118 results about "Clutter" patented technology

This invention discloses a clutter discrimination and suppression method based on multiple echo features, comprising: acquiring radar front-end clutter and target echo detection video data and dividing it into several connected regions; extracting multi-dimensional features and labeling prior information for each connected region; using the Relief feature selection algorithm to calculate and filter the weights of the multi-dimensional features, removing redundant features to obtain the optimal feature vector; using this feature vector to train a support vector machine classifier, and obtaining the optimal parameter model through cross-validation; acquiring measured clutter data, extracting corresponding features and inputting them into the model; and determining whether to remove clutter and retain targets based on the output. This invention effectively filters out dynamic clutter spots, avoids false deletion of weak targets, significantly reduces the false alarm rate of the system, and alleviates the computational burden of subsequent track processing.

This invention relates to the field of radar target detection technology, and provides a robust method and apparatus for radar extended target detection under weighted generalized inverse Gaussian clutter. The invention uses a clutter prior information vector and a moment estimation order vector to estimate the clutter parameter vector; based on the training sample matrix, the data matrix to be detected, and the estimated values ​​of the clutter parameter vector, it estimates the inverse expectation of the texture component; based on the training sample matrix, the data matrix to be detected, the signal steering vector, and the estimated values ​​of the inverse expectation of the texture component, it constructs a test statistic; and based on the test statistic, it determines the target state. This invention solves the problems of radar extended target detection failing to meet real-time processing requirements and exhibiting poor robustness.

The present application relates to radar signal processing technical field, specifically provide a kind of staggered pulse repetition interval time-frequency domain dual-polarized radar detection variable estimation method, comprising: in frequency domain, the estimation of echo signal-to-noise ratio, reflectivity factor, differential reflectivity of horizontal / vertical polarization based on power spectral density;And the estimation of average Doppler velocity and velocity spectrum width;In time domain, after ground clutter filtering based on odd-even pulse, respectively, odd-even pulse correlation parameter estimation is carried out, so that two detection variables of dual-polarized radar correlation coefficient and differential propagation phase shift of odd-even pulse are obtained respectively, then odd-even combination can obtain the estimation result of accurate correlation coefficient and differential propagation phase shift.The present application realizes the accurate estimation of dual-polarized radar multi-class detection variable, ensures that estimation result is not disturbed by ground clutter interference, simultaneously adapts single pulse repetition interval and staggered pulse repetition interval two working modes, improves the reliability and applicability of radar detection.

This invention discloses a multi-frequency, multi-target range-velocity deambiguation method and system based on phased array fire control radar, mainly addressing the low accuracy of existing methods in handling multi-target ambiguity. The implementation scheme is as follows: acquiring measurement points from the phased array fire control radar and generating a full-space candidate point set; discretizing the range-velocity plane into a grid, counting the number of candidate points and non-repeating frequency groups within each grid block; dividing the grid into blocks based on block-level neighborhood statistics to generate the candidate point set; calculating the unique frequency support number of the candidate point set and performing clustering to obtain candidate point clusters; statistically estimating the candidate point clusters, outputting target range and velocity estimates, forming the deambigued target set for the current frame. This invention can reduce the probability of false clustering structures formed by erroneous unfolding points, clutter points, and false alarm points, improving the accuracy and real-time processing efficiency of multi-target range-velocity joint deambiguation in complex scenarios, and can be used for multi-target signal processing and target tracking in phased array fire control radar.

The application discloses an airborne radar downburst optimized detection method and belongs to the technical field of radar signal processing. An airborne Doppler radar is used to emit a radar waveform to a possible wind shear dangerous area in front of a route, and wind speed estimation in a transmission direction is obtained by analyzing radar echo IQ data. The application sets a pitch angle of radar scanning according to a take-off or landing stage of an airplane. After receiving the IQ signal, frequency domain correction is performed, data is preprocessed according to spectrum width information, and a wild value area is eliminated. After the preprocessing is completed, a frequency domain clutter suppression algorithm is used to suppress a ground clutter area near zero frequency, and then wind shear wind speed estimation is performed. The application proposes a novel vertical wind speed estimation model, establishes a connection between a downburst core area range and a vertical wind speed gradient, forms a detection and sensing capability for a three-dimensional area of the downburst, and proposes an airplane scanning strategy for optimizing the model.

This invention discloses a radar clutter signal simulation method based on dynamic scene simulation, mainly addressing the problems of low computational efficiency and insufficient flexibility of traditional clutter simulation methods under conditions of large scenes, long time series, and multiple dynamic factors. The implementation scheme includes: constructing a digital simulation scene based on radar signal parameters and dynamic scene information to determine the clutter region to be simulated; discretizing the clutter region into a uniform basic grid in the range-azimuth plane, and then aggregating it into multiple independently processable clutter channels; calculating the amplitude, Doppler frequency, and phase parameters of the center point of each channel in parallel, constructing a two-dimensional convolution kernel reflecting spatial correlation, and using this convolution kernel to efficiently expand the center point parameters to the entire channel, generating a continuously distributed parameter field; and transmitting the parameter fields of each channel to a hardware logic unit to complete signal modulation and synthesis, outputting a high-fidelity radar clutter signal. This invention can achieve rapid, high-fidelity parameter generation from point to field, and significantly improves computational efficiency while strictly maintaining physical accuracy. It can be used for radar system simulation, electronic countermeasures testing, and radar performance evaluation.

The present application relates to the radar signal processing neighborhood, specifically to a kind of target detection method based on target space-time stability.The method includes: based on the time-domain signal representation of multiple frames sliding, generate subframe by dynamic matching filter, obtain subframe peak point information, including the energy, distance, doppler information of peak point on each subframe RD spectrum;Based on the spatial signal processing of single snap angle, generate multi-subframe peak point RDA spectrum, obtain the azimuth information of peak point on each subframe RD spectrum, utilize the feature information of each subframe peak point, remove false alarm point using false alarm suppression method based on mahalanobis distance;After false alarm suppression, the result is further removed discrete false alarm point using target track based on space-time stationarity, the method proposed in the present application improves the problem of too many false alarms in the RD spectrum after traditional clutter suppression algorithm;It can effectively reduce false alarm rate while retaining weak target signal, improve target detection capability;It is suitable for multiple system radars, and the application range is wide.

This application discloses a method, apparatus, medium, and device for selecting vibration measurement points based on radar. The method includes: acquiring echo data of the object under test collected by a radar device; determining multiple candidate vibration points based on the echo data; estimating the stationary clutter component of each candidate vibration point to obtain the stationary clutter component; filtering out stationary measurement points from the candidate vibration points based on the stationary clutter component to obtain at least one vibration measurement point; and performing vibration measurement on the object under test based on each vibration measurement point. Using this application embodiment, stationary measurement points that do not vibrate can be filtered out from the candidate vibration points, thereby improving the accuracy of radar vibration measurement results.

The application discloses a pre-detection tracking method based on random set theory under unknown signal-to-noise ratio, and belongs to the field of radar target detection and tracking. The pre-detection tracking is a kind of technology for effectively solving the problem of low signal-to-noise ratio (SNR) target detection and tracking. The application proposes a solution for the case that the target signal-to-noise ratio is unknown and time-varying based on a label multi-Bernoulli (LMB) filter. Based on the second-order expansion of Taylor series, a new likelihood function is designed under the condition that the target SNR is unknown. Meanwhile, an ordering update strategy is proposed to solve the problem that the target and clutter share the same measurement when they are close to each other. The simulation results show that the method can effectively detect and track the target under the condition that the target SNR is unknown. Compared with the simulation results under the condition that the target SNR is known, the performance loss is negligible.

The application provides a radar monitoring and evaluation method for a shale gas well site safety situation, comprising the following steps: collecting original vibration amplitude data, initial clutter power spectrum information and target Doppler shift signals, and obtaining filtered vibration amplitude data, purified clutter spectrum data and refined Doppler shift values after filtering; performing clustering analysis on the current vibration intensity level of the energy distribution characteristics of different frequency bands in the overlapping interval range, evaluating the interference level caused by the target detection of the clutter power spectrum broadening amount, and obtaining the interference intensity grading result; performing boundary feature extraction and trend fitting processing on the interference intensity grading result and the frequency spectrum overlapping boundary by using a support vector machine, and identifying a dynamic threshold adjustment amplitude to determine an adjusted detection threshold value.

This application provides a sea clutter suppression method based on complex numbers and adaptive dynamic weighting, relating to the field of marine radar signal processing technology. The method includes performing a short-time Fourier transform on the raw radar echo data to generate a spectrum; standardizing preprocessing to obtain preprocessed in-phase and quadrature component signals; performing frequency domain pre-separation to obtain low-frequency sea clutter component signals; performing phase compensation and outputting the IMF corresponding to the low-frequency sea clutter components through improved complex EMD decomposition; performing multi-criteria adaptive selection to obtain the filtered IMF; calculating the high-frequency residual of the original signal; and performing dynamic weighted reconstruction and energy calibration based on the high-frequency residual and the filtered IMF to output the target signal after clutter suppression. This application improves the method's ability to capture signal phase information and suppresses mode aliasing by extending the time-domain EMD to the complex domain; and introduces an adaptive dynamic weighting mechanism to adjust the weight allocation in real time during the decomposition process, thereby improving sea clutter suppression performance.

This invention belongs to the field of radar signal processing and target detection technology, specifically relating to a statistical scattering method for clutter particles in radar detection. The method includes: acquiring single scattering parameters and simulation control parameters of the clutter particles; dynamically determining the simulation space length based on the single extinction coefficient and asymmetry factor; initializing a quasi-Monte Carlo low-discrepancy sequence; after initializing each photon packet, randomly generating a propagation step size and updating the cumulative propagation step size; when the photon packet does not exceed the boundary, determining whether scattering has occurred based on the single extinction coefficient and cumulative propagation step size; if scattering has occurred, updating the scattering direction based on the Henyey-Greenstein phase function and attenuating the energy weight; employing an adaptive dynamic threshold roulette wheel mechanism to dynamically adjust the survival probability based on scattering efficiency, photon position, and propagation direction, and maintaining statistical unbiasedness through energy weight compensation; after traversing all photon packets, calculating the total transmitted energy and total reflected energy, and calculating the equivalent extinction coefficient and radar cross section. This invention significantly improves simulation efficiency while ensuring statistical accuracy through physically guided importance sampling and a quasi-Monte Carlo low-discrepancy sequence, providing reliable support for radar anti-environmental clutter interference design.

This invention relates to the field of signal processing technology and provides a ground clutter frequency domain suppression method based on correlation spectrum. Its key feature is the use of phase information from the autocorrelation spectral density of weather radar echo signals to identify and suppress ground clutter signals. The method includes: adaptively selecting an optimal data window function based on the clutter-to-noise ratio of the weather radar echo signal and performing power-preserving processing; calculating the autocorrelation spectral density and power spectral density, and effectively distinguishing clutter from broadband weather signals using the local deviation characteristics of the autocorrelation spectral density relative to the ideal linear phase, thereby performing clutter identification and filtering. Based on the autocorrelation spectral density after filtering out ground clutter, an inverse transform is performed to calculate the autocorrelation function, completing the time-domain estimation of the weather radar detection variables. This invention effectively solves the problem that traditional ground clutter suppression methods cannot accurately identify and eliminate ground clutter mixed in with weather signals.

The present application belongs to the technical field of external radiation source radar signal processing, and particularly relates to a radar clutter suppression method based on alternating projection, aiming at the coupling problem of clutter suppression performance and memory space requirement of existing algorithms, realizing an iterative optimization scheme, and different from traditional iterative clutter suppression schemes (CLEAN algorithm, etc.), the radar clutter suppression method based on alternating projection proposed by the present application can guarantee that the algorithm converges linearly to the overall least square solution in norm, that is, there is theoretical guarantee and actual improvement in convergence and convergence speed.

This invention provides a radar target detection method and apparatus based on dual-polarization maximum eigenvalues. The method includes: receiving first polarization echo data and second polarization echo data, and determining a detection unit and at least two reference units based on the first polarization echo data and the second polarization echo data; for any unit, determining a cross-covariance matrix based on the first polarization echo data and the second polarization echo data, and determining the maximum eigenvalue corresponding to the unit based on the cross-covariance matrix; determining an average maximum eigenvalue based on the maximum eigenvalues ​​corresponding to each reference unit; and determining a target detection result based on the maximum eigenvalue corresponding to the detection unit, the average maximum eigenvalue, and a preset threshold factor, wherein the target detection result includes whether the target exists or does not exist. This reduces the computational complexity of radar target detection, fully leverages the distinguishability of target echo signals and sea clutter, and improves the detection accuracy of radar target detection.

The application discloses a kind of ground-based radar multi-resource adaptive joint scheduling methods for high-speed target, first in noise and clutter background, ground-based radar system receives and carries out signal processing to the echo in detection airspace, obtains the plot information of detection airspace, then the plot velocity of each wave position is analyzed, then the wave position that appears cross-range cell walk phenomenon calculates the cross-cell walk energy loss coefficient of high-speed target and constructs multi-resource optimization function, finally the adaptive scheduling result of space-time resource and false alarm probability resource is obtained by using CVX toolbox solution, until the overall target detection process is completed.The method of the application considers the energy loss caused by high-speed target range migration, and combines ground-based radar multi-resource cooperative scheduling, realizes the reasonable use of resources while improving the detection performance, the detection performance of high-speed target in clutter and noise environment is significantly improved, and the adaptive optimization of multi-resource cooperative scheduling is realized.

A radar for an Advanced Driver Assistance System (ADAS) comprises at least one transmitting antenna coupled to a vehicle, wherein the at least one transmitting antenna is configured to send radar signals from the vehicle in the direction of a scenario of interest, at least one receiving antenna coupled to the vehicle, wherein the at least one receiving antenna is configured to receive reflected radar signals, and at least one processor.The processor is configured to execute computer-executable instructions to obtain multiple time samples of the reflected radar signals from the at least one receiving antenna at predetermined periodic intervals, to execute an autocorrelation algorithm on the obtained multiple time samples, to normalize a zero-delay-first delay ratio according to an output of the autocorrelation algorithm, and to compare the normalized zero-delay-first delay ratio with a specified threshold to determine whether the reflected radar signals contain an object identified as road clutter.

This invention provides a method and system for modeling sea clutter in a multi-channel spaceborne bistatic radar, including: solving the spaceborne-sea geometric relationship; calculating the two-way radiation pattern of the spaceborne bistatic radar antenna; modeling the time-varying motion of the sea surface; calculating the bistatic scattering coefficient of sea clutter; and synthesizing the multi-channel sea clutter echo of the spaceborne bistatic radar. This invention can achieve high-precision modeling of multi-channel sea clutter for spaceborne bistatic radar under different bistatic configurations and system parameters, overcoming the problems of existing models that do not consider the internal motion of multi-channel sea clutter in spaceborne bistatic radar, and do not consider the influence of different polarization modes and azimuth bistatic angles on the bistatic sea clutter scattering coefficient.

The application discloses a non-measured area electromagnetic clutter deduction method and system based on similar terrain areas, and the method comprises the following steps: acquiring geographic information data of a target non-measured area, and matching one or more reference areas with similar terrain characteristics; extracting measured electromagnetic clutter data and corresponding environmental parameters of each reference area, and constructing a terrain-electromagnetic correlation feature mapping model by using a deep feature learning network; dynamically generating a correction factor suitable for the area according to the terrain-electromagnetic correlation feature mapping model; based on the correction factor, deducing the spatial clutter intensity distribution of the target non-measured area under a specified frequency band and polarization mode; performing statistical feature conformity test and spatial continuity optimization on the spatial clutter intensity distribution, and finally outputting the electromagnetic clutter deduction result of the non-measured area. By using the embodiment of the application, the accuracy and spatial continuity of the non-measured area electromagnetic clutter deduction can be improved, and the dependence on field measurement can be reduced.

This invention provides a category detection method, apparatus, electronic device, and storage medium, belonging to the field of image detection technology. By standardizing and logarithmically compressing radar data, and combining it with a compact scattering prior generated by a learnable mapping, the interference caused by heavy-tailed fluctuations in reflection intensity is resolved at the data source. Based on this, gated modulation processing is used to explicitly filter radar features, effectively suppressing the weight of unreliable echoes in feature aggregation, making the semantic features output by the radar backbone network more robust. Finally, through joint local and global modeling and deep fusion with visual features, the detection error caused by outliers and multipath clutter is significantly reduced, achieving accurate and stable identification of target object category information in complex environments, and improving the overall robustness of the target detection system.

This invention discloses a clutter false alarm suppression method based on multi-feature fusion, comprising: acquiring radar echo signals and performing pulse compression processing to obtain time-domain echo data; performing clutter suppression and coherent accumulation on the time-domain echo data to obtain a range-Doppler matrix; performing constant false alarm detection on the range-Doppler matrix to obtain several potential target points; extracting the multi-pulse echo signals and unsuppressed frequency domain signals corresponding to the potential target points from the time-domain echo data, extracting the corresponding clutter-suppressed frequency domain signals from the range-Doppler matrix, and constructing multi-dimensional features for each potential target point based on the extracted signals; inputting the multi-dimensional features into a pre-trained classifier, determining whether the potential target point is a target or false alarm clutter based on the output results, and outputting the final target point trace. This invention effectively suppresses clutter false alarms in complex environments and improves radar detection reliability through multi-dimensional feature fusion and secondary decision-making by the classifier.

The application discloses a sea surface weak target detection method based on a residual network and a hypersphere constraint. The method can solve the problems of training underfitting or overfitting caused by the fact that the target sample is much smaller than the sea clutter sample under the sea clutter background, and the specific steps include: 1, performing short-time Fourier transform on each piece of data obtained after dividing the radar echo signal sample to obtain a time-frequency spectrum; 2, constructing an anomaly detection network model combined with a support vector data description based on a variational autoencoder of a residual network; 3, inputting the time-frequency spectrum into the anomaly detection network model for end-to-end training; 4, performing online detection of the target based on the trained model; and 5, evaluating the performance of the application from the constructed multi-dimensional index. The application can effectively enhance the separability between the target and the sea clutter, and improve the weak target detection precision under the condition of a real small sample.

This invention belongs to the field of integrated radar signal processing technology, specifically involving a two-stage clutter suppression method based on the space-time domain, which solves the problem of clutter suppression in complex low-altitude environments. First, the first stage, based on CLAM (Clear-and-Field Analysis), eliminates the dependence on the radial velocity difference between the target and clutter, directly and accurately shielding clutter from the physical space dimension, efficiently eliminating strong static ground object interference while preserving the energy of low-speed UAVs without loss. Second, the second stage, with its candidate angle traversal and circle fitting algorithm, accurately estimates and reduces the DC bias caused by residual static clutter, successfully overcoming the blind spot problem when the target and clutter spatial angles are close, enabling accurate extraction of weak, low-speed UAV targets at close range.

The application discloses a kind of based on dynamic weighting joint area CFAR's unmanned vehicle target detection and speed-distance dictionary construction method, it is related to target detection technical field, including: two-dimensional FFT processing is carried out to radar echo signal, constructs distance-Doppler joint spectrum matrix;In distance dimension, detection is carried out using ordered statistics CFAR, in Doppler dimension, detection is carried out using minimum selection CFAR, and respectively generates continuous type confidence score;Through introducing clutter intensity index and target density index, the weight of distance dimension and Doppler dimension is dynamically calculated, and joint confidence index is obtained by weighted fusion;Adopt adaptive dynamic threshold to carry out target decision, finally the detected target unit is mapped as speed-distance pair, constructs distance aggregation dictionary indexed by speed;The present application effectively suppresses false alarm propagation under dense target environment, improves detection robustness, and provides structured speed-distance dictionary data support for target tracking.

The application discloses a global average sea clutter near real-time estimation method, system, device and storage medium, including the following steps: obtaining multi-source satellite-borne radar historical backscattering coefficient data, and performing time and space matching and binning on the ERA5 wind field; an average sea clutter estimation model is established according to a geophysical model function fitting; the satellite-borne microwave scatterometer wind field is corrected in deviation and fused with a numerical weather prediction wind field, so that a high-coverage, continuous near real-time global sea surface wind field is obtained; the relative azimuth is calculated in combination with radar observation parameters and fused wind direction, and global average sea clutter data is obtained by using the model; the application improves the precision and application range of sea clutter estimation under the condition of medium and small incident angles, provides a high-quality input wind field through a multi-source wind field fusion technology, realizes rapid, stable and near real-time estimation of global average sea clutter, has high timeliness and business feasibility, and can meet the needs of near real-time sea clutter monitoring and ocean remote sensing application.

This invention discloses a method for detecting respiratory signals using through-wall radar based on PCA and VMD. First, eigenvalue decomposition is performed on the ultra-wideband radar echo data matrix after removing the DC component. Then, mode decomposition is performed on the clutter-free dynamic signal matrix in the slow-time dimension. Relevant mode components within the respiratory frequency band are selected. The selected mode components are reconstructed. Fourier transform is performed in the slow-time dimension to construct a range-frequency spectrum. Finally, a two-dimensional Gaussian template conforming to the spectral characteristics of human respiration is constructed and matched. The range and frequency estimates of the human respiratory signal are calculated. This invention addresses the problems of weak anti-interference capability and high false alarm probability of single clutter suppression methods, thereby achieving accurate respiratory signal detection in strong clutter environments.

A method for detecting faint targets based on non-steady-state clutter suppression belongs to the field of target detection and recognition technology. The method involves: extracting the target-specific spectrum and the target-background consistency spectrum from the input infrared image using two learnable filters; employing a densely connected U-Net network with residual connection modules as the backbone network, which uses a bidirectional interactive attention mechanism to extract features from the spectral components, obtaining target-specific features and target-background consistency features; using an instance-level contrastive loss to assist filter learning, achieving decoupling between target-specific features and target-background consistency features; and supervising the target-specific features with real-world annotations to perform infrared small target detection. This invention proposes a frequency domain decoupling method that preserves the effective spectrum, improving target detection performance and achieving effective removal of complex backgrounds.

The application relates to a KCF-TBD method based on a non-cooperative bistatic radar and belongs to the weak target tracking technical field of the non-cooperative bistatic radar. The application can realize detection and tracking of multiple weak targets on the basis of not depending on a motion model, and in view of the fact that in the actual scanning process of the passive bistatic radar, the capture of weak targets between frames is unstable, the KCF tracking frame is easily affected by strong clutter, errors of the tracking system itself and the like, and thus the problems of tracking frame drift and loss are caused, track prediction is carried out by using deep learning, the position of the KCF tracking frame is effectively corrected, and the stability of the tracking process is improved.

This invention proposes a method and apparatus for synthesizing array radar radiation patterns using space-time joint modulation. The method includes: determining initial values ​​of the array radar element excitation coefficients; acquiring the array far-field radiation pattern under the influence of the initial values ​​of the element excitation coefficients; dividing the array far-field radiation pattern into main lobe and side lobe regions based on its characteristics; setting the random modulation phase of the main lobe region to zero, maintaining the same main lobe phase across different pulses, while modulating the phase of the side lobe region regularly or randomly, while keeping the array far-field radiation pattern amplitude constant, thus obtaining array radiation patterns with different phase characteristics under multiple pulses; performing Fourier transforms on the array radiation patterns under different pulses to obtain the space-time two-dimensional element excitation coefficients; and applying the space-time two-dimensional element excitation coefficients to the array radar system to synthesize the array radar radiation pattern after space-time joint modulation. This invention significantly improves the array radar's resistance to sidelobe clutter and interference.