275 results about "Radar radiation" patented technology

The invention belongs to the radiation source signal identification technology field and particularly relates to a radar radiation source signal identification method based on the one-dimensional convolutional neural network. The method comprises steps that (1), a radar radiation source signal data set is generated; (2), data pre-processing is carried out; (3), the convolutional neural network is constructed; (4), parameters are set, and the convolutional neural network is trained; (5), prediction classification is carried out; (6), precision calculation is carried out; and (7), the result is outputted. The method is advantaged in that 1, a network structure of the convolutional neural network is utilized to carry out characteristic extraction of signals, and a manual characteristic design process in a traditional algorithm can be avoided; 2, signals with a signal to noise ratio lower than -10dB can be accurately identified; and 3, the method can be simply realized.

The method discloses a method for sorting signals of a radar radiation source by using coverage in a complex dense environment and relates to a radar radiation source signal sorting method by using coverage, aiming at solving the problem of poor sorting ability of the prior art in the complex dense environment as well as in a serious overlapping condition of characteristic parameters. The method comprises the following steps of: choosing the characteristic parameters of a radar signal capable of being standardized as input characteristic vectors, initializing a radar signal sample parameter space; carrying out supervisory learning on the radar signal with parameter prior information in the radar signal sample parameter space by using a coverage algorithm; separating the radar signals of different kinds by using different fields by using the coverage algorithm; expressing the membership degree of the characteristic parameters of each radar signal to be sorted and identified in relative with each field by using a cloud model, and obtaining the membership value of the membership degree of the radar signal to be sorted and identified in relative with each field; and sorting the signal to be detected by using decision rules. The method for sorting signals of the radar radiation source by using coverage in the complex dense environment is suitable for sorting radar radiation source signals.

The present invention relates to a broadband radar target simulation method and system, characterized in that: the received radar radiation signal enters the broadband down-converter of the simulation system through an attenuator and a low-noise amplifier to perform conversion from radio frequency to intermediate frequency, and the down-conversion output IF 70MHz band-pass signal, 70MHz IF modulation signal is low-pass filtered and then band-pass samples the input analog modulation signal, and the output digital data is digitally down-converted in the FPGA, demodulated to the digital baseband signal and delayed; the delayed The data is output to the IQ data processing circuit of the FPGA, and finally the target simulation in the microwave frequency band is radiated to the radar through the signal output by the DAC. The invention adopts space coherent reception, which can fully meet the requirements of radar far-field testing.

The invention discloses a rough set-based radar radiation source signal identification method, relates to the technical field of signal identification and solves the problem of large calculated amount because the least square needs to be calculated and an optimal initial clustering center needs to be determined when the radar radiation source signal is identified by the conventional rough K-mean value method. The method comprises the following steps of: firstly, acquiring a pulse description word of a radar radiation source signal sample; secondly, determining a clustering number and the initial clustering center of the rough K-mean value by using rough set theory; thirdly, acquiring the centre of RBF neural network hidden layer neurons by using the rough K-mean value so as to acquire an RBF neural network structure; and finally, inputting the sample description word of the radar radiation source signal to be identified into the RBF neural network, and acquiring the identification result to finish the identification of the radar radiation source signal. The method of the invention is suitable for the identification of the radar radiation source signal.

A radar system and method with reduced multipath effects include a first component of a radar sensor module on which at least one antenna element is formed, the at least one antenna element having a surface at which radar radiation is received or transmitted, the at least one antenna element having a radiation aperture. A second component in proximity to the antenna element such that a portion of the radar radiation impinges on the second component comprises an angled surface forming an angle with the surface of the antenna element. The angled surface of the second component comprises a texture such that when the portion of the radiation impinges on the angled surface, the amount of multipath signal propagating through the radiation aperture of the antenna element is reduced.

The invention relates to a radar radiation source identification method based on feature fusion. The method comprises the steps of generating a radar radiation source unintentional modulation signal set; extracting AR model coefficients, Renyi entropy features and spectral kurtosis features; computing smoothed pseudo Wigner-Ville distribution, generating time-frequency images, and carrying out graying and adaptive binarization to obtain adaptive binarized images; extracting pseudo Zernike matrix and Hu matrix features of the images; extracting signal time-frequency image unintentional modulation features through application of an AlexNet convolutional neural network, carrying out normalization, and carrying out feature fusion to obtain fused feature vectors; and inputting the fused featurevectors into a support vector machine, training the support vector machine optimized through particle swarm optimization, and inputting the radar radiation source signal set into a system to finish identifying radar radiation sources. According to the method, the signals are analyzed from a time domain, a frequency domain and a time-frequency domain, various unintentional modulation features areextracted comprehensively, and the problems that the extracted unintentional modulation features are low applicability and reliability and the radiation sources are difficult to identify is solved.

The invention discloses a radar radiation source identification method based on phase noise unintentional modulation characteristic, relates to an identification method of a radar radiation source, and aims to solve the problem that the identification rate of an existing radiation source identification method based on phase noise is not high. The method disclosed by the invention comprises the following steps of analyzing the structure of a phase-lock frequency synthesizer in a radar transmitter system; building a model of phase noise generated by the phase-lock frequency synthesizer; calculating a bispectrum diagonal slice characteristic and a bispectrum non-diagonal slice characteristic; forming a characteristic matrix Y by using a bispectrum diagonal slice characteristic matrix A1 and a bispectrum non-diagonal slice characteristic matrix B1; performing PCA (Principal Component Analysis) dimensional reduction and building a type-known transmitter vector machine model; identifying a transmission signal of a type-unknown transmitter by utilizing the built vector machine model so as to realize the identification of a radar radiation source. The method disclosed by the invention is applicable to the identification of the radar radiation source.

The invention belongs to the field of radar radiation source signal recognition technology and particularly relates to an intra-pulse modulation recognition method based on time frequency image features. The method comprises the steps that (1) radar signals are subjected to Wigner-Vill distribution time frequency transformation to obtain a time frequency distribution image reflecting signal energy along with time and frequency; (2) the time frequency image obtained in the step (1) is subjected to image preprocessing; (3) the image obtained after preprocessing is subjected to interpolation to scatter an original track; (4) the track scattered into single points in the step (3) is analogized into a target location in target tracking, and joint probabilistic data association is introduced to recognize tracks of different radar signals; and (5) a central moment is utilized to extract shape features, etc. of the time frequency image. According to the method, the radar signals are converted into the time frequency image by means of time frequency distribution transformation, and modulation modes of the radar signals which arrive at the same moment under the condition that frequencies are overlapped are recognized respectively through digital image processing.

The invention relates to the technical field of radar testing, in particular to a radar radiating source simulator. The invention aims to solve the problem of limitation of a single-system single-radar radiating source. Therefore, the radar radiating source simulator disclosed by the invention comprises a modulation source module, an agile source module, a frequency mixing and filtering module, a radio frequency sending module and a signal processing and control module, wherein the modulation source module is used for generating a C-waveband output signal; the agile source module is used for generating a 11-17GHz radio frequency output signal; the frequency mixing and filtering module is used for receiving the output signals generated by the modulation source module and the agile source module and outputting a 6-18GHz radio frequency output signal; the radio frequency sending module is used for processing the 6-18GHz radio frequency output signal to generate a 0.8-18GHz radio frequency output signal of the radar radiating source simulator; and the signal processing and control module is used for carrying out upper control and coordination of the radar radiating source simulator. The radar radiating source simulator is a multi-system radar radiating source simulator. The broadband radio frequency output signal can be output. The limitation of the single-system single-radar radiating source is effectively avoided.

A radar system for motor vehicles has at least two radar sensors for emitting and receiving radar radiation for monitoring the surroundings of the motor vehicle, the at least two radar sensors being each positioned at an angle between 40 degrees and 50 degrees to an axis, and the at least two radar sensors being configured in such a way that the respective antenna has a pivotable visual range of an angle between at least −60 degrees and +60 degrees, in particular between −45 degrees and +45 degrees, relative to the main emission direction of the respective radar sensor.

A radar sensor for motor vehicles includes a printed circuit board which carries the mass and antenna structures of the radar sensor, and includes a housing accommodating the printed circuit board, the housing being formed on a transmit and receive side of the radar sensor by a radome which is transparent to radar radiation, characterized in that the radome has an essentially plane wall oriented obliquely to the printed circuit board.

In the case of a method for the material-removing fine machining of a workpiece surface of a workpiece, in particular for the honing or finishing of workpiece portions having substantially rotationally symmetrically curved workpiece surfaces, at least one fine machining tool machines the workpiece surface and, by means of a measuring system, a measurement of the workpiece surface is performed. In this case, at at least one measuring position, radar radiation is directed onto the workpiece surface, and the radar radiation reflected from the workpiece surface is acquired and evaluated for the purpose of determining at least one surface measurement value.

The invention discloses a radar radiation source identification method based on bispectral self-coding features. The method comprises the following steps that step 1, radar radiation source signals are acquired, and preprocessing and noise filtering are performed on the radar radiation source signals; step 2, the direct estimation method is applied to the discrete radiation source signals obtainedby sampling to calculate the bispectral image of the discrete signals; and step 3, feature learning is performed on the extracted bispectral image by using an over-limit learning machine sparse self-coding algorithm, and finally a radar radiation source identification model is constructed by using an over-limit learning machine classification algorithm. The integrated radar radiation source classification framework based on bispectral feature learning and over-limit learning machine identification is constructed so that the rapid and efficient radar radiation source signal identification method can be established.

The invention, which belongs to the technical field of data processing, discloses a radar radiation source signal feature selection method based on a membrane particle swarm multi-target algorithm. According to the method disclosed by the invention, a membrane calculation optimization theory is combined with a particle swarm optimization algorithm and the uniformity and diversity of a crowding degree maintenance set are utilized; and a data object is optimized by using two objective functions of correlation and redundancy and application to in-pulse feature selection of a radar radiation source signal is carried out. According to the invention, with non-dominated sorting and a crowding distance mechanism in a skin membrane, rapid convergence of the multi-target particle swarm optimizationalgorithm is kept by the algorithm and the solution set has high diversity. And then with KUT and ZDT series test functions, comparison testing is carried out on the algorithm and MOPSO, SPEA2 and PESA2 algorithms. Therefore, rapid convergence to the real Pareto leading edge is realized and the provided algorithm is feasible and effective.

The invention relates to a multi-parameter rasterizing sliding signal statistic screening processing method which is used for achieving the effect that a passive signal detection system sorts time-varying parameter complex signal sequences in the dense signal environment. High-density complex pulse signal flows which are detected and interwoven with the parameters changing randomly are subjected to parameter discretization segmentation according to time grids, time-varying parameter vectors of pulse signals are arranged corresponding to the time grids to form a vector parameter array sliding with time, a vector grid matching algorithm is adopted to conduct pre-sorting according to the time of arrival (TOA) and the angle of arrival (DOA) of signals and the carrier parameter change, and then fine sorting is conducted according to the signal pulse width and the repetition period change rule. By means of the method, sorting of the time-varying parameter complex signal sequences is decomposed into multistage vector clustering statistic screening and the least square error estimation processing, and accurate sorting and recognition can be conducted on complex signals of electronic scanning beams and phase control array radar radiation.

The invention discloses a radar radiation source signal identification method according to a three-dimensional entropy characteristic. The method of the invention is a novel identification method for settling defects in radiation source signal identification based on an in-pulse characteristic. According to the radar radiation source signal identification method, sample entropy, fuzzy entropy and normalized energy entropy are used as a three-dimensional characteristic vector of a signal. The sample entropy is used for describing complexity of a radiation source signal. The fuzzy entropy is used for measuring uncertainty of the signal. Furthermore the normalized energy entropy is utilized for describing distribution condition of the signal energy. According to the radar radiation source signal identification method, characteristic extraction is performed on six typical radar radiation source signals, and furthermore a support vector machine is used for performing classification testing. A testing result proves a fact that the extracted characteristic vector can well realize classification and identification on the radar radiation source signal in a relatively large signal-to-noise range, thereby preventing high effectiveness of the radar radiation source signal identification method.

A method for recognizing a vertical misalignment of the radiation characteristic of a radar sensor of a control system for a motor vehicle, in particular of a driving speed and / or adaptive driving speed system, including the following steps: the receive power of the radar radiation reflected from an object is determined; the distance dependence and the horizontal angular dependence are compensated according to the radar equation; the functional dependence of the receive power, processed in this way, on the distance from the object is compared to an expected and stored curve of the receive power over the distance, and from this the vertical misalignment of the radiation characteristic of the radar sensor is inferred.

The invention belongs to the technical field of radar signal processing, and particularly relates to a radar radiation source identification method and device based on an extended residual network. The radar radiation source identification method comprises the steps: carrying out the time-frequency analysis of a radar signal, and converting the time domain waveform of the radar signal into a two-dimensional time-frequency image; preprocessing the time-frequency image to obtain input data of a deep learning network; and constructing an extended residual deep learning network model, and for input data, self-learning signal time-frequency image features by using the network model and carrying out classified identification. According to the radar radiation source identification method, the problems that a traditional method is sensitive to noise, low in extraction characteristic effectiveness and universality and the like are solved, and the excellent identification effect can still be kept for complex multi-class radar signals in the environment with the low signal-to-noise ratio; the radar radiation source identification method can solve the problems that a simple depth model is weakin learning ability, confusion time-frequency image similar signals and the like, and is good in confusion resistance, accurate in identification result and high in identification accuracy; and the radar radiation source identification method can be applied to radar radiation source identification of more types, and has very high adaptability and popularization.

The invention belongs to the technical field of signal processing, and particularly relates to an ADS-B-based radiation source individual identification method and device, and the method comprises thesteps of obtaining an ADS-B pulse signal periodically broadcasted by a transponder in a working state, wherein the ADS-B pulse signal comprises a frame header and a data part; extracting individual characteristic parameters of the preamble pulse, decoding the data part to obtain individual identity information, and forming a characteristic vector by the individual characteristics and the individual identity information; dividing the feature vectors into a training data set and a test data set which are used for training a test neural network classifier; and for the target signal, carrying outradiation source individual identification by using the trained neural network classifier. According to the method, the operation complexity is reduced, and the problems of difficulty in individual information verification, low reliability due to pure dependence on simulation signals and the like in individual recognition are effectively solved. By means of the advantages of FPGA parallel computing and software decoding flexibility, the rapid and efficient decoding of the ADS-B messages is achieved, and the method has the important guiding significance for development of the radar radiation source individual recognition technology.

The invention belongs to the technical field of radar signal processing, and particularly relates to a method and device for individual identification of radar radiation sources based on unintentionalphase modulation characteristics, and the method includes the following steps of establishing a phase observation model of a radiation source, obtaining an unintentional phase modulation characteristic curve, and constructing a labeled training data set; performing offline training on the constructed convolutional network model by using the training data set; extracting unintentional phase modulation sequence characteristics of a target signal, and using the trained convolutional network model for online identification to obtain individual identification results of the radiation source. In the case of intentional modulation, the method provided by the invention provides a simplified observation model of the intra-pulse signal phase, performs demodulation processing on the observation phase sequence to extract the noisy estimation of unintentional phase modulation, uses the Bezier curve to fit the unintentional phase modulation to reduce the influence of noise, and finally uses the convolutional network to extract the joint characteristics of the unintentional phase modulation sequence to achieve the individual identification of radar radiation sources. The feasibility and effectiveness of the scheme are verified by simulation experiments to obtain a good engineering application prospect.

The invention provides a complex radar radiation source identification method based on a one-dimensional self-stepping convolutional neural network, and solves the problems that in the prior art, dimension transformation processing needs to be carried out on radar signals and the identification rate is low. The implementation scheme comprises the steps of collecting radar radiation source signalsto make a data set; dividing the data set into a training set and a verification set; constructing a one-dimensional self-stepping convolutional neural network; setting a self-stepping sample trainingstrategy and training the network by using the training set; and inputting the data of the test set into the trained one-dimensional self-stepping convolutional neural network, and outputting the recognition rate of the overall test signal. The one-dimensional self-stepping convolutional neural network constructed by the method is simple in structure and small in parameter quantity, can directlyextract the time domain signal characteristics of the one-dimensional radar radiation source, does not need dimension transformation, and is good in real-time performance. Meanwhile, a self-stepping sample training strategy is adopted, so that the network is close to the optimal point as much as possible in the training process, and the recognition rate is increased. The method can be used for radar radiation source identification in a complex electromagnetic environment.

The invention discloses a radar radiation source recognizing method based on a VAE-ResNet network. The problems that network training cannot be completed under few signals, the radar radiation sourcefeature extraction is not sufficient and the recognition accuracy is low in the prior art are mainly solved. By means of the scheme, seven different types of radar radiation source signals are generated; the radar signals are output in the form of a sequence, then time-frequency converting is conducted on the signals, and the signals are output in the form of a time-frequency graph; the data output in the two forms is concentratedly marked with the signal types, and a training set and a testing set are prepared; the VAE-ResNet network is established; the VAE-ResNet network is trained through the training set; the testing set is sent into the trained network, and the network output is of a radar radiation source type. The radar radiation source recognition feature extracting completeness degree and recognition precision are improved, and the method can be used for radar signal recognition under a complicated electromagnetic environment.

The invention provides a reflective material, adapted for the efficient retro-reflection of radiation emitted by radar, the material comprising a multiplicity of reflective entities which are typically embedded in a substrate, the multiplicity of reflective entities being comprised in at least one reflective surface or electrically conducting surface, and the at least one reflective surface or electrically conducting surface comprising an electrically conductive coating, a high permittivity material, a foil, a film or a fabric formed from electrically conducting fibres or filaments. The reflective entities may comprise discrete shaped entities, most preferably di- or tri-hedral shaped entities, which are preferably embedded in a high permittivity medium. More preferably, the reflective entities are comprised in the machined surface of a reflecting substance comprising a polymeric sheet material which is machined to provide an irregular patterned surface. Most preferably, the substrate comprises a textile material in the form of a garment. Reflective material and textile garments according to the invention provide a highly efficient means for the reflection of incident radar radiation and offer significant benefits in terms of the visibility of wearers to drivers of oncoming vehicles in poor and dark light conditions, thereby facilitating a marked improvement in road safety statistics and enhancing search and rescue detection and success rates, especially in severe and inclement weather conditions.

A radar radiation source individual identification method based on combination of a deep learning model and features comprises the following steps: 1) collecting intermediate frequency AD signal dataemitted by different radars, and intercepting intra-pulse signal data to generate a radar radiation source individual identification sample set; 2) performing normalization processing on radar radiation source individual identification samples and dividing the radar radiation source individual identification samples into a training sample set, a verification sample set and a test sample set; 3) constructing a radar radiation source individual identification model based on combination of the deep learning model and features; 4) training a radar radiation source individual identification model based on combination of the deep learning model and features; 5) using the test sample set to obtain a radar radiation source individual identification model result and a characteristic determination result; and 6) calculating a final identification result by using the radar radiation source individual identification model result and the feature determination result, and counting the identificationaccuracy. The method is high in universality, does not need manual feature extraction and a large amount of priori knowledge, is low in complexity, and is accurate and stable in classification results.

The invention provides a formation method of a MIMO tracking radar emission wave beam based on radio frequency stealth and belongs to the communication radar technology field. Through designing a weighting coefficient of an orthogonal transmitting waveform, a directional wave beam is formed at a target azimuth and a zero limit of a width is formed along an interception receiver direction. And a discrete long ball sequence method is used to form a wide wave beam along a target direction so as to simultaneously detect a wide sector. Finally, according to a wave beam gain of each target direction, radar radiation detection is performed on minimum power needed by a target. Through null steering and radiation power, radio frequency stealth performance of a MIMO radar is controlled and increased so that multi-target tracking detection performance of the MIMO radar is guaranteed and simultaneously radio frequency stealth performance is increased.

The invention relates to a radar signal feature extraction method based on residual depth learning. With a designed residual depth learning network, deep-level feature extraction is carried out on a radar radiation source signal in a complex electromagnetic environment. The method is implemented as follows: parameters of the residual depth network are trained by using the existing radar data in adatabase; the intercepted data are sent to the input terminal of the residual depth network and results are outputted after mapping of multiple hidden layers, wherein the outputted results are used asthe depth features of the pulse string; with a clustering method, the obtained depth features are clustered, and the correlation degree of each two radiation sources after clustering is calculated and processed according to a correlation criterion; and then the parameter of each radiation source after fusion is calculated and sorting is completed. Therefore, the depth features among data can be dug; the sorting precision is high; and the radar signal feature extraction method can be applied to fields of target reconnaissance and interference source localization.

The invention relates to a parameter agile radar signal sorting method based on sequence fragment mining. The parameter agile radar signal sorting method comprises the steps that a digital processingextension set firstly performs clustering processing on PDW, then performs mining of sequence fragments and transforms PDW information into pulse fragment information. The pulse fragment screening iscompleted by comparing with the information in a radar agile parameter database, for the screened pulse fragment, certain criteria are used for determining whether a radar radiation source exists or not, and if yes, radiation source parameters are calculated to generate EDW information.

The invention is applicable to the field of computers, and provides a radar radiation source identification method. The radar radiation source identification method comprises steps: to-be-identified radar radiation source signals are received; the to-be-identified radar radiation source signals are subjected to wavelet transformation to generate a two-dimensional wavelet matrix; the two-dimensional wavelet matrix is processed to generate a time-frequency domain gray image; and a radar radiation source corresponding to the time-frequency domain gray image is determined according to the time-frequency domain gray image and a radar radiation source identification model generated and trained based on a convolutional neural network algorithm. According to the radar radiation source identification method provided in the embodiment of the invention, the to-be-identified radar radiation source signals are subjected to wavelet transformation and are then processed to be the time-frequency domain gray image, the radar radiation source identification model generated and trained based on the convolutional neural network algorithm is used, the radar radiation source can be directly determined,and in comparison with the existing identification method, the data processing amount can be effectively reduced, the processing efficiency is improved, and the identification accuracy is good.

The invention discloses a radar radiation source identification method based on singular value decomposition and one-dimensional CNN network, and mainly solves the problems of complex identification time and low identification precision of a radiation source identification technology in the prior art. The method comprises the following steps: adopting a separation algorithm to carry out signal separation and splitting a radar time sequence into a matrix G; performing singular value decomposition on the matrix G, and extracting a diagonal element forming vector lambda from the decomposed sigmamatrix; making a training set, a verification set and a test set from singular value vectors extracted from a plurality of groups of radar data; designing the one-dimensional CNN network structure aiming at the vector lambda; training the one-dimensional CNN network by using the training set; testing and training the performance of the network by using the verification set, and judging whether thenetwork is available or not; the test set is sent to a trained network, and the network output is a radiation source category. The method reduces the identification time of the radiation source underthe condition of ensuring the available identification rate, and can be used for the identification of the radar radiation source under the complex electromagnetic environment.