89 results about "Data fusion center" patented technology

Systemic, adaptive procedural template comprised of common building blocks forming template frameworks i.e., self-organizing, mutually reinforcing service, system, process, procedure components derived situational understanding, state meta data signaling replication systems consisting of TCP / IP heartbeat, heartbeat messages signaling during micro-macro report cycles of state meta-data sync deltas <class> typed with <ORG_ID>, <URN> time stamped prior to data fusion-center insertion followed by reports aggregated, recalculated, relayed through synchronization, conversion gateways then merged into macro-cycle reports where metrics, metering are described by using Paul Revere meme linear, sequential hop count, water-drop in-pond meme geo-spatial temporal intensity measures, metrics recording sync deltas change across time / space viewed on appliqué displays using Russian Matryoshka doll techniques where each view adds to, changes the nature, meaning of composite views while retaining original appliqué views unique qualities as decision support aids in best effort, best practice by federated groups

The invention discloses a cooperative spectrum sensing method of a cognitive radio network considering malicious nodes. The cooperative spectrum sensing method is characterized in that the nodes involved in cooperative sensing are subjected to cyclic spectrum sensing to obtain characteristics of spectrum resources of a master user; normally sensed nodes and maliciously sensed nodes report sensed information to a data fusion center through an orthorhombic common control channel; the data fusion center perform data fusion on the collected sensed information and calculate the false alarm probability of the false alarm probability according to the malicious attach pattern of the malicious nodes; a secondary user and the master user share the spectrum resources, build a optimizing model, determine the constraint conditions of the transmitting power and the sensing time, solve the built optimizing problem, and repeat the steps to make the obtained average value to be the sensed parameter of the spectrum sensing model. The cooperative spectrum sensing method is fast in calculation and strong in pertinence and universality, and can correctly select the optimizing sensing period and the signal transmitting power in the sensing environment with the malicious nodes.

The invention discloses a ship's inertial navigation system (SINS) / Doppler velocity log (DVL) / global positioning system (GPS)-based autonomous underwater vehicle (AUV) combined navigation system, which comprises an SINS, a GPS receiver, a DVL and a data fusion center, wherein the SINS, the GPS receiver, the DVL and the data fusion center are arranged on an AUV; when the AUV is positioned on the water surface, an optimized filter module carries out filtering fusion calculation by combining navigation information of the SINS, a pseudo-range and a pseudo-range rate corresponding to the SINS and available ephemeris data output by the GPS receiver to obtain correction information; when the AUV is positioned underwater, the optimized filter module carries out filtering fusion calculation by combining the navigation information output by the SINS and three-dimensional navigational speed information output by the DVL to obtain correction information. The navigational positioning accuracy and the robustness of the system are improved, and the system realizes an uninterrupted high-accuracy underwater and water surface carrier navigating and tracking function.

The invention discloses a cooperative modulation signal identifying method based on data fusion of a decision layer, belonging to the technical field of wireless communication. According to the cooperative modulation signal identifying method provided by the invention, a judging result of each receiving node is obtained by obtaining characteristic values of sample signals collected by a plurality of receiving nodes and using a support vector machine based on a binary tree decision; and a data fusion center uses a decision with a maximum posterior probability, so as to finally determine a modulation manner of signals to be identified. With the adoption of the cooperative modulation signal identifying method provided by the invention, the quantity of the support vector machines to be trained is reduced by using the SVM (Support Vector Machine) based on a binary tree, so as to improve the classification efficiency. An error caused by single-user detection is corrected by multi-user cooperative identification, particularly the identification rate at a low signal-to-noise ratio can be improved; and compared with the traditional voting fusion decision, the fusion decision with the maximum posterior probability has higher reliability of an identifying result by considering influences caused by a prior identification condition in a system and the judging results of the receiving nodes.

The invention relates to a cooperative spectrum sensing method capable of saving cost on network communication. The prior network communication has the defect of high cost. The method comprises the following steps: a cognitive radio user uses an energy detection technique to sense the spectrum of a target frequency range, thereby obtaining a local sensing result; a data fusion center collects andfuses the local sensing results of the cognitive radio users, and makes a judgment on the spectrum service conditions, thereby obtaining a cooperative sensing judgment result; and the data fusion center and the cognitive radio users communicate messages through a control channel, each cognitive radio user transmits the local spectrum sensing result, and the data fusion center transmits the cooperative sensing judgment result. On the premise of not reducing the cooperative sensing accuracy, the method of the invention reduces the cost on cognitive radio network communication. For the cognitiveradio users, the method reduces the transmission of unnecessary sensing results, saves the valuable energy and prolongs the service lives of batteries.

The invention discloses a cooperative broadband spectrum sensing method based on double-threshold energy detection, which is a multiuser multichannel cooperative spectrum sensing method based on double-threshold energy detection. In the method, a secondary user in a cognitive radio network only needs to detect a few narrow band channels during wideband spectrum sensing, make local decisions based on the sensing results of these sub-channels, and upload the judgment results to a data fusion center in a network to perform secondary data fusion so as to make the final global decision. By adoption of the cooperative broadband spectrum sensing method provided by the invention, the requirements of the secondary user for the signal sampling rate and related devices during the broadband spectrum sensing can be reduced, the detection performance of the cognitive radio network is improved, and thus the interference to the normal communication of a primary user is avoided.

The present invention discloses a main user positioning method based on sensor and quantum intelligent computing, which is achieved based on a wireless sensor network assisting a cognitive radio network, and includes the following steps: step 1, a network deploy stage; step 2, a positioning information collecting stage; step 3, a distance measurement stage, wherein a data fusion center averages the sampled signal strength as a received signal strength RSS of an anchor node, and estimates the distance between a main user and the anchor node according to the RSS in a lognormal shadow path loss wireless broadcasting environmental model; and step 4, a positioning stage, wherein the positioning problem is converted into an optimization problem, and the optimization problem is solved by using a quantum genetic simulated annealing algorithm, thereby achieving positioning the location of the main user in a two-dimensional space. On the premise that a good positioning performance is ensured, the present invention can achieve the effect of reducing complexity of the algorithm and saving the energy consumption of the battery at the same time; and accurate location information of the main user can be obtained via the positioning method based on the quantum genetic simulated annealing algorithm.

In a cooperative spectrum sensing method and system for locationing primary transmitters, each of secondary users transmits to a corresponding one of cognitive radio (CR) base station location information thereof and a received signal strength indicator (RSSI) value generated thereby in response to sensing power signals from the primary transmitters. The CR base stations transmit the location information and the RDDI values of the secondary users to a data fusion center such that the data fusion center obtains the number and locations of the primary transmitters based on the location information and the RSSI values received thereby using a learning algorithm to thereby reconstruct a power propagation map of the primary transmitters.

The invention discloses a multi-point cooperative spectrum sensing method based on an HMM model. The method comprises the following steps: introducing a plurality of secondary users into a cognitive radio network, performing hidden Markov model modeling on the frequency spectrum of a master user, obtaining an observed value of each secondary user at each time slot according to spectrum sensing, training parameters of the hidden Markov model, and recursively calculating the prediction probability of the spectrum state of "busy" or "idle" of each secondary user at the next time slot; and counting the number of times that the cognitive spectrum state of all secondary users at the next time slot is "busy" or "idle", if the "busy" ratio is greater than a preset threshold, then judging that the spectrum state of the next time slot is "busy", otherwise "idle", outputting a spectrum state result, and returning the result to a spectrum sensing data fusion center. The multi-point cooperative spectrum sensing method based on the HMM model provided by the invention introduces the prediction functions of multi-node cooperative spectrum sensing and the hidden Markov model for the spectrum in the cognitive radio network, and performs the spectrum state prediction of the next time slot, thereby improving the reliability of spectrum prediction.

The invention discloses a body joint distributed information consistency estimation method based on interacting multiple models. The method comprises the following steps: initializing a skeleton joint position; estimating joint motion by use of a local sensor; constructing a motion model and an observation model by virtue of a human body joint, and realizing effective estimation on a joint state; estimating information consistency of a target joint between the sensors, i.e., each sensor transmits a joint information vector and an information matrix which are estimated by the sensor and corresponding information contribution and model probability to an adjacent communication sensor node and receives information of the surrounding sensors; by virtue of utilizing an information consistency algorithm and fusing the estimation results of the surrounding sensors, subsequent iteration is performed for multiple times, and convergence of the algorithm and the estimation result is realized; by virtue of constructing a distributed RGBD sensor network, and utilizing the information consistency algorithm, distributed estimation of human body joints is realized; no data fusion center exists in a network, so that robustness of a system for node information mistake and invalidity is improved, and expansion of the sensor network is relatively easily realized.

The invention discloses a multi user cooperative frequency spectrum sensing data fusion method and device. The method comprises the steps that a data fusion center receives local frequency spectrum sensing data from a number of cognitive radio users; the local frequency spectrum sensing data comprise local frequency spectrum sensing statistics of the cognitive radio users; the local frequency spectrum sensing statistics are based on generalized likelihood ratio and are the logarithm of the radio of arithmetic mean to geometric mean, wherein the arithmetic mean and the geometric mean are the characteristic values of a sample covariance matrix of signals received by the cognitive radio users; and the data fusion center carries out data fusion on the local frequency spectrum sensing statistics of a number of cognitive radio users according to the local frequency spectrum sensing data. According to the invention, the basis is the generalized likelihood ratio, thus the application performance is not affected by noise uncertainty; and whether the impact of the noise uncertainty is considered or not, in the situation of large reliability difference of local frequency spectrum sensing, a better frequency spectrum sensing performance can be acquired compared with energy detection based on equal gain combination.

The invention provides a cognition wireless broadband frequency spectrum compressed sensing method based on backtracking and centralized type cooperation. The method is characterized by comprising steps: firstly, respectively conducting reconfiguration of a compressed sensing part of each cognition wireless user; secondly, respectively conducting judgment for cognition wireless users, and transmitting judgment results to a data fusion center; and thirdly, according to a related algorithm, the data fusion center conducting summary fusion and giving global decision for uploaded frequency spectrum information. The method greatly improves spectrum sensing efficiency and accuracy of a broadband cognition wireless system, and simultaneously reduces complexity of the calculation by improving of a signal reconstructing algorithm in the process of compressed sensing and changing single user spectrum detection to centralized multi-user cooperation spectrum detection.

The present invention relates to the field of communications. Disclosed are a broadband spectrum sensing method, a fusion center (FC), a sensing node (SN) and a computer storage medium. The method comprises: the FC obtaining statistical idle probabilities of all current primary user (PU) physical channels; based on a sequence of the statistical idle probabilities from large to small, assigning SNs used for detecting channel spectrums to the PU physical channels in sequence; using the SNs assigned to the PU physical channels to detect the channel spectrums for the PU physical channels; and based on channel spectrum detection results, determining status of the PU physical channels.

The invention discloses a vehicle-road cooperative bus accurate data fusion center system which is operated in a bus management control center. The functions of the system comprises fusing the positioning information uploaded by a bus and the differential grid data stored in the center to obtain a more accurate real-time position of the bus, calculating and issuing the distances to the adjacent vehicles on real time, uniformly timing all buses through the Beidou NTP time synchronization protocol to realize time synchronization, and sending the real-time bus position information of the corresponding route and high-precision position of a station parking area to a bus stop platform. The issued information, such as folk news and advertisement information, can be displayed on display terminalsof the bus and the bus stop as needed.

The invention discloses a method for cooperative spectrum sensing optimization based on signal to noise ratio screening. The problem that part of cognitive nodes located in the severe environment affect the overall sensing performance when cooperative spectrum sensing is actually applied can be effectively solved. The method comprises the steps that local signal to noise ratios of the cognitive nodes are measured, judgment results and a real-time signal to noise ratio condition are reported to a data fusion center, a signal to noise ratio threshold value is set by the data fusion center according to the whole signal to noise ratio condition of a system, the signal to noise ratios of the cognitive nodes are compared and screened, the judgment results satisfy the requirement of a condition cognitive node will participate in overall cooperation, and a cooperation judgment result is an overall judgment result. When the signal to noise ratio threshold value is set and the signal to noise ratios are screened and compared, the value of an adjustment factor can be dynamically changed, the screening dimension is controlled, and therefore the system performance is optimized. The method is quite low in computation complexity, only simple comparison and screening operation is needed, computation is completed at the side of the data fusion center, and the method is simple and easy to conduct.

The invention provides a rainy season land slide deformation monitoring method and a rainy season land slide deformation monitoring system. A data integration center is used to acquire a displacement value and a speed value of a monitoring point of a current sampling time point according to a displacement sensor and a speed sensor, and is used to acquire displacement values and speed values of the previous one or two time point / s, and two given error covariance matrixes, and is used to acquire a measurement noise covariance matrix estimation value and a process noise covariance matrix estimation value of the current sampling time point according to the ordinal number of the current sampling time point and the above mentioned parameters. Kalman filtering of the displacement value and the speed value of the current time point can be carried out by adopting the acquired matrix estimation values, and then the optimal displacement estimation value and the optimal speed estimation value of the current sampling time point can be acquired. The displacement value and the speed value can be the displacement weighted average and the speed weighted average. The two covariance matrixes can be estimated according to the actually acquired displacement value and the actually acquired speed value, and are not given, and therefore the matrix estimation values are real, and the filtering result is accurate. The invention also relates to the rainy season land slide deformation monitoring system.

The invention relates to the technical field of OFDM cognitive wireless networks, and discloses a joint spectrum sensing and resource allocation method and apparatus based on game optimization. The method comprises the following steps: performing, by a slave user, energy value detection on a master user, and generating a hard decision value of a state of the master user; for a given partition combination, calculating, by the slave user, an own access probability, meanwhile, calculating, by a data fusion center, access benefits of all slave users in the partition combination and feeding back the access benefits to the slave users, and determining, by the salve user, whether to update the partition combination according to the access probability and the access benefit; and selecting an alliance to perform access according to the hard decision condition of the slave user on the state of the master user in the alliances of the partition combination, and maximizing the access benefit of thealliance. According to the joint spectrum sensing and resource allocation method and apparatus disclosed by the invention, the access fairness of the users and the partition iteration willingness ofthe users are fully considered, the access benefit of the actual alliance is maximized by making full use of channel time slot resources, and the channel and power resources in the network are effectively used.

Methods, computer-readable means, and systems for minimizing false alarm rates and operator loading while conducting multistatic active sonar operations are disclosed. A plurality of sensor nodes is established, with each sensor node including an active sonar source, and a receiver. Each sensor node has a local storage center, and each node is connected to a data fusion center. During active sonar operations, a high threshold (HTH) and a low threshold (LTH) setting is established. For each sensor node, all sonar return data above the LTH setting are stored at its respective storage center. The HTH is set so that only specular echo returns exceed the HTH; when the HTH setting is exceeded, the fusion center retrieves LTH data from each storage center corresponding to the same specular echo return, in order to establish a track for the contact.

The invention relates to a road data acquisition and simulation scene establishment integrated system. The integrated system comprises a sensor acquisition unit, a data fusion center and a road environment analog simulation platform which are connected in sequence. The sensor acquisition unit comprises: an assembly A which is a vehicle front-view camera and a millimeter-wave radar fusion group; anassembly B which is a laser radar group arranged at the half-vehicle-height position and comprises at least six 110-degree sub laser radar sensors; an assembly C which is a laser radar group arrangedon the roof and comprises three laser radars; and an assembly D which is an IMU and GPS group arranged in the middle of the vehicle. The collected environment data is used for scene analog simulationand generating a corresponding test case. The system is used for solving the problems that: in the prior art, the cost of collecting vehicle road tests is high, the vehicle road tests are not coupledwith a built simulation environment, and an environment scene cannot be automatically generated. The system can widely cover potential types of driving scenes possibly encountered by automatic driving vehicles, and greatly reduces kilometers needing road testing.

The invention discloses a Lora signal monitoring system and method for a multi-node software radio platform. The Lora signal monitoring system comprises a Lora signal monitoring part and a data fusioncenter part; the Lora signal monitoring part comprises a plurality of Lora signal monitoring nodes, and each Lora signal monitoring node comprises a radio frequency front-end processing module, a software radio platform hardware processing module, a signal data processing module and a monitoring node upper computer software module; and the data fusion center part comprises a cloud server module and a user application software module. A radio frequency front end is used for preprocessing the wireless signals, an energy detection and convolutional neural network modulation recognition algorithmis used for recognizing the LoRa signals, and the multi-node monitoring is deployed to upload the data to a data fusion center to judge whether the LoRa signals exist or not and estimate the parameters. According to the invention, the identification and parameter estimation can be carried out on the LoRa signal with the unknown frequency point information, and the probability of the LoRa signal detection is improved.

The invention discloses a collaborative information processing method and processing system suitable for networked operations. The method comprises the following steps: (A), when the radar of the combat unit normally tracks the target, the state information of the target is obtained by the radar processor of the combat unit and the estimation error of the state information is estimated; (B), for all combat units The state information and estimation error of the unit are calculated to obtain the fusion information and fusion error; (C), when each combat unit performs fire control calculation, if the combat unit normally tracks the target, the state information and estimation error of the combat unit are used. The error is used for fire control calculation. If the combat unit cannot track the target normally, the fusion information and fusion error distributed by the data fusion center are used for fire control calculation. The invention is easy to exchange information, can meet the requirements of networked combat modes such as cooperative detection / tracking and cross-fire unit control, and has the characteristic of high precision.

The invention provides a data transmission method in a cognitive radio network, which comprises the following steps: in an initialized subframe, enabling all k secondary users to report the perception results, setting an ordinary subframe number into 1 in a data fusion center, determining to enable an optimal secondary user to report a set Omega according to the perception results reported by all the k secondary users, judging whether a main user exists or not and enabling all the k secondary users to carry out data transmission if the main user exists; and in an ordinary subframe, enabling the secondary users in the set omega reported by the optimal secondary user to report the perception results, judging whether the main user exists or not in the data fusion center according to the perception results reported by the secondary users in the set omega reported by the optimal secondary user and enabling all the k secondary users to carry out data transmission if the main user does not exist. By applying the invention, on the premise that the main user is not disturbed, the report signaling cost is reduced, so that the throughout capacity of the secondary users in the cognitive radio network is improved.

The invention discloses a cooperative interference detection method based on a support vector machine. The method comprises the following steps: firstly, respectively operating different categories of interference signals to obtain the characteristic parameters of the interference signals, and training the support vector machine through the obtained characteristic parameters to obtain a classification model of the support vector machine; secondarily, carrying out energy detection on the receiving signals of a plurality of nodes to obtain an interference detection result; transmitting the interference detection result and the characteristic parameters of the signals to a data fusion center, determining whether the interference signals exist according to the interference detection result by using the data fusion center, and if the interference signals exist, carrying out interference identification on the interference signals based on a support vector machine algorithm to determine the interference category. The cooperative interference detection method disclosed by the invention can be used for accurately identifying the characteristics of the interference signals and effectively resisting multi-path fading.

The invention discloses a blind collaboration spectrum sensing method based on a soft fusion strategy. The method comprises the following processing flows: firstly, a sampling module in each sensing node samples a receiving signal from the same monitoring channel to obtain a sampling signal corresponding to each sensing node; and then, each sensing node computes the estimation variance of the estimation power and the instantaneous power of the corresponding sampling signal; each sensing node uploads the estimation variance of the estimation power and the instantaneous power of the corresponding sampling signal to a data fusion center; the data fusion center computes the test statistics; and finally, the data fusion center realizes the spectrum sensing by comparing the test statistics withthe judgment threshold, and judges whether an authorized user signal is existent in the monitoring channel; the method disclosed by the invention has the advantage that the hidden terminal problem ofthe sensing node can be well overcome without knowing the authorized user signal and the priori information of a wireless channel, and the sensing performance of the spectrum can be effectively improved.

The invention brings forward a cooperation spectrum perception method under Nakagami-m fading channel condition. In the invention, the perception efficiency of a user to authorization spectrum is raised through two links. In the first link, at a cognition user, a realized adaptive equalizer based on an improved reverse direction propagation nerve network is introduced, and the cognition user sends a received signal which passes through a fading channel to the adaptive equalizer for processing so as to raise the quality of the received signal. In the second link, at a data fusion center, idea of a Chair-Varshney criterion is used for an improvement algorithm of a decision criterion, and a BP nerve network based on an L-M algorithm is employed for realization. The related nerve network structures of the above two links can be subjected to adaptive adjustment according to a form of a Nakagami-m fading channel, through improvement of the two links, spectrum correct perception rate of the user can be effectively raised, and authorization spectrum resource can be used more reasonably and effectively.

The invention is suitable for the technical field of network monitoring, and provides a holographic construction safety monitoring system comprising: a construction site front-end module used for collecting construction site images and data; a cloud management module which is connected with the construction site front-end module through the wireless communication module and used for processing the construction site images and data; and a terminal module which is connected with the cloud management module through the wireless communication module and is used for carrying out holographic display on the site construction site image, wherein the cloud management module comprises a video data fusion center, the video data fusion center is used for fusing virtual scenes constructed by real construction site images and data to form a three-dimensional construction scene, and holographic display is carried out through the terminal module. The system greatly adapts to a complex environment of a construction site, provides an immersive safety operation experience through holographic display, and effectively restrains the occurrence of engineering construction safety accidents. The system has the advantages of being high in monitoring capacity, good in safety, good in experience and good in construction guidance.

The invention provides a two-coordinate ship-borne radar signal level fusion method. The two-coordinate ship-borne radar signal level fusion method comprises the steps of preprocessing echo data of aplurality of radars of the same type in formation, then uniformly transmitting the preprocessed data to a signal fusion center, converting the preprocessed data into a uniform coordinate system through coordinate transformation, and then realizing non-coherent accumulation of echo data of a plurality of radar targets by utilizing an improved dynamic programming algorithm to realize effective detection of a weak target. According to the two-coordinate ship-borne radar signal level fusion method, the data volume transmitted to the data fusion center by each ship is greatly reduced, and the weaktarget detection performance is improved; the problems of low target parameter measurement precision and difficulty in multi-homotype radar signal level fusion cooperative detection space registrationcaused by huge data volume transmitted between each ship and the data fusion center in the formation and the energy diffusion phenomenon of a dynamic planning method are effectively solved; and a powerful technical support is provided for detection of the weak target in a complex sea clutter environment.

The invention relates to a method for selecting cognitive users in coordinated spectrum sensing. According to the method, under the condition that prior knowledge of the signal-to-noise ratio of the cognitive user is unknown, the every-order autocorrelation coefficients of received signals are calculated by all the cognitive users so as to obtain an autocorrelation matrix, then the parameter B capable of expressing sensing properties of the cognitive user is obtained through analyzing calculation of elements of the autocorrelation matrix, the parameter B is transmitted to a data fusion center by the cognitive users, the property difference index delta Bi between each cognitive user and the best property cognitive user is calculated by the fusion center, the users bad in property are kicked out, and the cognitive users participating in coordinated sensing are selected out according to the from-small-to-large rule of delta Bi. With the method for selecting the cognitive users, while the cognitive sensing property is guaranteed, the number of nodes of the cognitive users participating in the cooperated sensing is reduced, network loads are reduced, occupancy of resources of a system is reduced, and the transmission efficiency of the system is also improved.

The invention relates to a multi-sensor fusion system. The multi-sensor fusion system comprises multiple ZigBee-based wireless monitoring nodes; data of each wireless monitoring node are respectively connected to a data pre-processing unit; the data pre-processing unit is connected with a data fusion center; and the data pre-processing unit assigns a a probability value to data of each node; fusion of data of each sensor is carried out by using a preset fusion rule by the data fusion center, and a decision result is obtained according to a decision rule. According to different assigned probability values of sensor data, data of multiple sensors are fused, the final result is obtained according to different rules, all-round monitoring can be obtained, the system has the advantages of low cost, high flexibility, easy configuration, high reliability and the like, and performances of the whole system are improved.

A system, apparatus, and method for the rapid inspection of shipping containers during transport and for intelligent data gathering for risk analysis are provided. More specifically, a portal based scanner is disclosed which includes a plurality of sensors positioned to create a target zone so that the shipping containers can be automatically scanned during loading and offloading operations. According to one aspect of the invention, the scanner is capable of wirelessly communicating with the containers, gathering data about each container, and reporting data to a Data Fusion Center for risk profile analysis.