49 results about "Cognitive radar" patented technology

In cognitive radar information networks (CRINs) human-like cognitive abilities of attention and intelligence are built into radar systems and radar information networks (RINS) to assist operators with information overload. A CRIN comprises a plurality of radar sensing nodes monitoring an environment, a repository or memory, and a cognitive radar controller. Each radar sensing node includes a radio frequency transmitter, a transmitting antenna, and a receiver and receiving antenna. The receiver includes a digital radar processor for generating receiver information from the received echoes about the environment. The repository is configured for receiving and storing the receiver information generated by the digital radar processor. The cognitive controller is configured to automatically focus the system's attention on a region of interest within the surveillance volume in response to an attention request, by selecting the transmitter's waveform, selecting the receiver's processing mode, and controlling the transmitter's antenna. The cognitive controller learns from the environment by exploiting the repository's historical receiver information and further learns from the consequences of its past decision.

In cognitive radar information networks (CRINs) human-like cognitive abilities of attention and intelligence are built into radar systems and radar information networks (RINS) to assist operators with information overload. A CRIN comprises a plurality of radar sensing nodes monitoring an environment, a repository or memory, and a cognitive radar controller. Each radar sensing node includes a radio frequency transmitter, a transmitting antenna, and a receiver and receiving antenna. The receiver includes a digital radar processor for generating receiver information from the received echoes about the environment. The repository is configured for receiving and storing the receiver information generated by the digital radar processor. The cognitive controller is configured to automatically focus the system's attention on a region of interest within the surveillance volume in response to an attention request, by selecting the transmitter's waveform, selecting the receiver's processing mode, and controlling the transmitter's antenna. The cognitive controller learns from the environment by exploiting the repository's historical receiver information and further learns from the consequences of its past decision.

This invention is related to a cognitive Multi-Input Multi-Output (MIMO) radio frequency (or laser) radar with large-area synchronized multi-dimensional hopping spread spectrum and Interference-Free Windows (IFWs) for autonomous vehicles comprising of (1) analog component; (2) digital baseband component; (3) multi-dimensional hopping code generator; (4) large-area time synchronization; (5) cooperative IFW; and (6) cognitive engine. The new MIMO radar can provide interference-free environmental perception intelligently by the multi-dimensional IFWs formulated by beat-frequency hopping, beat-time hopping, discrete sequence (DS), cooperative IFW, array processing, and image denoising and fusion, etc. This invention increases the frequency efficiency greatly, and be applied to autonomous vehicles and robotics under sparse, dense, mixed autonomous and human driving, or completely autonomous driving environments.

The invention discloses a velocity-deception-jamming-resistant phase encoding method for a cognitive radar, belongs to the technical field of radar jamming resistance, and particularly relates to a radar jamming-resistant waveform design technology. According to the method, relevant parameters of a target and jamming are extracted from prior information acquired by the cognitive radar, a stop band is arranged close to each real target Doppler according to a real target Doppler estimated value, a stop band is arranged at the same position as a constant-mode encoding sequence configured in advanced, and an optimal pulse-to-pulse phase encoding transmission waveform is designed according to the criterion that the spectrum energy of a coded signal in the stop band, thereby effectively inhibiting velocity deception jamming in a multi-target and multi-jamming scene, increasing the signal-to-jamming ratio of each real target, and realizing the effect of correct detection of a plurality of real targets.

The invention discloses a waveform optimization method based on target cognition and transmitted power distribution, which mainly solves the problem that the current waveform optimization algorithm is not suitable for a broadband radar. The method comprises the following steps of: (1) building a waveform-optimized signal model of a cognitive radar of a broadband; (2) utilizing a single echo to estimate the power spectral density of a target; (3) calculating the transmitted power of a transmit signal being a linear frequency modulation signal to be used as a constrained initial value of the transmitted power; (4) constructing a cost function; (5) solving the cost function; (6) optimally setting the power spectral density of transmitted waveform corresponding to the transmitted power; (7) calculating the related coefficient of the estimated value and the true value of the power spectral density of the target corresponding to the optimized waveform; (8) and judging and outputting the power spectral density of the transmitted power and the transmitted waveform according to whether the related coefficient meets the requirements. According to the method, the transmitted waveform optimization and power distribution of a cognitive radar system of the broadband can be realized, and the efficiency and overall performance of the system are increased.

The invention discloses a cognitive radar optimal waveform design method suitable for parameter estimation. The cognitive radar optimal waveform design method suitable for parameter estimation comprises the steps of building a channel model by use of actual target impulse response more consistent with the actual situation, and obtaining the energy spectrum density of the desired waveform by use of a water injection method according to the channel model so that the maximum target information quantity can be obtained from echo, taking the energy spectrum density and time-domain complex waveform amplitude as constant constraint conditions and obtaining the time-domain complex waveform of constant amplitude by use of an iteratively weighted least square method without wasting the power of a transmitter, next, transmitting the designed time-domain complex waveform by use of a radar to obtain target echo and performing parameter estimation by use of the target echo, after the expiry of the accumulated time of estimation of one parameter, updating the target impulse response by use of the current target echo and carrying out waveform design and transmission again so that the waveform transmitted at present changes along with the change of the target and the environment, and guaranteeing that the target echo obtained at present always contains the maximum target information quantity so that target parameter estimation can be carried out favorably.

The present invention belongs to the field of cognitive radar, and proposes a waveform optimization method based on the Tianniu search algorithm. The method is simpler and more convenient, and can achieve better effects than the existing methods. The technical scheme used in the present invention is a cognitive radar waveform optimization method based on the Tianniu search algorithm. The method comprises: using waveform information received by a radar receiver to estimate a target scattering coefficient, wherein in the estimation method, the maximum posterior probability estimation is used andthe Kalman filter is used for iterative estimation; comparing the estimated target scattering coefficient with the actual target scattering coefficient, and calculating the minimum mean square errorbetween the two; taking the transmitted waveform as the variable and the minimum mean square error as the objective function, and designing a mathematical model of the optimization objective; and using the Tianniu search algorithm to solve the mathematical model to obtain the transmitted waveform when the objective function is at a minimum. The method provided by the present invention is mainly applied to the design and manufacturing of cognitive radar.

The invention discloses a clutter environment based transmitting and receiving jointly optimized adaptive filtering method which mainly solves the problems that operation quantity is large while a receiver and a transmitter are not in jointly optimized utilization in the prior art. The method includes the implementation procedures: 1) transmitting a group of phase-coded signals; 2) calculating received echoes; 3) performing matched filtering for the received echoes; 4) feeding back a scattering center amplitude estimated value obtained by matched filtering to a transmitter, and setting a transmitted signal side lobe weight; 5) optimizing the phase-coded signals to enable weighted integral side lobe level to be the smallest; 6) transmitting the optimized phase-coded signals and calculating received echoes; and 7) performing adaptive filtering for the received echoes to obtain an amplitude estimated value of a scattering center. The method is optimal to an existing filtering method in amplitude estimation performance of the scattering center, is small in operation quantity, preliminarily achieves closed loop feedback of cognitive radar from a receiver to the transmitter, and can be used for radar clutter suppression and target detection.

The invention provides an intermittent sampling forwarding interference suppression method based on a cognitive radar waveform. The technical scheme comprises the following steps: the first step of intermittent sampling forwarding interference parameter estimation, a cognitive radar emits a linear frequency modulation pulse signal firstly, an echo signal is analyzed, and an estimation value of a sampling pulse width and an estimation value of a forwarding period are obtained; and the second step of intermittent sampling forwarding interference suppression, the cognitive radar emits a specific intra-pulse frequency modulation signal, interference interval signal zero setting is performed on a received radar echo signal, splicing is performed then, and matching filtering is performed on the signal to obtain a pulse pressure result of a jammer Real target detection performance can be effectively improved, and intermittent sampling forwarding interferences in direct forwarding, repeated forwarding and other forwarding modes are achieved.

The invention discloses a cognitive radar waveform designing method based on interference and side-lobe equilibrium suppression. The method comprises: on the basis of a self-adaptive framework, in a noise suppressing interference model, and through the MMSE criteria, introducing and structuring a cognitive radar waveform optimization model of interference and side-lobe equilibrium suppression; converting the model into a minimized multivariable and multi-constrained objective function model; then according to Lagrange multiplier method, developing the Lagrange function of the objective function, and according to the principle of duality, converting the model into the optimized dual function again, solved with the introduction of auxiliary variables and the KKT optimality; and finally, designing the alternating iterative method to combine with the optimized emission shape and the filter sequence; and estimating the evaluation of performance brought about by the comprehensive processing algorithm. The simulation results show that the proposed algorithm can realize the balance of the interfering signal suppression and the self-processing side-lobe, which improves the detection performance of the target.

Embodiments of the present invention relate cognitive radar and RF technologies, and more particularly, to spectrum sensing processing for rapidly monitoring the RF spectrum for channel availability and activity. The goal is to find and use unoccupied RF channels to broadcast and receive information. According to one embodiment, a method for analyzing a received RF signal to determine unused channels, or frequencies, therein, comprises: analyzing a received RF signal to determine anchor points that represent high energy frequency locations; calculating distances between the determined anchor points; identifying and eliminating clusters or isolated anchor points defined as a high energy region of interference based on the calculated distances; and selecting at least one remaining unoccupied frequency for transmitting or receiving a RF signal. The method may further include performing an optional quality or risk assessment on remaining frequencies of the waveform, and eliminating high risk frequencies from consideration in some instances.

The invention relates to an anti multi-hop clutter interference waveform designing method based on a cognition radar. The objective of the invention is to remove a disadvantage that normal detection of a target is affected since multiple Doppler detection regions are sacrificed and the size of the regions depends on the expansion width of the multi-hop clutters when the separation of the multi-hop clutters from the target in a certain range is achieved in the current method. The method comprises steps that, 1, radar outwards emits linear frequency modulated continuous waves, and carries out Doppler two-dimensional processing on an echo signal so as to obtain the widths and positions in a Doppler spectrum of the multi-hop clutters in the echo signal are obtained; 2, phase position modulation is performed based on the linear frequency modulated continuous waves, so UT(t) is obtained, and a three-time phase position coding efficient is obtained according to the obtained widths of the multi-hop clutters, so compression of the multi-hop clutters is achieved; and 3, secondary phase position coding is performed on the UT(t), so the multi-hop clutters are allowed to have controllable shifting on the Doppler spectrum. The method is used in the technical field of radar clutter resisting.

The invention provides a passive interference suppression method in a complex environment based on cognitive radar waveform design. The passive interference suppression method comprises the followingsteps: a radar transmitting and receiving signal model is determined; response prior information of a target, clutter, passive interference and noise is determined; a detector is determined and detector performance is obtained; and waveform design is carried out according to the bandwidth and the transmitting energy, and transmitting signal frequency domain energy distribution is calculated, wherein the obtained signal frequency domain energy spectrum is used for transmitting signals. Through the clutter and passive interference confrontation-oriented waveform design, the interference suppression-oriented energy distribution mode can be designed in an ultra-wideband frequency domain based on the prior information of clutter and passive interference, so that the interference response is suppressed, and the target detection and recognition capability of the radar under complex environmental conditions is improved.

The invention discloses a robust cognitive radar transmitting-and-receiving combined designing method in a clutter environment, wherein the method belongs to the field of signal processing. The invention relates to the robust cognitive radar transmitting-and-receiving combined designing method in the clutter environment. Each iteration step in the method of the invention relates to a MVDR settlement problem and a plurality of one-directional fractional programming problems which can be efficiently settled by means of a Dinkelbach process. Simulation proves a fact that the algorithm can effectively improve an outputted SINR in short time, and realizes efficiency breakthrough and effect breakthrough relative to an SDR algorithm, thereby improving system testing performance.

The invention discloses a complementary waveform construction method and module, a cognitive radar system and a waveform emission method, solves the problem that an existing complementary waveform construction method cannot provide enough and flexible Doppler tolerance, and belongs to the technical field of radar emission. The waveform construction method comprises the steps of determining input parameters according to requirements, and constructing a constant vector and a constant matrix; under given high-order Doppler frequency spectrum zero constraint and energy constraint, constructing a constraint optimization problem by taking maximization of similarity between a received pulse weight and a given window function as an optimization target; solving to obtain a transmitting sequence vector and a receiving pulse weight vector, and further constructing a transmitting pulse string and a receiving reference signal to complete complementary waveform construction. According to the method,a complementary waveform construction module is obtained by using a computer, a cognitive radar system is formed by combining the radar and the waveform, and the waveform transmitting method can intelligently select to transmit a common pulse string and a Doppler-tolerant complementary waveform pulse string to realize target detection.

The invention belongs to the field of cognitive radars, and proposes a waveform optimization method based on particle swarm optimization. Compared with the existing methods, the method is simpler andmore convenient, and can acquire a better effect. According to the technical scheme provided by the invention, the cognitive radar waveform optimization method based on particle swarm optimization comprises the steps that a radar receiver uses received waveform information to estimate a target scattering coefficient, and the estimation method is to use maximum posterior probability estimation anduse Kalman filtering for iterative estimation; the estimated target scattering coefficient is compared with an actual target scattering coefficient; the minimum mean square error between two coefficients is calculated as the objective function of particle swarm optimization; and a waveform to be optimized is used as a variable to the most accurately estimate the target information. The method provided by the invention is mainly applied to the design and manufacture of cognitive radars.

The invention relates to a cognitive radar detection device based on a neural network, whose main structure comprises a chassis, a liquid crystal display, indicator lamps, a power supply switch, reset switches, signal transmitting adjustment and control devices, signal receiving adjustment and control devices, a transmitting antenna, a receiving antenna, a circuit board, a direct current power supply and a circuit board circuit. Aiming at limitation of the traditional radar, the cognitive radar detection device uses the advantages of the neural network, information processing is performed on signals of the transmitting antenna and the receiving antenna, the neural network consists of nerve cells with nonlinear mapping functions, and information of the neural network is stored in a continuous weight coefficient by weight coefficient connection, so that the network has very high fault tolerance; information loss is reduced; self-organizing and the adaptivity of the neural network are high; the recognition capability is improved; the neural network, digital signal processing, and digital information collection, receiving and processing are accomplished by a computer program, so that informatization, programming and automation of the device are achieved; and as a very ideal cognitive radar detection device based on the neural network, the device is advanced in design, compact in structure and accurate in measurement data, and can be widely applied to various industrial detection fields.

Methods and systems for a vehicle cognitive radar are provided. The system includes a transmitter is configured to transmit a first plurality of transmittal signals for a cognitive radar system of a vehicle, the cognitive radar system having at least a first modality. An interface is configured to receive sensor data from one or more sensors having a second modality that is different from the first modality. A processor is coupled to the interface, and is configured to select an adjusted waveform for a second plurality of transmittal signals for the cognitive radar system using the sensor data.

The invention discloses a cognitive radar target tracking method in a clutter environment, which acquires all measurement values ​​at the current moment and calculates the measurement values ​​falling into the correlation wave gate; calculates each measurement value falling into the correlation wave gate The value comes from the probability of the target; calculate the equivalent measurement value at the current moment; use it as the input of the sensory memory, and output the system equation parameters at the current moment; perform state and measurement prediction according to the system equation parameters and equivalent measurement values , to obtain the state prediction covariance; use it as the input of the execution memory, and output the emission waveform parameter set at the current moment; calculate the innovation covariance and the state error covariance under each waveform parameter; carry out optimal waveform parameter selection ; Calculate the innovation covariance, state error covariance and state estimation value at the current moment according to the optimal waveform parameters. The invention solves the problem that the existing cognitive radar target tracking technology cannot operate normally in the clutter environment, and can improve the tracking performance in the clutter environment.

The invention discloses a cognitive radar information processing platform architecture. Based on an existing cognitive radar system, an knowledge auxiliary co-processing system is added. The system ismainly used for prior knowledge caching and radar echo characteristic parameter extraction, simultaneously is cooperated with a signal / data processing system to complete the realization of a knowledge auxiliary radar signal processing algorithm, and is a core component of a cognitive radar system. The architecture has advantages that the information processing platform architecture can satisfy acognitive radar information processing requirement and improve a radar anti-interference ability; through introducing a big data fast access module, a coprocessor, an open exchange structure and othermodules, the architecture can completely satisfy demands of a cognitive radar construction dynamic database, prior knowledge calling and other aspects; and simultaneously, system expansibility is increased, which is convenient for follow-up maintenance and upgrading.

The invention relates to a biological heuristic algorithm based stealth target identification method of a cognitive radar, belongs to the technical field of cognitive radars, and aims at providing a biological heuristic algorithm based waveform optimization method which is simpler and more convenient and has a better effect compared with a present method. According to the method, a target scattering coefficient is estimated by using waveform information received by a radar receiver, maximal posterior probability estimation is used and then Kalman filtering is used for iterative estimation, theestimated target scattering coefficient is compared with a practical target scattering coefficient, a minimum mean squared error therebetween is calculated, a biological heuristic algorithm is used for solving by taking the sending waveform as a variable and the minimum mean squared error as a target function, and the sending form when the target function is minimal is obtained. The method is mainly applied to design and manufacture of the cognitive radar.

Embodiments of the present invention relate cognitive radar and RF technologies, and more particularly, to spectrum sensing processing for rapidly monitoring the RF spectrum for channel availability and activity. The goal is to find and use unoccupied RF channels to broadcast and receive information. According to one embodiment, a method for analyzing a received RF signal to determine unused channels, or frequencies, therein, comprises: analyzing a received RF signal to determine anchor points that represent high energy frequency locations; calculating distances between the determined anchor points; identifying and eliminating clusters or isolated anchor points defined as a high energy region of interference based on the calculated distances; and selecting at least one remaining unoccupied frequency for transmitting or receiving a RF signal. The method may further include performing an optional quality or risk assessment on remaining frequencies of the waveform, and eliminating high risk frequencies from consideration in some instances.

The invention discloses a closed-loop feedback judgment method of self-adaptive imaging of a cognitive radar resource, and relates to the technical field of the cognitive radar self-adaptive imaging.A problem of how to perform more objective and accurate assessment on the closed-loop feedback of the self-adaptive imaging of the cognitive radar resource is solved. The method comprises the following steps: (1) acquiring assessment index data of a priority target; (2) performing non-dimensionalization and assimilation processing on the assessment index; (3) performing priority sub-index assessment on image quality and resource consumption; and (4) assessing the target priority. By adopting the priority disclosed by the invention, the whole assessment result cannot be greatly influenced whensingle index is in error influence, the stability is stronger, and the assessment is more objective and accurate.

The invention relates to a cognitive radar robust sidelobe suppression method based on sequence optimization, comprising the following steps: dividing a to-be-detected region in range resolution cells, sequentially performing transmitting-receiving joint optimization on each range resolution cell, building an objective function and searching an optimal transmitted waveform and an optimal receivingfilter; solving the objective function by adopting a loop optimization method, so that sidelobe suppression performance can meet preset requirements, namely for a first range resolution cell, a transmitted waveform and receiving filter joint optimization problem is solved, and in each iteration, the joint optimization problem is decomposed into two independent optimization problems, one is that the receiving filter (described in the specification) is optimized by a fixed waveform (described in the specification), and the other one is that a waveform (described in the specification) is optimized on the basis of a known filter (described in the specification). The method provided by the invention adjusts a transceiver system according to scene information fed back in real time, and perception of radar on an environment is effectively improved, thus sidelobe suppression performance is improved, and anti-noise robustness is improved.

The invention discloses a radar target constant false alarm detection method based on clutter knowledge. Under the background of non-uniform and dynamic time-varying clutters, the processing capacity of a traditional constant false alarm detection method is greatly reduced, and the detection performance is obviously reduced. In order to improve the detection performance and improve the adaptive capacity of the radar under the complex clutter background, the method utilizes the obtained clutter priori knowledge to dynamically select parameters according to the background clutter change to calculate a threshold value, so that the radar can dynamically adapt to the complex clutter environment. The method has excellent detection performance under the backgrounds of uniform clutters, edge clutters and the like, the radar can be helped to adapt to clutters with different distribution characteristics, the detection capability of the radar in a complex environment is effectively enhanced, and the method is more in line with the development direction of cognitive radars.

The invention discloses a radar high-robustness low-sidelobe waveform design method, belongs to the technical field of radar communication, and aims to solve the problems that an existing radar waveform design method is low in stability, poor in interception resistance and poor in self-correlation distance sidelobe suppression performance. The method is technically characterized by integrating chaotic waveform initialization, a waveform index evaluation value and a resampling thought into constant modulus waveform sequence iterative optimization by adopting a particle distributed projection thought, and designing a constant modulus phase coding waveform meeting engineering requirements. Compared with an existing waveform design method, the method avoids the influence of a non-convex local region stagnation effect on waveform design, and has higher capability of suppressing sidelobe interference of a specific interval distance. By applying the method provided by the invention, the waveform design robustness is stronger, the anti-interception performance is better, the related sidelobe is lower, and a conventional radar, an MIMO radar, a cognitive radar and the like can have better detection performance.

The invention discloses an anti-range deception jamming waveform design method under spectrum coexistence. The method comprises the steps of obtaining prior information in a cognitive radar mode, establishing an optimization problem model, solving an optimization problem and obtaining an optimized waveform, and transmitting the optimized waveform according to radar parameters and processing echoes. In the optimization problem modeling stage, on the basis of known specific interference modulation types, an intra-pulse modulation waveform set is considered, and the design criterion is to reduce the weighted autocorrelation integral sidelobe level and the weighted cross-correlation integral level of the radar waveform set. In order to ensure that a power amplifier works in a saturated state and consider the finite phase number of a digital signal generator, constant modulus constraint and discrete phase constraint conditions are added to a designed waveform, and an optimization problem model is established. In the waveform optimization design stage, an imprecise alternating direction penalty method is used for solving the established quartic optimization problem, the obtained waveform can inhibit range deception jamming while ensuring spectrum coexistence, and the radar detection performance is improved.

The invention relates to a cognitive radar technology, and proposes a waveform optimization method. The calculation performance of the method is higher than the calculation performance of the existingmethod. According to the cognitive radar waveform optimization method based on a bat algorithm, a signal sent on a transmitting end is reflected by a target, and then is received by a receiving end;a target scattering coefficient is calculated according to the maximum posterior probability; each virtual bat represents a feasible position of the signal; the optimal optimization algorithm is acquired according to the calculated position of the bat after each iteration; a cost function corresponding to the fitness is set for each position of the signal in space; iteration is carried out throughthe bat algorithm to change the signal waveform, so that the mean square error calculated by comparing the actual target scattering coefficient with the estimated target scattering coefficient is gradually reduced; after multiple iterations, the best position Gest is found by P; the mean square error is minimized; and radar waveform optimization is realized. The method provided by the invention is mainly applied to radar design and manufacturing.

The invention discloses a closed-loop feedback determination method for cognitive radar resource adaptive imaging, relates to the technical field of cognitive radar adaptive imaging, and solves the problem of how to perform more objective and accurate closed-loop feedback for cognitive radar resource adaptive imaging The evaluation method includes the following steps: (1) Obtaining evaluation index data for priority targets; (2) Dimensionless and convergent processing of evaluation indicators; (3) Prioritizing image quality and resource consumption (4) Evaluate the priority of the target. By adopting the technical scheme of the invention, when a single index is affected by errors, it will not have a great impact on the overall evaluation result, the stability is stronger, and the evaluation is more objective and accurate.