11 results about "Cfar detector" patented technology

The invention provides a CA-CFAR detector detection probability analysis method and system under a reverberation background, and belongs to the field of detection probability calculation. The method aims at solving the problems that an existing Monte Carlo simulation method for analyzing the false alarm probability of a detector is large in calculation amount, low in efficiency and lack of theoretical support. According to the method, the calculation model of the detection probability is established based on the Gamma distribution and the PHCSR distribution, the detection probability of the CA-CFAR detector under the reverberation background can be directly calculated, the problems that a traditional Monte Carlo simulation method is large in calculation amount and long in consumed time are solved, the calculation efficiency is remarkably improved, and a theoretical basis is provided for evaluation of the detection probability.

The invention provides a non-uniform double-pulse modulation method based on a TCM. The non-uniform double-pulse modulation method comprises the steps that 1, a non-uniform double-pulse period modulation model is constructed; 2, carrying out radar echo modulation analysis; 3, analyzing a pulse compression result; and step 4, regulation and control effect analysis based on a CFAR detector. According to the method, the key parameter of the time delay factor is innovatively introduced, the modulation flexibility and unpredictability are greatly improved through the collaborative design of three degrees of freedom, and the problems of insufficient flexibility and the like existing in a radar target feature regulation and control method are solved. Compared with a regulation and control method based on TCM uniform monopulse period modulation, the generated harmonic peak amplitude distribution is diversified, screening of regular signals by a radar CFAR detector is effectively avoided, and the number of effective harmonic peaks passing detection is remarkably increased. According to the invention, precise suppression or enhancement of specific-order harmonics can be realized, and a technical approach is provided for implementing flexible and dynamic radar target feature regulation and control.

The invention provides a three-dimensional CFAR detection method based on semantic segmentation, and belongs to the field of radar target detection. Aiming at performance reduction caused by target amplitude flicker in single-frame RD image detection, a CFAR detector reference window is extended to range, Doppler and frame three dimensions so as to contain more effective samples, a reference unit is selected in combination with semantic information, and the method comprises the following steps: integrating multiple frames of RD images to form a three-dimensional data matrix, then carrying out semantic segmentation on three-dimensional radar data, and obtaining a three-dimensional data matrix; and constructing a three-dimensional semantic sign matrix. In CFAR detection, a reference window clutter unit is eliminated by means of the matrix so as to estimate background power, and finally whether a target exists or not is judged. According to the method, the problem that the detection performance is reduced due to target amplitude flicker in single-frame detection is effectively avoided, the adaptability of a detection algorithm to target fluctuation change is remarkably improved, the calculated amount is small, and engineering implementation is easy.

The invention discloses a vertical measurement ionogram O / X wave separation method based on polarization matched filtering, and the method comprises the steps: obtaining a dual-channel complex signal, and carrying out the incoherent accumulation to obtain echo intensity; using an OS-CFAR detector and morphological processing to generate a mask; calculating a phase difference based on the mask, and fitting through a double-Gaussian mixture model to obtain O-wave and X-wave characteristic phases; estimating a complex polarization ratio; constructing a polarization matched filter weight vector, multiplying the polarization matched filter weight vector with the dual-channel signal, and separating to obtain an O / X wave complex signal matrix; a separated ionogram is generated through amplitude calculation and detection. The method does not depend on circular polarization hypothesis, actual polarization parameters can be directly estimated, high-precision separation is achieved through polarization matched filtering, and robustness and accuracy are improved.

The invention relates to the field of radar signal CFAR processing, in particular to a radar signal CFAR algorithm implementation method based on GPU acceleration. According to the technical scheme, the method comprises the following steps: calculating module values of a main channel signal and an auxiliary channel signal after being subjected to an MTI / MTD algorithm; after signal noise parameter estimation of different sampling points is completed, signal noise parameter estimation data of different sampling points are compared with a signal threshold, and a distance dimension peak value is searched; the maximum value of each sampling point signal in the same distance dimension and different Doppler dimensions is calculated, when two-dimensional CFAR processing is carried out, zero Doppler output is detected through a clutter map, output of each of other Doppler filters is detected through a unit average CFAR detector, and the maximum value of each sampling point signal in the same distance dimension and different Doppler dimensions is calculated. The output of the clutter map detection and the output of the CFAR detector are combined in a large selection mode to serve as a final detection result. The method is suitable for radar signal CFAR processing.

The invention provides a CA-CFAR detector detection probability analysis method and system under a noise background, and belongs to the field of detection probability calculation methods. The objective of the invention is to solve the problem that there is no accurate analytical relationship between the detection probability and the detection threshold of the existing CA-CFAR detector. The method comprises the following steps: fitting probability distribution containing echo signal test statistics by using Gamma distribution, calculating signal amplitude, and calculating detection probability. According to the method, a calculation model of the detection probability is established, and the detection probability of the CA-CFAR detector under the exponential distribution noise background can be directly calculated; the problems that a traditional Monte Carlo simulation method is large in calculation amount and long in consumed time are solved, the calculation efficiency is remarkably improved, and a theoretical basis is provided for evaluation of the detection probability.

This invention discloses a SAR image target detection method based on the fusion of statistical and structural characteristics. It utilizes a CFAR detector and a Faster-RCNN detection network to iteratively identify targets in the SAR image under test until the alternating iteration termination condition is met, thus obtaining the target identification result of the SAR image. This invention fuses the CFAR detector and the Faster-RCNN detection network. Based on the large number of false alarm bounding boxes obtained from the CFAR detector, soft annotations of the bounding boxes are obtained through pixel-level to target-level fusion calculations. The Faster-RCNN detection network is used as the basic training framework, and the bounding boxes output by the Faster-RCNN detection network are used as the protected area size of the CFAR detector, reducing the mixing of target samples with clutter samples and making the parameter estimation of the statistical distribution model more accurate.

The invention discloses a self-adaptive high-frequency ground wave radar target detection method and system, and relates to the technical field of target detection. The target detection method comprises the following steps: performing two-dimensional Fourier transform on high-frequency ground wave radar echoes to obtain a distance Doppler spectrum so as to distinguish a shore-based radar from a shipborne radar; preliminarily detecting a shore-based radar spectrum through a constant false alarm detector to obtain a suspicious target index, traversing the index to divide connected regions, and judging single and group targets; positioning, tracking, category reasoning and tracking optimization are carried out after single-target secondary detection; and after the center of the group target is determined, positioning, tracking, identifying and associating AIS data. And carrying out preliminary detection on a ship-borne radar spectrum, then intercepting an image, compensating and denoising, detecting and determining a target unit index again, positioning, tracking and associating AIS data to obtain a category, predicting a motion direction, adjusting a posture, and continuing tracking. The problem that an existing high-frequency ground wave radar is large in error in the ocean target detection process is solved, and the accuracy of ocean target detection is improved.

The invention discloses a two-stage distance and speed estimation method for a frequency agile radar, and the method comprises the steps: carrying out the first-stage linear processing of an echo signal, calculating the square of the amplitude module value of each pulse, carrying out the summation, judging whether a target is an effective target or not at a peak point of a summation result through a constant false alarm detector, and carrying out the second-stage linear processing. Successively detecting new targets and circularly correcting parameters of the detected targets until a detection termination condition is met, and obtaining a final set of distances and amplitudes of all the targets; and performing second-stage linear processing on the echoes, and estimating the accurate distance and speed of each target through matched filtering and a Newton method according to the distance unit of each target obtained in the first stage. The invention also provides a two-stage distance speed estimation device. The method provided by the invention is used for solving the problems of poor precision, high complexity, difficult detection and the like when the frequency agile radar detects the distance and the speed of the target.

This invention discloses a two-stage range and velocity estimation method for frequency-agile radar, comprising: performing a first-stage linear processing on the echo signal, calculating and summing the squared amplitude magnitudes of each pulse, using a constant false alarm rate (CFAR) detector to determine whether a target is valid at the peak of the summation result, successively detecting new targets and iteratively correcting the parameters of detected targets until a detection termination condition is met, obtaining the final set of ranges and amplitudes of all targets; performing a second-stage linear processing on the echo, estimating the precise range and velocity of each target based on the range units of each target obtained in the first stage using matched filtering and Newton's method. This invention also provides a two-stage range and velocity estimation device. The method provided by this invention addresses the problems of poor accuracy, high complexity, and detection difficulties in detecting target range and velocity using frequency-agile radar.

The invention provides a CA-CFAR detector false alarm probability analysis method and system under a reverberation background, and belongs to the field of false alarm probability calculation. The method aims at solving the problems that an existing Monte Carlo simulation method for analyzing the false alarm probability of a detector is large in calculation amount, low in efficiency and lack of theoretical support. According to the method, the calculation model of the false alarm probability is established based on the Gamma distribution, the false alarm probability of the CA-CFAR detector under the K distribution reverberation background can be directly calculated, the problems that a traditional Monte Carlo simulation method is large in calculation amount and long in consumed time are solved, the calculation efficiency is remarkably improved, and a theoretical basis is provided for rapid and accurate setting of a detection threshold value.