51results about How to "Suppress false alarms" patented technology

The invention discloses a method for detecting a random access leader. After a random access channel (RACH) signal is treated with the simplified time domain-related operation, noise mean estimation is carried out to each root sequence; an absolute threshold is then detected according to a signal obtained by the noise estimation and signals in a search window are detected; the signals which exceed the detection threshold are screened and then a timing position can be determined in a fast and accurate way so as to provide reliable uplink timing adjustment information to a mobile terminal, guarantee the accuracy of the uplink synchronization and effectively eliminate the timing error due to the over-sampling and reduce the false alarm in the detection.

The invention discloses a system and a method for detecting a low-speed small target under a sea cluster background. The method includes that S1, a radar transceiving unit acquires signals of the low-speed moving small target under the sea cluster background; S2, the radar transceiving unit sends received signals to a radar echo processing unit to be processed to generate radar echo data and sends the radar echo data to a detection unit; S3, the detection unit processes multiple continuous radar echoes and establishes corresponding time frequency domain two-dimension planar graphs to effectively eliminate false-alarm targets in different areas. The system and the method are suitable for detection of dim and small targets moving at low speed under sea cluster background interference during downward-looking detection of sea-surface targets through radar. By the system and the method, the problem of radar downward-looking detection of the low-speed moving small target under the sea cluster background can be solved; time frequency domain CFAR (constant false alarm rate) processing technology is adopted to decompose target echoes and sea cluster data to a time frequency domain two-dimension plane for processing.

The invention discloses a heart-rate estimation method and device based on fusion of electrocardiogram signals and pulse signals. The heart-rate estimation method includes the steps that heart rate estimation is carried out based on the electrocardiogram signals and the pulse signals respectively; the electrocardiogram signals and the pulse signals are subjected to signal quality evaluation respectively, and the signal quality index of the electrocardiogram signals and the signal quality index of the pulse signals are obtained; based on the two signal quality indexes, the two estimated heart rate values are subjected to kalman filtering processing; the two estimated heart rate values subjected to kalman filtering processing are subjected to weight fusion. By means of the heart-rate estimation method, the accuracy of heart-rate estimation can be effectively increased, and a reference is provided for clinical caring.

Provided is a device and process for detecting and restraining the impact of mobile camera moving in a monitoring system, the device including: an image capturing module for capturing new images; an image processing module, for comparing the captured new images with the scene model, updating the scene model based upon the comparative result, and determining the pixel status in the image according to the updated result of the scene model to test whether the camera has moved. If the camera is detected to have moved, the error alarm resulting from camera moving is restrained.

A point target movement velocity detection method based on multiple linear moveout scanning, extending and sampling comprises the following steps: (1) establishing a multiple linear moveout scanning detection device; (2) using each linear detector to detect point targets in an extending and sampling manner; (3) processing Nt groups of image data acquired by each linear detector to obtain sub-pixel images; (4) performing non-uniformity correction and sub-pixel match on two sub-pixel images processed by the two adjacent processed linear detectors, then performing difference computation to complete the background subtraction; (5) performing threshold filtering on difference images processed by the two adjacent processed linear detectors, extracting positive and negative point pairs in the difference images by adopting the neighborhood constraint criterion, so as to complete the target detection and extraction of moving points in once scanning process; (6) marking regions of paired positive and negative points extracted in the step (5) respectively, calculating movement velocity and movement directions of the targets according to the position relation of the marked regions of the positive and negative points; (7) averaging the movement velocity and the movement directions obtained in the step (6) to obtain the velocity and the direction of a detected target.

The invention discloses a fire behavior early warning method based on machine learning monitoring video image smog. The method is characterized by comprising the steps of firstly, collecting and marking an image data set of various smog scenes, wherein the type of non-fire-behavior early warning smog scenes is A, and the type of fire behavior early warning smog scenes is B; secondly, conducting non-fire-behavior early warning smog scene training through a context target detection layer; thirdly, conducting fire behavior early warning smog scene training through the context target detection layer, and repeating the second step, wherein training images are B-type fire behavior early warning smog images; fourthly, detecting suspected fire behavior images. The method has the advantage of solving the problem that a traditional machine learning method classifier cannot accurately distinguish whether detected smog is produced by fire disasters or not. The context relation of the area where smog is located is judged through a context target detection method, and the false warning rate and warning omission rate are reduced on the premise of improving the fire behavior early warning rate.

The invention discloses a method for detecting a moving object in video. The method comprises the steps of calculating texture pattern characteristics of a current video frame, dividing the current video frame into small blocks, combining every four adjacent small blocks into a large block, calculating a texture pattern characteristic histogram of each large block, and updating a texture pattern characteristic background model in each large block; according to the texture pattern characteristic background models and the texture pattern characteristic histograms of the large blocks, obtaining the probability, belonging to the background, of the large blocks under texture characteristics, and therefore performing average solving on the overlapped small blocks to obtain the probability, belonging to the background, of the small blocks under the texture characteristics; according to color information, updating a current main background image; according to the main background image and the color information, obtaining a color difference value of the current video frame and the small blocks of the main background image; according to the probability, belonging to the background, of the small blocks under the texture characteristics and the color difference between the small blocks and the main background image, judging whether the small blocks belong to the background or not; according to a judgment result of a foreground and the background, obtaining foreground blocks through segmentation, and using a communication domain for performing analysis to obtain a moving object detection result.

The embodiment of the invention discloses a moving target detecting method and wireless sensor network head node equipment. The method comprises the following steps of: detecting a signal which is transmitted by a moving target by using a first sub-node; activating other sub-nodes within a length specified by the first sub-node to detect the moving target when the moving target is detected by the first sub-node; searching a triangle which meets three conditions from the sub-nodes within the specified length when the moving target is detected by the sub-nodes; when the triangle which meets the three conditions is searched out, determining a circular area by taking the mass center of the triangle as a circle center and taking the detecting radius of the first sub-node as a radius, and calculating the total number of nodes that the probability of finding the moving target by using the sub-node is more than a node detecting threshold in the area; and comparing the total number of the nodes with a preset decision threshold. The problem that the probability of finding the target by using a single node is low can be effectively solved by implementing the invention.

The invention provides a vehicle detection method based on multiple sub-images and image saliency analysis. The method comprises the following steps: S1) carrying out saliency pretreatment based on multiple sub-image fusion; S2) carrying out boundary correction on a candidate target area comprising a target vehicle; S3) carrying out accurate judgment on the corrected target candidate area; S4) carrying out multi-frame joint and de-superposition; and S5) outputting a target window area coordinate and finishing vehicle detection. The method is low in time complexity and high in real-time performance, and can be suitable for various different scenes, such as rainy days or night.

Provided is a point object detection method based on multiple-linear time difference scanning and expansion sampling. The point object detection method comprises the steps of (1) constructing a double-linear-detector imaging device; (2) performing point object detection through each linear detector in an expansion sampling method; (3) respectively processing Nt groups of image data collected by each the linear detectors to obtain sub-pixel images, performing heterogeneity correction on two sub-pixels processed by the two linear detectors, performing sub pixel matching, and performing calculus of differences to complete background removal; (4) performing threshold filtering on residual images obtained through difference in the step (3), extracting positive and negative point pairs in the residual images through a neighborhood constraint criterion to complete detection and extraction of a moving point object in a scanning process, and forming neighborhood constraint criterion (as specified in the specification) according to imaging time differences of different linear detectors and the target movement speed, wherein delta D represents a distance between a positive area and a negative area, vmin represents the minimum movement speed of the detected object, vmax represents the maximum movement speed of the detected object, and GDS represents ground sampling distances of the linear detectors.

The invention provides a method and device for transmitting messages. An aggregated link trunk is set between a first device and a second device. A state machine of a maintenance association end point(MEP) of the first device is currently located on a first single board of the first device. The level of the MEP of the first device is different from that of the MEP of the second device. The methodcomprises the steps that when the state machine of the MEP of the first device is migrated from the first single board to a second single board of the first device, the first device suppresses faultalarm and sends an alarm indication signal (AIS) to the second device according to a preset frequency; and when the state machine of the MEP of the first device is migrated completely from the first single board to the second single board of the first device, the first device recovers the fault alarm and stops sending the AIS. The false alarm resulting from state machine migration timeout is effectively suppressed through utilization of the alarm indication signal (AIS). The method and the device are simple and feasible. Interconnection is not influenced.

The invention discloses a method for inhibiting random access code false alarm. The method comprises the following steps: measuring a peak value corresponding to the random access code in a target detection window; deducting non-target detection window leakage energy from the peak value to obtain a correction peak value corresponding to the random access code; according to the correction peak value and an absolute threshold, judging whether a user appearing in the target detection window is false alarm; if so, marking in the target detection window. After the embodiment of the invention is applied, the false alarm is effectively inhibited under the condition that the multi-user normal access probability is guaranteed.

The invention belongs to the technical field of complete polarization synthetic aperture radar image processing, and particularly relates to a polarization information fused landslide identification method and device under a complex background, and the method comprises the steps: carrying out the registration of an image, which has the same polarization combination mode as the single-polarized SAR image before the landslide, in a single-polarized SAR image before the landslide and a PolSAR image after the landslide; constructing a dual-time-phase SAR image pair, and generating a coherence graph through coherent change detection to extract a change region; extracting a plurality of polarization characteristic parameters from the preprocessed PolSAR image after landslide, and fully fusing the polarization characteristic parameters by using TOPSIS with weight to carry out landslide detection so as to obtain a suspected landslide area; and fusing the change area and the suspected landslide area extracted from the coherence map through logic AND operation to obtain a final landslide area. According to the method, the TOPSIS is used for fusing the polarization information to perform landslide detection, and coherent change detection is combined, so that the accuracy of landslide detection can be greatly improved under the condition of effectively inhibiting the false alarm rate.

The invention discloses a water treatment monitoring method and system. The method comprises steps of setting monitoring points at different nodes of a water treatment process, setting a time window at each monitoring point, and setting a time window length, an alarm threshold and a monitoring frequency; calculating the relative position of each time window; monitoring data of each monitoring point; moving the time window position of each monitoring point; calculating an alarm state of each monitoring point time window; summarizing alarm states of the time windows of the monitoring points, analyzing summarized data, and judging whether alarm logic is met or not; if so, generating an alarm; if not, inhibiting the alarm, skipping to a step S130, and continuing execution according to the methods in steps S130 to S160. The method is advantaged in that an alarm conforming to the technological process is accurately generated in a water treatment scene, and a problem of false alarm caused byabnormal data is effectively inhibited.

The invention discloses a divide-and-conquer detection algorithm based on a semantic segmentation network. According to the method, clutter region segmentation of a distance Doppler domain is realizedby means of a semantic segmentation network, a detection strategy is formulated according to the characteristics of different clutter region main bodies and edges, appropriate detectors and reasonable detection parameters are selected from alternative detector groups to complete divide-and-conquer detection, and false alarms are effectively suppressed while a target is detected to the greatest extent. The technology is of great significance to radar detection of low-altitude small targets. Compared with a detection plane normalization method, the method has the advantages that detection planeconversion is not needed, the calculated amount is smaller, the influence of a parameter estimation result on the detection plane conversion is avoided, and the robustness is higher.

The invention discloses a loss resisting alarm device based on wireless, the device is used to prevent the loss of expensive box and other things. The device is made up of signal transmitting part and receiving part. The transmitting part is placed in the box, the receiving part is carried by user, when the distance between the user and the box exceeds a certain distance, the receiver sends out harsh noise or vibration to alarm. The invention uses microchip to control the wireless transiting and receiving models. At the same time, it reduces the energy consumption of wireless transmitting model and receiving model, prolongs the lifespan of battery.

The invention provides a vehicle-mounted radar data association method and device. The method comprises the steps of setting two sets of data association thresholds for each track prediction point, namely, a first data association threshold and a second data association threshold; for each track prediction point, determining whether the measurement value is a candidate measurement value of the track prediction point or not through the first data association threshold and the cumulative number of times that the measurement value falls into the data association thresholds of other track prediction points; and weighting all candidate measurement values of the track prediction point, and taking the weighted candidate measurement values as final association data of the track prediction point to be used for updating the track, so that the phenomenon of misassociation is reduced. The measurement value which falls into the second data association threshold and does not fall into the first data association threshold is labeled, so that the measurement value cannot be used as a new track start, false alarm of the same target caused by large measurement value distribution is inhibited, and track continuity and stability are improved.