262 results about "Micro doppler" patented technology

Aspects of the present invention relate to visual presentation techniques for mD signatures.

Aspects of the present disclosure relate to visual presentation techniques for micro-Doppler signatures.

Aspects of the present invention relate to visual presentation techniques for mD signatures.

Aspects of the present disclosure relate to visual presentation techniques for micro-Doppler signatures.

The invention discloses a two-dimensional ISRA imaging method of an object with micro rotation in the air. The method comprises the following steps that: (1), a radar matriculates an ISAR echo; (2), translation compensation is carried out; (3), time frequency distribution map is drafted; (4), micro doppler distance units are determined; (5), echo separation of a distance unit; (6), it is determined whether all distance unit have been traversed; (7), a distance-doppler method is used to carry out imaging on a rigid body echo; and (8), imaging is carried out on a rotary part echo. According to the invention, a low frequency modulation rate matched filtering method is employed to carry out echo separation, so that an adaptive chirplet decomposition imaging method's disadvantages including large calculated amount, high time consumption and insufficience of real-time property are overcome; therefore, the method has advantages of simple realization, high efficient and high real-time property. According to the invention, an I-Radon conversion is employed to carry out imaging on a rotary part; therefore, defects of an EHT algorithm are overcome, wherein the defects include high image sidelobe of a rotary object, low precision of estimation position and inaccuracy of object identification; and the method has advantages of good image focusing, high position estimation precision and accurate object identification.

The invention provides an object micro Doppler feature separation and extraction method based on edge detection. The method comprises that smooth pseudo Winger-Ville processing is carried out on echo signal to obtain a time-domain distribution matrix of the echo signals; a time-domain distribution map of the time-domain distribution matrix is seen as a grayscale image, the contrast of the grayscale image is improved, the edge of the grayscale image is sharpened, and edge detection is carried out on the processed image; separation and boundary extraction are carried out on object micro Doppler features, corresponding to two components, in an edge detection result on the basis an edge gradient inertial principle; and the gravity center of energy distribution within the boundary is calculated in the time-domain distribution matrix, transient frequency change rule curves of the two components are obtained, and the object micro Doppler features are resolved. The object micro Doppler features can be separated when the echo signals are two-component signals, and the method is simple in calculation and high in practicality, has low requirements for object prior information, and overcomes disadvantages of a present component extraction method.

The invention discloses a precession target micro-Doppler extracting method based on instant frequency modulation rate estimation. The precession target micro-Doppler extracting method based on instant frequency modulation rate estimation comprises the steps that (1) an equivalent scattering point model of a spatial precession conical target is established, echo signals of the conical target are received by a radar, echo sequences are obtained from the echo signals by using the equivalent scattering point model; (2) frequency modulation rates of the echo sequences are estimated with a relaxation demodulation line frequency modulation method; (3) the frequency modulation rates of the echo sequences are processed through a random sampling consistency algorithm, and P frequency modulation rate curves are obtained; (4) integral operation is carried out on the P frequency modulation rate curves respectively, and P instant micro-Doppler frequency estimation curves are obtained. According to the precession target micro-Doppler extracting method based on instant frequency modulation, high estimation accuracy can be obtained under the situation of the lower signal to noise ratio. The precession target micro-Doppler extracting method based on instant frequency modulation can be applied to analysis of the micro-Doppler frequency of the spatial precession conical target.

The invention provides a micro moving target feature extracting method based on micro Doppler effect, which comprises the steps that HHT (Hilbert-Huang transform) is introduced into micro moving target feature extracting, an HHT algorithm based on the down sampling EMD (empirical mode decomposition) is provided by aiming at the problem of the modal mixing of the feature extracting of the HHT, and procedures of resolving, summing and averaging on the noisy EMD are performed by the multiple groups of data obtained by performing the down sampling on original signals, thus effectively solving the mixing problem of spectrogram modes in the vibration target feature extracting of the HHT, inhibiting the noise of the original signals, improving the signal to noise ratio, reducing the EMD operating complexity of the multiple groups of data, greatly reducing the operating amount, improving the operating speed and achieving a better micro Doppler feature extracting effect. The micro Doppler feature extracting model based on the improved HHT is provided by integrating the advantages of the traditional time frequency analysis method and the improved HHT algorithm, a spectrogram peak value estimation method is added into the model, the resolution in the traditional time frequency spectrogram is improved to be used as an assisting method for the HHT feature extracting, and the requirements on accuracy and practicability of the extracted vibration target feature are achieved.

The invention discloses a plane target classification method based on a time domain and a Doppler domain, and relates to classification methods for moving targets in the air. According to the implementation process of the plane target classification method, a radar conducts observation many times and receives echo signals of a plane target for clutter rejection; time domain waveform entropy and Doppler domain waveform entropy of the signals are calculated; an entropy matrix S' is constructed; the time domain waveform entropy mean value, the time domain waveform entropy 1 order moment and the Doppler domain waveform entropy mean value of each row of the entropy matrix S' are figured out; classifiers are trained by means of the time domain waveform entropy mean values, the time domain waveform entropy 1 order moments and the Doppler domain waveform entropy mean values; test samples are input to the classifiers for classification. The method mainly solves the problems that when the radar carries out observation once under the condition that beam dwell time is shorter than a time domain echo cycle of a rotating part, a micro-doppler modulation spectrum is broadened and the resolution ratio of the micro-doppler modulation spectrum is lowered. Classification accuracy is obviously improved, and the method is used for classification and identification of plane targets.

The invention discloses an aircraft target recognition method based on a single frequency network passive radar. Micro Doppler modulation characteristics generated by an aircraft target rotating part (such as a rotor, a propeller or a jet engine) are used for target recognition. Firstly, two-dimensional cross correlation processing is carried out on signals of a reference channel and a monitoring channel, and thus target detection is realized; then, micro Doppler characteristics corresponding to each transmitting station are extracted from different distance unit positions on a range-Doppler spectrum; and finally, information fusion technology is used for carrying out decision level fusion recognition on multi-angle characteristics. Micro Doppler characteristics provided by each transmitting station in the single frequency network at the same time are combined, attitude sensitivity of the target micro Doppler characteristics is reduced, and recognition accuracy is thus improved.

The invention provides an unmanned aerial vehicle (UAV) classification method and device based on dual frequency radar signal time-frequency distribution. The method comprises steps that S1, short-time Fourier transform is utilized to process time domain data of each UAV acquired by a dual band radar system, and time-frequency maps of two bands of each UAV are acquired; S2, characteristic extraction of the time frequency maps of two bands of each UAV is carried out through a principal component analysis algorithm; and S3, for each UAV, the characteristics of the two bands of the UAV are fusedto acquire fusion characteristics, each fusion characteristic is taken as a sample to input to a support vector machine to classify the UAV. The method is advantaged in that the dual band radar systemis utilized to emit electromagnetic waves in different bands to the UAV, characteristic extraction of the micro Doppler information of UAV echo is carried out, then dual band characteristic fusion analysis is carried out, so different UAV categories are acquired, and UAV classification accuracy is improved.

Systems and methods for labeling radar tracks for machine learning are disclosed. According to some aspects, a machine accesses data from radar unit(s), the data from the radar unit(s) comprising radar tracks, each radar track comprising one or more of the following: Doppler and micro-Doppler measurement(s), range measurement(s), and angle measurement(s). The machine accesses data from computer vision device(s), the data from the computer vision device(s) comprising image(s), the data from the computer vision device(s) being associated with a common geographic region and a common time period with the data from the radar unit(s). The machine labels, using an image recognition module, objects in the image(s). The machine determines, based on the common geographic region and the common time period, that labeled object(s) in the image(s) map to radar track(s). The machine labels the radar track(s) based on labels of the labeled objects.

The invention relates to a human body recognition method based on multi-base radar micro-Doppler and a convolutional neural network, and belongs to the technical field of radar target recognition. According to the method, the multi-base radar is used so that the echo signal difference caused by the change of view can be alleviated, the recognition robustness can be enhanced and the recognition accuracy can be enhanced. The convolutional neural network is applied to perform data processing without manual feature design so as to have certain universality and excellent recognition accuracy performance. According to the method, the transfer learning technology is used, the RGB optical image is utilized in the convolutional neural network to pre-train the weight and the three-channel multi-resolution time-frequency graph having the similar RGB optical image is used as the input of the convolutional neural network so that the pre-training weight dimension is matched, more information is provided in comparison with that of the single-resolution time-frequency graph. Great recognition accuracy can be obtained in multiple human body recognition tasks by the method.

The invention discloses a multi-person through-wall time varying breathing signal detection method based on VMD, comprising the steps of: an ultra wide band emission antenna emitting narrow pulse, through human thoracic cavity micro-doppler vibration, echo being received by a receiving antenna, and performing slow time sampling on ultra wide band radar echo to obtain a through-wall human body echo signal matrix; calculating the variance of each distance door through a distance door selection algorithm, the one with a largest variance being a distance door where a multi-person target exists, and employing a low pass filter to eliminate high frequency interference and superfluous frequency components; utilizing a VMD algorithm to perform mode decomposition on filtered signals, and iterating sub-signals to obtain effective information meeting the number and frequency band of breathing; and performing Hilbert transformation and time frequency treatment to obtain dynamic instantaneous information including smooth breathing characteristics. The method can effectively eliminate interference harmonic wave of different breathing components, remove metope interference, and enhance weak breathing signals, and has the characteristics of strong interference immunity and accurate time varying tracking characteristics.

The invention relates to an underwater moving target identification method. The underwater moving target identification method includes the following steps: conducting preprocessing to target signals received by active sonar, conducting self-mixing (SM) time frequency processing to the target signals after being preprocessed, and extracting a micro-doppler spectrum; extracting intra-frame micro-doppler features and interframe micro-doppler features according to the micro-doppler spectrum; and classifying underwater moving targets based on the intra-frame micro-doppler features and the interframe micro-doppler features. The underwater moving target identification method extracts micro-doppler distribution feathers through echo waveform of underwater moving small target of an active high frequency sonar. A SM algorithm can produce self-correlation items which are identical with west data (WD), mutual disturbance terms cannot exist, the problem that micro-doppler variation restricted by linear frequency modulation (LFM) signal ambiguity function can not be achieved is solved, classification of underwater moving small target of active high frequency sonar single beam echo waveform is achieved, influences of a random channel are reduced through statistics stability of the interframe micro-doppler features, and identification stability is improved.

A multi-function, range-Doppler, synthetic aperture and micro-Doppler, coherent laser radar system having improved spatial resolution and immunity to undesired platform motion utilizing two or more simultaneous, spatially offset transceiver apertures.

The invention relates to a k-nearest neighbor and micro-Doppler feature-based human body action identification method. The method comprises the following steps of 1) establishing a radar time-frequency image database; 2) extracting micro-Doppler features: a) a Doppler frequency of a trunk; b) a total Doppler signal bandwidth; c) a total Doppler offset; d) a Doppler signal bandwidth of the trunk; and e) a body movement cycle; and 3) performing human body action identification by utilizing radar time-frequency images.

The invention provides a laser radar system for tracking and identifying a low, small and slow object based on the laser micro-Doppler effect, belongs to the field of laser radar detection, and aims at solving the problem that a present micro-Doppler microwave radar is interfered by ground clutters seriously. A laser source module outputs relatively strong signal light and relatively weak local oscillation light, a frequency shift modulation module carries out frequency shift on the signal light, and the signal light after frequency shift is input to a heterodyne detection module; the heterodyne detection module uses the signal light after frequency shift to scan a target area and obtains a target echo; beam combination is carried out on the target echo and the local oscillation light, the light after beam combination is detected, an intermediate-frequency signal is collected and processed, signal spectral distribution including target motion and micro-motion information is obtained, whether an object is the low, small and slow object is determined, and if YES, the speed and distance of the object are obtained; and the object is tracked according to the obtained speed and distance of the object.

The invention discloses a method for detecting a micro-motion target of a through-wall radar, and belongs to the field of radar detection and the field of through-wall radar detection. The method fordetecting the micro-motion target of the through-wall radar comprises the following steps that collected intermediate frequency echo signal is subjected to pulse compression treatment; a narrowband filter is designed according to Doppler frequency shift characteristics of a micro-motion target and used for suppressing out-of-band clutter; the signal after clutter suppression treatment is subjectedtocoherent integration; the treated signal is subjected to constant false alarm detection; the pulse compression increases the signal-to-noise ratio and distance accuracy; the clutter suppression caneffectively suppress strong clutter behind the obstacle; according to the method for detecting the micro-motion target of the through-wall radar,on the basis of the analysis of the Doppler frequencyshift characteristics of the micro-motion target, and weak echo waves of the micro-motion target behind a wall are subjected to treatments such as targeted wave filtering, amplification, and extraction, and effective detection of the micro-motion target is achieved.

The invention discloses an empirical mode decomposition based moving vehicle target classification method which is used for mainly solving the problems that the conventional similar methods are sensitive to variation in translational velocities of targets, extra clutter suppression is required and special structural information of the target cannot be utilized. A realization process of the methodcomprises the following steps of: performing empirical mode decomposition on a Doppler echo signal; finishing clutter suppression by rejecting a remainder; defining a Doppler spectrum of a first intrinsic modular function and Doppler spectrums of remaining intrinsic modular functions by using a decomposition result; judging whether doubling translation micro-Doppler components exist according to the defined spectrums and preliminarily discriminating a track-laying vehicle; if the discrimination fails, extracting characteristics of the intrinsic modular functions and the defined spectrums; andclassifying the extracted characteristics by using a classifier. By adopting the method, influence of variation in the translational velocities of the targets on positions and widths of the Doppler spectrums of the targets can be eliminated, the clutter suppression is automatically performed, and the method can be used for classifying moving vehicle targets with maneuvering parts by using the special structural information of a track.

The invention discloses a micro-motion target characteristic extraction method based on the micro-Doppler effect. According to the micro-motion target characteristic extraction method based on the micro-Doppler effect, firstly, HHT is introduced into micro-motion target characteristic extraction, an HHT algorithm based on downsampling EMD is put forward for the problem of mode mixing of HHT characteristic extraction, noise-plus EMD is conducted on multi-set data obtained through downsampling of original signals, summation average is calculated, and therefore the problem of the pattern mode mixing in vibration target characteristic extraction of HHT is effectively solved. Noise of the original signals is restrained, signal to noise ratio is improved, the EMD arithmetic complexity of the multi-set data is reduced, arithmetic amount is greatly reduced, arithmetic speed is improved, and the better micro-Doppler characteristic extraction effect is achieved. The advantages of a traditional time-frequency analysis method and an improved HHT algorithm are combined, a micro-Doppler characteristic parameter extraction model based on improved HHT is put forward, and the resolution problem in a traditional time-frequency analysis spectrogram is improved due to the fact that a spectrogram peak value estimation method is added into the model. The micro-motion target characteristic extraction method based on the micro-Doppler effect is used as an auxiliary means for HHT characteristic extraction, and the requirement of improving accuracy and practicability of vibration target characteristic extraction is met.

The invention discloses a human body target motion state identifying method based on improved generalized S conversion, and belongs to the technical field of radar life detection and identification. The method comprises the following steps of: establishing a human body target walking space model based on an empirical mathematic parameter and an Euler rotation matrix, then establishing a radar return model for micro Doppler modulation, and extracting target micro Doppler characteristics from a return signal by utilizing improved generalized S conversion, so that the human body target motion state can be identified. By utilizing the method, three specific target states of low-speed walking, normal-speed walking and high-speed walking can be identified.

The present invention relates generally to a long range eye-safe laser radar (LADAR) system for use in an environment where real-time non-cooperative identification of an object is required. In particular, a laser beam is aimed at an object, the laser energy reflected from the object is collected by a detector array for use in generating a composite of both a high resolution 3-Dimensional (3D) shape of the object and the object's high resolution micro-Doppler vibration spectrum, a characteristic of the object as unique as a fingerprint. The composite is then used to automatically identify the object by comparison to a database of similar composite sets of 3D shape and vibration spectrum information with the results of the identification conveyed to the user.

The present disclosure may be embodied as systems and methods for action recognition developed using a multimodal dataset that incorporates both visual data, which facilitates the accurate tracking of movement, and active acoustic data, which captures the micro-Doppler modulations induced by the motion. The dataset includes twenty-one actions and focuses on examples of orientational symmetry that a single active ultrasound sensor should have the most difficulty discriminating. The combined results from three independent ultrasound sensors are encouraging, and provide a foundation to explore the use of data from multiple viewpoints to resolve the orientational ambiguity in action recognition. In various embodiments, recurrent neural networks using long short-term memory (LSTM) or hidden Markov models (HMMs) are disclosed for use in action recognition, for example, human action recognition, from micro-Doppler signatures.

The invention discloses an indoor user behavior monitoring method and device, electric appliance equipment and a home monitoring system. The method comprises the following steps: transmitting a microwave radar signal; collecting a return signal of the microwave radar signal; extracting micro-Doppler characteristics in the return signal; and judging whether a preset user is existent in the currentindoor environment according to the micro-Doppler characteristics, wherein the preset user is the user collecting the Doppler characteristic data in advance. Through the method disclosed by the invention, an effect of flexibly monitoring the indoor user behavior is reached.

The invention discloses a high-resolution human body target motion feature detecting method. The method comprises the steps that firstly, a radar demodulation target echo signal model with micro-Doppler features is built, then signals are over-sampled, the time frequency distribution of the signals is calculated through time frequency analysis, binaryzation and other optimizing processing are carried out on the time frequency distribution of the signals, and a time frequency curve matrix is obtained; then, straight line detection is carried out on the matrix, human body main trunk motion parameters are obtained, a parameter space is set, the motion parameters of all the joints are detected, and the human body motion parameters are obtained. Compared with a traditional detecting method, the terahertz frequency band feature is fully utilized, the defect that the binding force of an existing human body target motion feature visual image sequential detection method on detecting conditions and the resolution ratio of a traditional radar detecting technology are low is overcome, and the human body target motion parameters can be detected faster and more accurately within a certain distance through the method.

The invention provides a micro-motion target characteristic extraction method and system based on wavelet multi-scale analysis. The micro-motion target characteristic extraction method based on wavelet multi-scale analysis comprises the steps that a micro Doppler model of a precessional motion target and a micro Doppler model of a rolling target are established, narrow-band radar scattering cross section data of the precessional motion target and narrow-band radar scattering cross section data of the rolling target are obtained based on ballistic trajectory simulation, the high-frequency component of the narrow-band radar scattering cross section data of the precessional motion target and the high-frequency component of the narrow-band radar scattering cross section data of the rolling target are extracted by means of wavelet multi-scale analysis, and column diagram characteristics are extracted aiming at the high-frequency component of the precessional motion target and the high-frequency component of the rolling target. According to the micro-motion target characteristic extraction method based on wavelet multi-scale analysis, the micro Doppler high-frequency components of the targets in different micro-motion forms are separated out by means of wavelet multi-scale analysis, new characteristics are extracted for recognizing the truth of targets, and the recognition accuracy of precessional motion targets and rolling targets is improved.

The invention relates to an ultra-wideband radar-based old person tumble detection method and belongs to the field of radar target detection signal processing. According to the ultra-wideband radar-based old person tumble detection method, on the basis of the time, Doppler frequency and distance change characteristics of the radar echoes of the different actions of a human body, a multi-domain peak point tracking method is used to replace a traditional time-frequency analysis method; on the basis of the acquisition of the time characteristics and Doppler frequency characteristics of the actions of the human body, the distance characteristic information of the actions of the human body is obtained; and therefore, the influence of micro-Doppler frequency spectra on a result which is caused by the swinging of the limbs of the human body during the movement of the human body is avoided, and the realization of more accurate action characteristic analysis results can be benefitted. Compared with the prior art, the method disclosed by the invention can reserve time and frequency information obtained by a traditional time-frequency analysis method, realize the accurate detection of the action distance information of the human body and improve the recognition accuracy of the falling action of the human body.