101 results about "Envelope (radar)" patented technology

The invention relates to an ultra-low signal-to-noise ratio medium and high orbit satellite target ISAR imaging method, and mainly relates to the technical field of inverse synthetic aperture radar imaging. The method provided by the invention comprises the steps that the slant range course between a ground radar and a target satellite is calculated by using a target satellite orbit and the phasecenter position of a radar antenna, and pulse compression is carried out on original echo data received by the radar; according to the slant range course and radar imaging geometric parameters, the curvature walk of a target envelope is corrected; the delay phase of an ionized layer is compensated along azimuth, and a linear model is used to correct an envelope alignment error caused by a radar geometric parameter measurement error and a satellite orbit measurement error; the translation phase of the target is compensated, and fast Fourier transform (FFT) along azimuth is carried out to acquire an initial ISAR image; and finally, the quadratic translation phase error of the target is estimated and compensated according to the initial ISAR image, so as to acquire a final medium and high orbit satellite target ISAR image.

The invention relates to a real-time detection and separation method of multiple moving objects by a through-the-wall radar. The method comprises: a complex background signal is rejected by using a clutter suppression algorithm based on an exponential smoothing filter and information of moving human objects can be kept rapidly and accurately; with an envelope detection algorithm based on Hilbert transform, all moving objects are arranged at different envelope peaks; and all moving objects can be separated at different areas rapidly according to an algorithm based on combination of a fixed threshold value and an adaptive threshold value. According to the invention, the method which is simple has the high execution efficiency and is suitable for detection on multiple moving human objects by a portable through-the-wall radar; a powerful guarantee can be provided for accurate positioning of all moving objects by the through-the-wall radar; and the working efficiency of the through-the-wall radar can be improved effectively.

The invention relates to a maneuvering trajectory synthetic aperture radar self-focusing imaging method. The method comprises the steps: echo data of a synthetic aperture radar are acquired; BP sub-images of the data at each azimuth moment are obtained; the phase error of each azimuth moment is estimated to obtain a phase error vector; the estimated phase error vector is unwrapped; the data envelope offset of each azimuth moment is calculated according to the unwrapped phase error vector and the emission signal wavelength of the synthetic aperture radar, and the distance vector is transferredaccording to the envelope offset to obtain echo data after envelope compensation; then the echo data subjected to distance compression is replaced with the echo data subjected to envelope compensationto obtain a sub-image at each azimuth moment; all the steps are repeated to obtain a phase error at each azimuth moment; and performing compensation on corresponding phase errors for the sub-images to obtain the full-aperture SAR image. According to the method, the envelope offset and the phase error are compensated at the same time, and the influence of the motion error on SAR imaging can be eliminated more accurately.

The invention provides a millimeter-wave radar material identification method based on machine learning, and relates to the technical field of material identification, and the method comprises the steps: collecting target envelope information through a millimeter-wave radar sensor; de-noising the electromagnetic wave envelope, and manually marking the de-noised data to generate a sample library; constructing a machine learning classifier; measuring the distance between a sensor and an object through millimeter-wave radar, and judging the material of the object by combining a machine learningclassifier. Object materials are marked by detecting millimeter waves to obtain a large number of samples, a sample library is generated, a classifier is trained through a machine learning method, andfinally the object materials are judged in combination with the machine learning classifier and the envelope of electromagnetic waves reflected by a detection object. Compared with an existing identification method, the method has the advantages of being free of damage, low in power consumption, low in cost and low in complexity, and materials of to-be-detected targets or targets made of different materials can be normally identified in non-transparent plastic bags or paper bags or plastic boxes or paper boxes.

The invention discloses a radar target recognition framework based on a single-layer bidirectional recurrent neural network. The method comprises the steps of firstly, carrying out pretreatment to reduce the sensitivity in an HRRP sample; establishing a dynamic adjustment layer, and dynamically adjusting the sample; enabling a subsequent CNN layer to extract information contained in an HRRP envelope while reserving sequence correlation contained in the HRRP sample; carrying out time sequence correlation modeling through a bidirectional RNN, and extracting high-level features of the time sequence correlation; and finally carrying out target classification through a softmax function.

The invention discloses a radar radiation source individual identification method and device, electronic equipment and a storage medium thereof. The method comprises the steps that a radar radiation source envelope image set is obtained, and the radar radiation source envelope image set comprises a radar radiation source envelope training image set, a radar radiation source envelope verification image set and a radar radiation source envelope test image set; constructing a convolutional neural network model, training the convolutional neural network model according to the radar radiation source envelope training image set and the radar radiation source envelope verification image set, and performing verification to obtain a trained convolutional neural network model; and inputting the radar radiation source envelope test image set into the trained convolutional neural network model to obtain the radar radiation source individual recognition rate. According to the method, radar individual characteristics can be automatically extracted by a machine through the constructed convolutional neural network model, and the defects that in traditional radar radiation source individual identification, the manual characteristic extraction process is tedious, and database updating is slow are overcome.

The invention discloses a photon-assisted pulse system microwave radar detection method, and the method comprises the steps: carrying out the double-sideband modulation of carrier suppression of an optical carrier through a linear frequency modulation microwave pulse signal; performing minimum point modulation on the generated carrier-suppressed double-sideband modulation signal by using an envelope signal of the linear frequency modulation microwave pulse signal through an electro-optical modulator to generate a high extinction ratio optical pulse, dividing the high extinction ratio optical pulse into two paths, one path being used as an optical intrinsic reference signal, and the other path being used as an optical intrinsic reference signal; converting the other path into an electric pulse signal to be emitted as a radar detection signal; and carrying out optical domain mixing dechirping processing on the radar echo signal and the optical intrinsic reference signal subjected to timedelay matching to realize two-dimensional imaging of the target. The invention further discloses a photon-assisted pulse system microwave radar detection device. According to the invention, the detection requirements of long-distance and high-resolution imaging radar can be met.

The invention belongs to the technical field of radar signal processing, and particularly discloses a free-air high-speed target detection method based on long-time accumulation, and the method comprises the steps: segmenting an echo signal after pulse compression according to the number of pulses, and compensating the range walk and Doppler spread enveloped by the echo signal in each segment; performing intra-segment phase-coherent accumulation on the result after the compensation in each segment; and finally, carrying out inter-segment envelope movement and non-coherent accumulation on all echo signals, so that the energy of the echo signals is effectively accumulated. A long-time accumulation method of time segmentation, intra-segment coherent accumulation and inter-segment non-coherent accumulation is adopted, so that the problem of long-time coherent accumulation calculation amount is solved, and the problem of poor coherence of echo signals is solved; and the detection performance is improved, and engineering realization is easy.

The invention discloses an old people tumble detection method based on a non-contact radar technology. The method comprises the following steps: acquiring an original signal of a radar sensor; solvinga maximum value point and a minimum value point of the original signal; calculating an envelope curve of the maximum value point and the minimum value point of the original signal, and solving a centroid point of the envelope curve; subtracting the centroid point sequence from the original data sequence to obtain a new data sequence without low frequency, repeating the step of subtracting the centroid point sequence from the original data sequence to obtain a sequence of IMF components, and separating out low-frequency and high-frequency components; subtracting the obtained IMF component fromthe original signal to form a new signal; inputting the maximum absolute amplitude, the zero point number and the autocorrelation maximum amplitude in the iteration process to serve as stop criteria,carrying out detecting and classifying on different types of noise, and detecting tumble of old people accurately according to positioning and classification of radar echo signals. The calculation load of a traditional CEEMD algorithm is reduced, and automatic detection, positioning and classification of the tumble features are achieved.

The invention discloses a navigation radar signal sorting method based on envelope analysis, and the method comprises the steps: carrying out the carrier frequency and pulse width filtering of an original pulse description word set, and obtaining a navigation pulse description word set; carrying out carrier frequency orientation parameter clustering on the navigation pulse description word set, carrying out envelope division on the obtained pulse cluster set, and extracting envelope description words of a single scanning envelope exceeding a pulse number threshold; establishing a navigation target table; traversing the navigation target table, backtracking the navigation pulse description word set, and calculating the scanning period of each batch of targets in the target table; and traversing the navigation target table again, calculating a navigation display mark of the target table, setting the navigation display mark of the batch number corresponding to the maximum amplitude as 1,setting the rest matched batch numbers as reflection signals, setting the navigation display mark as 0, and sending the radiation source parameters of the batch number corresponding to the display mark as 1 to navigation display. According to the method, the accuracy of the sorting result is effectively improved.

The invention relates to an imaging millimeter-wave radar point cloud target classification method based on machine learning, and the method comprises the following steps: S1, obtaining an input point cloud of a millimeter-wave radar, preprocessing the input point cloud, obtaining a detected target point cloud cluster, and constructing a target point cloud data set according to the detected target point cloud cluster; S2, generating a minimum cubic envelope frame of the target point cloud, calculating and extracting feature parameters of the target point cloud, and generating a feature vector of target classification; and S3, inputting the feature vector into a pre-trained classification model to obtain a target type corresponding to the detected target point cluster. Compared with the prior art, the method has the advantages that the practicability is high, various types of target classification can be realized, and the target classification capability of the millimeter wave radar is greatly enhanced.

The invention relates to an X-band radar target detection and tracking method based on density clustering, and the method comprises the following steps: 1), obtaining the point cloud data of a radar under a polar coordinate, converting the point cloud data from the polar coordinate into a Cartesian coordinate, introducing the ship body position and heading correction, and obtaining the radar pointcloud data under a global coordinate system; (2) adopting a DBSCAN clustering algorithm, dividing points with the close distances into one group according to the density degree between the points, outputting different point cloud sets, obtaining the minimum envelope circle of each point cloud set, wherein the minimum envelope circle represents the detection target of the radar; and 3) performingoptimal data combination on the detection target and the existing target, and estimating the position and speed of the target to realize multi-target tracking. Compared with the prior art, the accuracy and stability of radar target detection and tracking are effectively improved, and accurate target position, speed and size information can be provided, so that a reliable reference basis is provided for safe navigation and collision avoidance of ships.

The invention discloses a motion compensation method based on inertial navigation system parameters, which comprises the following steps of establishing a radar coordinate system and a motion error model, and collecting the inertial navigation system parameters in real time, establishing an instantaneous slant distance equation of the airborne SAR to obtain an instantaneous slant distance error formula, and separating an influence value deltaR of position errors of the aerial carrier along the Y axis and the Z axis on the instantaneous slant distance error and an influence value deltaRx of position errors of the aerial carrier along the X axis direction on the instantaneous slant distance error, then calculating the actual course angle and the actual course speed of the aerial carrier by using the parameters of the inertial navigation system, projecting the speed of the actual course of the aerial carrier to a radar coordinate system to obtain the actual positions of the aerial carrier along the X-axis, Y-axis and Z-axis directions, sequentially calculating deltaR, deltaRx and an envelope compensation function H1, and performing envelope compensation on the echo signal, and finally, calculating a phase compensation function H2, and performing phase compensation on the echo signal, thereby completing motion compensation of the echo signal.

The invention discloses a radar HRRP target recognition method based on spectrogram segmentation preprocessing and a convolutional neural network. The method comprises the steps of firstly, carrying out pretreatment to reduce the sensitivity in an HRRP sample; carrying out spectrogram transformation on the sample; enabling the next CNN layer to extract the information contained in the HRRP envelope while retaining the sequence correlation contained in the HRRP sample; then extracting the high-level features through time sequence correlation modeling by the bidirectional RNN; and finally carrying out target classification through the softmax function.

The invention discloses a target micro-motion parameter estimation method, system and device based on IEEMD and a storage medium. The method comprises the steps that an echo signal is acquired, and ahigh-resolution distance image of a target is obtained according to the echo signal; a plurality of scattering centers are obtained according to the high-resolution distance image, and a pulse of eachscattering center is acquired; envelope information of each scattering center is acquired through an IEEMD algorithm according to the pulses, and corresponding phase information is obtained accordingto the envelope information; phase unwrapping is performed on the phase information by using a least square method to obtain phase information after unwrapping, and a micro-distance curve of the target is obtained according to the phase information after unwrapping; and a target micro-motion parameter estimation value is obtained according to the micro-distance curve. According to the method, theenvelope information can be accurately extracted under the condition of a low signal-to-noise ratio or a low pulse repetition frequency through the IEEMD algorithm, Doppler ambiguity is effectively solved through the least square method, and the precision of target micro-motion parameter estimation is improved. The method can be widely applied to the technical field of radars.

The embodiment of the invention provides a radar scene matching terminal guided missile flight trajectory planning method, and relates to the technical field of missile trajectory planning methods. According to the method, the planning operation time can be greatly shortened, the application range of a launching area is increased, one set of digital reference map can be shared when multiple adjacent targets are hit, and the radar reference map guarantee capability and the weapon combat effectiveness can be effectively improved. The method comprises the steps of 1, preprocessing a radar reference map, and adjusting the resolution of the radar reference map to complete binarization processing; 2, planning a tail-section flight path, planning a tail-section attack direction, generating an envelope control point, and generating a tail-section flight path; step 3, performing control guidance planning, designing a control guidance rate, and realizing flight trajectory shift planning; 4, planning the change of the strike point, and planning the navigation correction amount of the changed strike point; and 5, planning available emission areas, and checking and generating an available emission area list.

The invention discloses a motion error compensation method of a synthetic aperture radar, and relates to the field of synthetic aperture radars. According to the method, a distance error model is established to obtain a distance error varying along with a distance gate space, then the scene center distance is used as a reference, primary compensation of the distance error is carried out, envelopeand phase errors can be compensated at the same time, and only the space-variant envelope and phase errors are left after compensation; then, secondary compensation is carried out, the secondary compensation is carried out in a distance time domain, a space-variant phase error is compensated, and an envelope space-variant error is left after the secondary compensation; and finally, the echo signalsubjected to secondary compensation is segmented in a distance time domain, a compensation function is constructed according to the center distance in each small section, envelope space-variant errors are compensated, after compensation is completed, echo signals of all the small sections are spliced again, signals with envelope space-variant error compensation completed are obtained, the problemthat a traditional motion error compensation method cannot compensate the envelope space-variant errors is solved, and the compensation reliability is improved.

The embodiment of the invention provides a circular synthetic aperture radar self-focusing imaging method and system and electronic equipment. The method comprises the following steps: dividing an imaging region into a plurality of rectangular regions; for each of the plurality of rectangular regions, calculating a projection matrix for each pulse; performing image offset compensation on the projection matrix; performing phase error estimation and phase error compensation on the projection matrix after image offset compensation; and on the basis of the projection matrix after phase error compensation, obtaining a full-scene image of the imaging area through splicing. According to the invention, the envelope error and the phase error are compensated at the same time, the SAR imaging precision is improved, meanwhile, the imaging area is subjected to block processing, the influence of phase error space-variant characteristics on SAR imaging is reduced, and the requirement of SAR imaging for the storage space is lowered.

The invention relates to an algorithm for a terrain shielding radar scanning range envelope surface, and the algorithm comprises the steps: constructing an xOz plane rectangular coordinate system by employing a radar transmitting point as an original point, and enabling the horizontal right direction to be the x-axis forward direction and the vertical upward direction to be the z-axis forward direction; starting from the original point, taking z coordinate values of the terrain at intervals of unit distance along the forward direction of the x axis to obtain a slope kn of a connecting line from one point on the terrain to the original point; selecting a terrain occlusion reference point, recording a terrain occlusion parameter set (xn, kn) corresponding to the terrain occlusion reference point, and the slope kn of the connecting line from the terrain occlusion reference point to the original point satisfying the following conditions that kn is larger than the slope kn + 1 obtained later, and kn being the maximum value of all slopes recorded last time; and starting from xn, selecting a connecting line from the terrain occlusion reference point to an original point as a lower boundary of a radar scanning range. According to the method, complex terrain data is simplified, a small number of parameters of terrain occlusion information in a large range can be transmitted to a GPU through a UBO, and the drawing performance is greatly improved.

The invention relates to a time difference ranging radar structure with low power consumption and simple structure, and belongs to the technical field of radar structures and radar ranging. The structure comprises a digital gradient generator, a digital control type oscillator, a power amplifier, a transmitting and receiving antenna, a low-noise amplifier, an envelope detector and a time-to-digital converter. The time difference ranging radar structure can solve the problem that the radar resolution of a frequency modulated continuous wave radar is seriously limited by bandwidth, meanwhile, anoscillator in a transmitter part is controlled by an enable signal, and when a multi-bit digital signal is input, the oscillator only works in a half cycle that the multi-bit digital signal is in anupward gradient, so that the power consumption is greatly reduced; in the receiver part, the demodulation and measurement circuit part adopts an envelope detector to detect the envelope of an echo signal, and adopts a time-to-digital converter to measure the time difference, so that compared with a coherent demodulation and frequency difference measurement method of a frequency-modulated continuous wave radar, the structure is simple.

The embodiment of the invention provides an MIMO radar target detection method and device, and the method comprises the steps: receiving a radar echo signal, sampling each pulse of the radar echo signal in one wave beam along a distance dimension, then carrying out non-coherent accumulation among pulses, and obtaining an envelope data vector; determining a suspected target echo of the envelope data vector, determining a rule for calculating a local mean value according to sampling points at preset distances at the left side and the right side of the suspected target echo, and calculating the local mean value of the suspected target echo according to the rule; calculating a local standard deviation of the suspected target echo according to the rule of calculating the local mean value and the local mean value of the suspected target echo; and determining a detection threshold vector according to the local mean value and the local standard deviation, performing element-by-element over-threshold detection on the envelope data vector, and outputting a final detection result. According to the embodiment of the invention, the method is high in adaptability in a complex non-uniform environment, and is high in operability.

The invention discloses a double-beam microwave landing radar high-precision distance measuring method and system. The method comprises: obtaining echoes of a landing beam irradiation area at the sametime through two beams of an antenna convex beam and an antenna concave beam; the landing radar signal processor calculating the envelope ratio of the convex beam echo and the concave beam echo; calculating the minimum value point position of the echo envelope ratio notch to obtain high-precision measurement of the beam distance; and calculating the distance ratio of two distance points with theecho envelope ratio of 1, and looking up a table to obtain the estimation of the beam center incident angle. Echo waves of a landing wave beam irradiation area are obtained at the same time through configuration of convex-concave double wave beams for distance measurement. The problem of echo envelope asymmetric center estimation caused by discrete strong scattering points due to inconsistent echopower distance path attenuation of each resolution unit in the wave beam is eliminated. While the ranging precision is improved, an estimated value of a beam center incident angle can be obtained andserves as an important parameter of safe landing, and the technology is already applied to design and application of a lunar exploration four-stage microwave landing radar system at present.

The invention provides a drivable area detection method, device and equipment, and the method comprises the steps: obtaining a three-dimensional point cloud of a target detection area through a laser radar, determining the type label of each point in the three-dimensional point cloud through a semantic segmentation network, and enabling the type labels to comprise the ground and a ground object; determining the position of the laser radar as a coordinate origin, dividing the three-dimensional point cloud into different fan-shaped regions, and dividing each fan-shaped region into different grids according to the distance from the point to the origin; performing grid traversal on each fan-shaped region, and determining boundary points of a drivable region of each fan-shaped region based on type labels of points contained in grids; performing segmentation fitting on each boundary point, performing resampling on boundary lines after segmentation fitting according to a preset interval, and determining an envelope point set of a drivable area; according to the method, the problems of unstable road edge shielding identification, poor rugged road robustness, insufficient detection precision and the like in the prior art are effectively solved.

A device comprises at least: one rotary antenna including at least two parallel rectilinear waveguides; a radar emitting a continuous-wave microwave signal towards the emission guide of the antenna and receiving the signals from the guides of the antenna, which signals are captured by the movable beam, the received signals are the direct component I and the quadrature component; a stereoscopic video camera oriented in the same direction as the movable beam of the rotary antenna and able to record the clothing envelope of the individual, the envelope serving as a reference surface for the measurement of distances to the device; a processor that computes an SAR image of that portion of the body of the individual targeted by the radar and the video camera and who is possibly equipped with one or more objects, from signals received from the radar and the distances measured by the video camera.

The invention discloses a multi-station radar system interference identification method based on a convolutional neural network. The method comprises the following steps: obtaining K groups of slow-time random complex envelope sequences according to K radars in a multi-station radar system; horizontally linking the K groups of slow-time random complex envelope sequences in sequence to obtain a first two-dimensional data block; inputting the first two-dimensional training data block into a first interference discrimination network to obtain a first classification output result; adopting a gradient descent method to obtain a minimum value of a loss function of the first interference discrimination network so as to obtain a second interference discrimination network; inputting the first two-dimensional data block into a second interference discrimination network, and obtaining a third interference discrimination network when the error sum of the second interference discrimination network is smaller than or equal to a threshold value; inputting the second two-dimensional data block into the third interference discrimination network to obtain a final classification output result. According to the interference identification method, the utilization rate of sampling data in the information processing process of the multi-station radar system is improved, and the identification probability of deception interference is improved.

The invention discloses an embedded lightweight millimeter wave radar target identification method, and relates to the technical field of target identification, and the method comprises the steps: receiving electromagnetic wave envelope signals returned by an object at different distances or different speeds; de-noising the electromagnetic wave envelope signals through a wavelet threshold method, manually marking the de-noised electromagnetic wave envelope signals to generate a sample library, and constructing a category set; the sample library is used for training a lightweight convolutional network model, the convolution layer number range of the lightweight convolutional network model is 36-58, and the compression factor range of a compression layer is 0.3-0.5; collecting an electromagnetic wave envelope signal returned by a to-be-detected target through a millimeter wave radar, denoising the electromagnetic wave envelope signal through a wavelet threshold method, inputting the denoised electromagnetic wave envelope signal to the lightweight convolutional network model, and identifying the category of the to-be-detected target; object category identification is realized based on the lightweight convolutional network model and in combination with the millimeter wave radar sensor, the physical characteristics of the target object can be more effectively revealed, the flexibility is high, and the practical value is higher.

The invention relates to an interference source individual identification method based on envelope fingerprint features and belongs to the technical field of target identification. The method comprises the following steps of 1) establishing a radar interference source signal model; 2) establishing a simulation model of a wireless channel; 3) extracting a leading edge feature enveloped by a radar interference source signal as a fingerprint feature identified by a radiation source individual; 4) repeating the step 3), extracting the envelope leading edge feature of each radar interference source signal, and establishing an envelope fingerprint feature library; 5) processing the radar echoes to obtain envelope fingerprint features of the standard radar echoes; 6) identifying active interference signals, and classifying the active interference signals into suppressing interference and deceptive interference; and 7) receiving and analyzing the echo envelope fingerprints of the deceptive interference identified in the step 6), and comparing the echo envelope fingerprints with the envelope fingerprints in the envelope fingerprint feature library in the step 4). The method is simple and convenient; scientific basis is provided for electronic system design, and identification efficiency is improved.

The invention relates to the field of radar target signal generation and simulation. The purpose is to provide a non-breakpoint multi-target signal synthesis method. First, the user inputs signal parameters through the interface provided by the signal generation software; then calculates the signal memory utilization, calculates the size of the memory space occupied by the signal, and adjusts the pulse signal according to the accuracy requirements of the multi-target signal. PRI, arrange the adjusted multi-target signal timing and perform gain calculation to obtain the multi-target signal envelope information; then perform intra-pulse information processing, add signal intra-pulse information, and form a multi-target waveform file; finally generate a multi-target waveform through arbitrary waveforms The multi-target waveform file is called by the converter to generate a multi-target analog signal. The multi-object signal synthesized by the method of the invention has the advantages of high utilization rate of hardware resources and high precision of the multi-object signal.

The invention provides a high-frequency ground wave radar weak target accumulation detection method and computing equipment, and the method comprises the steps: obtaining an echo signal of a high-frequency ground wave radar, and carrying out the pulse compression of the echo signal to obtain a high-frequency ground wave radar pulse pressure signal; setting search parameters of three-dimensional combined search of radial distance, radial speed and real speed according to the parameters of the high-frequency ground wave radar; performing target track signal extraction of the high-frequency ground wave radar according to the search parameters; constructing a phase compensation function along the slow time dimension of the high-frequency ground wave radar; compensating phase fluctuation of the extracted target track signal among pulses along a high-frequency ground wave radar pulse sequence, and adding echo envelopes of the compensated target track signal in the same phase to complete coherent accumulation; performing three-dimensional joint constant false alarm rate detection based on a coherent accumulation result, and judging whether a target is detected or not; and performing parameter estimation on the detected target, and outputting the parameter. According to the invention, the detection performance of the high-frequency ground wave radar on a high-speed and high-maneuverability weak target can be improved.