342 results about "Signal processing algorithms" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention discloses an electromechanical equipment health assessment and early warning method based on multi-mode dynamic perception, and belongs to the field of intelligent operation and maintenance of electromechanical equipment. The problems that in the prior art, a single physical quantity cannot comprehensively reflect the equipment state and a traditional signal processing algorithm cannot adapt to the equipment degradation mode change are solved, a panoramic sensing system covering multiple physical fields such as vibration, temperature and noise is constructed through a multi-mode sensor network and a dynamic weight fusion algorithm, and the multi-physical-field multi-physical-field panoramic sensing method is applied to the multi-physical-field multi-physical-field panoramic sensing system. The problem of isolated island of traditional single-dimensional monitoring information is solved; through a physical-depth mixed feature extraction architecture, combining interpretable engineering features with abstract features extracted by a deep neural network to form a health assessment model with mechanism transparency and mode generalization ability; through deep integration of the digital twin platform and the RPA technology, the manual inspection frequency and workload are reduced, the fault recognition accuracy is promoted to increase year by year, and continuously optimized intelligent operation and maintenance ecology is formed.

The invention relates to the technical field of optical signal processing, in particular to an optical signal processing algorithm based on biological agent fluorescence detection, which comprises the following steps: S1, collecting fluorescence signals with different wavelengths emitted by each probe, and generating a multi-channel fluorescence spectrum data set; s2, carrying out spectral de-mixing on the fluorescence signals in the fluorescence spectrum data set, separating a plurality of overlapped fluorescence signals, and decoupling the fluorescence signals with different wavelengths; s3, for highly overlapped fluorescence signals, carrying out secondary overlapping separation treatment on the fluorescence signals by using a nonlinear optimization method; s4, extracting the intensity, wavelength and time change characteristics of each fluorescent probe signal, and constructing a multi-dimensional fluorescent signal characteristic vector; and S5, by amplifying the characteristic peak value of the weak fluorescence signal, it is ensured that the signal is not covered by strong signals of other probes in multi-probe detection. According to the invention, the resolution of fluorescence detection is improved, so that the high-precision detection effect can be maintained under complex experimental conditions.

The embodiment of the invention provides a mining lithium battery thermal runaway acoustic signal processing algorithm based on machine learning. The mining lithium battery thermal runaway acoustic signal processing algorithm comprises the steps of synchronously obtaining a target acoustic signal and a battery environment parameter of a mining lithium battery in a thermal runaway process; preprocessing the target acoustic signal to obtain a preprocessed acoustic signal; performing spectral analysis, time domain analysis and wavelet transformation on the preprocessed acoustic signals, extracting thermal runaway related features, and correspondingly obtaining thermal runaway related key frequency features, vibration features and local frequency changes of the mining lithium battery; fusing the key frequency characteristic, the vibration characteristic and the local frequency change related to the thermal runaway and the battery environment parameters to obtain a thermal runaway fusion characteristic vector; and based on the thermal runaway fusion feature vector, performing thermal runaway monitoring on the mining lithium battery to obtain a thermal runaway monitoring result, and when an abnormal signal is monitored, automatically sending out early warning information and starting corresponding safety measures by the system.

The invention relates to the technical field of fault positioning, and particularly discloses a digital distribution network fault traveling wave range finder and method, and the range finder employs fault traveling wave collection devices disposed at all key nodes of a distribution network to collect fault traveling wave signals in real time, and obtains stable line mode traveling wave signals through signal filtering and line mode component extraction. Then introducing a signal processing algorithm based on deep learning to carry out time domain feature analysis on the line mode traveling wave signal of each key node so as to detect the wave head position of the signal, further determining a power distribution network section where a fault point is located by comparing the signal arrival time of each key node, and determining the fault location of the power distribution network based on a double-end traveling wave distance measurement algorithm. And calculating the distance between the fault point and the nodes at the two ends according to the signal arrival time difference of the nodes at the two ends of the section and the signal propagation speed. Thus, through deep time domain analysis of the fault traveling wave signal, the wave head position of the signal can be accurately identified, and the reliability of distribution network fault traveling wave distance measurement is improved.

The invention discloses a wireless communication battery management system with a signal adaptive adjustment function, and relates to the field of wireless communication networks. The signal monitoring unit is used for monitoring parameter data of wireless signals in real time and transmitting the parameter data to the signal processing unit, and the signal processing unit comprises a multi-band antenna and a signal acquisition circuit; and the signal processing unit is connected to the signal monitoring unit and is used for receiving the parameter data of the wireless signal transmitted by the signal monitoring unit and carrying out filtering, noise reduction and enhancement processing on the signal through a preset signal processing algorithm. The distributed multi-band antenna design is combined with the automatic gain control technology, the signal receiving range is expanded, blind areas are reduced, the stability of signal collection in a complex environment is improved, signal multi-scale noise reduction is achieved through the wavelet transform algorithm, the signal quality trend is pre-judged in advance in combination with machine learning prediction, and the signal quality is improved. And the control unit is supported to pre-adjust the transmitting power and switch to a high-quality frequency band, so that the probability of communication interruption is reduced.

The invention relates to the technical field of industrial park intelligent monitoring, and particularly discloses a data acquisition and monitoring control method based on an intelligent industrial park, which comprises the following steps: acquiring key state data of equipment in real time, including operation time, operation temperature and operation power; an advanced signal processing algorithm is used for calculating an operation temperature abnormal index and an operation power abnormal index, the system can comprehensively evaluate the state of equipment and accurately judge whether slight or serious abnormal conditions exist or not based on detailed data analysis results, and the system can not only give out instant early warning notifications but also give out alarm information according to the serious abnormal conditions. According to the method, a machine learning model is adopted to predict the future operation state of the equipment, the equipment state abnormal coefficient in each future monitoring period is predicted based on the equipment state data in the current period, and classification and dynamic regulation and control of the equipment state are carried out according to a preset threshold value.

The invention discloses a non-contact sleeping posture detection and vital sign monitoring method based on a millimeter-wave radar, and the method employs a multi-transmitting and multi-receiving millimeter-wave radar, employs an innovative signal processing algorithm and a data processing algorithm, and carries out sleep monitoring through comprehensive consideration of frequency spectrum information and point cloud information. Comprising the steps of extraction of vital sign data such as respiration and heartbeat and judgment of in-bed, body movement and sleeping posture states. Breathing and heartbeat components are effectively extracted through cross-correlation entropy operation, then the signal-to-noise ratio of the breathing and heartbeat components is increased, then fast Fourier transform is carried out on cross-correlation entropy, and it is proved that the breathing and heartbeat frequency is effectively extracted.

The invention discloses an intelligent sensing network connection monitoring method and system for a high-low voltage switch cabinet, and belongs to the technical field of electrical monitoring. The method comprises the following steps: acquiring physical quantity data of the high-low voltage switch cabinet according to an adaptive data acquisition period; generating a high-precision original data set through a signal processing algorithm and a data fusion algorithm; according to the high-precision original data set, the operation state of the high-low voltage switch cabinet is evaluated through a state evaluation algorithm, and a state evaluation result set is generated; and outputting a prediction result set of the high-low voltage switch cabinet through a state prediction method according to the state evaluation result set. According to the invention, on the basis of self-adaptive data acquisition, a state prediction method is adopted to accurately predict the fault occurrence probability of the high-low voltage switch cabinet according to the state evaluation result set, so that the effects of planning an operation and maintenance strategy in advance and ensuring stable operation of a power system are realized; the problem that the high-low voltage switch cabinet state prediction precision is low in the prior art is solved.

The invention discloses a multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.

The invention discloses a power system inertia estimation method based on multi-mode decomposition and reconstruction, and belongs to the technical field of power system inertia identification control, and the method comprises the steps: obtaining a system frequency deviation signal and three-phase voltage and current data in real time; inputting the frequency deviation signal into a signal processing algorithm, extracting a multi-mode oscillation signal, and reconstructing the multi-mode oscillation signal into an equivalent single oscillation mode signal; the frequency change rate of the reconstructed signal is calculated, and the frequency change rate is obtained and analyzed; after a fault occurs, analyzing the moment corresponding to the maximum value of the frequency change rate, and combining power deviation to trigger inertia constant calculation; according to the frequency change rate and the power deviation, calculating a system inertia constant through a dynamic equation at a triggering moment, and converting the system inertia constant into a per unit value; and reconstructing a frequency signal of a non-measurement node by adopting a weighted least square method to finish inertial estimation. Through a synchronous phasor data dynamic decomposition and reconstruction technology, high-precision online sensing of inertial parameters under complex working conditions is realized.

The invention relates to the field of medical information monitoring, and discloses a doctor monitoring bracelet for receiving infusion information of a patient, comprising: a multispectral sensing module for collecting an original physiological light intensity signal of the wrist of the patient; the motion sensing module is used for collecting motion signals; the micro-electro-mechanical motion compensation module is configured on the multispectral sensing module; the processor is electrically connected with the multispectral sensing module, the motion sensing module and the micro-electro-mechanical motion compensation module; and the communication module is electrically connected with the processor. By integrating a high-sensitivity multispectral sensing module and a micro-electro-mechanical active motion compensation system and combining an advanced signal processing algorithm, the accuracy of real-time monitoring of key vital signs of a patient in a complex infusion scene is remarkably improved, more reliable patient infusion information is provided for a subsequent multi-head infusion intelligent switching control system, and the infusion safety of the patient is improved. High fidelity of a data source is ensured, and tedious work of medical staff is reduced.

The invention relates to the technical field of mining area vehicle excavation operation statistics, in particular to a mining area vehicle excavation operation statistics method, system and device and a storage medium. The statistical method comprises the steps that the relative position between the excavator and the mine car is determined through beacon signals, and the distance between the excavator and the mine car is calculated according to position data of the relative position; a high-density vibration sensor is used for detecting periodic vibration signals generated during operation of the excavator, and SD periodic burr signals are extracted through a signal processing algorithm and used for judging whether the excavator is loading or not; when it is judged that the excavator is loading, a loading event is recorded, and a loading record is generated. According to the invention, by fusing various sensor signals, the accuracy of mining area vehicle excavation operation statistics is improved. Specifically, the beacon signal enables the relative position between the excavator and the mine car to be accurately determined, so that misstatistics caused by wrong position judgment is avoided.

The invention discloses a self-adaptive rotating baseline direction finding method, device and equipment, a medium and a terminal. Signals are continuously collected through the base line rotatable antenna array; rapidly identifying and outputting environment parameters by using the trained environment evaluation model, and evaluating the environment grade; constructing a dynamic direction-finding angle model by combining the attitude parameters and the motion parameters of the mobile platform, the control parameters of the antenna array and the environmental parameters; according to the environment parameters and the environment grades, a dynamic parameter optimization rule is formulated, and control parameters, direction-finding resolving parameters and signal processing algorithm parameters of the antenna array are adjusted; and based on the environmental parameters, the adjusted control parameters of the antenna array, the adjusted direction-finding resolving parameters and the adjusted adaptive filter parameters, calculating an incoming wave angle by using a dynamic direction-finding angle model. According to the method, high-precision incoming wave signal direction finding is realized in a dynamic environment, and the method is suitable for mobile scenes such as unmanned aerial vehicles and vehicle-mounted communication.

The invention discloses a refrigerator door body vibration induction detection control system, and relates to the technical field of refrigerator detection, the system comprises the following components: a vibration data acquisition subsystem, a data processing and analysis subsystem, a machine learning analysis module, a fault early warning and maintenance prompt subsystem and a data storage and management subsystem; through the high-precision piezoelectric vibration sensor and an advanced signal processing algorithm, the vibration state of the refrigerator door body can be monitored in real time, the system can accurately capture the key information of the vibration amplitude, frequency, phase and waveform of the door body, and the vibration data is deeply analyzed by using a machine learning algorithm, so that the accuracy of the vibration state of the refrigerator door body is improved. The door body fault early warning and accurate positioning function is achieved, the problems that due to the door body fault, the sealing performance of the refrigerator is reduced, and energy consumption is increased are effectively solved, the service life of the refrigerator is prolonged, and meanwhile the maintenance cost and time cost of a user are reduced.

The invention relates to the technical field of power system monitoring, and discloses a power circuit breaker state monitoring and recording system and method based on a signal processing algorithm, and the system comprises a data collection module which is used for collecting the real-time operation data of a power circuit breaker, and storing the real-time operation data as a monitoring record; the signal preprocessing module is used for preprocessing the real-time operation data by using a filtering and denoising algorithm before the state of the power circuit breaker is analyzed; the state analysis module is used for performing state analysis on the preprocessed data based on PLC program optimization and a signal processing algorithm so as to judge the working state of the power circuit breaker; and the recording module is used for recording and storing the analyzed state of the power circuit breaker for subsequent query and analysis. The working state of the circuit breaker can be accurately judged, and potential faults can be found in time, so that electric power accidents are effectively prevented, and the stability and the safety of an electric power system are improved.

The invention relates to the technical field of power system fault detection, and discloses a 10kV cable single-phase earth fault detection method based on an adaptive algorithm, and the method comprises the following steps: S1, employing a voltage collector and a current collector to collect the power frequency and transient signals of a switching station integrated intelligent terminal strip; s2, filtering and de-noising the collected signals through a self-adaptive digital signal processing algorithm; s3, adaptively determining a single-phase earth fault signal component, and identifying the most significant frequency band; s4, extracting features of the fault signal by using a multi-band multi-criterion adaptive algorithm; and S5, based on the extracted features, selecting the most significant criterion for single-phase earth fault detection. Through an adaptive algorithm and a deep learning model, the detection and recognition precision of a complex single-phase earth fault is improved, real-time intelligent processing is realized through an integrated monitoring module, misjudgment is reduced, the operation and maintenance efficiency is improved, the system adapts to multi-scene application, and the intelligentization and power supply reliability of the system are enhanced.

The invention discloses a method and system for analyzing high-voltage electromagnetic resonance noise of an electric vehicle, and relates to the technical field of automobile electromagnetic vibration noise, and the method comprises the steps: obtaining noise information data during the operation of the vehicle, the key parts of the vehicle comprising a high-voltage motor and a battery pack; the collected noise data are preprocessed and analyzed, and the noise data are decomposed into different frequency components. The invention further discloses a system for analyzing the high-voltage electromagnetic resonance noise of the electric vehicle. According to the system of the method for analyzing the high-voltage electromagnetic resonance noise of the electric vehicle, the high-voltage electromagnetic resonance noise of the electric vehicle can be rapidly analyzed through the automatic data acquisition and signal processing flow, the analysis efficiency is improved, and the analysis accuracy is improved by adopting an advanced signal processing algorithm and analysis technology. According to the method, the resonant frequency can be accurately identified, the resonant mechanism can be accurately analyzed, the analysis precision is improved, and the electrical system and structural characteristics of the electric vehicle are considered.

The invention provides an ultrasonic three-dimensional imaging method and system based on an FPGA, and relates to the technical field of ultrasonic imaging, and the method comprises the steps: transmitting a chirp signal, receiving an echo signal reflected by a target object, and carrying out the preprocessing; carrying out adaptive filtering, constant false alarm rate detection, adaptive beam forming and three-dimensional positioning algorithm processing on the preprocessed signal to generate target position data; and performing three-dimensional point cloud image reconstruction on the target position data by using a density-based spatial clustering algorithm and a deep learning model, and outputting a target three-dimensional image. Through the innovation of a signal processing algorithm and the collaborative design of the system, the technical bottlenecks of a traditional ultrasonic sensor in detection distance, anti-interference, real-time performance and positioning precision are solved, and the three-dimensional sensing capability with high reliability and strong environmental adaptability is provided for intelligent driving.

The invention discloses a breath and heartbeat detection method and system based on an FMCW (Frequency Modulated Continuous Wave) radar. The method comprises the following steps: acquiring a continuous wave signal sent by an FMCW millimeter wave radar to detect a target object so as to obtain a reflected echo signal; the echo signals are preprocessed; separating a respiration signal from a heartbeat signal of the preprocessed echo signal based on an EPH multi-stage cooperative signal processing algorithm; and carrying out anomaly correction on the separated instantaneous frequency anomaly data based on a local weighted regression algorithm. Therefore, the vital sign detection accuracy of the radar can be improved under the interference conditions of background noise, signal harmonic waves and the like.

The invention provides a respiratory tract phlegm blockage monitoring method and device, and belongs to the technical field of respiratory tract phlegm blockage monitoring, and the monitoring method specifically comprises the following steps: obtaining respiratory tract multi-mode biophysical signals of a target domain patient and a plurality of source domain patients; the method comprises the following steps: acquiring a target domain signal through a micro sensor array, and constructing an optimal source domain set by using a signal processing algorithm formed by a coding module and a classification module; inputting a target domain signal and the optimal source domain set into a global adapter, extracting shallow layer features through a feature extraction module, and realizing conditional distribution alignment through a global adaptation unit; edge distribution alignment is guided through adversarial training containing a gradient inversion layer; and the discriminator and the global adapter parameters are fixed, and a classifier is trained to identify and evaluate the sputum blockage state. According to the method, the sputum blockage monitoring accuracy is improved through multi-mode signal processing and confrontation training.

The invention discloses a method and system of an unmanned aerial vehicle signal detection and automatic control platform in a complex environment, and relates to the technical field of unmanned aerial vehicles. In the complex environment, multi-mode signal acquisition equipment is deployed, a quantum encryption communication link is established, and electromagnetic, optical and acoustic signals of an unmanned aerial vehicle are acquired in real time and preprocessed; the method comprises the following steps: preprocessing a multi-modal signal to obtain a preprocessed multi-modal signal, inputting the preprocessed signal into a depth feature extraction module, extracting electromagnetic, optical and acoustic features by using a convolutional neural network, a target detection algorithm and an acoustic signal processing algorithm, fusing to form a feature vector, comparing the feature vector with a database for analysis, identifying unmanned aerial vehicle information, and judging an abnormal condition. And after the abnormity is identified, the control decision module formulates a control instruction according to a preset strategy, sends the control instruction to the unmanned aerial vehicle through a quantum encryption link, records the control instruction in a block chain account book, monitors the instruction execution condition in real time and adjusts the strategy until the unmanned aerial vehicle returns to normal or is effectively disposed.

The invention provides an automatic detection method and system based on a GNSS receiver hardware module, and relates to the technical field of electronic equipment testing, and the method comprises the steps: carrying out the analysis of a preprocessed signal data set, extracting a signal feature boundary based on a convex hull algorithm, and carrying out the detection of the signal feature boundary; calculating key performance indexes such as signal acquisition time, tracking precision, bit error rate and receiving sensitivity; comparing the key performance index with a preset performance threshold value, automatically judging whether the hardware module of the GNSS receiver is qualified or not according to a comparison result, and generating a performance judgment result; and automatically generating a structured detection report based on a performance judgment result, and outputting the report to a specified terminal or a storage position. According to the invention, by simulating GNSS signal excitation, response signal acquisition, a signal processing algorithm and an automatic performance judgment criterion, a scheme for efficiently and fully automatically detecting various key performance indexes of the hardware module of the GNSS receiver is realized.

The invention provides a cable lead sealing internal defect positioning method based on a multi-view TFM. The method comprises the following steps: acquiring an original ultrasonic signal when viewpoint scanning is carried out on a cable lead sealing every time; processing the original ultrasonic signal into a signal intensity distribution image by using a full-focus algorithm, and performing three-dimensional conversion on the signal intensity distribution image; simulating propagation paths and scattering intensity of ultrasonic waves in various defects to calculate theoretical reflection intensity data of various defects; obtaining effective actual measurement signals of the defects and extracting related data; a Bayesian joint probability distribution function is constructed for estimation, and defect parameters are obtained in combination with theoretical reflection intensity data and extraction data of defects; a three-dimensional model of cable lead sealing is simulated, and automatic defect positioning and marking are carried out according to the original ultrasonic signals and defect parameters. By implementing the method, the defects existing in the aspects of acoustics, signal processing, algorithm models, defect classification and the like in an existing mode can be overcome, and the detection probability and depth measurement precision of the tiny pores are remarkably improved.

The invention discloses a digital stethoscope system based on a piezoelectric film, and relates to the technical field of medical electronics and biomedical engineering. An existing digital stethoscope based on a piezoelectric film has the challenges of signal noise and interference, limitation of a signal processing algorithm, nonlinear response and time migration, insufficient diagnosis accuracy and automation and the like. A piezoelectric film and microphone dual audio acquisition mode is fused, various physiological signals such as low-frequency vibration and high-quality heart and lung sound are captured, and background noise is removed through a self-adaptive filter and a frequency spectrum subtraction technology; an MFCC feature extraction and dynamic time warping algorithm is utilized to carry out fine feature analysis and time axis alignment on the preprocessed audio signal, and high-precision matching with a storage template is realized; different physiological states are accurately distinguished through intelligent feature processing, real-time and visual diagnosis feedback is achieved through wireless transmission and state indication, and the reliability and efficiency of medical detection are improved.

The invention discloses a method for detecting and inhibiting resonance of a two-axis photoelectric pod. Belongs to the technical field of photoelectric pod control systems, and particularly relates to the technical field of two-axis photoelectric pod resonance detection and suppression. According to the invention, a stable precision index is taken as a discrimination standard of a resonance condition, a self sensor is utilized, a resonance frequency automatic detection and identification method is adopted, filter parameters are adaptively adjusted according to resonance frequency point distribution characteristics, and the resonance frequency is suppressed through a signal processing algorithm, so that interference of the resonance frequency on a test is effectively avoided. According to the method, the self-adaptive capability of the photoelectric pod is enhanced, so that the photoelectric pod can cope with different external interference factors, and especially in a special vibration environment, the pod can keep relatively high stability and precision. According to the method, the problem of vibration interference in a traditional method can be effectively solved, and the adaptability of the photoelectric pod in a vibration environment can be remarkably improved.

The invention relates to a sound source positioning method based on a microphone array. Comprising the following steps: arranging a plurality of microphones to form an array, and collecting sound source signals; preprocessing the collected signals, wherein the preprocessing comprises filtering, denoising and gain control; calculating the time difference (TDOA) of the signals received by the microphones, obtaining the time difference through cross correlation operation, and carrying out preliminary positioning through a geometric positioning algorithm; a plurality of signal processing algorithms, such as a least square method, a maximum likelihood estimation method and a high-resolution spectrum estimation method, are applied to optimize the preliminary positioning result; designing an adaptive filter, dynamically adjusting filter parameters according to environmental noise and interference changes, and ensuring high-precision positioning in different environments; and outputting the final sound source position, and performing visual display.

The invention provides a photoelectric pod tracking method and system for an unmanned aerial vehicle platform. The photoelectric pod tracking method comprises the steps of collecting multi-mode sensor original data of the unmanned aerial vehicle platform in a target scene through a photoelectric pod; performing image processing on the original data of the multi-modal sensor by using a self-adaptive image signal processing algorithm based on a deep reinforcement learning model to obtain an optimized multi-modal image; performing semantic feature extraction on the optimized multi-modal image by using a double-flow neural network to obtain deep multi-semantic association features, and performing fusion processing on the deep multi-semantic association features to obtain a unified feature map in a target scene; based on the unified feature map, using a Transform model to track a target of the unmanned aerial vehicle platform in the target scene to obtain real-time coordinate information of the target; when the target is tracked, self-adaptive image processing is carried out based on the original data of the multi-mode sensor, and stable and efficient tracking of the target in the target scene by the photoelectric pod is facilitated.

The invention belongs to the technical field of underwater sound detection, and discloses a detection efficiency evaluation method and device for a radiation noise continuous spectrum of an underwater vehicle. The method comprises the following steps: S1, modeling an underwater vehicle radiation noise continuous spectrum signal, and constructing a marine acoustic channel model library and an array signal processing algorithm library; s2, designing detection statistics of an underwater target, and setting a detection threshold value to detect and identify the continuous spectrum of the radiation noise of the underwater vehicle; and S3, by changing the parameters or algorithms in S1 and S2, evaluating the detection efficiency under the combination of distribution modes, channel propagation modes and array signal processing algorithms of different continuous spectrums. According to the method, the detection efficiency of the radiation noise continuous spectrum of the underwater vehicle can be evaluated, and guidance can be provided for stealth performance optimization of the underwater vehicle.

The invention relates to the technical field of remote detection, and discloses an unmanned aerial vehicle human body vital sign detection method based on an ultra wide band radar, and the method comprises the steps: planning a flight path of an unmanned aerial vehicle; acquiring a reflection signal received by the ultra-wideband radar, position information of the unmanned aerial vehicle and image data of the thermal imager in real time; a reflection signal received by the ultra wide band radar is acquired and preprocessed in real time; processing the reflected signal based on a signal processing algorithm and extracting human body vital sign characteristic parameters; the human body vital sign characteristic parameters comprise respiratory frequency and heartbeat frequency; according to the human body vital sign characteristic parameters, the normal range of the preset human body vital sign characteristic parameters and the image data of the thermal imager, the existence condition of the human body life is judged. According to the invention, vital signs can be remotely detected, and human body vital sign signals under ruins can be rapidly and accurately detected in a large range by using the high maneuverability of the unmanned aerial vehicle.