578results about "Radio wave finder details" patented technology

The invention discloses an unmanned aerial vehicle integrated defense system and a method. The unmanned aerial vehicle integrated defense system comprises a reconnaissance equipment unit, an interference equipment unit and a control unit, and is characterized in that the reconnaissance equipment unit integrates a radar reconnaissance technology, a photoelectric reconnaissance technology and a radio reconnaissance technology to carry out monitoring on a designated area so as to recognize an unmanned aerial vehicle and acquire movement and position information and instruction and image transmission information of the unmanned aerial vehicle and azimuth information of a manipulator of the unmanned aerial vehicle; and the control unit realizes interference for instruction signals, navigation signals and image transmission signals of the unmanned aerial vehicle through the interference equipment unit according to the acquired information. The unmanned aerial vehicle integrated defense system increases electronic reconnaissance on the basis of integrating the radar, photoelectric/infrared reconnaissance and electronic interference technologies so as to carry out reconnaissance analysis on wireless signals of the unmanned aerial vehicle, is applied to guidance interference, and improves the interference effectiveness and the efficiency; and meanwhile, the unmanned aerial vehicle integrated defense system can carry out remote positioning on the manipulator of the unmanned aerial vehicle, provides capture guidance for a law-enforcing department while realizing unmanned aerial vehicle defense, and improves the crime committing cost of unmanned aerial vehicle crime committing.

A model construction module (MCM) for constructing a probabilistic model (PM) of a wireless environment (RN) in which a target device (T) communicates using signals that have a measurable signal value (x), such as signal strength. The model construction module forms several submodels (611-631) of the wireless environment (RN). Each submodel indicates a probability distribution (F₁-F₃) for signal values at one or more locations (Q₁-Q_Y) in the wireless environment. The module combines the submodels to a probabilistic model (PM) of the wireless environment (RN), such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations comprises combining the inverse cumulative distribution functions of the submodels or existing locations.

The invention discloses an estimation method of a co-prime array DOA (Direction Of Arrival) angle based on sparse reconstruction, and mainly solves the problems that a prior art is higher in operand, less in identification information source amount and large in passive location evaluated error, and needs more priori knowledge. The method comprises the realizing steps of forming a co-prime array by an antenna receiver; obtaining observation data to spatial signal sampling; receiving data vectors by a virtual array element obtained by observation data; dividing spatial grids to form over-complete bases; receiving a spare relationship between the data vectors and the over-complete bases by the virtual array element to build a spare restraint equation; resolving the spare restraint equation by adopting a convex optimization method to obtain sparest resolution; drawing a magnitude spectrogram by a relative relationship between the sparest resolution and the spatial angle to obtain a DOA angle value. According to the method provided by the invention, the passive direction-finding precision and operating speed can be improved under a condition of low priori knowledge, the number of the recognized information source can be improved, and the estimation precision of a signal direction angle can be improved in a low signal to noise ratio, therefore the estimation method can be used for target reconnaissance and passive location.

Substantial improvements in frequency reuse in microwave communications systems is achieved by canceling co-channel interference and transmitter leakage. Interferometric beam-narrowing reduces beamwidth without reducing preak magnitude of the beam pattern. Frequency-dependent beam-shaping compensates for frequency-dependent distortions of the beam pattern thereby improving bandwidth. A spatial demultiplexing technique utilizes spatial gain distributions of received signals to separate signals, even from co-located transmit sources, and uses microwave lensing to enhance received spatial gain distributions. Predetermined cross-polarization interference is used to separate differently polarized receive signals. A reference branch provides a cancellation signal to a receiver to cancel transmitter leakage signals. An error signal controls an impedance-compensation circuit that is responsive to changes in antenna impedance but not to receive signals. A dc bias magnetic field applied to a magnetic permeable material adjusts non-linear distortion in a cancellation circuit for canceling distortion in a transmitter leakage signal. Discreet impedance elements approximate a circuit having distributed impedance.

Direction findings by radio comprises arraigning an array of antennas, to receive signals from emitters, selecting individual antenna signals using a first multipole switch and determining, antenna signal strengths. Individual antenna signals are also selected by mutipole switch, which routes a selected signal to a third multipole switch. The third switch switches a phase shifter into and out of an antenna signal path. An adder is employed to add an antenna signal in a first signal path extending via the first multipole switch to a different antenna signal in a second signal path extending via the second and third switches. This determines combined signal strengths between pairs of antenna signals, one of which either has or has not been relatively phase shifted depending on the third switch position. Covariance matrix elements are determined from signal strengths enabling emitter bearings to be derived.

The invention discloses a CPU (Central Processing Unit) realizing method based on amplitude-comparison direction finding of a multi-frequency point omnibearing passive radar. A CPU and GPU (Graphic Processing Unit) heterogeneous hardware platform is adopted; a processing module with strong parallelism and the processing module with strong logicality are respectively realized on the GPU and the CPU; the platform is simple and high in use ratio; a system is simple in operation and strong in flexibility when being expanded; the coagulation and the angle measurement are carried out in the CPU; specifically, a coagulating-while-comparing amplitude angle measuring method is adopted; trace point coagulation is combined with amplitude-comparison angle measurement, so that the time for recombining the data after coagulation is omitted; the coagulation and angle measurement processes are embedded in multithreading, so that the time is further saved; and a target point containing the angle information is transferred to a terminal display control platform. According to the CPU realizing method provided by the invention, the problems that a target positioning effect of a single-frequency passive radar detecting system is inferior, the multi-frequency precision is high but the calculated quantity is large and the consumed time is long are solved. The CPU realizing method has the advantages of high processing speed, high angle measuring precision, and wide angle measuring scope, and can be applied to the target detection and positioning for the passive radar.

The invention discloses a fast sparse Bayesian learning based direction-of-arrival estimation method and mainly solves the problems of heavy computation and large location estimation error in the prior art. The method includes the implementation steps: (1) adopting antenna receivers to form a uniform linear array; (2) sampling space signals and computing an array covariance matrix R; (3) vectorizing R to obtain a sparse model vector y; (4) dividing space domain grids, and constructing an over-complete base phi(theta) according to the structure of the sparse model vector y; (5) establishing a sparse equation according to the sparse representation relation of the sparse model vector and the over-complete base; (6) defining a hyper-parameter vector alpha, and adopting a fast sparse Bayesian learning algorithm to solve the sparse equation; (7) drawing a magnitude spectrogram according to an optimal estimation value of alpha to obtain a direction-of-arrival value. By the method, estimation accuracy of target reconnaissance and passive location under the conditions of low signal to noise ratio and small snapshot number is improved, computational complexity is lowed, and the method can be applied to target reconnaissance and passive location.

The invention provides a distributed source center direction-of-arrival estimation method based on Bayesian compressed perception, and belongs to the technical field of wireless mobile communication. The invention mainly aims to solve the problem concerning inherent error of center direction-of-arrival estimation when the center angle of arrival of an information source is not on an angle sampling grid. According to the invention, an antenna array composed of parallel uniform linear arrays is arranged; an approximate array data reception model of a distribution source is established; the space-domain angle is sampled; a parameterized over-complete redundant dictionary is constructed by using an array steering vector so as to make the problem of distributed source center direction-of-arrival estimation converted into the problem of sparse matrix equation solving; a Bayesian compressed perception method is adopted to solve the equation set and obtain the most sparse solution of an unknown sparse vector; and the estimated value of the center direction of arrival is obtained according to the one-to-one correspondence relationship between sparse solutions and space-domain angles. The method of the invention is low in computing complexity, and has the characteristics of high resolution and accuracy under the condition of a small number of snapshots.

A detection device is described configured for receiving radar and laser signals. The detection device is a self contained stand alone assembly having a watch like configuration and positionable on an arm of a vehicular driver such as a motorcyclist. The detection device is a warning receiver configured to receive signals, identify the signals as being in frequency bands for vehicular speed measuring systems used in law enforcement, and providing a warning to the vehicular driver. In addition, the detection device is configured for calculating the angle and distance from the detection device to the source of the speed measuring system.

The invention discloses a single-snapshot data-based coherent signal DOA estimating method and relates to the technical field of array antenna systems or methods capable of indicating different signal directions. The method comprises rearranging the single-snapshot received data of an array to obtain two pseudo-covariance matrixes, and then through two pseudo-covariance matrixes, expanding the covariance matrix of a subspace restructuring algorithm; performing singular value decomposition on the new pseudo-covariance matrixes to obtain a signal subspace and a noise subspace, and performing DOA estimation on incoming coherent wave signals through a MUSIC spectrum estimating method. The single-snapshot data-based coherent signal DOA estimating method can eliminate coherence among source signals under single-snapshot conditions and meanwhile further improve the DOA estimating precision. The single-snapshot data-based coherent signal DOA estimating method is mainly applied to rapid estimation of incoming directions of coherent signals under the single-snapshot conditions.

This invention relates to a method for testing directions of coherent signal sources under non-stable noise background including: collecting data received by channels and storing them in an EMS memory of the system, making adaptive uniform process to data received by the channels to form an array covariance matrix, utilizing a forward space smooth technology to preprocess it smoothly to constitute a forward space smooth covariance matrix and utilizing a forward and backward space smooth technology to smoothly pre-process the matrix to constitute a generalized covariance matrix based on the covariance matrix and a conjugated matrix and carrying out spectrum estimation to the generalized matrix with a sub-space class algorithm to identify a real incident direction of image false peak and signal source and output the real direction of the estimated signal source.

An arrival angle estimation system includes a transmitting device and a receiving device and estimates an arrival angle at which frames transmitted by radio from the transmitting device arrive at the receiving device. The transmitting device includes two directional antennas each arranged so that its directivity is tilted at ±θ degrees with respect to the front of the transmitting device. The receiving device includes a directional antenna having directivity toward the front of the receiving device. Frames are transmitted alternately from the respective directional antennas of the transmitting device, and received signal strengths of the frames on the receiving device side for the respective transmitting directional antennas are compared with each other to estimate the arrival angle.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention discloses a joint estimation method for an azimuth angle and an elevation angle of a signal on the basis of an L-type sensor array. The joint estimation method is used for estimating a direction of arrival of an incidence signal emitted onto the L-type sensor array, wherein the L-type sensor array is placed on an x-z plane and is provided with two mutually vertical uniform linear arrays, and M omnidirectional sensors are equidistantly arranged in different spatial positions along a straight line on each of the uniform linear arrays. The joint estimation method is characterized by comprising the following steps: (1) estimating a covariance matrix of signals received by two rows of uniform linear arrays on x axis and z axis, and then obtaining an M*2M expanding cross covariance matrix by calculating according to the covariance matrix of the signals received by the two rows of uniform linear arrays; (2) cutting the uniform linear array on the z axis or x axis into two rows of non-coincident forward/backward sub-arrays, and then estimating the elevation angle by utilizing the expanding cross covariance matrix of data received by the two rows of uniform linear arrays according to a linear operation one-dimensional subspace method; and (3) estimating a corresponding azimuth angle by linearly operating by utilizing feasible regions of the azimuth angle and the elevation angle, the two rows of sub-arrays on the z axis or x axis and the cross covariance between one of the sub-array and the uniform linear array on the x axis or z axis.

The invention discloses a rapid two-dimensional spectral-peak searching method suitable for engineering realization. The method comprises the following steps of: 1, carrying out two-dimensional global search for a spatial spectrogram composed of a noise subspace and antenna array flow pattern vectors, and obtaining global search spectral-peak points; 2, carrying out fine search near the global search spectral-peak points, and obtaining spectral-peak candidate points; 3, classifying the spectral-peak candidate points, and finding real spectral-peak points corresponding to the number of signals; and 4, and utilizing false spectral peak information around a real spectral peak, and finally using a weighted value of the real spectral peak and false spectral peaks around the real spectral peak as an estimated value of an incoming wave direction. Aiming at using a 2D-MUSIC algorithm to estimate the direction of arrival of incident signals, a two-dimensional spectral-peak searching process occupies most of the time of the whole algorithm so as not to obtain the direction of arrival of the signals in real time or on time. According to the method disclosed by the invention, on the basis of characteristic analysis of the spatial spectrogram, a data cluster and grading searching method is used to accelerate the spectral-peak searching process and the precision of search results are ensured.

The invention discloses a passive direction-finder antenna array of a phase interferometer and the phase interferometer. The passive direction-finder antenna array comprises three layers from inside to outside. The first layer is a circular antenna array composed of a plurality of dual-polarization omnidirectional antenna array elements which are arranged on a circumference, each dual-polarization omnidirectional antenna array element comprises a horizontal polarization oscillator and a perpendicular polarization oscillator and is connected with one-out-multiple antenna switch through a polarization choice output matching board, and the horizontal polarization oscillators and the perpendicular polarization oscillators on the circumference are adjacently arranged; the second layer is a horizontal polarization antenna array which comprises a horizontal polarization annular oscillator; the third layer is a perpendicular polarization antenna array which comprises a perpendicular polarization biconical antenna oscillator and two cones in the perpendicular polarization biconical antenna oscillator are respectively connected with a matching circuit; the horizontal polarization antenna array of the second layer and the perpendicular polarization antenna array of the third layer are connected with the input ends of the same one-out-two antenna switch. The passive direction-finder antenna array has the advantages of being simple in structure, low in production cost, high in testing sensitivity and high in accuracy.