Target detection method, device, equipment, medium and product

Abstract

The invention discloses a target detection method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a preprocessed target detection response signal, and determining the spectrum data of the target detection response signal; based on the frequency spectrum data, the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal is determined, and the detection frequency energy ratio is related to the energy sum of the subcarrier in a set detection frequency range; and determining the signal-to-noise ratio of each subcarrier in the set detection frequency range, and determining whether a detection target exists currently based on the detection frequency energy ratio and the signal-to-noise ratio. According to the technical scheme, the accuracy and reliability of target detection are improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a target detection method, apparatus, device, medium, and product. Background Technology

[0002] With the continuous increase in car ownership, vehicles have become an indispensable means of transportation for people's daily travel. However, while cars are widely used and bring many conveniences to people's lives, the problems left behind by children, the elderly, and stationary people in cars are gradually becoming a safety hazard that cannot be ignored.

[0003] Currently, the industry uses Child Presence Detection (CPD) technology based on Wi-Fi Channel State Information (WiFiCSI) to detect targets. This method mainly relies on activity values ​​to detect targets. However, for weak targets (such as infant models, stationary people, etc.), the signal changes they produce are relatively weak, and the activity value algorithm often fails to capture them accurately, resulting in low detection accuracy. In addition, because the signal characteristics of weak targets are not obvious, they are easily masked by environmental noise or noise from the device itself, further affecting the accuracy of detection. Summary of the Invention

[0004] This invention provides a target detection method, apparatus, equipment, medium, and product, which improves the accuracy and reliability of target detection.

[0005] In a first aspect, embodiments of this disclosure provide a target detection method, including:

[0006] Acquire the preprocessed target detection response signal and determine the spectral data of the target detection response signal;

[0007] Based on the spectrum data, the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal is determined, and the detection frequency energy ratio is related to the energy of the subcarrier within a set detection frequency range;

[0008] Determine the signal-to-noise ratio (SNR) of each subcarrier within the set detection frequency range, and determine whether a detection target exists based on the detection frequency energy ratio and the SNR.

[0009] Secondly, embodiments of this disclosure provide a target detection device, comprising:

[0010] The spectrum data determination module is used to acquire the preprocessed target detection response signal and determine the spectrum data of the target detection response signal;

[0011] An energy ratio determination module is used to determine the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal based on the spectrum data, wherein the detection frequency energy ratio is related to the energy of the subcarrier within a set detection frequency range;

[0012] The target detection module is used to determine the signal-to-noise ratio of each subcarrier within the set detection frequency range, and to determine whether a target is currently being detected based on the detection frequency energy ratio and the signal-to-noise ratio.

[0013] Thirdly, embodiments of this disclosure provide an electronic device, including:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor can perform a target detection method provided in the first aspect embodiment described above.

[0017] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing computer instructions that are used to cause a processor to execute and implement the target detection method provided in the first aspect of the embodiments described above.

[0018] Fifthly, this disclosure provides a computer program product, which includes a computer program that, when executed by a processor, implements a target detection method provided in the first aspect of the embodiment.

[0019] This invention discloses a target detection method, apparatus, device, medium, and product, comprising: acquiring a preprocessed target detection response signal and determining the spectral data of the target detection response signal; based on the spectral data, determining the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal, wherein the detection frequency energy ratio is correlated with the energy of the subcarrier within a set detection frequency range; determining the signal-to-noise ratio (SNR) of each subcarrier within the set detection frequency range; and determining whether a target is currently detected based on the detection frequency energy ratio and the SNR. This technical solution improves the accuracy and reliability of target detection.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a target detection method provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a target detection method provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a target detection device provided in Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," and "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] WiFi CSI technology currently uses activity values ​​for CPD detection of targets such as infants and children, identifying the presence or movement of a target by quantifying the degree of change in the CSI signal. This technology shows great potential in wireless sensing of targets such as infants and children due to its low cost and ease of deployment, but it also faces challenges such as low accuracy in weak target detection and negative scene interference, specifically:

[0029] For weak targets (such as infant models or stationary people), the signal changes they produce are relatively weak, making it difficult for the activity value algorithm to accurately capture them, resulting in low detection accuracy. The signal characteristics of weak targets are not obvious and are easily masked by environmental noise or noise from the device itself, further reducing detection accuracy.

[0030] In some negative scenarios (such as heavy rain outside a vehicle), the WiFi CSI signal may fluctuate significantly due to environmental interference. These fluctuations are similar to the signal changes of weak targets, easily leading to false alarms. The environmental noise and interference signals in negative scenarios are complex and variable, making them difficult to effectively distinguish and filter using simple activity value algorithms, thus increasing the risk of false alarms.

[0031] Current WiFi CSI CPD technologies exhibit relatively weak algorithm robustness and limited adaptability to different environments, devices, and targets. In practical applications, even minor changes in environmental conditions or differences in device performance can significantly impact the algorithm's detection performance, reducing the technology's reliability and stability. Therefore, this invention employs a target detection method to address these technical problems.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a target detection method provided in Embodiment 1 of the present invention. This embodiment can be applied to the situation of detecting whether there is a living body left in a vehicle. The method can be executed by a target detection device, which can be implemented in hardware and / or software.

[0034] like Figure 1 As shown, the method includes:

[0035] S101. Obtain the preprocessed target detection response signal and determine the spectral data of the target detection response signal.

[0036] In this embodiment, the target detection response signal can be understood as the signal obtained after preprocessing and optimizing the initially acquired WiFi CSI channel impulse response (CIR) signal; it is a time-domain signal. The spectrum data is the result of converting the target detection response signal from the time domain to the frequency domain, reflecting the energy distribution of the signal at different frequencies.

[0037] Specifically, the initially detected WiFiCSI signal is subjected to a series of filtering processes to obtain a target detection response signal. The target detection response signal is then windowed to obtain a two-dimensional target detection response signal with the number of subcarriers multiplied by the number of window segments. Fourier transform is then performed on the signal corresponding to each window segment to obtain the corresponding spectrum data.

[0038] S102. Based on the spectrum data, determine the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal. The detection frequency energy ratio is related to the energy of the subcarrier within the set detection frequency range.

[0039] In this embodiment, the detection frequency-energy ratio can be understood as an index used to quantify the energy distribution relationship of subcarriers within a set detection range. The set detection frequency range can be understood as a pre-defined frequency range related to liveness detection, including a target detection frequency range corresponding to the normal breathing frequency range of a living organism and an extended detection frequency range that has a certain extension range compared to the normal breathing frequency range of a living organism, wherein the extended detection frequency range includes the target detection frequency range.

[0040] Specifically, the target detection response signal is a two-dimensional signal corresponding to multiple subcarriers. For each subcarrier, based on the spectrum data corresponding to that subcarrier, the energy sum within the target detection frequency range and the energy sum within the extended detection frequency range are determined respectively. Based on the ratio of the two energy sums, the detection frequency energy ratio corresponding to that subcarrier is determined, thereby obtaining the detection frequency energy ratio corresponding to each subcarrier.

[0041] S103. Determine the signal-to-noise ratio of each subcarrier within the set detection frequency range, and determine whether a detection target exists based on the detection frequency energy ratio and the signal-to-noise ratio.

[0042] In this embodiment, the signal-to-noise ratio (SNR) can be understood as the ratio of the detection frequency energy to the noise floor, and it is a core indicator for measuring signal quality. The detection target can be understood as a living being left inside the vehicle, including at least infants and children.

[0043] Specifically, the average energy of all subcarriers within the extended detection frequency range is determined as the noise floor of the extended detection frequency range. Based on this noise floor and the ratio of the detection frequency energy of each subcarrier within the target detection frequency range, the signal-to-noise ratio (SNR) of the corresponding subcarrier is determined, and further, the maximum SNR value is determined from the SNR values ​​of each subcarrier. The maximum SNR value is compared with the corresponding threshold, and the detection frequency energy ratio is compared with the corresponding threshold. If the results of both threshold comparisons meet specific conditions, it is determined that a detection target exists; otherwise, it is determined that no detection target exists.

[0044] This invention provides a target detection method, comprising: acquiring a preprocessed target detection response signal and determining the spectral data of the target detection response signal; based on the spectral data, determining the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal, wherein the detection frequency energy ratio is correlated with the energy of the subcarrier within a set detection frequency range; determining the signal-to-noise ratio (SNR) of each subcarrier within the set detection frequency range; and determining whether a target is currently detected based on the detection frequency energy ratio and the SNR. This technical solution, through steps such as data preprocessing, spectral analysis, and detection frequency energy ratio calculation, improves the accuracy, reliability, and robustness of target detection.

[0045] As a first optional embodiment of this method, the method further includes:

[0046] If a target is detected, a notification message is generated and sent to the associated client.

[0047] In this embodiment, the prompt message can be understood as a message used to inform the user that a living being remains in the vehicle.

[0048] Specifically, if a target is detected, it indicates the presence of a living being inside the vehicle. A corresponding notification is generated and sent to the client associated with the vehicle containing the target, thus alerting the user (vehicle owner) to the presence of a living being inside the vehicle and effectively ensuring the safety of the detected target.

[0049] Example 2

[0050] Figure 2 This is a flowchart of a target detection method provided in Embodiment 2 of the present invention. This embodiment is a further optimization of any of the above embodiments and can be applied to the situation of detecting whether there is a living body left in a vehicle. The method can be executed by a target detection device, which can be implemented in hardware and / or software.

[0051] like Figure 2 As shown, the method includes:

[0052] S201. Obtain the preprocessed target detection response signal. The preprocessing includes at least abnormal data filtering and static noise processing.

[0053] In this embodiment, initial WiFi CSI cir data is acquired, and abnormal data filtering is performed on the WiFi CSI cir data to remove outliers caused by device malfunctions, environmental interference, etc., ensuring the accuracy and reliability of the data. Here, cir is two-dimensional data, calculated by multiplying the number of subcarriers N by the number of samples fs. N*fsThe filtered CSI data undergoes static noise processing to reduce the impact of background noise on subsequent analysis, improve the signal-to-noise ratio, and obtain the preprocessed target detection response signal.

[0054] S202. Perform cumulative windowing processing and Fourier transform processing on the target detection response signal to obtain the spectrum data.

[0055] In this embodiment, the target detection response signal is processed by cumulative windowing, and the window is called fftWindow. The cumulative fftWindow data is represented as cir. N*fftWindow Perform a Fourier transform on the signal for each time period to obtain the frequency domain spectrum data fftCir. N*fftWindow This provides a basis for subsequent calculation of the detection frequency-energy ratio, where N is the number of subcarriers.

[0056] S203. For a single subcarrier of the target detection response signal, based on the spectrum data corresponding to the subcarrier, determine the first energy sum of the subcarrier in the target detection frequency range and the second energy sum in the extended detection frequency range.

[0057] In this embodiment, the target detection frequency range can be understood as the range used to detect the normal breathing frequency of a living organism, such as [-1.5Hz to +1.5Hz]. The first energy sum can be understood as the signal energy of the subcarrier within the target detection frequency range, intended to assess the channel state or signal strength distribution. The extended detection frequency range can be understood as an extended range for detecting the normal breathing frequency of a living organism under the influence of external environmental factors, such as [-2.5Hz to +2.5Hz]. The extended detection frequency range includes (is greater than) the target detection frequency range. The second energy sum can be understood as the signal energy of the subcarrier within the extended detection frequency range, intended to assess the channel state or signal strength distribution.

[0058] Specifically, for a single subcarrier i of the target detection response signal, based on the spectral data fftCir corresponding to that subcarrier i... N*fftWindow ,pass The first energy and percent1(i) of subcarrier i within the target spread frequency range [-f, f] are calculated, where i = 1, 2, 3…N. For the same subcarrier i, based on the corresponding spectral data fftCir... N*fftWindow ,pass The second energy and percent2(i) of subcarrier i in the extended frequency range [-F, F] are calculated.

[0059] S204. The ratio of the first energy sum to the second energy sum is determined as the detection frequency energy ratio of the subcarrier.

[0060] In this embodiment, based on the first energy and percent1(i) and the second energy and percent2(i) of subcarrier i, through The detection frequency energy ratio percent(i) of subcarrier i is calculated.

[0061] S205. Determine the average frequency energy of all subcarriers corresponding to the target detection response signal within the extended detection frequency range.

[0062] In this embodiment, the average frequency energy of the subcarrier within the extended detection frequency range can be understood as the noise floor of the extended detection frequency range, which is the level of feedback environmental noise.

[0063] Specifically, through The average frequency energy of all subcarriers in the extended detection frequency range is calculated to obtain the noise floor in the extended frequency range.

[0064] S206. For a single subcarrier, the signal-to-noise ratio of the subcarrier is obtained based on the ratio of the first energy of the subcarrier to the average frequency energy.

[0065] In this embodiment, for a single subcarrier i, through The signal-to-noise ratio (SNR) of subcarrier i is obtained by calculating the ratio of the first energy and percent1(i) of subcarrier i in the target detection frequency range to the noise floor energy noisePower(i), which further highlights the characteristics of the live signal.

[0066] S207. Compare the detection frequency energy ratio of each subcarrier with a first threshold to obtain a first comparison result, wherein the first comparison result includes the number of subcarriers when the detection frequency energy ratio is greater than the first threshold.

[0067] In this embodiment, the first threshold can be understood as a detection frequency energy ratio threshold, which is used to determine the minimum detection frequency energy ratio at which a detection target exists. The first comparison result can be understood as the result of comparing the detection frequency energy ratio of each subcarrier with the first threshold, including at least the magnitude relationship between the detection frequency energy ratio of the subcarrier and the first threshold and the number of subcarriers with a detection frequency energy ratio greater than the first threshold, and may also include the number of subcarriers with a detection frequency energy ratio less than or equal to the first threshold.

[0068] Specifically, the first threshold PERCENT_SH is determined, and the probe frequency energy ratio percent(i) of each subcarrier is compared with the first threshold PERCENT_SH to determine the relationship between percent(i) and PERCENT_SH, and the number of subcarriers percent_cnt when percent(i)>PERCENT_SH is determined.

[0069] S208. Take the maximum signal-to-noise ratio from all subcarriers, compare the maximum signal-to-noise ratio with the second threshold, and obtain the second comparison result.

[0070] In this embodiment, the maximum signal-to-noise ratio (SNR) can be understood as the maximum SNR corresponding to N subcarriers. The second threshold can be understood as the SNR threshold, which is used to determine the minimum SNR required to detect a target. The second comparison result can be understood as the result of comparing the maximum SNR with the second threshold, including situations where the maximum SNR is greater than the second threshold and where the maximum SNR is less than or equal to the second threshold.

[0071] Specifically, the second threshold POWER_SH is determined. The maximum signal-to-noise ratio (SNR) value is selected from the N SNR values ​​corresponding to the N subcarriers of the target detection response signal and determined as the maximum SNR value max_power. The maximum SNR value is then compared with the second threshold to determine the size relationship and obtain the second comparison result.

[0072] S209. Determine whether a detection target exists based on the first comparison result and the second comparison result.

[0073] In this embodiment, based on the number of subcarriers percent_cnt when the detection frequency energy ratio included in the first comparison result is greater than the first threshold, and the relationship between the maximum signal-to-noise ratio corresponding to the second comparison threshold and the second threshold, it is determined whether the current condition is met, so as to determine whether there is a detection target.

[0074] Optionally, determining whether a detection target exists based on the first and second comparison results includes:

[0075] If the number of subcarriers corresponding to the first comparison result is greater than the number threshold, and the maximum signal-to-noise ratio represented by the second comparison result is greater than the second threshold, it is determined that there is a target to be detected; otherwise, it is determined that there is no target to be detected.

[0076] In this embodiment, the number threshold can be understood as the number threshold of subcarriers whose detection frequency energy ratio is greater than the first threshold, which is the minimum value used to determine the existence of a detection target.

[0077] Specifically, a threshold CNT_SH is determined. If the number of subcarriers corresponding to the detection frequency energy ratio of the first comparison result is greater than the first threshold (percent_cnt>CNT_SH), and the maximum signal-to-noise ratio of the second comparison result is greater than the second threshold (max_power>POWER_SH), it is determined that a detection target exists. Otherwise, if any condition is not met, it is determined that no detection target exists.

[0078] The present invention provides a target detection method, comprising: acquiring a preprocessed target detection response signal, wherein the preprocessing includes at least abnormal data filtering and static noise processing; performing cumulative windowing processing and Fourier transform processing on the target detection response signal to obtain spectrum data; for a single subcarrier of the target detection response signal, determining a first energy sum of the subcarrier within the target detection frequency range and a second energy sum within the extended detection frequency range based on the spectrum data corresponding to the subcarrier; determining the ratio of the first energy sum to the second energy sum as the detection frequency energy ratio of the subcarrier; and determining the target detection response signal... The average frequency energy of all corresponding subcarriers within the extended detection frequency range; for a single subcarrier, the signal-to-noise ratio (SNR) of the subcarrier is obtained based on the ratio of the subcarrier's first energy to the average frequency energy; the detection frequency energy ratio of each subcarrier is compared with a first threshold to obtain a first comparison result, wherein the first comparison result includes the number of subcarriers whose detection frequency energy ratio is greater than the first threshold; the maximum SNR value is taken from the SNR values ​​of all subcarriers, and the maximum SNR value is compared with a second threshold to obtain a second comparison result; based on the first comparison result and the second comparison result, it is determined whether a detection target exists. The above technical solution uses spectral energy ratio for target detection, significantly improving the detection accuracy of weak targets and making the identification of weak signal sources such as infants and children more accurate and reliable. By combining two different detection frequency ranges to calculate the spectral energy ratio, and then combining the spectral energy ratio with the noise floor energy to calculate the signal-to-noise ratio, it can adapt to different vehicle models and environmental noise conditions, effectively enhancing the robustness of the algorithm and ensuring stable detection performance in different scenarios. By utilizing the difference in spectral energy ratio between live targets such as infants and children and targets in heavy rain outside the vehicle, and by accurately analyzing the energy distribution in the spectrum, it effectively improves the ability to identify targets interfered with by heavy rain. This not only enhances the system's detection accuracy for weak targets (such as infants and children) but also significantly reduces the false alarm rate under severe weather conditions such as heavy rain, ensuring the accuracy and reliability of target detection.

[0079] Example 3

[0080] Figure 3 This is a schematic diagram of the structure of a target detection device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0081] The spectrum data determination module 31 is used to acquire the preprocessed target detection response signal and determine the spectrum data of the target detection response signal;

[0082] The energy ratio determination module 32 is used to determine the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal based on the spectrum data, wherein the detection frequency energy ratio is related to the energy of the subcarrier within a set detection frequency range;

[0083] The target detection module 33 is used to determine the signal-to-noise ratio of each subcarrier within the set detection frequency range, and to determine whether a target is currently being detected based on the detection frequency energy ratio and the signal-to-noise ratio.

[0084] The target detection device used in this technical solution improves the accuracy and reliability of target detection.

[0085] Optionally, the detection frequency-energy ratio determination module 32 is specifically used for:

[0086] For a single subcarrier of the target detection response signal, based on the spectral data corresponding to the subcarrier, the first energy sum of the subcarrier within the target detection frequency range and the second energy sum within the extended detection frequency range are determined respectively;

[0087] The ratio of the first energy sum to the second energy sum is determined as the detection frequency energy ratio of the subcarrier.

[0088] Optionally, the target detection module 33 is specifically used for:

[0089] Determine the average frequency energy of all subcarriers corresponding to the target detection response signal within the extended detection frequency range;

[0090] For a single subcarrier, the signal-to-noise ratio of the subcarrier is obtained based on the ratio of the first energy of the subcarrier to the average energy of the frequency.

[0091] Optionally, the target detection module 33 includes:

[0092] The first comparison unit is used to compare the detection frequency energy ratio of each subcarrier with a first threshold to obtain a first comparison result, wherein the first comparison result includes the number of subcarriers when the detection frequency energy ratio is greater than the first threshold;

[0093] The second comparison unit is used to take the maximum signal-to-noise ratio from the signal-to-noise ratios of all the subcarriers, compare the maximum signal-to-noise ratio with a second threshold, and obtain a second comparison result.

[0094] The target detection unit is used to determine whether a target is currently being detected based on the first comparison result and the second comparison result.

[0095] Optionally, the target detection unit is specifically used for:

[0096] If the number of subcarriers corresponding to the first comparison result is greater than the number threshold, and the second comparison result indicates that the maximum signal-to-noise ratio is greater than the second threshold, it is determined that there is a detection target.

[0097] Otherwise, determine that there is currently no target to detect.

[0098] Optionally, the spectrum data determination module 31 is specifically used for:

[0099] Acquire the preprocessed target detection response signal, wherein the preprocessing includes at least abnormal data filtering and static noise processing;

[0100] The target detection response signal is subjected to cumulative windowing processing and Fourier transform processing to obtain spectral data.

[0101] Optionally, the device further includes a prompting module for:

[0102] If a target is detected, a prompt message is generated and sent to the associated client.

[0103] Optionally, the extended detection frequency range includes the target detection frequency range.

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

[0105] Example 4

[0106] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The electronic device can also be a vehicle with processing capabilities. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0107] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0108] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0109] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as object detection methods.

[0110] In some embodiments, the target detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the target detection method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the target detection method by any other suitable means (e.g., by means of firmware).

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

[0112] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

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

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

[0115] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0116] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0117] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0118] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A target detection method, characterized in that, include: Acquire the preprocessed target detection response signal and determine the spectral data of the target detection response signal; Based on the spectrum data, the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal is determined, and the detection frequency energy ratio is related to the energy of the subcarrier within a set detection frequency range; Determine the signal-to-noise ratio (SNR) of each subcarrier within the set detection frequency range, and determine whether a detection target exists based on the detection frequency energy ratio and the SNR.

2. The method according to claim 1, characterized in that, The step of determining the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal based on the spectrum data includes: For a single subcarrier of the target detection response signal, based on the spectral data corresponding to the subcarrier, the first energy sum of the subcarrier within the target detection frequency range and the second energy sum within the extended detection frequency range are determined respectively; The ratio of the first energy sum to the second energy sum is determined as the detection frequency energy ratio of the subcarrier.

3. The method according to claim 2, characterized in that, Determining the signal-to-noise ratio of each subcarrier within the set detection frequency range includes: Determine the average frequency energy of all subcarriers corresponding to the target detection response signal within the extended detection frequency range; For a single subcarrier, the signal-to-noise ratio of the subcarrier is obtained based on the ratio of the first energy of the subcarrier to the average energy of the frequency.

4. The method according to claim 1, characterized in that, The step of determining whether a target exists based on the detection frequency-energy ratio and the signal-to-noise ratio includes: The detection frequency energy ratio of each subcarrier is compared with a first threshold to obtain a first comparison result, wherein the first comparison result includes the number of subcarriers when the detection frequency energy ratio is greater than the first threshold; Take the maximum signal-to-noise ratio from all the subcarriers, and compare the maximum signal-to-noise ratio with the second threshold to obtain the second comparison result; Based on the first comparison result and the second comparison result, it is determined whether there is a detection target.

5. The method according to claim 4, characterized in that, The step of determining whether a detection target exists based on the first comparison result and the second comparison result includes: If the number of subcarriers corresponding to the first comparison result is greater than the number threshold, and the second comparison result indicates that the maximum signal-to-noise ratio is greater than the second threshold, it is determined that there is a detection target. Otherwise, determine that there is currently no target to detect.

6. The method according to claim 1, characterized in that, The step of acquiring the preprocessed target detection response signal and determining the spectral data of the target detection response signal includes: Acquire the preprocessed target detection response signal, wherein the preprocessing includes at least abnormal data filtering and static noise processing; The target detection response signal is subjected to cumulative windowing processing and Fourier transform processing to obtain spectral data.

7. The method according to claim 1, characterized in that, Also includes: If a target is detected, a prompt message is generated and sent to the associated client.

8. The method according to claim 2, characterized in that, The extended detection frequency range includes the target detection frequency range.

9. A target detection device, characterized in that, include: The spectrum data determination module is used to acquire the preprocessed target detection response signal and determine the spectrum data of the target detection response signal; An energy ratio determination module is used to determine the detection frequency energy ratio of each subcarrier corresponding to the target detection response signal based on the spectrum data, wherein the detection frequency energy ratio is related to the energy of the subcarrier within a set detection frequency range; The target detection module is used to determine the signal-to-noise ratio of each subcarrier within the set detection frequency range, and to determine whether a target is currently being detected based on the detection frequency energy ratio and the signal-to-noise ratio.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a target detection method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute and implement the target detection method according to any one of claims 1-8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements a target detection method according to any one of claims 1-8.

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