41 results about "Cfar detector" patented technology

The invention relates to a self-adaptive constant false alarm rate target detection method. The method includes the following steps: 1), transmitting data received by radar into a matching filter; 2), transmitting signals output by the matching filter into a square-law detector for processing; 3), transmitting signals output by the square-law detector into a CFAR (constant false alarm rate) detector for processing, and acquiring an estimation value Z of cluster power level generated by a reference unit according to a corresponding CFAR algorithm through sampling; 4), outputting a final judgment by the CFAR detector according to the estimation value Z acquired in step 3), namely detecting whether a target exists in the unit or not. In the method, according to statistical average value and variance of sampling values in a reference sliding window, the sampling value with the variance larger than a certain numerical value is deleted, an average value of remaining valid sampling values is used to replace the sampling value, and an average value of the sampling values is recalculated.

A method and system, for detecting closely spaced objects from a sensor that images a scene and provides image data, are disclosed. A CFAR detector performs a CFAR detection on the image data at a specific threshold level. A processor runs a first algorithm on the exceedances above the CFAR threshold level, and the first algorithm that is for single target detection includes a local-max algorithm for determining whether there are multiple exceedances within a 2×2 matrix of image data. If so, the first algorithm only keeps the exceedance with the maximum value. All the leftover exceedances in the whole image are the single target detections. The processor then runs a second algorithm to determine whether there are multiple exceedances (single target detections) within a local regional area across the whole image. The local regional area size can be set as a 3×3, 4×4, or 5×5 matrix of image data depending on the specified CSO separations. This algorithm can be run by using a regional moving window across the whole image. An alternative, simpler, and with more accuracy method using the indices of all the exceedances' (x,y) coordinates is also disclosed.

The invention discloses a CFAR detector suitable for complex inhomogeneous clutters, belongs to the field of radar signal processing, relates to a radar self-adaptive detection technology and in particular relates to a self-adaptive constant false alarm rate (CFAR) detection method. A radar radiation region is divided into even small regions by use of a morphological classification algorithm, each small region serves as a reference window, the reference windows of the same terrain are fused, and the terrains of the various reference windows after fusion are numbered to obtain the terrain classes of the whole radar radiation region. According to the CFAR detector suitable for complex inhomogeneous clutters, cells to be detected and resolution cells in the reference window are independent identically distributed even in an inhomogeneous background, and the performance of the detector is not affected by the inhomogeneous background. As a result, the CFAR detector suitable for complex inhomogeneous clutters is capable of solving the problem of acute drop of the performance of the CFAR detector when the radar scans a complex region in practical application, and has the outstanding advantages of high accuracy and good practicability.

The invention relates to a radar target and shadow segmentation method based on wavelets and constant false alarm rates. The method includes the following eleven steps: inputting a synthetic aperture radar image; selecting a wavelet function; conducting wavelet multi-dimensioned decomposition; selecting a high frequency sub-image after wavelet decomposition; inputting a CFAR detector I; segmenting the selected sub-image; performing wavelet inverse transformation; removing a mean value; inputting a CFAR detector II; inputting a target that finally needs to be segmented; and segmenting the image again. The advantages of the method are that the influence of spot noise on an SAR image is reduced; the defect that a contrast ratio between a target and a background has to be high in terms of a single CFAR detection method is overcome, and a target zone and a shadow zone can be detected at the same time; weak scattering targets in the SAR image can be effectively segmented, and the method exhibits high universality. An experiment shows that the mentioned SAR image segmentation method based on CFARs and wavelet transformation is a feasible and effective segmentation method, and has great application prospects.

The invention discloses a high-efficiency method for estimating covariance matrix structures, which belongs to the field of radar signal processing and mainly realizes estimating covariance matrix structures in adaptive detection of radar targets on the background with non-Gaussian clusters. The high-efficiency method aims to solve the problem that a traditional sampling covariance matrix and a traditional normalized sampling covariance matrix cannot lead an adaptive detector to a complete CFAR (constant false alarm rate) characteristic. A sampling covariance matrix is solved after pretreatment by means of dividing real-part data from virtual-part data, an obtained initiated matrix realizes the complete CFAR characteristic for clusters, a real part and a virtual part of auxiliary data are sufficiently utilized for iteration, accordingly, computation complexity in an iteration process is reduced, estimation precision is improved beneficially, and the high-efficiency method for estimating covariance matrix structures has a popularization and application value in adaptive CFAR detectors for radar targets.

The invention discloses a radar signal detection method based on a convolutional neural network. According to the method, a distance-Doppler frequency spectrum obtained after pulse Doppler processingis used as an image sample, different characteristics of a time-frequency spectrum with or without a target are taken as identification characteristics, radar signal detection is realized by utilizingthe classification capability of a convolutional neural network, existence of a target signal is judged, the convolutional neural network is applied to radar signal processing, and signal detection is realized from the perspective of image processing. Compared with a traditional CFAR detector, the method of the inventiom has the advantages that the detection probability is improved, the influencecaused by the correlation of background clutters when the CFAR detector is used is avoided, and the limitation that the fixed CFAR detector is suitable for a specific environment is improved.

A method and system for detecting a point source target using multiple matched filters are disclosed. A sensor, which is preferably an infrared focal plane array, includes a plurality of pixels for providing image data. A processor processes the image data from a selected pixel corresponding to a potential point source target and from a plurality of neighboring pixels. The system includes multiple filters having a plurality of different phases, including at least a center phase and four corner phases. The multiple filters filter the image data to obtain different filtered spatial images. A detector, which is preferably a CFAR detector, detects a target based upon the spatially filtered images from the multiple matched filters.

The invention relates to the technical field of radar target detection, and in particular, relates to an FPGA-based two-dimensional ordered statistical constant false alarm detector implementation method. According to the method disclosed by the invention, detection is carried out in each clock period from two-dimensional power matrix input, so that the repeated reading time delay of the data cache is reduced, and the detection speed can be effectively improved. Aiming at the condition of large calculated amount in a traditional OS-CFAR (Ordered Statistical Constant False Alarm Rate) sorting process, the FPGA implementation method is completed by adopting parallel comparison, so that the sorting complexity is reduced. The reusability of a module is fully considered, two-dimensional slidingwindow structure setting is completed through a shifting register set, two-dimensional OS-CFAR detection is achieved, and BRAM memory resources and DSP resources of the FPGA can be saved.

The invention provides a high-frequency radar target joint detection method using time-frequency analysis and a constant false alarm technology. Firstly, radar echo signals are preprocessed to conveniently extract a time-frequency ridge. The preprocessing process comprises the following steps: performing time-frequency representation on radar echo signals, performing binarization processing, performing vertical projection on a binary image, determining an approximate time-frequency ridge extraction area, and extracting the time-frequency ridge. After the time-frequency ridge is obtained, a CFAR detector is used for carrying out first-stage detection on time-frequency points on the time-frequency ridge, whether the time-frequency points on the time-frequency ridge are target points or not is judged, then the number of the target points is accumulated, second-stage detection is carried out, and if the accumulation number reaches a second threshold value set by a binary accumulation algorithm, it is judged that the time-frequency ridge is a ship target. Verification is carried out through actual radar data and AIS information, and under the condition of the same false alarm rate, a union set of ship targets matched by TF-CFAR and CFAR methods is taken, so that the radar target matching number can be greatly improved, and the radar target detection probability is further improved.

The invention discloses a sea surface floating type dim target detecting method.The problem that the sea surface floating typedim target is flooded by strong sea clutter in time domain and frequency domains, and consequently, the traditional detection algorithm is helpless is overcome. According to thetechnical scheme of the sea surface floating type dim target detecting method, a radar signal processing system receives pulse-echo data on a certain position, a large amount of noise and low energy sea clutter are removedthrough a Log-t / CFAR detector; two times of threshold decision-making are conducted on adjacent N frames of data, M / N detection technology with radial velocity is used for joint N frame M / N processing, data which have not passed a threshold are discarded, and the data whichhave passed the threshold are retained; N frames of original data are called back along a M / N accumulation path which has passed the threshold, and characteristics of the N frames of the original dataare extracted, then characteristic domain decision are conducted according to the characteristics, and target track and velocity information results are outputted. According to thesea surface floating type dim target detecting method,the sea surfacefloating typedim target can be effectively detected.

The invention discloses a method for simultaneously tracking and jamming based on RF stealth. The method comprises the steps of: firstly, analyzing a relationship between a radar detection distance and an interception distance of an enemy passive system, and acquiring a transmission power range required by a radar for detecting a target on the premise of ensuring that a radar signal is not intercepted; secondly, adopting a Kalman filtering and covariance control method, increasing a radar interval to reduce radiant energy on the premise of ensuring radar tracking precision; thirdly, analyzinga relationship between a detection probability of a constant false alarm rate (CFAR) detector and power subjected to blanket jamming, so as to obtain a jamming power boundary condition of effective jamming for the enemy passive system; and analyzing a relationship between detection performance of the CFAR detector and a number of jamming pulses, reducing the number of jamming pulses on the premiseof ensuring the jamming effect, and making the jamming pulses do not overlap with radar echoes. The method provided by the invention reduces the radiation energy under the condition of satisfying theradar detection performance and the jamming shielding performance, and improves the RF stealth performance of the airborne radar.

The invention discloses a CFAR detector based on KL divergence unit screening, and relates to the technical field of radar target detection. In allusion to the problems that the existing detector needs the priori information of the number of interference targets under a multi-target background, and the large-number deletion number in TM-CFAR needs to be manually set and cannot be effectively extracted from the environment, the invention designs a novel detector by combining KL divergence and an Otsu method. Abnormal values with relatively high amplitudes can be effectively eliminated on the basis of sequencing the detection units, the large-value deletion number in the TM-CFAR is adaptively and effectively determined according to the change of the environment, the number of the anti-impacttargets is adaptively adjusted according to the condition of a reference unit, prior information of the number of the interference targets is not needed, and the method has good detection performancein a multi-target environment.

A method for detecting a robust CFAR detector solves the problem of detection performance loss under the conditions of unknown or inaccurate prior information. The realizing steps of the method include obtaining a sample histogram, obtaining a mean ratio of front and rear data windows of the detector, determining whether clutters in an observation area contain uniform clutters, clutter edges and interference targets according to the sample histogram and mean ratio, and selecting appropriate constant false alarm detection algorithm for detection according to the clutter environment. Types of clusters are primarily determined by estimating the sample amplitude statistical characteristics and mean ratio of the clusters in an observation area, and target detection probability and false alarm probability are obtained by adaptively selecting the appropriate CFAR detection algorithm. Since the method can be used to effectively screen observed cluster samples, good detection performance in different clutter environment can be ensured.

The invention discloses a RATM-CFAR detector and detection method based on distance and angle joint estimation, and the method comprises the steps: collecting radar echo data, carrying out the 2D-FFT of the data to obtain an RDM distance Doppler matrix, employing a distance-angle estimation result for the priori estimation of a CFAR algorithm in a process of carrying out the CFAR of the RDM matrix, converting a frequency domain into a spatial domain, and carrying out the processing; eliminating interference targets near the to-be-detected unit and prerequisite targets far away from the to-be-detected unit, and then performing CFAR algorithm selection based on background clutter estimation on the remaining units to obtain a final detection result. According to the method, the distance-angle estimation result is used for priori estimation of the CFAR algorithm for the first time, conversion from the frequency domain to the spatial domain is achieved, the method has the advantages of being simple, capable of avoiding missing detection of small targets, high in detection precision, high in environmental adaptability and high in anti-interference performance, the detection performance of the radar in a complex environment and a multi-target environment is improved, and a new method is provided for the CFAR algorithm.

The invention discloses a radar-based sleep characteristic event detection method, device and a storage medium. The method comprises the steps as follows: S10, transmitting waveform to a sleeper in adetection range of radar and receiving a target echo signal by radar, establishing an echo signal model; and S20, preprocessing the received echo signal to obtain a restrained echo signal. The radar-based sleep characteristic event detection method fits distribution of power burst curves of sleep events and sleep characteristic events by matlab by analyzing power burst curve distribution of the sleep events and sleep characteristic events, judges whether distribution of P(Z|H1) and P(Z|H10) obeys lognormal normal distribution, and then detects numerical values of power burst curves by an adaptive logarithm double-threshold constant false-alarm detector; and the technical scheme can determine that power burst curve characteristics conform to normal distribution after taking logarithm valuesand accordingly, designs the adaptive logarithm double-threshold constant false-alarm detector to detect sleep characteristic events.

The invention relates to a target detection method of a matrix CFAR detector based on tKL divergence. The method comprises the following steps of: calculating a covariance matrix according to echoes received by each distance gate; calculating a right median matrix of the covariance matrix corresponding to a reference distance gate according to the definition of the tKL divergence; calculating a covariance matrix corresponding to a target to obtain the tKL divergence of a target covariance matrix and the right median matrix; determining a detection threshold value; and comparing the tKL divergence of the target covariance matrix and the right median matrix with the detection threshold value, and if the target tKL divergence is greater than the detection threshold value, determining that thetarget exists; and otherwise, determining that the target does not exist. By adoption of the target detection method of the matrix CFAR detector based on the tKL divergence provided by the technicalscheme, the problems of high difficulty and low accuracy of the existing target detection method for target detection under complex non-uniform clutter and short pulse sequence conditions can be effectively solved.

The invention provides a sea surface weak target detection method based on an AR spectrum singular intensity function, and the method comprises the steps: estimating an AR spectrum of a sea clutter through employing a time domain echo signal of the sea clutter, judging the fractal characteristics of the AR spectrum of the sea clutter, calculating the value interval width of the singular intensityfunction of the AR spectrum of the sea clutter, and carrying out the design of a constant false alarm detector. According to the method, the non-Gaussian, non-stationary and non-linear characteristicsof the sea clutter are utilized, and the defect that the detection performance of a traditional target detection method is reduced due to sea clutter model mismatch is overcome by analyzing the fractal characteristics of the AR spectrum of the sea clutter; AR power spectrum analysis is adopted, the defects of traditional time domain and frequency domain fractal analysis are overcome, the influence of time correlation on fractal characteristic analysis is fully considered, and the frequency resolution of the AR power spectrum is greatly improved; and the detection performance and stability ofthe sea surface weak target under the low signal-to-clutter ratio background are improved.

The invention discloses an improved two-dimensional change index constant false alarm target detection method. The method comprises the following steps: selecting a reference unit on a two-dimensionalR-D spectrum, and building a left reference window, a right reference window, an upper reference window and a lower reference window; describing clutter states and summing according to reference unitvalues in the reference window; selecting corresponding logic according to the background estimation condition; and calculating a detection threshold according to the weight to realize constant falsealarm processing. According to the method, an existing single-dimensional VI-CFAR detection strategy is expanded to a two-dimensional R-D spectrum for 2D-VI-CFAR detection, so that the background estimation effectiveness is improved, and the method has higher adaptability to various complex clutter environments. According to the invention, the respective excellent detection performances of the three mean value type constant false alarm detectors in a uniform clutter, a multi-target environment and a clutter edge environment are integrated, and the background reference unit is selected by using the two-dimensional spectrum information to estimate the background clutter power so as to select a more appropriate detection strategy.

A radar circuit for use with a host system such as a vehicle includes a radio frequency (RF) signal generator configured to generate a predetermined RF waveform, an RF antenna connected to the RF signal generator, and an ECU which executes a method. As part of such a method, a signal generator transmits the RF waveform toward different radar target types, and receives return signatures reflected therefrom. The ECU receives the return signatures from the antenna, processes the return signatures via parallel constant false-alarm rate (CFAR) subdetectors each with defined cells under test to detect the target types as a set of detection events, merges the detection events into a merged set, and executes a control action aboard the host system responsive to the merged set. Each subdetector detects a corresponding one or more of the radar target types via corresponding detection parameters.

The invention discloses a high-efficiency method for estimating covariance matrix structures, which belongs to the field of radar signal processing and mainly realizes estimating covariance matrix structures in adaptive detection of radar targets on the background with non-Gaussian clusters. The high-efficiency method aims to solve the problem that a traditional sampling covariance matrix and a traditional normalized sampling covariance matrix cannot lead an adaptive detector to a complete CFAR (constant false alarm rate) characteristic. A sampling covariance matrix is solved after pretreatment by means of dividing real-part data from virtual-part data, an obtained initiated matrix realizes the complete CFAR characteristic for clusters, a real part and a virtual part of auxiliary data are sufficiently utilized for iteration, accordingly, computation complexity in an iteration process is reduced, estimation precision is improved beneficially, and the high-efficiency method for estimating covariance matrix structures has a popularization and application value in adaptive CFAR detectors for radar targets.

The invention discloses an intelligent clutter partitioning method based on false alarm preprocessing time, and the method comprises the steps: step 1, carrying out average constant false alarm detection of a leading edge reference unit and the average CFAR detection of a trailing edge reference unit (Back of Off, BO) of each frame of data (kth frame) outputted by a detector at the same time; step2, fusing the detection results of the FO-CFAR detector and the BO-CFAR detector; step 3, if frame data is the first frame data, initializing a time storage matrix, otherwise, deleting the data in the lowest row of the storage matrix, integrally moving down the rest data by one row, and storing the data transmitted in the step 2 into the first row of the storage matrix to update the storage matrix; step 4, performing time accumulation on each distance unit of the time storage matrix, and judging whether each point after accumulation is a clutter boundary point or not; and step 5, obtaining aclutter partitioning result and outputting the result.

The invention discloses a BVI-CFAR target detection algorithm of a variation index based on Bayesian interference control, and the algorithm comprises the steps: firstly dividing a sliding window into a front detection window and a rear detection window, then dividing the front detection window and the rear detection window into two parts, obtaining four detection windows, then employing the variation index to carry out the analysis of the process of the four sliding windows, judging the position of a window where interference possibly exists, then adopting a Bayesian sliding window interference control method, predicting and compensating window interference, and deducing an expression of the detection process of a Bayesian variation index algorithm. According to the invention, the Bayesian sliding window interference control method is applied to the variation index detection process for the first time, a good multi-target detection effect is achieved, compared with a traditional VI-CFAR algorithm, the invention has the advantages that the problem that the detection probability is greatly reduced when targets appear on the two sides of a VI-CFAR detector is solved, and by means of the application of Bayesian interference control, the influence of interference on the detection process is reduced, the detection performance of the radar in a complex environment and a multi-target environment is improved, and a new method is provided for the CFAR algorithm.

The invention belongs to the technical field of passive MIMO radar signal processing, and relates to a passive radar network centralized detection method based on distance weighting. For a passive radar network system with multiple external radiation sources and multiple passive radar receivers under a low signal-to-noise ratio, all preprocessed reference signals and monitoring signals are uploaded to a data processing center, and signals on different propagation paths are endowed with different weights by using implicit distance priori knowledge of a target position; and a signal model is established according to propagation attenuation characteristics of electromagnetic waves in space, and a constant false alarm detector is deduced based on a generalized maximum likelihood ratio test criterion, so that centralized joint detection is carried out. The method has the beneficial effects that the intensity of signals received by each receiver is considered, the intensity difference of each signal path is also considered, and the target detection performance in a passive radar network system is greatly improved under a low signal-to-noise ratio by using a centralized detection method.

The invention discloses a multi-strategy Weibull CFAR detection method based on a change index, relates to the technical field of radar adaptive detection, and provides an RWVI-CFAR detector aiming at the problem that the detection performance of a detector in Weibull distribution clutter is reduced in the prior art, the detector has very small CFAR loss in a uniform Weibull clutter environment, and the detection performance of the RWVI-CFAR detector is improved. The method has the detection performance close to TOS-CFAR in a multi-target environment, has the false alarm control capability superior to TGO-CFAR in a clutter edge environment, and effectively solves the problem that the detection performance of a detector is reduced in Weibull distribution clutter. The RWVI-CFAR detector effectively solves the problem that the detection performance of VI-CFAR is reduced in an environment that interference targets exist on both sides by designing a self-adaptive target rejection algorithm.

The invention relates to an adaptive constant false alarm rate target detection method, comprising the following steps: 1): inputting data received by a radar into a matched filter; 2): inputting a signal output by a matched filter into a square law processing in the detector; 3): Finally, the signal output from the square law detector is passed to the CFAR detector for processing, and the estimated value Z of the clutter power level generated by the reference unit sampling according to the corresponding CFAR algorithm is obtained; 4): according to 3) Obtained reference unit sampling According to the estimated value Z of the clutter power level generated by the corresponding CFAR algorithm, the CFAR detector outputs the final decision, that is, whether there is a target in the detection unit. According to the statistical mean value x and variance of the sampling values ​​in the reference sliding window, the method deletes the sampling values ​​whose variance is greater than a certain value, replaces the sampling value with the mean value of the remaining effective sampling values, and recalculates the mean value of the sampling values.

A method for detecting a robust CFAR detector solves the problem of detection performance loss under the conditions of unknown or inaccurate prior information. The realizing steps of the method include obtaining a sample histogram, obtaining a mean ratio of front and rear data windows of the detector, determining whether clutters in an observation area contain uniform clutters, clutter edges and interference targets according to the sample histogram and mean ratio, and selecting appropriate constant false alarm detection algorithm for detection according to the clutter environment. Types of clusters are primarily determined by estimating the sample amplitude statistical characteristics and mean ratio of the clusters in an observation area, and target detection probability and false alarm probability are obtained by adaptively selecting the appropriate CFAR detection algorithm. Since the method can be used to effectively screen observed cluster samples, good detection performance in different clutter environment can be ensured.