24 results about "Cognitive radar" patented technology

The invention relates to the technical field of radar waveform optimization, in particular to a synthetic broadband waveform optimization method based on reinforcement learning. According to the method, a deep reinforcement learning framework is adopted, and end-to-end optimization is carried out on the center frequency and amplitude parameters of multiple sub-pulses. The method comprises the following steps: constructing a synthetic broadband waveform signal model, and parameterizing sub-waveforms to determine a search space of an optimization problem; converting a waveform optimization problem into a reinforcement learning form, and defining a state space, an action space, a reward function and a state transition mechanism; based on a deterministic soft action value iteration strategy algorithm, point spread function features are extracted in combination with a Transform encoder, and sub-pulse parameters are dynamically adjusted. Through the method, the main lobe resolution can be effectively suppressed and improved, the target detection performance of a radar system is improved, and the method has good global optimization capability and environmental adaptability and is suitable for cognitive radar and adaptive waveform design scenes.

The invention discloses a radar signal intelligent sorting method based on a GraphGated graph convolutional neural network, relates to the radar signal intelligent sorting method based on the GraphGated graph convolutional neural network, and belongs to the field of information and communication engineering. The objective of the invention is to solve the problem of difficulty in signal sorting of unknown signal sensing in a high-pulse-density environment, complex inter-pulse signal modulation and transient conditions of a current passive radar, an electronic reconnaissance radar, a cognitive radar, frequency spectrum situation sensing and the like. The method comprises the following steps of: 1, assuming that V pulse description words are received within time T; 2, constructing an adjacent matrix A based on the V preprocessed pulse description words, and constructing a radar pulse description word graph marked with node category attributes based on the adjacent matrix A; 3, obtaining a trained graph convolution model; 4, acquiring B to-be-tested pulse description words, and outputting feature vectors of the B pulse description words based on the trained model; and obtaining a clustering result through a clustering algorithm.

The invention discloses a dynamic modeling method of a hidden Markov model, which comprises the following steps of: defining a radar-jammer master-slave confrontation mode, namely, when a radar is not interfered, setting an HMM-based information scheduling strategy matrix A and a transmitted pulse time-frequency parameter control strategy matrix B in coherent processing time according to a target detection task of the radar; a transmission pulse sequence is generated. An interference party extracts radar state parameters by using intercepted radar pulses, estimates a radar information scheduling strategy matrix A and a transmitting pulse time-frequency parameter control strategy matrix B, formulates an interference strategy matrix Aj and an interference signal time-frequency parameter control strategy matrix Bj, sends interference signals to the radar, and estimates Aj and Bj of the interference party after the radar receives the interference signals. And judging whether interference is valid or not, correspondingly adjusting the matrix A and the matrix B, and repeating the process to realize dynamic modeling of cognitive radar countermeasure.

The invention relates to automatic driving, in particular to a dynamic environment modeling and active adaptation system for an automatic driving vehicle, which comprises a multi-band cognitive radar module and a millimeter wave radar integrated with multiple bands, adopts an intelligent switching and cooperative working mechanism, and automatically optimizes the duty cycle proportion of the bands according to the environment condition. Radar waves of different frequency bands are transmitted and received, and multi-dimensional information of a target is obtained; the data processing and dynamic environment modeling module adopts a multi-sensor data fusion algorithm to fuse target multi-dimensional information acquired by radars of different frequency bands, performs dynamic environment modeling on the periphery of the vehicle, and updates a dynamic environment model in real time; the decision-making and active adaptation control module is used for making a reasonable driving decision, planning a path and carrying out active adaptation control by adopting a deep learning and artificial intelligence algorithm; according to the technical scheme provided by the invention, the defects that an accurate and real-time dynamic environment model is difficult to construct and the vehicle driving state cannot be timely and reasonably adjusted can be effectively overcome.

The invention discloses an external radiation source radar target identification method under same frequency interference. The method comprises the following steps: step 1, spectrum sensing guidance; step 2, polarization space joint isolation; step 3, carrying out cooperative airspace beam splitting; 4, reconstructing a time-frequency fingerprint; 5, dynamic gaming of the cognitive radar is carried out; according to the method, due to the fact that interference signals leaked by adjacent-channel broadcast signals have the advantages of being high in out-of-band stray power, continuous in time domain coverage and non-uniform in frequency domain distribution, out-of-band leakage of the adjacent-channel signals can be dynamically recognized and restrained through intelligent trapped waves guided by spectrum sensing, signal distortion caused by traditional fixed trapped waves is avoided, and the method has the advantages of being high in reliability and high in reliability. Meanwhile, fractional order Fourier domain adaptive filtering is used for separating interference and target signals in a fractional order domain, the Gibbs effect of conventional frequency domain filtering is avoided, a received signal can be projected to an orthogonal complementary space of an interference polarization vector by utilizing polarization space joint isolation, and the actually measured isolation degree reaches 35dB.

The embodiment of the application relates to the technical field of cognitive radar, and provides a cognitive radar constant modulus waveform design method and device based on maximum signal-to-clutter-noise ratio, the method comprising: obtaining a feedback signal echo obtained after a cognitive radar transmitting signal, wherein the feedback signal echo comprises a target echo component and a clutter echo component, the target echo component is output after the transmitting signal is convolved with a unit impulse response of a target, and the clutter echo component is output after the transmitting signal is reflected by a clutter scattering point; calculating a signal-to-clutter-noise ratio of an output signal after a matched filter based on the feedback signal echo; obtaining an optimal energy spectrum density of the transmitting signal when the signal-to-clutter-noise ratio is maximum under a preset energy constraint condition; and determining a target constant modulus signal waveform based on the optimal energy spectrum density. Therefore, the radar waveform is dynamically adjusted through the interaction between the radar and the environment, better adaptability is achieved in a complex clutter environment, the balance between time domain and frequency domain characteristics is maintained, and target detection performance is improved.

Techniques are disclosed for implementing a CMOS-compatible millimeter wave matrix computing network architecture, which enables high-speed matrix operations for deep learning neural networks through a reconfigurable feedforward architecture using matrix computing meshes. Each mesh may include hybrid couplers and adjustable phase shifters. The architecture may be configured in various arrangements with programmable weights. The architecture offers advantages over existing solutions through full CMOS compatibility, the elimination of optical-electrical conversion, improved scalability, total latency, and superior power efficiency. Applications include massive MIMO systems and cognitive radar, in which the network may be implemented as part of RF front ends to reduce ADC requirements, system complexity, and power consumption.

The application relates to automatic driving, in particular to a dynamic environment modeling and active adaptation system for an automatic driving vehicle, a multi-frequency band cognitive radar module, a millimeter wave radar integrating multiple frequency bands, an intelligent switching and cooperative working mechanism, automatic optimization of a frequency band working cycle proportion according to an environment condition, emission and reception of radar waves of different frequency bands, acquisition of target multidimensional information, a data processing and dynamic environment modeling module, a multisensor data fusion algorithm for fusing target multidimensional information acquired by different frequency band radars, dynamic environment modeling around the vehicle, and real-time updating of a dynamic environment model, a decision and active adaptation control module, a deep learning and artificial intelligence algorithm for formulating reasonable driving decisions and planning paths and performing active adaptation control, and the technical scheme can effectively overcome the defects that it is difficult to construct an accurate and real-time dynamic environment model and a vehicle driving state cannot be timely and reasonably adjusted.

This invention relates to the field of radar signal processing and provides an optimized design method for Doppler filters in cognitive radar. The method includes: determining M transmitted wave azimuth angles; determining the Doppler frequency of the corresponding m-th main clutter wave based on the m-th transmitted wave azimuth angle, where m ranges from 1 to M; determining the corresponding Doppler channel using the Doppler frequency of the m-th main clutter wave; adjusting the frequency range of the Doppler filter based on the Doppler channel of the m-th main clutter wave to obtain an adjusted Doppler filter; receiving multiple sets of echo signals within a preset range threshold, and determining the output signal-to-noise ratio of the Doppler filter based on the multiple sets of echo signals and the adjusted Doppler filter, to determine the maximum detectable target area within the preset range threshold. The improved filter based on the method provided by this invention can provide a larger detectable area and higher detection accuracy, and can adapt well to complex clutter scenarios.

The application provides a clutter suppression method for cognitive radar based on double-base projection angle nonlinear time-varying weighting, calculates a double-base distance sum of an lth double-base distance unit to be detected of the echo signal; calculates a distance between an intersection of a double-base distance ring in a receiving main beam azimuth direction and a transceiver radar according to the double-base distance sum; calculates a projection angle of a double-base angle on the ground and an updating variable according to the system prior information and the distance between the intersection of the double-base distance ring in the receiving main beam azimuth direction and the transceiver radar; judges whether the projection angle of the double-base angle on the ground is a non-obtuse angle, if yes, calculates an adaptive weight vector of nonlinear time-varying weighting by using the projection angle, the updating variable and echo signal data; and performs space-time adaptive processing on the echo signal according to the adaptive weight vector to obtain a space-time adaptive processing result. The application can improve the clutter suppression performance of the double-base airborne radar.

The present invention relates to the technical field of cognitive radar maneuvering target tracking and processing, and particularly relates to a method and system for selecting a cognitive radar tracking waveform assisted by amplitude information. By setting up a parametric radar waveform library and combining prior knowledge, the target measurement amplitude likelihood ratio is obtained by analyzing target fluctuations and environmental clutter background. In the state prediction stage, the tracking waveform parameters are selected based on the minimum information entropy criterion. In the state update stage, the target measurement amplitude likelihood ratio is used to correct the probability of the associated measurement in the unscented Kalman filter data probability data association, so that the weight of the probability of the associated measurement originating from the true target increases. The multi-model state estimation is updated using the corrected unscented Kalman filter probability data association, and the interactive multi-model state estimation is fused. The present invention uses target amplitude information to assist target tracking to reduce the cognitive radar maneuvering target tracking error and the loss-of-track rate, and improve the cognitive radar target tracking performance.

The invention provides a cognitive radar data processing system and method based on a Lambda architecture, and relates to the field of cognitive radars, the cognitive radar data processing system comprises an application layer, a data processing and storage layer, a data source layer and the Lambda architecture, and the Lambda architecture comprises a batch processing layer, an acceleration layer and a service layer; the data processing and storage layer comprises a data access gateway, a universal library, an offline processing module, a real-time calculation module, a historical data service interface Restful api and a real-time data interface Websocket; the batch processing layer is connected with a historical data service interface Restful api, and the acceleration layer is connected with a real-time data interface Websocket. According to the invention, a big data processing scheme is introduced into the data processing architecture of the cognitive radar, so that a large amount of valuable historical global data can be stored and managed on the premise of realizing the conventional radar function; the powerful data processing and storage capability has the function of data collaborative reasoning with other devices, and can support development of a comprehensive function system.

The application discloses a detection-before-track method for reducing target missing alarm probability of airborne cognitive radar, relates to the technical field of radar, and solves the problem that weak high-speed targets are prone to missing alarm under the influence of ground clutter in the prior art.The method comprises the following steps: performing data processing on echo data of a target received by an airborne radar, performing space-time adaptive processing on the processed data, performing constant false alarm detection, and obtaining a target data set; performing distance extension in the distance direction on each frame of data in the target data set by using a maximum non-ambiguous distance of the radar, and obtaining an extended data set; performing one-stage DP-TBD processing between M pulse repetition frequency data in each frame of data in the extended data set, and performing two-stage DP-TBD processing between the frames of data, and obtaining a two-stage processed data set; and performing completion on the two-stage data set, and obtaining a target track; and the method realizes detection and tracking of multiple weak high-speed targets under the influence of ground clutter, and has a wide application range.

In view of the problem that existing active jamming waveforms are difficult to counter the cognitive radar waveforms, the present application discloses a radar active jamming waveform library, which comprises a jamming task information acquisition and analysis module, a suppression jamming parameter analysis and judgment module, a deception jamming parameter analysis and judgment module, a smart jamming parameter analysis and judgment module, a suppression jamming waveform module, a deception jamming waveform module and a smart jamming waveform module. The suppression jamming waveform module comprises a classical suppression jamming waveform library, a suppression jamming waveform design rule library, a suppression jamming waveform design rule updating module and a suppression jamming waveform design module; and the smart jamming waveform module comprises a jamming modulation waveform library, a jamming modulation waveform design rule library, a jamming modulation waveform design rule updating module and a jamming modulation waveform design module. The present application realizes flexible, complete and adaptive jamming waveform design, and improves the jamming capability of the active jamming system against complex radar waveforms.

The application discloses a kind of under combined constraint based on directional diagram matching anti-interference waveform design method, establishes the MIMO cognitive radar system of antenna layout using uniform linear array;Directional diagram model is constructed;Under the condition that waveform constant modulus constraint and similarity constraint are satisfied, the problem model of directional diagram matching design based on minimum mean square error criterion is established;For optimization problem under combined constraint, the similarity constraint is simplified using Lagrange multiplier method, and only the optimization problem with constant modulus constraint is obtained;The geometric characteristics of non-convex constraint are excavated, the constant modulus constraint is associated with Riemannian manifold in nature, so that the constrained non-convex optimization problem is converted into an unconstrained convex optimization problem on the manifold;Based on optimization algorithm, the optimization problem model on the manifold is solved, and the anti-interference waveform is obtained.The application can generate optimal transmit waveform to resist specific interference, significantly improve the efficiency of transmit beam directional diagram matching, and obtain transmit waveform with better blur characteristics.

The invention provides a self-adaptive environment cognitive radar system based on waveform optimization and an optimization method, and belongs to the technical field of radar systems.The system comprises a radio frequency module and a baseband signal processing module connected with the radio frequency module, and the baseband signal processing module comprises a processing system unit and a programmable logic unit; the programmable logic unit adopts a waveform optimization algorithm to process the preprocessed digital echo signal so as to dynamically generate an anti-interference waveform digital signal for next signal emission; the waveform optimization algorithm is a closed-loop iterative algorithm based on environmental perception and a signal-clutter-noise ratio maximization criterion; the processing system unit adopts a preset radar signal processing algorithm to process the preprocessed digital echo signal; the programmable logic unit and the processing system unit work cooperatively in parallel to form a first processing link for dynamic waveform optimization and a second processing link for radar signal processing. The method has the advantages of real-time environment cognition, dynamic spectrum optimization and efficient parallel processing.

The present invention discloses a method and system for generating active anti-interference waveforms for cognitive radars based on DQN and MAB, wherein the method comprises: obtaining a radar interference time-frequency diagram based on radar transmission signals and interference signals; simulating a radar countermeasure process and constructing a transmission waveform strategy library based on the radar interference time-frequency diagram; solving the waveform strategy library using a DQN algorithm, and optimizing the solution using a MAB algorithm to obtain an optimal transmission waveform; detecting the optimal transmission waveform using a CFAR detector to generate an optimal anti-interference waveform and parameters. The present invention integrates the global strategy search of DQN with the local parameter optimization of MAB, effectively solving the problem of dynamic anti-interference adaptation in multi-pulse timing interaction scenarios, and is suitable for airborne radar electronic countermeasure systems and real-time anti-interference requirements in complex electromagnetic environments.

The application discloses a dynamic modeling method of a hidden Markov model, defines radar-jammer as a master-slave confrontation mode, namely, when the radar is not interfered, sets an information scheduling strategy matrix A and a transmission pulse time-frequency parameter control strategy matrix B in a coherent processing time according to a target detection task of the radar, and generates a transmission pulse sequence. An interference party extracts radar state parameters and estimates the information scheduling strategy matrix A and the transmission pulse time-frequency parameter control strategy matrix B of the radar by using intercepted radar pulses, formulates an interference strategy matrix A j and a jamming signal time-frequency parameter control strategy matrix B j , and sends a jamming signal to the radar, wherein the radar estimates the A j and B j of the interference party after receiving the jamming signal. Whether the interference is effective is judged, and the matrixes A and B are adjusted accordingly, and the process is repeated, so that dynamic modeling of cognitive radar confrontation can be realized.

The present application relates to a radar optimized waveform generation method, a cognitive radar system, a device, and a storage medium. The method includes constructing a corresponding optimal waveform generation model according to different game types existing in actual situations. In this way, the optimal waveform calculated under different circumstances is more accurate. For cognitive radars, the signal-to-noise ratio is improved, making it easier to detect targets. For cognitive jammers, the signal-to-noise ratio is reduced, which can interfere with the radar, making it more difficult for the radar to detect targets.

The present invention discloses a target recognition and tracking method based on a cognitive FDA-MIMO radar network, comprising the following steps: S1: establishing a measurement model of radar network targets and mainlobe deceptive trajectory interference; S2: using mainlobe deceptive trajectory interference and the distribution characteristics of targets in space relative to different radars to perform FDA-MIMO radar network target recognition; S3: extracting target measurement information from the target recognition results of each radar and initializing the target state; S4: predicting the target state for each radar and updating the corresponding target state; S5: fusing the target states obtained by each radar to obtain the final target state; S6: repeating steps S4 and S5 to achieve closed-loop cognitive target tracking. This method can accurately identify real targets and mainlobe deceptive trajectory interference in a mainlobe deceptive trajectory interference environment, and initialize a tracker when the target prior information is unknown, thereby achieving target tracking.

The application discloses a detection-before-track method for weak target distance walk of cognitive radar, and relates to the technical field of radar data processing.The method solves the problem of the prior art that the state transition step is too long, the search interval is too large, the processing efficiency is low, and the engineering practicability is not high when directly performing DP-TBD processing on the measurement data between different frames of the radar.The method comprises the following steps: converting radar echo data to a two-dimensional plane, extending the two-dimensional radar data set in the range and azimuth directions by using the maximum unambiguous distance of the radar, and obtaining an extended data set; setting a state transition step, performing first-stage DP-TBD processing on each frame of data in the extended data set, and obtaining a first-stage processing data set; performing second-stage DP-TBD processing between each frame of data in the first-stage processing data set, and obtaining a second-stage processing data set; performing track backtracking on the second-stage processing data set, and obtaining a TBD result.The method realizes high-speed target tracking under low SNR, has little influence of distance walk, and has a wide application range.

This invention discloses a multi-index power resource collaborative allocation method based on cognitive radar threshold constraints, comprising: constructing a feasibility-aware utility function set according to the performance threshold constraints and radiation concealment threshold constraints of radar multi-target tracking tasks; training and generating a power allocation strategy set covering different risk preferences based on the utility function set; iteratively detecting and filling the front gaps according to the Pareto front coverage of the strategy set, and outputting a complete strategy library to support dynamic power allocation decisions. This invention introduces a utility expression mechanism with performance threshold gating to suppress unusable extreme strategies and improve training efficiency; and combines a Pareto front gap detection and completion mechanism to construct a distributed Pareto strategy library covering different risk preferences, thereby providing beam power allocation strategies that balance reliability and concealment for different task requirements.

The present invention discloses a method and device for adaptively generating an anti-interference strategy for a cognitive radar based on PPO, wherein the method comprises: obtaining an echo spectrum and inputting it into an anti-interference strategy selection model to obtain anti-interference parameters; the model is trained by: constructing a cognitive radar anti-interference model, wherein the cognitive radar model continuously transmits radar pulses with variable frequency, and the jammer model intercepts the radar pulses and releases interference; using the PPO algorithm to train an actor network and a commentator network; the actor network outputs the expectation and standard deviation of the behavior based on the echo spectrum; the behavior corresponds one-to-one with the center frequency of the radar pulse; the commentator network outputs the reward value based on the echo spectrum; and constructing an anti-interference strategy selection model based on the trained actor network. The present invention solves the problem that existing anti-interference strategies are overly dependent on rules and experience and are difficult to meet the anti-interference requirements in complex electromagnetic interference environments, enabling cognitive radars to quickly and accurately achieve anti-interference.

The invention provides an 8-18GHz radar interference system and a method thereof, which solve the problem that the effectiveness of resisting novel cognitive radar interference is reduced, and the like, and comprises a control subsystem, the control subsystem is connected with a plurality of modulation subsystems through a communication network, each modulation subsystem is provided with a control terminal, the control terminal is connected with a signal processing host, and the signal processing host is connected with a wireless communication network. The signal processing host is connected with a receiving antenna body and is connected with a transmitting antenna body through a power amplifier host. The method has the advantages of good cooperative control effect, good interference effectiveness and the like.