35 results about "Radar signal detection" patented technology

The invention belongs to the technical field of radar signal processing, and provides a radar signal detection and identification method and device based on deep learning. The method comprises the following steps: performing time-frequency transformation on a time-frequency aliasing radar signal to obtain two-dimensional time-frequency data, drawing a time-frequency graph in a matching manner, combining the time-frequency graph into a mask region of each time-frequency component in the time-frequency graph, and mapping the mask region of the time-frequency graph into the two-dimensional time-frequency data; filtering time-frequency components outside a mask area in the two-dimensional time-frequency data, performing mask filtering on a time-frequency overlapping area to filter an overlapping part, performing time-frequency inverse transformation on the filtered time-frequency data to obtain a time-domain incomplete waveform, and reconstructing a complete radar signal; obtaining a reconstructed radar signal time-frequency diagram; graying processing is carried out, an optimal image segmentation threshold value is determined, a radar signal and a background signal are completely stripped, and a radar signal contour is extracted; and obtaining a modulation mode corresponding to the current radar signal based on the radar signal contour and a pre-trained modulation identification model.

The invention discloses a radar target detection method based on graph node dual-channel feature attention fusion, and belongs to the technical field of radar signal detection, and the method comprises the following steps: 1, carrying out the graph node division of received frame radar echo data; 2, respectively extracting time domain amplitude and time frequency characteristics from echo time sequence data corresponding to each graph node; step 3, establishing a feature preprocessing sub-network; step 4, constructing a node feature fusion sub-network; 5, constructing a signal classification graph neural sub-network; 6, connecting the feature preprocessing sub-network, the node feature fusion sub-network and the signal classification graph neural sub-network in series to form a radar target detection neural network; and 7, inputting the test set into the trained radar target detection neural network, and outputting a dichotomy result of which the corresponding node is a target or clutter signal. Through the scheme, the target detection capability of the radar in the clutter environment can be improved.

A cardiopulmonary radar signal detection system based on a pre-training-fine tuning normal form comprises a signal acquisition module, a waveform demodulation module, a signal waveform pre-training module, a signal waveform fine tuning classification module and a classification result semantization module. Wherein the signal acquisition module adopts an under-bed radar module based on a millimeter wave sensor to acquire cardiopulmonary activity information on the surface of a thoracic cavity; the waveform demodulation module extracts a cardiopulmonary activity displacement curve from the cardiopulmonary activity information by adopting a radar signal demodulation algorithm; the signal waveform pre-training module carries out waveform covering processing according to the cardiopulmonary activity displacement signal waveform information to obtain a reconstructed signal waveform result; the signal waveform fine tuning classification module performs classification label processing according to the abnormal cardiopulmonary displacement signal waveform marking information to obtain a matching result between the signal waveform and a classification label; and the classification result semantization module performs label analysis and interpretation under the combination of the knowledge base according to the classification label matching information to obtain a semantic comprehensive result of the cardiopulmonary radar signal detection system. The method is high in overall link performance, high in integration level and large in application potential.

A wireless device is configured to transmit radar signals at extremely low spectral density so that the receiver can be operated at the same time without saturating and thereby enabling full-duplex operation. To achieve sufficient range, beamforming is used together with a very long correlation time for detection of the radar signal, in which case the receiver can resolve backscattered or reflected radar signals far below the noise floor, while the bandwidth of the transmitted radar signal can be very high. The wide bandwidth enables the wireless device to maintain a low power spectral density while increasing output power, and also increase the depth resolution. The extremely low power spectral density even in the transmission beam direction makes the probability that communication will be disturbed very low. The high bandwidth calls for digital filters for both transmitted and received signals to equalize the radar frequency response characteristics of the radio unit.

A hydraulic cylinder assembly includes a cylinder body, a piston configured to slide within an interior of the cylinder body, and a cylinder head coupled to the cylinder body. The cylinder head includes a cavity, and a bore that extends between the cavity and the interior of the cylinder body. The hydraulic cylinder assembly includes a radar sensing unit disposed within the cavity. The radar sensing unit includes a radar signal emitter and a radar signal detector oriented respectively to emit and detect radar signals through the bore into the interior of the cylinder body. The hydraulic cylinder assembly includes a dielectric lens between the radar sensing unit and the interior of the cylinder body, the dielectric lens having a convex side facing the radar sensing unit and a planar side opposite the convex side facing the interior of the cylinder body.

The invention discloses an adaptive radar signal detection method and system based on reinforcement learning. The method comprises the following steps: S1, collecting pulse radar echo signals; s2, dividing a reference unit and a to-be-detected unit for the signal; s3, extracting basic features of the reference unit and the to-be-detected unit signals; s4, extracting deep features of the reference unit signals; s5, extracting deep features of the to-be-detected unit signal, and outputting a confidence coefficient; s6, inputting the joint deep features of the reference unit and the to-be-detected unit into a reinforcement learning agent as a state, and adjusting an action output detection threshold by the agent based on the current state; s7, performing target detection on the to-be-detected unit, and calculating a reward value; s8, repeating the steps S3-S7, constructing empirical data, training and updating the intelligent agent by adopting a reinforcement learning algorithm, and outputting a signal detection decision intelligent agent model; and S9, obtaining a confidence coefficient after the test set passes through the steps S3-S5, inputting the deep features into the trained intelligent agent to obtain a threshold, judging that the intelligent agent with the confidence coefficient exceeding the threshold value is a signal, and otherwise, judging that the intelligent agent is a noise.

The invention belongs to the field of centralized MIMO (Multiple Input Multiple Output) radar signal detection, and relates to a self-adaptive detector algorithm for a weak moving target, combined with an optimized quantizer based on a PSO-SA (Particle Swarm Optimization-Sub-Association) algorithm and based on G-LMP (Gradient-Local Maximum Potential) detection of a local maximum potential algorithm under a low-bit quantization condition. The method specifically comprises the following steps: S1, setting a radar system model, setting a centralized MIMO radar, and performing multi-input and multi-output; s2, transmitting and receiving at the same time by each antenna to obtain a radar echo signal, and performing low-bit quantization processing on the received signal; s3, adopting a similar GLRT thought, and designing a G-LMP detection algorithm based on a local maximum potential; and S4, designing a quantizer based on a PSO-SA algorithm obtained by combining a particle swarm algorithm and a simulated annealing algorithm, and carrying out multiple iterations to obtain an appropriate quantization threshold value so as to improve the detection performance. Under the condition that a large amount of echo data is obtained by a centralized MIMO radar, a low-bit sampling method is introduced, the occupied bandwidth is reduced, the detector with better performance is designed, and compared with a single traditional detector, the detection performance is effectively improved while the operation cost is reduced.

The present application relates to a radar signal detection method, a processing chip, a radar, a device, and a detection apparatus, which relate to the technical field of radars. The method comprises: receiving a first instruction, wherein the first instruction is used for indicating a first threshold value; receiving a first radar signal; and in response to the similarity between the first radar signal and a preset signal being greater than or equal to the first threshold value, sending an alarm signal, wherein the preset signal is used for representing that there is a moving target object in a target area. In this way, the accuracy of radar detection can be improved.

The present application relates to a method, apparatus, device, and readable storage medium for identifying autonomous vehicles, and relates to the field of autonomous vehicle technology. The method comprises obtaining radar signals sent by other vehicles; detecting whether a fusion signal exists in the radar signal, wherein the fusion signal includes an amplitude modulation signal and a frequency modulation signal; and determining that the other vehicle is an autonomous vehicle if a fusion signal exists in the radar signal. The present application adds an amplitude modulation signal to the radar of each autonomous vehicle, so that a unique fusion signal containing an amplitude modulation signal and a frequency modulation signal exists in the radar signal of the autonomous vehicle, and effectively identifies the autonomous vehicle through the unique fusion signal.

A monobit analog-digital converter (ADC) system and method are disclosed, the system comprising a front-end signal conditioning system equipped with a programmable attenuator and amplifier for processing analog signals, where the attenuator, which may include a cascade of programmable attenuator cells, is controlled by an attenuation control signal to adjust signal attenuation to cause a signal-to-noise ratio (SNR) in the negative domain, after which an amplifier amplifies the attenuated signal with the suitable SNR, where the amplified signal is then converted to a monobit signal by a monobit ADC, thereby achieving an ADC system that enables conditioning and digitizing of RF signals over a wide range of input signal powers with control over the output spectrum signal power and harmonics to achieve low-power real-time general-purpose broadband blocker detection for adaptive radios and for interference detection, main beam radar signal detection, and instantaneous frequency measurement, among other fields requiring general-purpose blocker identification with low size, weight, power, and cost (SWaP-C).

To provide a kick operation sensing device which has a wide sensing distance and is resistant to influence of a peripheral environment by sensing user's kick operation on the basis of a radar signal.SOLUTION: A device for sensing kick operation by using a radar signal related to an embodiment of the present invention includes a transmission and reception part for transmitting the radar signal toward an object and receiving the radar signal reflected from the object, a characteristic derivation part for deriving characteristic information for the object on the basis of the radar signal, an operation collection part for determining the existence / nonexistence of an approach to a kick operation determination state on the basis of the characteristic information, and a kick operation determination part for determining whether operation of the object is effective kick operation on the basis of the collected characteristic information in the case of approaching the kick operation determination state.SELECTED DRAWING: Figure 1

The invention relates to a microwave frequency testing technology, and aims to solve the problems that an existing microwave frequency measuring method based on Brillouin scattering is limited by the Brillouin gain spectrum width, and the frequency measuring resolution is difficult to improve; the invention discloses a high-resolution microwave frequency measurement technology based on a tunable Brillouin fiber laser, and the technology comprises the steps: outputting two narrow-linewidth optical carriers through a laser source, enabling an unknown signal of an upper branch to serve as pump light to excite Brillouin after intensity modulation, enabling a signal outputted by a vector network analyzer of a lower branch to serve as probe light after modulation, and enabling the probe light to serve as the probe light after intensity modulation. The probe light is selectively amplified after interacting with the Brillouin gain, an electric delay line is introduced into the Brillouin resonant cavity, and stepping tuning of the laser frequency is realized by changing the voltage applied to a medium on the electric delay line, so that a to-be-measured frequency signal is measured at high resolution; while the broadband frequency measurement range is maintained, the frequency resolution of 100 kHz is achieved, and the application potential in the fields of high-resolution radar signal detection and the like is shown.

Radar signal detection and parameter estimation is central in passive surveillance systems, providing inputs for many information processing modules in order to detect, localize, indentify and intercept hostile targets. The proposed method for detecting radar signals and estimating their intra-pulse parameters in time-varying noisy environments consists of several stages: magnitude-squared envelopes calculation, adaptive noise floor estimation, detection statistics calculation, rising edge detection, time of arrival estimation, falling edge detection, time of departure estimation, pulse width estimation, amplitude estimation and center frequency and bandwidth estimation. Estimated intra-pulse parameters are wrapped into pulse descriptor words (PDWs) for information processing tasks, where each PDW consists of time of arrival, time of departure, pulse width, pulse amplitude, center frequency, signal bandwidth, noise floor level and additional useful information. The method is sequential, implemented in hardware platforms for real-time surveillance applications. The proposed method yielded much better performance than classical threshold-based edge (TED) detection methods.

Disclosed is a driver assistance system including a first processor that receives a radar signal from a radar and detects one or more first objects based on the radar signal, a second processor that receives a camera signal from a camera and detects one or more second objects based on the camera signal, a laser controller that controls a beam generator to radiate the laser beam, a segmentation unit that extracts one or more first segments and one or more second segments corresponding to the one or more second objects, a mapping unit that maps at least one of the first segments and at least one of the second segments, a classification unit that classifies a target object based on an image signal and determines a possibility of collision with the target object, and a vehicle controller that controls a vehicle drive device based on the possibility of collision.

The present invention discloses a radar signal detection method and system based on multi-task learning of a time-frequency domain feature fusion network. The method includes the following steps: establishing a radar signal mathematical model and using the mathematical model to obtain various types of radar signal sequences; processing the radar signal sequences to obtain radar signal I sequences and radar signal Q sequences; labeling the radar signal I sequences and radar signal Q sequences and constructing a training data set; constructing a time-frequency domain feature fusion network and training the time-frequency domain feature fusion network using the training data set to obtain a signal detection and recognition model; obtaining a radar signal to be detected and completing radar signal detection using the signal detection and recognition model. The present invention performs combined modeling and analysis on multiple radar signals, and uses a time-frequency domain feature fusion network to extract local and global signal features from the perspective of signal I / Q, and fuses frequency domain features to reduce the impact of noise on signal recognition.

The invention discloses a reflected signal multi-channel synchronous processing system suitable for an active phased array radar, relates to the technical field of radar signal detection, and solves the technical problem that in the prior art, a radar system cannot perform synchronous analysis and synchronous processing on time, phase and amplitude multi-dimensional data when performing signal transmission. The time synchronization processing unit is used for collecting hardware synchronization embodying data and software synchronization embodying data, carrying out time synchronization analysis according to data comparison and carrying out synchronization processing; the phase synchronization processing unit is used for acquiring phase difference information and phase difference control information, and performing phase synchronization processing analysis and regulation according to information comparison; the amplitude synchronous processing unit is used for performing amplitude synchronous analysis processing on each channel, acquiring amplitude deviation data and amplitude continuous data, and performing amplitude synchronous processing analysis according to data analysis; the signal transmission interference detection unit carries out signal transmission interference detection on the radar reflection signal transmission process.

The invention provides a radar signal intelligent detection method and system based on digital channelization and PointNet + + in order to solve the problem that an existing electronic reconnaissance system is difficult to detect a single signal and multiple signals under the condition of a low signal-to-noise ratio, and belongs to the field of radar signal detection. The method comprises the following steps: constructing a signal receiving model; designing a prototype low-pass filter of one order, and obtaining a multi-phase filter bank after the prototype low-pass filter is modulated; enabling the signal received by the receiving model to pass through a multi-phase filter bank, and completing the receiving of a digital channelized receiver; processing the multi-channel signal received by the digital channelized receiver to obtain a data set which comprises a training set and a test set; setting PointNet + + network parameters, and training a PointNet + + network by adopting the training set; and calling the trained PointNet + + network, and inputting the test set to obtain a task segmentation result. The method is suitable for the field of single-signal and multi-signal intelligent detection under the condition of low signal-to-noise ratio.

The invention relates to a radar signal detection method, a processing chip, a radar, equipment and a detection device, and relates to the technical field of radars. The method comprises the steps of receiving a first instruction, wherein the first instruction is used for indicating a first threshold value; receiving a first radar signal; when the similarity between the first radar signal and a preset signal is larger than or equal to a first threshold value, an alarm signal is sent, and the preset signal is used for representing that a moving target object exists in the target area. Therefore, the accuracy of radar detection can be improved.

The invention belongs to the technical field of radar signal detection, and particularly relates to a radar clutter environment uniformity judgment method under heavy-tailed non-Gaussian distribution, which comprises the following steps of: constructing an environment knowledge base, and judging a prior structure of an interference covariance matrix of a clutter environment of an undetermined region; establishing a uniformity binary hypothesis test model by using auxiliary data of a clutter environment of a region of interest collected in real time, and giving a test statistic calculation formula according to a generalized likelihood ratio rule; obtaining covariance matrix estimation under zero hypothesis and alternative hypothesis according to a prior structure, and substituting the covariance matrix estimation into a test statistic calculation formula; performing multiple Monte Carlo independent experiments under zero hypothesis to obtain a likelihood ratio statistic value, and further calculating a test threshold; judging whether the to-be-determined interference area environment has uniformity or not according to the test statistic and the test threshold; according to the method, the covariance structure prior information contained in the data in the environment knowledge base is fully utilized, the non-Gaussian property of the clutter environment is considered, and the judgment of the environment uniformity under the complex interference is realized.

The embodiment of the invention provides a radar signal detection method and a related device, and the method comprises the steps: converting a radar signal into structural data based on the pulse time domain data of the radar signal; performing semantic segmentation processing on the radar signal based on the time-frequency graph of the radar signal to generate semantic data; performing fusion coding on the structured data and the semantic data to obtain fusion features of the radar signal; and detecting the fusion features based on a vertical class large model to obtain a detection result. According to the embodiment of the invention, fusion coding is carried out on the structured data and the semantic data, so that the obtained fusion features not only retain the physical characteristics of the radar signal, but also capture the time-frequency joint features, thereby providing complete semantic input for the output detection result of the vertical large model, and improving the detection accuracy. Loss of high-dimensional physical features in the radar signals is effectively avoided, and the accuracy of radar signal detection is improved by combining with the large vertical model.

The invention discloses a radar communication machine based on EBPSK / MPPSK modulation, which comprises a transmitter power amplifier, a receiver intermediate frequency amplifier, a transceiving switch and an antenna / feeder line, and further comprises a data frame memory, an EBPSK / MPPSK signal modulator, an impact filter, a matched filter, two time gating gates and a radar signal detector, and a communication data demodulator. The data frame memory processes the randomized communication data code stream, and sends the randomized communication data code stream to the matched filter; and the EBPSK / MPPSK signal modulator processes a carrier signal of the radar transmitter by using the data in the data frame memory, and sends the carrier signal to the transmitter power amplifier. Under different combinations of the framework, the single-path EBPSK / MPPSK signal modem and the matched filter, the radar communication integrated equipment function can be obtained by using the same system.

A monobit analog-digital converter (ADC) system and method are disclosed, the system comprising a front-end signal conditioning system equipped with a programmable attenuator and amplifier for processing analog signals, where the attenuator, which may include a cascade of programmable attenuator cells, is controlled by an attenuation control signal to adjust signal attenuation to cause a signal-to-noise ratio (SNR) in the negative domain, after which an amplifier amplifies the attenuated signal with the suitable SNR, where the amplified signal is then converted to a monobit signal by a monobit ADC, thereby achieving an ADC system that enables conditioning and digitizing of RF signals over a wide range of input signal powers with control over the output spectrum signal power and harmonics to achieve low-power real-time general-purpose broadband blocker detection for adaptive radios and for interference detection, main beam radar signal detection, and instantaneous frequency measurement, among other fields requiring general-purpose blocker identification with low size, weight, power, and cost (SWaP-C).

The present invention provides a method for improving the detection rate of radar signals by utilizing a window function. By multiplying the window function during the convolution process, not only can the sidelobe energy be suppressed, but also the problem of the inseparability of target signals and noise signals is solved. On this basis, a real part detection model and a corresponding neural network model are established, the neural network model is trained, and the trained neural network model is used to detect echo signals to distinguish target signals and noise signals in the echo signals. Due to the use of real part detection and deep learning methods, the signal-to-noise ratio is further improved, thereby improving the detection rate of radar signals.

The invention belongs to the technical field of radar signal detection and classification, and particularly discloses a radar signal detection and classification method based on low signal-to-noise ratio time-frequency image enhancement, and the method comprises the steps: obtaining original IQ data, inputting the original IQ data into a lightweight detection model, carrying out the judgment of whether there is a signal, and if there is a signal, carrying out the recognition of the IQ data; if so, carrying out time-frequency transformation on the original IQ data segment in which the signal exists to obtain an initial time-frequency image; inputting the image into a CycleGAN enhancement model comprising two generators and two discriminators, and mapping the noise-containing initial time-frequency image into an enhanced time-frequency image through antagonism training and cyclic consistency loss between the generators and the discriminators; and inputting the enhanced time-frequency image into a depth classification model for feature extraction and classification, and outputting a final judgment result, including whether radar signals exist or not and the number category of the signals. According to the invention, the accuracy and robustness of radar signal detection can be effectively improved in a low signal-to-noise ratio and strong interference environment.

This invention discloses an adaptive radar constant false alarm rate (CFAR) detection method based on dynamic length truncation and segmented threshold compensation, mainly addressing the problem that existing technologies cannot simultaneously achieve both detection accuracy and real-time computation in dense multi-target scenarios. The scheme includes: 1) acquiring the amplitude data of the radar echo signal and constructing reference sliding windows on both sides of each target unit; 2) calculating the mean ratio of the left and right reference units and determining whether to perform truncation processing based on it; 3) obtaining the estimated noise power value after truncation using a preset gradient strategy; 5) determining the compensation factor value based on the effective sample length; 7) obtaining the nominal factor through a dynamic sample generation module, and calculating the final detection threshold by combining the estimated value and the compensation factor, and outputting the detection result. This invention has a constant data throughput and short processing time, and can achieve high-precision anti-masking radar signal detection while maintaining deterministic low latency, meeting the requirements of autonomous driving for deterministic low latency.

The present invention provides a radar signal detection device and method, wherein the device includes a signal transceiver component, a laser ranging module, and a controller electrically connected to the signal transceiver component and the laser ranging module: the signal transceiver component is used to, in a detection mode, sweep the radar signal emitted by the radar to be measured according to a sweeping mode corresponding to the frequency band of the radar signal emitted by the radar to be measured, and transmit the swept radar signal to the controller; the laser ranging module is used to transmit a laser beam to the radar to be measured and receive a reflected laser beam from the radar to be measured, determine the distance between the device and the radar to be measured based on the laser beam and the reflected laser beam, and transmit the distance value to the controller; the controller is used to determine the radiation power and frequency band of the radar to be measured based on the radar signal and the distance value, thereby reducing the cost of detecting the radar to be measured and enabling the radiation power and frequency band to be obtained more quickly.

The invention belongs to the field of large-scale MIMO radar signal detection, and relates to an algorithm for carrying out target detection on a long-distance weak signal under a low-bit quantization condition. The method specifically comprises the following steps: S1, setting a radar system model, setting multiple-input multiple-output of a large-scale MIMO radar, and setting a clutter covariance matrix to obey inverse Wishart distribution; s2, transmitting and receiving at the same time by each antenna to obtain a radar echo signal, and performing low-bit quantization processing on the received signal; s3, designing a Bayesian thought and a traditional Rao detector algorithm to be combined with a B-Rao detection algorithm; and S4, according to a particle swarm algorithm, performing multiple iterations to obtain an appropriate quantization threshold value so as to improve the detection performance. Under the condition that a large amount of echo data is obtained by a large-scale MIMO radar, a low-bit sampling method is introduced, the data processing amount is reduced, after low-bit sampling, an algorithm combining the Bayesian thought and a traditional detector is introduced, and compared with the single use of the traditional detector, the detection performance is effectively improved while the operation cost is reduced.

A hydraulic cylinder assembly includes a cylinder body, a piston configured to slide within an interior of the cylinder body, and a cylinder head coupled to the cylinder body. The cylinder head includes a cavity, and a bore that extends between the cavity and the interior of the cylinder body. The hydraulic cylinder assembly includes a radar sensing unit disposed within the cavity. The radar sensing unit includes a radar signal emitter and a radar signal detector oriented respectively to emit and detect radar signals through the bore into the interior of the cylinder body. The hydraulic cylinder assembly includes a dielectric lens between the radar sensing unit and the interior of the cylinder body, the dielectric lens having a convex side facing the radar sensing unit and a planar side opposite the convex side facing the interior of the cylinder body.

Disclosed is a radar detection assistant apparatus for increasing detection accuracy when detecting an object using a radar detection device. The radar detection assistant apparatus includes a radar signal receiver for adjusting a radar candidate signal received through a reception antenna to a predetermined magnitude and filtering out a signal other than the radar candidate signal, a radar signal analyzer for receiving the radar candidate signal from the radar signal receiver and determining whether or not the radar candidate signal is a preset radar signal, a radar signal production controller for generating a control signal for an assistant apparatus production signal or an assistant apparatus reproduction signal on the basis of at least one of pieces of information on a type, a bandwidth, and a period of the radar candidate signal received from the radar signal analyzer, and a radar signal producer.

The disclosure relates to a technology for identifying ghost targets using a radar apparatus. An antenna section includes a plurality of transmitting antennas and a plurality of receiving antennas. An extractor extracting a side lobe based on a virtual channel combination including M number of predetermined virtual channels, where M is a natural number equal to or greater than 2, and angle information of a target. A signal processor controls transmission and reception of a radar signal with respect to the target, and if the side lobe is extracted, determines that a ghost target is detected by the radar signal and processes the radar signal based on the determination.