variance threshold for a preset judgment value will a detection target value be recorded once. This strict verification mechanism can effectively filter out false alarms caused by noise or interference, thereby reducing the false alarm rate.

[0023] Furthermore, obtaining the final detection target value in the detection distance range further includes:

[0024] Circularly detect the variance array of the detection frequencies for each target distance dimension in the detection distance range until all variance arrays are detected, so as to obtain the final detection target value.

[0025] By circularly detecting the variance array of the detection frequencies for each target distance dimension in the detection distance range, it is possible to comprehensively cover all target distance dimensions in the detection distance range, ensure the comprehensiveness of target detection, avoid omission of target detection, and enhance the accuracy and reliability of target detection. By circularly detecting the variance array of each target distance dimension, it is possible to effectively identify and distinguish multiple targets, support multi-target detection, and meet the target detection requirements in complex scenarios.

[0026] Furthermore, determining a false alarm in target detection includes:

[0027] Compare the final detection target value with a preset distance detection value. If the final detection target value is less than the preset distance detection value, it is determined that there is no living target in the detection distance range, and a false alarm in target detection is determined.

[0028] If the final detection target value is greater than or equal to the preset distance detection value, it is determined that there is a living target in the detection distance range, and the target detection is determined to be accurate.

[0029] By comparing the final detected target value with the preset distance detection value, it is possible to effectively distinguish real moving targets from detection false alarms, avoid misjudgments caused by detection false alarms, and effectively improve the accuracy of detection false alarms. Only when the final detected target value is greater than or equal to the preset distance detection value will it be determined that there is a living target, thereby effectively reducing the false alarm rate of target detection.

[0030] Based on the same inventive concept, the present application also proposes a system for a method of identifying target detection false alarms, the system comprising:

[0031] A frequency acquisition module, configured to acquire the maximum frequency value of each target distance dimension in the detection distance range within the determination frequency range.

[0032] A target detection module, configured to compare the maximum frequency value with a preset breathing frequency range to obtain a living target within the detection distance range.

[0033] A numerical statistics module, configured to obtain the final detected target value within the detection distance range based on the living target.

[0034] And a false alarm identification module, configured to compare the final detected target value with the preset distance detection value, and when the final detected target value is less than the preset distance detection value, determine a target detection false alarm.

[0035] Further, the numerical statistics module further includes a loop detection module, the loop detection module being configured to loop-detect the variance array of the detection frequencies of each target distance dimension in the detection distance range, and obtain the detected target value based on the variance array until the variance arrays of the detection frequencies of all target distance dimensions are detected, and obtain the final detected target value.

[0036] Based on the same inventive concept, the present application also proposes a computer-readable storage medium storing computer-executable instructions that can be read and executed by a control processor to perform the method of identifying target detection false alarms.

[0037] Compared with the prior art, the present application has at least the following beneficial effects:

[0038] The method provided by this application effectively solves the problem of false alarms caused by abnormal changes in radar signals due to interference from swinging objects, rain, or noise when in-vehicle radar devices detect living targets. By obtaining the maximum probability value of each target distance dimension within the detection distance range in the judgment frequency range and comparing it with the preset breathing frequency range, living targets can be effectively identified, and the accuracy of target detection is effectively improved. By obtaining the final detection target value within the detection distance range based on the living target and comparing the final detection target value with the preset distance detection value, it is determined that the target detection is a false alarm when the final target value is less than the preset distance detection value. In this way, the occurrence of false alarms in target detection can be effectively reduced, and wrong judgments caused by environmental noise or interference can be avoided. Through the frequency analysis of each distance dimension, the system can adapt to the target detection requirements in complex environments, improving the adaptability and reliability of the system. By comparing the maximum frequency value with the preset breathing frequency range, the process of target detection is optimized, and the efficiency and accuracy of target detection are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flowchart of the method for identifying false alarms in target detection shown in an embodiment of this application.

[0040] Figure 2 is a schematic diagram of the system for identifying false alarms in target detection shown in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0042] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0043] Embodiment 1:

[0044] Please refer to Figure 1 , and the false alarm recognition method for target detection mainly includes steps S1 to S4.

[0045] Among them, step S1 includes: obtaining the maximum frequency value of each target distance dimension in the detection distance range within the judgment frequency range. The detection distance range can mainly be the detection range of all distance dimensions in the distance-frequency spectrum obtained after conventional static filtering processing of the echo signal of the radar device and then through Fourier transform. The radar device can mainly be a UWB (Ultra-Wideband) radar, but is not limited thereto. The target distance dimension is mainly the distance dimension corresponding to the energy ratio of the first energy sum of each distance dimension in the preset judgment frequency range to the second energy sum in the total detection frequency range in the detection distance range being greater than the preset energy threshold.

[0046] Step S2 includes: comparing the maximum frequency value with the preset breathing frequency range to obtain the living target in the detection distance range. Among them, the preset breathing frequency range can mainly be 0.1Hz to 2Hz. Those skilled in the art can adjust the preset breathing frequency range according to the actual situation, and it is not limited thereto. When the maximum frequency value is within the preset breathing frequency range, it is determined that there is a living target in the current detection distance range; when the maximum frequency value is not within the preset breathing frequency range, it is determined that there is no living target in the current detection distance range.

[0047] Step S3 includes: obtaining the final detection target value in the detection distance range based on the living target. Specifically, when a living target is detected, by calculating the variance array of the detection frequencies of all target distance dimensions within the preset time range, and comparing the preset variance threshold with the variance array, when there is a variance of the preset judgment value in the variance array, the detection target value is obtained. The preset time range can mainly be 8S, and by detecting once every 2S, that is, accumulating 4 times, the variance array of the detection frequencies of each target distance dimension within 8S is obtained. The preset variance threshold is mainly set according to the noise level and signal intensity in the actual application scenario, and those skilled in the art can set the preset variance threshold according to the actual application scenario. For example, the preset variance threshold is set to 0.4. The preset judgment value can mainly be 3, that is, only when there are 3 variances in the variance array, a detection target value is recorded. By circularly detecting the variance arrays of all target distance dimensions, the final detection target value is obtained.

[0048] Moreover, step S4 includes: comparing the final detected target value with a preset distance detection value, and when the final detected target value is less than the preset distance detection value, determining that the target detection is a false alarm. The preset distance detection value is set according to the main detection distance range and detection accuracy requirements. For example, the preset distance detection value can be set to 2. Those skilled in the art can set the preset distance detection value according to the actual situation, based on the detection distance range and detection accuracy requirements. Only when the final detected target value is less than the preset distance detection value, it is determined that the target detection is a false alarm. For example, when the final detected target value is 1 and the preset distance detection value is 2, that is, the final target value is less than the preset distance detection value, it is determined that the target detection is a false alarm.

[0049] In the specific implementation process, the above technical content can mainly be applied inside the vehicle to detect whether there are dangers caused by the presence of children or pets left behind. For example, one or more UWB radar devices can be installed inside the vehicle. After static filtering processing of the received UWB radar echo signals, the distance frequency domain spectrum is obtained by performing Fourier transform on the statically filtered return signals. Based on the distance frequency domain spectrum, the detection distance range, the total detection frequency range, and the judgment frequency range are set. By calculating the energy ratio of the sum of the energies of each distance dimension in the distance frequency spectrum within the judgment frequency range and the total detection frequency range, the target distance dimension and the maximum frequency value of the target distance dimension within the judgment frequency range are obtained. By comparing the maximum frequency value with the preset breathing frequency range, when the maximum frequency value is within the preset breathing frequency range, it is determined that there is a living target within the detection distance range. When there is a living target, by calculating the variance array of the detection frequencies of all target distance dimensions within a preset time range, and comparing the preset variance threshold with the variance array, when there is a variance of the preset judgment value in the variance array, the detected target value is obtained. The final detected target value is compared with the preset distance detection value, and when the final detected target value is less than the preset distance detection value, it is determined that the target detection is a false alarm.

[0050] In some embodiments, the obtaining of the maximum frequency value of each target distance dimension in the detection distance range within the judgment frequency range includes:

[0051] Obtain the first energy sum of each distance dimension in the detection distance range within the judgment frequency range and the second energy sum in the total detection frequency range; obtain the energy ratio of each distance dimension in the judgment frequency range based on the first energy sum and the second energy sum; obtain the values where the energy ratio is greater than the preset energy threshold, and when the value is greater than the preset numerical threshold, obtain the maximum frequency value of each target distance dimension in the judgment frequency range; wherein, the target distance dimension is the distance dimension corresponding to the energy ratio greater than the preset energy threshold.

[0052] Among them, the detection distance range can mainly be the detection distance range in the distance-frequency domain spectrum obtained by performing Fourier transform processing on the radar signal. For example, the detection distance range can be [r1, r2, r3, r4,..., rn], the judgment frequency range can mainly be [f1, f2], and the total detection frequency range can mainly be [F1, F2]. Obtaining the first energy sum of each distance dimension in the judgment frequency range can mainly be S r1 (f1, f2), S r2 (f1, f2),..., S rn (f1, f2), and the second energy sum of each distance dimension in the total detection range can mainly be SS r1 (F1, F2), SS r2 (F1, F2),..., SS rn (F1, F2), and obtaining the energy ratio of each distance dimension in the judgment frequency range based on the first energy sum and the second energy sum can mainly be rate1 = S r1 (f1, f2) / SS r1 (F1, F2),..., raten = S rn (f1, f2) / SS rn (F1, F2). The preset energy threshold can mainly be a data threshold set according to the actual situation. For example, the preset energy threshold can be RATE_SH = 0.5. The target distance dimensions obtained based on the preset energy threshold can mainly be r_i1, r_i1,..., r_irate_cnt. The preset numerical threshold can be 3. For example, when the value rate_cnt > NUM_TH = 3, the maximum frequency value max_fre of each distance dimension in the judgment frequency range is obtained. Those skilled in the art can set this preset numerical threshold according to the actual situation and are not limited thereto.

[0053] Optionally, the first energy sum and the second energy sum are obtained at least through the distance-frequency domain spectrum, and the distance-frequency domain spectrum is obtained at least by performing Fourier transform on the radar signal.

[0054] Among them, the radar signal can mainly be the echo signal obtained after the UWB radar emits a radar signal and is reflected by an obstacle or a target. The distance-frequency domain spectrum is obtained by performing a Fourier transform on the echo signal. Based on the detection distance range in the distance-frequency domain spectrum, the frequency range and the total detection frequency range are judged to obtain the first energy sum and the second energy sum.

[0055] Optionally, obtaining the living target in the detection distance range includes:

[0056] Comparing the maximum frequency value with a preset breathing frequency range. When the maximum frequency value is within the preset breathing frequency range, it is determined that there is a living target in the current detection distance range; otherwise, it is determined that there is no living target in the current detection distance range.

[0057] For example, the preset breathing frequency range can be from 0.1 Hz to 2 Hz, and the preset breathing frequency range mainly covers the breathing frequencies of humans and common pets. When the maximum frequency value max_fre is within the preset breathing frequency range, it is determined that there is a living target in the current detection distance range; otherwise, there is no living target in the current detection distance range. For example, when the maximum frequency value max_fre = 0.8 Hz, since 0.1 Hz < 0.8 Hz < 2 Hz, it is determined that there is a living target in the current detection distance range.

[0058] Optionally, obtaining the final detection target value in the detection distance range based on the living target includes:

[0059] When there is a living target in the detection distance range, obtain the variance array of the detection frequencies within the preset time range for each target distance dimension.

[0060] Based on a comparison between a preset variance threshold and the variance array, when there is a variance in the variance array that is greater than the preset variance threshold for a preset judgment value, obtain the detection target value.

[0061] The preset time range can mainly be 8S, and the sampling interval is set to 2S. For example, when there is a living target in the detection distance range, obtain the variance arrays vas(i, 1), vas(i, 2), vas(i, 3), vas(i, 4) within 8S for each target distance dimension r_i1, r_i2,..., r_irate_cnt. The preset variance threshold can mainly be set by those skilled in the art according to the actual situation. For example, the preset variance threshold can be set to VAR_SH = 0.4. The preset judgment value can mainly be 3, that is, when there are at least 3 variances in the variance array that are greater than the preset variance threshold, obtain a detection target value detect_cnt = 1.

[0062] Optionally, obtaining the final detected target value within the detected distance range further includes:

[0063] Circularly detect the variance array of the detection frequency of each target distance dimension within the detected distance range until all variance arrays are detected, so as to obtain the final detected target value.

[0064] By circularly detecting the variance array of the detection frequency of each target distance dimension, when the variance greater than the preset value in the variance array is greater than the preset variance threshold, the detected target value detect_cnt = detect + 1 is obtained. After all the variance arrays of the target distance dimensions are detected, the final detected target value is obtained.

[0065] Optionally, determining false alarms in target detection includes:

[0066] Compare the final detected target value with the preset distance detection value. If the final detected target value is less than the preset distance detection value, it is determined that there is no living target within the detected distance range, and a false alarm in target detection is determined.

[0067] If the final detected target value is greater than or equal to the preset distance detection value, it is determined that there is a living target within the detected distance range, and the target detection is determined to be accurate.

[0068] Those skilled in the art can set the preset distance detection value according to the actual situation. For example, the preset distance detection value can be set to RANGE_DETECT_MIN_CNT = 2. If the final detected target value is detect_cnt = 3, that is, the final detected target value is greater than the preset distance detection value, it is determined that there is a living target within the detected distance range, and the detection of the living target is accurate. If the final detected target value is detect_cnt = 1, that is, the final detected target value is less than the preset distance detection value, it is determined that there is no living target within the detected distance range, and a false alarm in the detection of the living target is determined.

[0069] Embodiment 2:

[0070] Please refer to Figure 2 , this application also proposes a system adopting the method for identifying false alarms in target detection described in Embodiment 1. The system mainly includes: a frequency acquisition module, a target detection module, a numerical statistics module, and a false alarm identification module.

[0071] Among them, the frequency acquisition module is used to obtain the maximum frequency value of each target distance dimension in the detection distance range within the judgment frequency range. The detection distance range can mainly be the detection distance range in the distance-frequency domain spectrum obtained by performing Fourier transform processing on the radar signal. By calculating the energy ratio of each distance dimension in the preset judgment frequency range and in the total detection frequency range within the detection distance range, the maximum frequency value of each target distance dimension in the judgment frequency range is obtained based on the energy ratio.

[0072] The target detection module is used to compare the maximum frequency value with the preset breathing frequency range to obtain the living target in the detection distance range. The preset breathing frequency range can mainly be the breathing frequencies of humans and common pets preset to cover, such as 0.1 Hz to 2 Hz. Only when the maximum frequency value is within the preset breathing frequency range is it determined that there is a living target in the current detection range.

[0073] The numerical statistics module is used to obtain the final detected target value in the detection distance range based on the living target. When there is a living target, by circularly detecting the variance array of each target distance dimension within a preset time range, when the variance of the preset judgment value in the variance array is greater than the preset variance threshold, the detected target value is recorded once until the variance arrays of all target distance dimensions are detected, and the final detected target value is obtained.

[0074] And, the false alarm identification module is used to compare the final detected target value with the preset distance detection value, so that when the final detected target value is less than the preset distance detection value, it is determined that the target detection is a false alarm. For example, the preset distance detection value can be set to 2. When the final detected target value is greater than or equal to 2, it is determined that there is no false alarm in the current target detection. When the final detected target value is less than the preset distance detection value, it is determined that there is a false alarm in the target detection.

[0075] Optionally, the numerical statistics module further includes a circular detection module. The circular detection module is used to circularly detect the variance array of the detection frequency of each target distance dimension in the detection distance range, obtain the detected target value based on the variance array, until the variance arrays of the detection frequencies of all target distance dimensions are detected, and obtain the final detected target value.

[0076] When there is a variance of the preset judgment value in the variance array of the detection frequency of any target distance dimension in the detection distance range that is greater than the preset variance threshold, the detected target value is recorded once. Circular detection can cover the detection of all target distance dimensions, avoiding inaccurate detected target values caused by missed detection and improving the accuracy of false alarm identification in target detection.

[0077] Embodiment 3:

[0078] The present application also provides a computer-readable storage medium, which includes:

[0079] The computer-readable storage medium stores computer-executable instructions.

[0080] When the computer-executable instructions are executed by a control processor, the false alarm recognition method for object detection described in Embodiment 1 is implemented.

[0081] In the computer-readable storage medium, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive SolidState Disk (SSD)).

[0082] In summary, the method provided in this application effectively solves the problem of false alarms caused by abnormal changes in radar signals due to interference from swinging objects, rainwater, or noise when in-vehicle radar devices detect living targets. By obtaining the maximum probability value of each target distance dimension within the detection distance range in the judgment frequency range and comparing it with the preset breathing frequency range, living targets can be effectively identified, and the accuracy of target detection is effectively improved. By obtaining the final detection target value within the detection distance range based on the living target and comparing the final detection target value with the preset distance detection value, a false alarm in target detection is determined when the final target value is less than the preset distance detection value. In this way, the occurrence of false alarms in target detection can be effectively reduced, and incorrect judgments caused by environmental noise or interference can be avoided. Through frequency analysis of each distance dimension, the system can adapt to the target detection requirements in complex environments, improving the adaptability and reliability of the system. By comparing the maximum frequency value with the preset breathing frequency range, the process of target detection is optimized, and the efficiency and accuracy of target detection are improved.

[0083] In several embodiments provided in this application, it can be understood that each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved.

[0084] If the described function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this 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 enable an electronic device to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0085] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above are only specific embodiments of the present application and are not used to limit the protection scope of the present application. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for identifying false positives in object detection, characterized in that, Including: Obtaining the maximum frequency value of each target distance dimension within the detection distance range in the judgment frequency range; Comparing the maximum frequency value with a preset breathing frequency range to obtain a living target within the detection distance range; Obtaining the final detection target value within the detection distance range based on the living target; And comparing the final detection target value with a preset distance detection value, and when the final detection target value is less than the preset distance detection value, determining that the target detection is a false alarm.

2. The method for identifying false positives in object detection according to claim 1, wherein The obtaining the maximum frequency value of each target distance dimension within the detection distance range in the judgment frequency range includes: Obtaining the first energy sum of each distance dimension within the detection distance range in the judgment frequency range and the second energy sum in the total detection frequency range; Obtaining the energy ratio of each distance dimension within the judgment frequency range based on the first energy sum and the second energy sum; Obtaining the value where the energy ratio is greater than a preset energy threshold, and when the value is greater than a preset value threshold, obtaining the maximum frequency value of each target distance dimension within the judgment frequency range; Wherein, the target distance dimension is the distance dimension corresponding to the energy ratio greater than the preset energy threshold.

3. The method for identifying false alarms in object detection according to claim 2, wherein, The first energy sum and the second energy sum are obtained at least through a distance-frequency domain spectrum, and the distance-frequency domain spectrum is obtained at least through Fourier transform of the radar signal.

4. The method for identifying false alarms in object detection according to claim 1, wherein The obtaining the living target within the detection distance range includes: Comparing the maximum frequency value with a preset breathing frequency range, and when the maximum frequency value is within the preset breathing frequency range, determining that there is a living target within the current detection distance range; otherwise, determining that there is no living target within the current detection distance range.

5. The method for identifying false positives in object detection according to claim 1, wherein The obtaining the final detection target value within the detection distance range based on the living target includes: When there is a living target within the detection distance range, obtaining the variance array of the detection frequency within the preset time range of each target distance dimension; Comparing a preset variance threshold with the variance array, and when there is a variance in the variance array that is greater than the preset variance threshold for a preset judgment value, obtaining the detection target value.

6. The method for identifying false alarms in object detection according to claim 5, characterized in that, The obtaining the final detection target value within the detection distance range further includes: Circularly detecting the variance array of the detection frequency of each target distance dimension within the detection distance range until all variance arrays are detected, so as to obtain the final detection target value.

7. The method for identifying false alarms in object detection according to claim 1, wherein The determining the target detection false alarm includes: Comparing the final detection target value with a preset distance detection value, if the final detection target value is less than the preset distance detection value, determining that there is no living target within the detection distance range, and determining that the target detection is a false alarm; If the final detection target value is greater than or equal to the preset distance detection value, determining that there is a living target within the detection distance range, and determining that the target detection is accurate.

8. A system for the method of identifying false alarms in object detection according to any one of claims 1-7, characterized in that, The system includes: A frequency acquisition module for obtaining the maximum frequency value of each target distance dimension within the detection distance range in the judgment frequency range; A target detection module, configured to compare the maximum frequency value with a preset breathing frequency range to obtain a living target within the detection distance range; A numerical statistics module, configured to obtain a final detected target value within the detection distance range based on the living target; And a false alarm recognition module, configured to compare the final detected target value with a preset distance detection value, and determine a target detection false alarm when the final detected target value is less than the preset distance detection value.

9. The system for the method of identifying false alarms in object detection according to claim 8, characterized in that, The numerical statistics module further includes a loop detection module, configured to loop through and detect the variance array of the detection frequencies of each target distance dimension within the detection distance range, obtain a detected target value based on the variance array, and obtain a final detected target value until the variance arrays of the detection frequencies of all target distance dimensions are detected.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that can be read and executed by a control processor to perform the target detection false alarm recognition method according to any one of claims 1-7.

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