MEMS-assisted handheld MIMO-SAR security check imaging method

Abstract

The invention discloses a handheld security check imaging method based on MEMS assistance, and aims to combine an MEMS sensor with an MIMO radar plate to form a handheld convenient security check imaging method with high precision and low cost according to the rapid and fine security check requirement of people in a security check place with dense people. Firstly, the radar board transmits a linear frequency modulation signal and receives an echo signal reflected by a target, and during the period, the MEMS sensor feeds back the motion track and position information of the handheld security check system in real time, and provides accurate attitude and position information. And secondly, position calibration is carried out on the track information of the MIMO radar antenna through the data of the MEMS sensor, and the zero drift of the accelerometer is compensated based on the wavelet packet change, so that the error caused by the system motion and the position change is reduced. And finally, residual phase errors are estimated and eliminated based on a particle swarm algorithm, non-contact three-dimensional imaging of the target is realized in combination with a wavenumber domain imaging algorithm on this basis, and hazardous article inspection is realized in combination with technologies such as deep learning and image recognition.

Description

Technical Field

[0001] The invention belongs to the field of radar signal processing and relates to a MEMS-assisted handheld MIMO-SAR security inspection imaging method. Background Art

[0002] Millimeter-wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) near-field imaging technology has become a new means of security inspection application due to its high resolution and penetration ability. However, existing millimeter-wave radar security inspection equipment is generally bulky and difficult to install and use, which limits its application scope and flexibility in certain environments.

[0003] The development of miniaturized, handheld security inspection equipment can greatly facilitate the operation of security personnel, making the security inspection process more flexible and efficient, thereby broadening the application prospects of millimeter-wave radar technology. On the one hand, handheld security inspection equipment can use the swing of the arm to form a synthetic aperture, combining the real aperture formed by the MIMO array with the bandwidth of high-frequency radar signals such as millimeter-wave terahertz, to obtain high-resolution three-dimensional imaging results of the target to be detected. On the other hand, handheld security inspection methods are more easily applied to occasions with dense crowds or narrow spaces, such as large-scale events, public transportation systems, etc., to improve the convenience and practicality of security inspections.

[0004] However, in practical applications, handheld millimeter-wave radar devices face some technical difficulties, especially in the challenges of swing trajectory estimation and swing error compensation. In response to these problems, this patent designs a radar imaging method based on microelectromechanical systems (MEMS). This method uses MEMS sensors to obtain coarse information on device acceleration and position, compensates for zero drift of MEMS sensor data through efficient signal processing algorithms, and accurately estimates motion parameters from radar echoes based on particle swarm optimization minimum entropy algorithm, further eliminating phase errors, thereby achieving high-precision three-dimensional imaging of the object to be detected. Based on the three-dimensional image, deep learning, image recognition and other methods can be used to detect dangerous goods. This new handheld imaging technology will provide broader application prospects for the application of millimeter-wave radar in security inspection and other fields. Summary of the invention

[0005] The purpose of the present invention is to improve the mobility and flexibility of the security inspection imaging system by using a handheld MEMS and MIMO-SAR radar system, combining the MEMS signal and radar signal processing method, enhancing the quality and accuracy of security inspection imaging, simplifying the operation and improving user friendliness. Its application scenarios are as follows: Figure 1 It specifically includes the following steps:

[0006] The first step is to use a handheld radar system that includes multiple transmitting antennas and receiving antennas for scanning. That is, the MIMO radar system transmits signals one by one, and the receiving antennas receive the signals in sequence.

[0007] The signal is sent from the mth transmitting antenna, reflected by the target, and received by the nth receiving antenna. The position of the target point from the transmitting and receiving antennas is expressed as:

[0008]

[0009]

[0010] Where (x, y, z) is the target position, and is the location of the transmitting and receiving antennas.

[0011] The reflected signal received by the nth receiving antenna from the mth transmitting antenna is:

[0012]

[0013] in, is the instantaneous distance, σ p is the reflection coefficient of the target P at the position (x, y, z), and O is the set of all point targets in the imaging scene. The synthesizer generates a linear frequency modulation pulse, which is transmitted through the transmitting antenna and received by the receiving antenna, and then mixed with the received signal through the mixer to finally obtain the processed signal.

[0014] Step 2: Swing the handheld MIMO-SAR radar device to form a three-dimensional imaging model, where the position of the antenna changes with the swing of the arm. The swing diagram is shown in the figure below. Figure 2 This change can be described as follows:

[0015] Assume that the position of the antenna changes with time due to the swing of a person's arm. After the original antenna position changes, the swing of the arm will cause the azimuth and elevation angles to change, and this angle changes with time t. This change leads to the phase φ in the signal model nm (t) also changes with the position of the antenna. The real aperture of the MIMO antenna itself determines the system's resolution in one-dimensional space. Each antenna element can be regarded as an independent sensor that can capture signals from a specific direction. Through the movement of the arm, the MIMO system uses the principle of synthetic aperture radar to enhance imaging capabilities. The system's moving path allows it to collect data from different angles. Through the comprehensive processing of this data, it can construct a two-dimensional plane image of the target object, that is, obtain the resolution of the antenna movement direction. Its azimuth resolution ρ az It is expressed as:

[0016]

[0017] Among them, L SAR is the synthetic aperture length. The bandwidth of the signal is a key factor in determining the resolution of the radar system in the range direction. In three-dimensional imaging, this allows the system to distinguish multiple targets that are very close together, thereby providing depth information to obtain distance resolution. Range resolution ρ range It can be expressed as:

[0018]

[0019] By combining these three resolutions, the MIMO antenna system can create a detailed three-dimensional image of the target area by fusing information from different dimensions.

[0020] Step 3: Use MEMS sensors to obtain the three-dimensional motion parameters of the security scanner, including acceleration, angular velocity, and roll axis parameters. Based on the preliminary three-dimensional parameters of the MEMS device, the antenna position is Corrections are made to update the signal model.

[0021] The corrected antenna position is used to update the signal model:

[0022]

[0023]

[0024] Based on the new antenna position, the signal reflection signal is recalculated and the estimated value is Construct the distance, and then obtain the three-dimensional radar image of the target based on the following imaging mechanism:

[0025]

[0026] The signal model is combined with wavenumber domain processing and wavenumber domain approximation (WDBA) is used. First, based on the wavenumber domain support domain approximation theory, an efficient three-dimensional reconstruction model is developed. The wavenumber support domains of the scattering point and the reference point are approximately the same. According to the wavenumber support domain, the wavenumber of the scattering point can be expressed as:

[0027]

[0028]

[0029]

[0030] in, and Represent the 3D wavenumber support domains for transmission and reception respectively.

[0031] Taylor expansion is used to process the wavenumber domain approximate phase error (WDBAPE), analyze and correct the 3D distortion caused by the first-order terms, and establish a 3D geometric mapping from the WDBA imaging position to the actual position to correct these distortions.

[0032] Φ=(k' x x+k' y y+k' z z)+Δφ (12)

[0033] where Δφ is the phase error term given by:

[0034]

[0035] After processing the distortion, an adaptive sub-scene segmentation and stitching strategy is used to reduce computational overhead and handle large-scale datasets. This involves effectively covering the imaging target area instead of the entire scene, thus reducing the number of necessary sub-scenes and optimizing the processing process.

[0036] Each sub-scene is processed independently in the wavenumber domain, and a 3D inverse fast Fourier transform (3D-IFFT) is used to reconstruct a 3D image based on the wavenumber domain signal:

[0037]

[0038] Through the above detailed steps, we not only improved the accuracy of signal processing, but also significantly reduced the computational complexity of the algorithm, making it more suitable for real-time and large-scale security screening applications.

[0039] Step 4: Although the antenna position has been corrected, the new antenna position (x' a ,y' a ,z' a ) may still have errors, which will cause phase errors, thus affecting the imaging quality and causing the image to appear blurred, defocused, and other problems.

[0040] The motion error model is applied to the radar echo signal to calculate the imaging result before correction. The output signal data of the MEMS accelerometer, including the signal with zero drift error, can be collected, and the output signal of the accelerometer can be decomposed by wavelet packet to decompose the signal into sub-signals in different frequency bands. The formula is as follows:

[0041]

[0042] in is the wavelet packet coefficient of the kth node in the jth layer, ψ j,k (t) is the wavelet packet basis function, and x(n) is the original signal. Then extract features from the sub-signals after wavelet packet decomposition, and the features may include the statistical characteristics of the sub-signals. With the features obtained by wavelet packet decomposition as input and zero drift error as the target output, a discrete high-pass filter is designed to detect its performance. The loss function can be the mean square error:

[0043]

[0044] Among them, y i is the actual zero drift error, is the zero drift error after filtering. Perform wavelet packet decomposition on the new data, extract features and input them into the compensation model to perform zero drift error, and perform zero drift compensation on the accelerometer output signal based on the results. Verify and evaluate the compensated signal to ensure a good compensation effect. The model performance can be evaluated by experimentally comparing the signal stability and accuracy before and after compensation.

[0045] The accuracy of the antenna position information obtained based on MEMS will seriously affect the accuracy of the imaging results. The phase error caused by the distance error will cause serious deformation and defocusing in the imaging results, affecting the imaging quality. Therefore, phase error correction is required.

[0046] Step 5: During the imaging process, due to the dynamic change of the antenna position, the phase error becomes a key factor affecting the imaging quality. This phase error is mainly caused by the non-ideal movement of the antenna (such as the swing of the arm), resulting in a phase deviation between the received signal and the actual reflected signal. Use a small amount of data to perform minimum entropy and particle swarm algorithm to estimate the phase error of the antenna. Specifically, set the initial particle swarm parameters and calculate the image entropy as the cost function. The phase formula is:

[0047]

[0048] in

[0049]

[0050]

[0051] In order to accurately compensate for the phase error, we first obtain the real-time antenna position through the MEMS sensor and initialize the parameters of the particle swarm based on this position information. Each particle represents a set of potential antenna parameters (velocity and acceleration). The particle swarm is optimized to estimate the parameters that can achieve the best imaging quality. is the position of particle i at the beginning, is the velocity of particle i at the beginning. For a single particle, the imaging result under the current parameters is calculated, and the image entropy is used as the cost function to evaluate the imaging quality. The formula of image entropy is as follows; the state of the particle is adjusted by the following update rule: update the particle position and velocity, and iterate to find the optimal solution.

[0052]

[0053] Among them, p k is the probability that the pixel intensity in the imaging result is k. Update the position and velocity of the particle to find the optimal solution:

[0054]

[0055]

[0056] Among them, w is the inertia weight, which controls the influence of the previous speed, c1 and c2 are learning factors, r1 and r2 are random numbers in the interval [0,1], and p i is the optimal position currently found by particle i, and g is the global optimal position. If the maximum number of iterations is reached or the change in particle position is less than a certain threshold, the optimization is stopped. If the target is re-imaged using the optimal parameters found, the phase error can be eliminated to obtain a higher quality image.

[0057] Step 6: Compensate the phase error of all data according to the optimal parameters found by the particle swarm optimization algorithm, perform fine imaging on the compensated signals, and obtain high-quality three-dimensional imaging results to ensure that all motion and phase errors are corrected:

[0058]

[0059] Before performing a three-dimensional Fast Fourier Transform (FFT), the updated signal data is interpolated. Interpolation is to estimate values ​​between discrete data points so that the data converted to the frequency domain is smoother and more continuous.

[0060] If V(x,y,z) is the corrected data, the interpolated data V′(x′,y′,z′) can be expressed as:

[0061]

[0062] Perform a three-dimensional FFT on the interpolated data to convert the data from the time domain to the frequency domain, which is necessary for high-quality imaging. The mathematical expression of FFT is:

[0063]

[0064] Among them, (kx ,k y ,k z ) is the coordinate in the frequency domain, is the data in the frequency domain.

[0065] Use the output of the 3D FFT to construct the final imaging result. Convert the complex frequency domain data back to real time domain data and extract the amplitude information as the imaging result. Analyze the imaging results to confirm whether all motion-induced phase and position deviations have been properly compensated. Based on the preliminary imaging results, further adjust the interpolation and FFT parameters to optimize the imaging quality. Ensure that all steps work closely together to achieve the best imaging performance.

[0066] This additional step ensures that the radar system can effectively process and compensate for any phase errors caused by motion when performing complex dynamic imaging, while utilizing high interpolation and FFT processing to speed up imaging while ensuring high quality and high accuracy of the imaging results.

[0067] Step 7: Use deep learning and other methods to detect dangerous goods. First, collect a large amount of MIMO-SAR imaging data, including image data of known categories of dangerous goods and non-dangerous goods. These data will be used to train deep learning models. During the deployment phase, the imaging data provided by the MIMO-SAR system is received in real time, and the CNN model quickly identifies and classifies each scanned object. The model will output the classification probability of each object. The system determines whether it is a dangerous good based on the probability threshold and feeds back the results to the operator in real time. In addition, in order to adapt to the ever-changing security inspection environment and emerging types of dangerous goods, the model needs to be regularly learned and updated online. By continuously monitoring the performance of the model and collecting new data samples for model fine-tuning, ensure that the system remains efficient and accurate in new security inspection scenarios. The system workflow diagram is shown below Figure 3 shown.

[0068] The present invention has advantages that traditional imaging methods do not have: first, timely feedback and applicability to dynamic environments (handheld devices can provide real-time or almost real-time imaging results, which is very important for application scenarios that require immediate diagnosis and decision-making. Handheld devices are particularly suitable for use in dynamic or changing environments because operators can instantly adjust the position and angle to adapt to on-site conditions). Second, it has high resolution and accuracy (by using linear frequency modulation signals, the distance, speed and angle information of the target object can be accurately captured. This technology can provide high-resolution imaging effects and more accurately capture details). Third, it has strong anti-interference ability (combined with the wavelet packet transform method, zero drift compensation can be effectively performed, thereby improving the system's resistance to external interference. Traditional methods often have difficulty in dealing with interference problems in complex environments). Third, it can automatically calibrate and compensate for errors (by automatically calibrating and compensating the system errors of MEMS sensors, the accuracy and reliability of data in the imaging process are ensured, and the system errors are effectively reduced). Fourth, it can perform fast non-uniform imaging (with the help of fast non-uniform imaging algorithms, it can efficiently process the collected data, accurately process non-uniform sampling points, and improve the imaging speed and quality. This advantage makes this method have significant advantages in real-time imaging and large-scale data processing).

[0069] After adopting the above-mentioned solution, the present invention can achieve the following beneficial effects:

[0070] 1. The radar system can obtain information such as the distance, speed and angle of an object, which can achieve accurate positioning and tracking of the target. Especially in a dynamic environment, this multi-dimensional information can improve the accuracy and reliability of target tracking.

[0071] 2. The radar system has strong anti-interference ability to changes in ambient light and object occlusion, and can work stably in complex environments. In contrast, the performance of the optical tracking system may decline when there is insufficient light or severe reflection interference.

[0072] 3. Combined with the wavelet packet transform method of MEMS sensors, effective zero drift compensation and error calibration are performed to improve the overall accuracy of the system.

[0073] This is especially important in long-term monitoring and dynamic target detection.

[0074] 4. The fast non-uniform imaging algorithm is used to process the collected data, and the non-uniform sampling points are processed through the wavenumber domain interpolation method, which improves the data processing speed and imaging accuracy, enabling the system to respond to and process large amounts of data in real time.

[0075] 5. The particle swarm optimization algorithm is used to estimate antenna parameters and compensate for phase errors, which can adapt to complex motion trajectories and ensure the accuracy of imaging and tracking. This flexibility enables the system to perform better in changing practical application scenarios.

[0076] 6. By establishing an echo signal model and analyzing scattering points, the radar system can realize simultaneous identification and monitoring of multiple targets, and is suitable for applications in multi-target environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A schematic diagram of the application scenario.

[0078] Figure 2 Schematic diagram of swing

[0079] Figure 3 System workflow diagram DETAILED DESCRIPTION

[0080] Step 1: Deploy a MIMO radar system with multiple transmitting and receiving antennas. The transmitting antennas transmit signals one by one, and the receiving antennas receive the signals reflected by the target one by one. The signal model is defined as: By receiving signals reflected from a target, radar can measure the distance, speed and angle of the target.

[0081] Step 2: As the arm swings, the antenna position changes. First, the target direction is determined using the angular resolution of the antenna array, then the image horizontal details are enhanced using synthetic aperture technology, and finally, high-bandwidth signals are used to achieve accurate depth resolution.

[0082] Step 3: Process the data obtained after scanning, including the acceleration and angular velocity obtained by the sensor, correct the preliminary three-dimensional parameters of the antenna, and update the signal model. The reconstruction imaging algorithm is

[0083] Step 4: Correct the antenna position error and use the wavelet packet transform method to compensate for the system error of the MEMS sensor. The signal decomposition formula is: as well as h and g are the filter coefficients of the wavelet transform.

[0084] Step 5: Compensate for phase error, use particle swarm optimization algorithm to optimize parameters, evaluate the fitness of each particle and iteratively update the particle state until convergence. The update formula is: i =v i,0 + learning factor × (local optimal position - current position), x i =x i,0 +v i .

[0085] Step 6: Apply the optimal parameters found by the above particle swarm algorithm to compensate the phase error, interpolate the compensated data, set the sampling number to N, and perform three-dimensional FFT on the calculated results. And perform high-precision imaging processing.

[0086] Step 7: Collect a large amount of known classified dangerous goods image data as a training set, and use data enhancement technology to improve the generalization and robustness of the model. Using the transfer learning strategy, you can use the pre-trained network structure and parameters as a starting point to accelerate the learning process and improve recognition accuracy.

[0087] The above description is only a specific implementation mode of the present invention. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other alternative features that are equivalent or have similar purposes; all the disclosed features, or all the steps in the methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

Claims

1. A MEMS-assisted handheld MIMO-SAR security inspection imaging method, comprising the following steps: Step 1: The handheld radar scans the target to be inspected. The MIMO radar transmits a linear frequency modulation signal, which is reflected by the target and received by the antenna. The nth receiving antenna receives the reflected signal of the mth transmitting antenna, which can be abbreviated as σ p is the reflection coefficient of the target P at the position (x, y, z), O is the set of all point targets in the imaging scene, and f = f c ±B / 2 is the signal frequency domain, f c , B are the center frequency and bandwidth of the signal, respectively. The transmitting antenna position Receiving antenna position It will change over time, resulting in a phase change. Step 2: The handheld MIMO radar forms a MIMO-SAR three-dimensional imaging model through arm movement. The model can first obtain the resolution of the array direction through the real aperture of the antenna array; secondly, the model uses synthetic aperture technology to obtain the resolution of the antenna movement direction; finally, the resolution of the MIMO array normal, that is, the distance direction, is obtained through high-bandwidth signals. Through this imaging model, the system has three-dimensional imaging capabilities. Step 3: Obtain the three-dimensional motion parameters of the security scanner based on the MEMS device, build a MIMO-SAR signal model, and use the estimated value of the antenna coordinates Establish instant distance Based on the idea of ​​matched filter imaging algorithm, the uncorrected imaging result can be expressed as follows Then, an efficient three-dimensional model is established based on the wavenumber domain approximation. However, due to the systematic error of the sensor, there will be phase errors in the imaging results, causing the image to be deformed and defocused. Step 4: In view of the system error problem of MEMS sensors, the zero drift compensation can be effectively performed by combining the wavelet packet variation method. The output signal of the accelerometer is decomposed into and To extract the characteristics of each frequency band. Then use the processed data as input and the zero drift error as the target output to design a discrete high-pass filter. Finally, use the filter to integrate into the sensor's use environment, continuously monitor its performance, and adjust the model according to new conditions or problems that arise in actual applications to predict and compensate for new data to achieve zero drift error calibration. Step 5: For the remaining phase error, a compensation phase error estimation model can be established to convert the phase error problem into an optimization problem, and then the optimal solution of the problem can be found through the particle swarm algorithm. Initialize the static parameter vector to be estimated, and each particle represents a possible parameter set. Evaluate the fitness of each particle based on the matching degree between the radar echo signal and the theoretical model. Optimize the parameters based on the optimal position of the particle itself and the optimal position of the group. and is the position and speed of particle i at the beginning. According to the optimal position of the particle itself and the optimal position of the group, the speed and position of each particle are updated, and the iterative steps are repeated until the error threshold is reached. The compensation phase is constructed through the obtained optimal static parameters to compensate the phase of the echo signal to eliminate the residual phase error. Step 6: After compensating for the phase error, use the non-uniform imaging algorithm to achieve fast and fine imaging, and use three-dimensional interpolation technology to fill in the data gaps caused by the scanning interval to ensure the continuity and smoothness of the imaging. Then, according to the distance from the reference point to the transmitting antenna and the receiving antenna, and the three-dimensional wave number support domain of the echo corresponding to the reference point, the processing is performed. Step 7: Integrate deep learning technology into the MIMO-SAR security inspection imaging system to achieve automatic identification and classification of dangerous goods. First, extract the characteristics of the target object, including shape, size, scattering intensity and other information, through the high-precision three-dimensional imaging data generated in the previous steps. Then, build a deep convolutional neural network model for training and identifying different types of dangerous goods.

Images