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.

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 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.

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 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 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.