Methods and equipment for enhancing the detection of stationary human targets

Abstract

This application proposes a method and device for enhancing the detection of static human targets. Based on the target echo model of radar penetration detection, the method uses adaptive frequency hopping technology to compare the energy difference between the actual detection data and the local echo template, and dynamically optimizes the transmission frequency band of radar detection. While reducing the coherent accumulation time, it significantly improves the signal-to-noise ratio of weak human life signals after medium penetration, and effectively enhances the detection performance of ultra-wideband radar for static human targets in medium penetration scenarios.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and device for enhancing the detection of static human targets. Background Technology

[0002] Ultra-wideband (UWB) radar, with its large instantaneous bandwidth, high range resolution, strong medium penetration capability, low probability of intercept, and strong anti-interference capability, can penetrate non-metallic obstructions such as walls and ruins by transmitting broadband pulses or frequency-modulated / stepped-frequency signals. This enables non-contact detection and positioning of targets behind obstacles, making it a core detection equipment in emergency rescue, special operations, and non-contact vital sign monitoring, possessing broad engineering application value and industrial prospects. However, during the propagation of UWB signals through non-metallic media such as walls and ruins, significant frequency-selective amplitude attenuation occurs. This means that different frequency components of the signal experience varying degrees of energy loss when penetrating the same medium, resulting in non-uniform distortion of the UWB signal's spectral energy. However, in the existing technical solutions for detecting human targets behind a medium using ultra-wideband radar, the ultra-wideband synthesis of multi-frequency signals is directly completed through pulse compression technology. The adverse effects of frequency-selective energy attenuation are not fully considered and improved. This results in a significant decrease in the effective signal-to-noise ratio of the synthesized ultra-wideband signal, which significantly weakens the ability of ultra-wideband radar to detect weak human life signals behind a medium. The detection accuracy, engineering application accuracy, and reliability all have obvious shortcomings. Summary of the Invention

[0003] This application proposes a method and device for enhancing the detection of static human targets, which can solve one of the problems existing in the background art.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] Firstly, a method for enhancing and detecting stationary human targets is provided, including:

[0006] After the stepping frequency ultra-wideband radar transmits stepping sub-pulse signals to the non-wall-penetrating uninhabited area, it receives the echo signals from the non-wall-penetrating uninhabited area.

[0007] After the step-frequency ultra-wideband radar transmits a step sub-pulse signal to the wall-penetrating target area, it receives the echo signal from the first wall-penetrating target area.

[0008] The wall penetration attenuation is obtained from the amplitude of the echo signal from the non-wall-penetrating uninhabited area and the echo signal from the first wall-penetrating target area.

[0009] Based on the comparison results between the wall penetration attenuation and the preset attenuation threshold, a preferred set of transmission frequency bands is selected.

[0010] After the stepped-frequency ultra-wideband radar transmits a stepped sub-pulse signal to the wall-penetrating target area using the preferred set of transmission frequency bands, it receives the echo signal from the second wall-penetrating target area.

[0011] Furthermore, based on the echo signal from the second through-wall target area, static human target detection is performed.

[0012] Based on the above technical solution, and based on the target echo model of radar penetration detection, the energy difference between the actual detection data and the local echo template is compared by adaptive frequency hopping technology. The transmission frequency band of radar detection is dynamically optimized. While reducing the coherent accumulation time, the signal-to-noise ratio of weak human life signals after medium penetration is significantly improved, effectively enhancing the detection performance of ultra-wideband radar for static human targets in medium penetration scenarios.

[0013] In one possible design approach of the first aspect, a preferred set of transmission frequency bands is selected based on the comparison result of the wall penetration attenuation and the preset attenuation threshold. Specifically, based on the comparison result of the wall penetration attenuation and the preset attenuation threshold, low attenuation frequency bands with an attenuation value less than the attenuation threshold are selected as the preferred set of transmission frequency bands.

[0014] In one possible design of the first aspect, the step-frequency ultra-wideband radar transmits step-pulse signals to non-wall-penetrating uninhabited areas as follows: the step-frequency ultra-wideband radar transmits N step-pulse signals to non-wall-penetrating uninhabited areas; the step-frequency ultra-wideband radar transmits step-pulse signals to wall-penetrating target areas as follows: the step-frequency ultra-wideband radar transmits NM step-pulse signals to wall-penetrating target areas; the method for enhancing the detection of stationary human targets further includes:

[0015] Before obtaining the wall penetration attenuation from the amplitude of the echo signal from the non-wall-penetrating uninhabited area and the echo signal from the first wall-penetrating target area, the echo signal from the non-wall-penetrating uninhabited area and the echo signal from the first wall-penetrating target area are interpolated, registered, and aligned.

[0016] In one possible design approach of the first aspect, the echo signal from the non-wall-penetrating uninhabited area is interpolated and registered with the echo signal from the first wall-penetrating target area, specifically including:

[0017] Using the sinc interpolation algorithm, the same number of frequency point data points as the echo signal of the non-wall-penetrating uninhabited area are reconstructed from the echo signal of the first wall-penetrating target area.

[0018] Extract the phase difference between the frequency point data and the echo signal from the non-wall-penetrating uninhabited area;

[0019] Based on the linear phase error model constructed for the phase difference, fitting parameters are extracted from the phase difference. The linear phase error model is constructed based on a one-dimensional phase unwrapping algorithm.

[0020] Furthermore, based on the fitting parameters, a compensation phase factor is constructed, and the frequency point data is phase compensated and calibrated to obtain the first through-wall target area echo signal after interpolation registration and alignment.

[0021] In one possible design approach of the first aspect, the fitting parameters include: equivalent time delay error and system fixed phase offset.

[0022] In one possible design approach of the first aspect, human static target detection is performed based on the echo signal from the second through-wall target area, specifically including:

[0023] The echo signal from the second through-wall target area is pulse-compressed based on inverse Fourier transform to obtain a range image matrix in slow time dimension and range dimension.

[0024] Perform a Fourier transform on the range image matrix to obtain the range-Doppler spectrum matrix;

[0025] Extract the target spectrum index and the frequency spectrum amplitude of each range gate from the range-Doppler spectrum matrix;

[0026] Furthermore, in the frequency spectrum amplitude, human static target detection is performed on the range data of the target spectrum index to obtain human static target detection results.

[0027] In one possible design of the first aspect, human stationary target detection is performed on the range data of the target spectrum index within the frequency spectrum amplitude, specifically as follows:

[0028] The constant false alarm rate (CFAR) algorithm is used to detect the distance data to obtain the detection results of the static human target.

[0029] In one possible design of the first aspect, the unit average constant false alarm algorithm is as follows: a one-dimensional sliding window is set on the range data vector, the sliding window includes: a unit to be detected and a reference unit. During the sliding process, the average value of the background clutter power in the reference unit is calculated, and the average value is multiplied by a scaling factor determined by a preset false alarm probability to obtain a dynamic judgment threshold. The amplitude of the unit to be detected is compared with the dynamic judgment threshold to determine whether there is a stationary human target at the current distance gate, and the absolute distance of the stationary human target is obtained.

[0030] In one possible design approach of the first aspect, the detection of a stationary human target based on the echo signal from the second through-wall target area further includes:

[0031] Before performing pulse compression based on inverse Fourier transform on the echo signal of the second wall-penetrating target area, the echo signal of the second wall-penetrating target area is preprocessed. The preprocessing includes zero-padding and exponentially weighted background cancellation.

[0032] In a second aspect, an electronic device is provided, comprising: a processor and a memory coupled to the processor, the memory for storing a computer program; the processor for executing the computer program stored in the memory to cause the electronic device to perform the human static target signal enhancement detection method as described in any possible implementation of the first aspect. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a technical flowchart provided in the embodiments of this application;

[0035] Figure 2 This is an amplitude map of 256 aligned local reference echo data and through-wall echo data obtained in the radar adaptive frequency hopping enhancement strategy provided in this application embodiment;

[0036] Figure 3 This is a phase map of 256 aligned local reference echo data and through-wall echo data obtained in the radar adaptive frequency hopping enhancement strategy provided in this application embodiment;

[0037] Figure 4 It is the amplitude difference between the local reference echo data and the through-wall echo data provided in the embodiments of this application;

[0038] Figure 5 This application provides a range-Doppler image for detecting stationary human targets, removing radar blind spots;

[0039] Figure 6 The embodiments of this application provide a distance-Doppler image of a stationary human target in the detection of human stationary target signals;

[0040] Figure 7 The one-dimensional distance image of a stationary target with a human body obtained by the method of removing high-frequency signals and the method of this embodiment are obtained by the average constant false alarm rate detection of the unit provided in this application.

[0041] Figure 8 This is a schematic diagram of the Doppler frequency signal corresponding to the distance gate for a stationary human body provided in the embodiments of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification and the above-mentioned figures are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0045] This embodiment proposes a radar adaptive frequency hopping method for enhancing the detection of stationary human targets. By comparing the energy difference between the detection data under medium penetration and the local echo reference data in the air scene, the signal frequency band used by the radar life detector during the detection process is optimized, thereby improving the detection performance of the radar life detector for stationary human targets.

[0046] like Figure 1 The diagram shown is a technical flowchart of this embodiment, which mainly includes radar adaptive frequency hopping enhancement strategy and weak human life signal enhancement detection. The following is a detailed description of the technical solution of this application, which takes a stepped-frequency ultra-wideband radar as an example for illustration.

[0047] 1. Radar transmits stepped-frequency signals in air-to-air scenarios:

[0048] A stepped-frequency ultra-wideband radar transmits refined N frequency stepped sub-pulse signals over a non-wall-penetrating uninhabited area within one pulse repetition period. The transmitted signal can be represented as: (1)

[0049] in This represents the amplitude of the transmitted signal, where N is the number of frequency steps. For frequency step indexing, Represents a refined signal frequency. This indicates the initial frequency of the step-frequency radar. Indicates the frequency step size. Indicates the initial phase of the frequency point. This indicates the time when the radar receives the signal. The total combined bandwidth of the radar is approximately... Distance resolution is ,in It represents the speed of light.

[0050] The echo signal from a non-wall-penetrating, uninhabited area acquired by radar can be simply represented ,in Indicates the time when the radar receives the signal. This represents the time delay due to inherent antenna coupling and fixed scattering in free space. Refined, acquired ultra-wideband echo data of the empty scene is used as local echo reference data.

[0051] 2. The radar transmits stepped-frequency signals to the ruins scene.

[0052] (1) Preliminary scan

[0053] While ensuring complete consistency between the total synthesized bandwidth and the refined acquisition, the number of sub-pulse transmissions is reduced by increasing the frequency step interval. This achieves a preliminary approximate scan of the medium-penetrating region, albeit at the cost of sacrificing some unambiguous detection range. For rapid approximate scanning, the stepped-frequency ultra-wideband radar maintains consistent core radar parameters and transmits NM frequency-stepped sub-pulse signals to the target region penetrating the wall within one pulse repetition cycle. The transmitted signals can be expressed as: (2)

[0054] Where NM is the number of frequency steps. For frequency step indexing, Indicates approximate signal frequency. This indicates the approximate frequency step size. The initial phase of the approximate frequency point is given, and the total synthesized bandwidth of the radar is approximately... .

[0055] The radar receives the echo signal from the target area after the medium has penetrated, which can be simply represented as... ,in Indicates the time when the radar receives the signal. This represents the time delay of electromagnetic waves penetrating the medium of the ruins.

[0056] (2) Interpolation alignment:

[0057] The approximate scan shortens the detection time by reducing the number of sub-pulses (from N to NM), enabling a rapid scan of wall attenuation in the medium-penetrating area. In contrast, the local reference data is refined detection data of N complete frequency points obtained in a non-penetrating environment. Since the number of frequency points of the approximate scan echo data and the local reference echo data are inconsistent, they cannot be directly compared point by point and attenuation calculated in the frequency domain.

[0058] To eliminate calculation errors caused by frequency mismatch, the approximate scan data with a lower sampling rate needs to be reconstructed and aligned to the same scale as the reference data. The sinc interpolation algorithm is used to reconstruct the approximate scan echo data to obtain the same number of frequency points as the local template, achieving frequency domain alignment between the two sets of data and eliminating attenuation calculation errors caused by frequency mismatch. The echo data interpolation formula can be expressed as: (3)

[0059] in Indicates the local reference data corresponding to the first A refined frequency point, This indicates the i-th approximate frequency point corresponding to the approximate scan. Indicates frequency points during the approximate scanning process Complex frequency domain echo data acquired at the location, For the normalized sinc kernel function, The signal is reconstructed through interpolation. The reconstructed frequency points correspond one-to-one with the local template frequency points, completing the initial alignment of the frequency point positions.

[0060] The least-squares linear phase fitting calibration is employed, and the measured phase difference at each corresponding frequency point is extracted by multiplying the local template with the conjugate of the reconstructed signal. (4)

[0061] in To reconstruct the conjugate of the echo data through interpolation, To obtain the phase angle of the complex signal, The measured phase difference at the nth corresponding frequency point. This is local template echo data. Because the initial phase value extracted by this function is wrapped in... ,exist The phase jump is so large that a one-dimensional phase unwrapping algorithm is first used to unfold the phase and obtain the continuous absolute measured phase difference.

[0062] For stepped-frequency radar, the phase error caused by medium penetration and system hardware mainly manifests as a linear phase component that varies with frequency and a fixed phase offset. Therefore, the following linear phase error is established for the absolute phase difference after unwrapping. Model: (5)

[0063] in To find the optimal equivalent time delay error, For the system to be optimized, a fixed phase shift is required. This is random phase noise. The measured values ​​obtained after unwrapping using equation (4) are... Using the sequence as the observation dataset, the optimal parameters can be estimated and extracted by solving the linear model of equation (5) using the least squares fitting algorithm. and The value of .

[0064] Finally, based on the extracted optimal fitting parameters and A phase compensation factor is constructed, and the interpolated reconstructed signal is calibrated with phase compensation to obtain the final medium penetration echo data that is fully aligned with the local template. : (6)

[0065] 3. Obtain the frequency amplitude attenuation parameters for each signal:

[0066] Using echo data from the target area under dielectric penetration and local echo reference data from uninhabited areas without wall penetration, the amplitude of the data is interpolated to obtain the wall penetration attenuation of different frequency signals in the echo data from the target area under dielectric penetration. : (7)

[0067] in The amplitude value of the local template echo data. The amplitude values ​​of the aligned medium penetration echo data. The optimal frequency band selection is obtained by combining this with the attenuation threshold. (8)

[0068] in The attenuation threshold, This is the set of optimal transmission frequency bands after screening. It can be adaptively adjusted according to different medium detection scenarios. In order to improve the penetration detection performance while ensuring the detection timeliness, the threshold value in this embodiment is typically 3dB. The final number of frequency points selected as the optimal frequency band is NM, which is consistent with the number of approximate scanning sub-pulses. Under limited pulse resources, frequency points that are severely affected by medium interference are eliminated, and the transmission energy is concentrated on the optimal frequency band with high penetration.

[0069] 4. Enhanced detection using optimal transmission frequency:

[0070] During the phase of enhancing and detecting weak human vital signs, the radar performs its detection task based on an optimal frequency band strategy. It only retains the selected low-attenuation frequency bands for signal transmission, skips the eliminated high-attenuation frequency bands, and actually transmits only NM sub-pulses of the optimal frequency bands, transmitting low-repetition-rate step-frequency signals corresponding to the optimal frequency bands. : (9)

[0071] in For N full-band reference transmission signals, For the frequency domain weighted window function, satisfying In the echo preprocessing stage, in order to ensure that the distance resolution remains unchanged after pulse compression, the untransmitted frequency points are zero-padding in the digital baseband data to construct a complete N-point frequency domain sequence.

[0072] 5. Echo signal preprocessing:

[0073] (1) Pulse compression:

[0074] To improve the range resolution of the echo signal and obtain range dimension information, it is necessary to perform pulse compression on the received step-frequency pulse train (i.e., the step-frequency echo signal). In one embodiment of this example, using... Point-fast discrete inverse Fourier transform is used to compress the step-frequency pulse train. For the k-th slow time index, the pulse compression result is... Step frequency echo data of a distance gate This can be expressed as: (10)

[0075] in This represents the distance-oriented index, and k represents the slow time index.

[0076] (2) Index-weighted background cancellation:

[0077] In medium penetration detection scenarios, the target signal is often submerged in clutter due to strong interference from direct-coupled waves and background clutter. Therefore, to obtain the target signal, clutter suppression of the radar echo is necessary. In one embodiment of this invention, exponentially weighted background cancellation is used to remove the clutter signal, resulting in a clutter-suppressed echo signal. It can be represented as: (11)

[0078] in For the k-th slow time period, the th Background clutter signal updated by a distance gate These are the exponential weighting coefficients, with a range of values. .

[0079] 6. Static human target detection:

[0080] (1) Distance-Doppler spectrum:

[0081] After pulse compression and exponentially weighted background cancellation preprocessing, the obtained clean target range image data can be represented as a dataset containing the range dimension L. r and slow time dimension M is Two-dimensional matrix Perform the distance image matrix along the slow time dimension Point fast discrete Fourier transform yields the range-Doppler spectrum matrix. ,but No. The distance gate, the first The amplitude of each frequency spectrum can be expressed as: (12)

[0082] in For distance gate, For frequency spectrum, Indicates slow time sampling points. Indicates the first The fast time corresponding to each distance gate Represents the preprocessing matrix China corresponds to fast time With slow time points The signal value.

[0083] (2) Target detection:

[0084] In the weak human target detection stage, the target frequency needs to be determined first. By extracting the frequency domain maxima points in the range-Doppler spectrum matrix (RDM) that correspond to typical human vital signs, the physical frequency of these points can be identified as the target frequency. After determining the target spectrum index, in Centered on the corresponding frequency point, several neighboring frequency bands are extracted as effective life signal energy, while the energy of non-neighboring background frequency bands is regarded as noise. By calculating the ratio of the two, the signal-to-noise ratio after enhanced detection is obtained, which is used to evaluate the signal detection quality.

[0085] Therefore, we only need to configure the frequency in the RDM. Distance data Target detection can be performed to obtain the target distance. Here, ":" indicates retrieving all elements of that dimension, i.e., extracting the index corresponding to the Doppler frequency cell of the target. For all range gate sequences, with respect to a single-sided spectrum, assuming the pulse repetition frequency of the step-frequency radar is... Then the target frequency spectrum With target frequency The correspondence can be expressed as: (13)

[0086] In one embodiment of this example, the Cell-Averaging Constant False Alarm Rate (CA-CFAR) algorithm is used to analyze the range data. The target distance was obtained by performing a detection. : (14)

[0087] in, This represents the operation function for executing the CA-CFAR detection algorithm.

[0088] The specific processing procedure of CA-CFAR is as follows: A one-dimensional sliding window is set on the range data vector, which consists of the target cell (CUT), guard cells, and reference cells (background training cells). During the sliding process, the average value of the background clutter power within the reference cells is calculated and multiplied by a scaling factor determined by a preset false alarm probability to obtain a dynamic decision threshold. The amplitude of the target cell is compared with this dynamic threshold. If it is greater than the set threshold, it is determined that a target exists at the current range gate, thus obtaining the final absolute distance of the stationary human target. .

[0089] To further demonstrate the effectiveness of this embodiment, the following supplementary explanation is based on simulation results.

[0090] The radar employs a single-transmit, single-receive frequency-stepping system, with a frequency range of 600MHz to 1.12GHz, 256 frequency points, and a pulse repetition frequency of 10Hz. During detection, the radar is placed close to a concrete wall approximately 0.3m thick.

[0091] Figure 2 and Figure 3 The amplitude and phase of the local reference echo data and through-wall echo data obtained after alignment of 256 frequency points in the radar adaptive frequency hopping enhancement strategy are shown respectively. It can be seen that there are significant differences in amplitude between the through-wall echo data and the local reference echo data. Figure 4 The amplitude difference between the local reference echo data and the through-wall echo data is used as the threshold. The corresponding frequency points with an attenuation greater than 3dB are removed. Figure 5 and Figure 6 These refer to the range-Doppler image after removing radar blind zones and the range-Doppler image with the presence of a stationary human target in human target signal detection, respectively. Figure 6As can be seen, the target frequency of 0.4Hz in this embodiment stands out significantly in the spectrum, while exhibiting better noise suppression. In contrast, the target frequency of the high-frequency signal removal method (i.e., the default mode) is approximately consistent with the noise intensity, indicating that this embodiment has a better and faster ability to detect weak vital signals. Figure 7 As shown, the one-dimensional distance images of a static human target obtained after cell-averaged constant false alarm rate detection are presented using the high-frequency signal removal method (i.e., the default mode) and the method of this embodiment. Figure 8 To correspond to the Doppler frequency signal of the stationary human target range gate, the target frequency is located at 0.4Hz, and the detection signal-to-noise ratio is calculated. The signal-to-noise ratio SNRe of the method in this embodiment is 7.8015, while the signal-to-noise ratio SNR of the method that removes high-frequency signals is 4.7268 (i.e., the reference signal-to-noise ratio). The method in this embodiment has a better signal-to-noise ratio for detecting weak human vital signals, which shows that the method in this embodiment has a stronger detection capability for weak vital signals.

[0092] In summary, this embodiment proposes a radar adaptive frequency hopping method for enhancing the detection of static human targets, thereby improving the detection capability of ultra-wideband radar for weak human life signals and achieving enhanced detection of static human targets. This embodiment uses a frequency-stepped continuous wave ultra-wideband radar system as an example. The method in this embodiment mainly consists of two stages: radar adaptive frequency hopping enhancement strategy and weak human life signal enhancement detection. In the radar adaptive frequency hopping enhancement strategy stage, firstly, ultra-wideband radar is used to detect non-wall-penetrating empty scenes, and ultra-wideband echo data is collected in detail as local echo reference data. Secondly, while maintaining the consistency of the radar's core parameters, in actual rubble penetration detection, the number of steps and the step frequency are adjusted to perform a rough scan of the rubble with the same bandwidth. Through interpolation processing, the rough scan data is aligned with the local echo template in terms of frequency points. Subsequently, the amplitude differences between the two sets of aligned data are compared to obtain the amplitude attenuation characteristics of different frequency signals during medium penetration. Severely attenuated frequency bands are eliminated, and frequency bands with minimal energy attenuation after penetration are selected as the optimal transmission signal frequency band. Then, the radar performs detection based on the optimal frequency band strategy, retaining only the selected low-attenuation frequency band for signal transmission, transmitting the low-repetition-rate step-frequency signal corresponding to the optimal frequency band, and simultaneously receiving the step-frequency echo signal after medium penetration. Next, the step-frequency echo signal undergoes preprocessing operations such as background accumulation averaging, exponentially weighted background destructive suppression, and pulse compression. Finally, a fast discrete Fourier transform is performed on the preprocessed step-frequency echo signal, and the target frequency is determined based on the weak human vital sign signal frequency band, completing the enhanced detection of stationary human targets. This embodiment is based on the target echo model of radar through-medium detection. By comparing the energy difference between the actual detection data and the local echo template through adaptive frequency hopping technology, the transmission frequency band of radar detection is dynamically optimized. While reducing the coherent accumulation time, it significantly improves the signal-to-noise ratio of weak human life signals after medium penetration, effectively enhancing the detection performance of ultra-wideband radar for static human targets in medium penetration scenarios.

[0093] This application also provides an electronic device, including: a processor, and a memory coupled to the processor, the memory being used to store a computer program; the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method as described in any of the above embodiments.

[0094] Electronic devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. These electronic devices may include, but are not limited to, processors and memory.

[0095] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting various parts of the device via various interfaces and lines.

[0096] The memory can be used to store the computer program, and the processor implements various functions of the electronic device by running or executing the computer program stored in the memory and calling the data stored in the memory.

[0097] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0098] This application also provides a storage medium, which is a computer-readable storage medium. The computer program is stored in the computer-readable storage medium, and when executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0099] This application also provides a computer program product, including: a computer program or instructions that, when the computer program or instructions are run on a computer, cause the computer to perform any of the above possible implementation methods.

[0100] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method of human motionless target signal enhancement detection, characterized in that, include: After the stepping frequency ultra-wideband radar transmits stepping sub-pulse signals to a non-wall-penetrating uninhabited area, it receives the echo signals from the non-wall-penetrating uninhabited area. After the step-frequency ultra-wideband radar transmits a step-sub-pulse signal to the wall-penetrating target area, it receives the echo signal from the first wall-penetrating target area. The wall penetration attenuation is obtained from the amplitude of the echo signal from the non-wall-penetrating uninhabited area and the echo signal from the first wall-penetrating target area. Based on the comparison results between the wall penetration attenuation and the preset attenuation threshold, a set of preferred transmission frequency bands is selected. After the stepped-frequency ultra-wideband radar transmits a stepped sub-pulse signal to the wall-penetrating target area using the preferred set of transmission frequency bands, it receives the echo signal from the second wall-penetrating target area. Furthermore, based on the echo signal from the second through-wall target area, static human target detection is performed; Based on the comparison result of the wall penetration attenuation and the preset attenuation threshold, a preferred set of transmission frequency bands is selected. Specifically, based on the comparison result of the wall penetration attenuation and the preset attenuation threshold, low attenuation frequency bands with an attenuation value less than the attenuation threshold are selected as the preferred set of transmission frequency bands.

2. The method of claim 1, wherein the step of enhancing the target signal is performed by using a beamformer. The step-frequency ultra-wideband radar transmits step-pulse signals to non-wall-penetrating uninhabited areas as follows: the step-frequency ultra-wideband radar transmits N step-pulse signals to non-wall-penetrating uninhabited areas. The step-frequency ultra-wideband radar transmits step-pulse signals to wall-penetrating target areas as follows: the step-frequency ultra-wideband radar transmits NM step-pulse signals to wall-penetrating target areas. The method for enhancing the detection of stationary human targets further includes: Before obtaining the wall penetration attenuation from the amplitude of the echo signal from the non-wall-penetrating uninhabited area and the echo signal from the first wall-penetrating target area, the echo signal from the non-wall-penetrating uninhabited area and the echo signal from the first wall-penetrating target area are interpolated, registered, and aligned.

3. The method of claim 2, wherein the step of enhancing the target signal is performed by using a matched filter. The echo signal from the non-wall-penetrating uninhabited area is interpolated and registered with the echo signal from the first wall-penetrating target area. Specifically, this includes: Using the sinc interpolation algorithm, the same number of frequency point data points as the echo signal of the non-wall-penetrating uninhabited area are reconstructed from the echo signal of the first wall-penetrating target area. Extract the phase difference between the frequency point data and the echo signal from the non-wall-penetrating uninhabited area; Based on the linear phase error model constructed for the phase difference, fitting parameters are extracted from the phase difference. The linear phase error model is constructed based on a one-dimensional phase unwrapping algorithm. Furthermore, based on the fitting parameters, a compensation phase factor is constructed, and the frequency point data is phase compensated and calibrated to obtain the first through-wall target area echo signal after interpolation registration and alignment.

4. The method of claim 3, wherein the step of enhancing the target signal is performed by using a correlation function. The fitting parameters include: equivalent time delay error and system fixed phase offset.

5. The method of claim 1, wherein the step of enhancing the target signal is performed by using a matched filter. Based on the echo signal from the second through-wall target area, static human target detection is performed, specifically including: ​ The echo signal from the second through-wall target area is pulse-compressed based on inverse Fourier transform to obtain a range image matrix in slow time dimension and range dimension. Perform a Fourier transform on the range image matrix to obtain the range-Doppler spectrum matrix; Extract the target spectrum index and the frequency spectrum amplitude of each range gate from the range-Doppler spectrum matrix; Furthermore, in the frequency spectrum amplitude, human static target detection is performed on the range data of the target spectrum index to obtain human static target detection results.

6. The method for enhancing and detecting static human targets as described in claim 5, characterized in that, In the frequency spectrum amplitude, human stationary target detection is performed on the range data of the target spectrum index, specifically as follows: The constant false alarm rate (CFAR) algorithm is used to detect the distance data to obtain the detection results of the human static target.

7. The method for enhancing and detecting static human targets as described in claim 6, characterized in that, The unit average constant false alarm rate algorithm is as follows: a one-dimensional sliding window is set on the range data vector, the sliding window includes a target unit and a reference unit. During the sliding process, the average value of the background clutter power in the reference unit is calculated, and the average value is multiplied by a scaling factor determined by a preset false alarm probability to obtain a dynamic judgment threshold. The amplitude of the target unit is compared with the dynamic judgment threshold to determine whether there is a stationary human target at the current range gate, and the absolute distance of the stationary human target is obtained.

8. The method for enhancing and detecting static human targets as described in claim 5, characterized in that, Human static target detection based on the echo signal of the second through-wall target area also includes: Before performing pulse compression based on inverse Fourier transform on the echo signal of the second wall-penetrating target area, the echo signal of the second wall-penetrating target area is preprocessed. The preprocessing includes zero-padding and exponentially weighted background cancellation.

9. An electronic device, characterized in that, The electronic device includes: a processor, and a memory coupled to the processor. The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the human static target signal enhancement detection method as described in any one of claims 1-8.

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