440 results about "Fusion center" patented technology

A spectrum sensing method for cognitive radio to detect spectrum holes in an environment of bandwidth scarcity. The method comprises first receiving a wireless signal at a cognitive radio user, and then discovering the frequency edges of allocated frequency bands by using wavelet transform coefficients to detect discontinuities in the power spectral density of the received signal. After determining the allocated frequency bands, the method determines frequency band availability by detecting the in band energy using a bi-threshold energy detector, where the energy detector makes hard decisions and soft decisions. Finally, a fusion center combines hard and soft decisions collected from a cooperative spectrum sensing network of cognitive radio users and makes a final decision using a hybrid of data fusion and decision fusion to determine the final decision regarding spectrum availability.

The invention discloses a Bayesian theory-based multi-sensor detecting and tracking combined processing method, mainly used for solving the problem that the traditional sensor fusion system has poor performance. The implementation process of the method comprises the following steps of: 1, setting a motion model of a target; 2, setting an observation model of the target; initializing all sensors for predicting probability distribution; 4, calculating posterior probability distribution of the target in a combined state by each sensor according to respective observation and transmitting the posterior probability distribution to a fusion center; 5, performing fusion by the fusion center to obtain a posterior probability of the existence of the target after fusion; 6, detecting whether the target exists according to a set detection threshold; 7, performing fusion by the fusion center to obtain a posterior probability of the motion state of the target after fusion; 8, forecasting the combined state of the target by every sensor; and 9, repeating the steps from the step 4 to the step 8 to detect and track the target continuously. The Bayesian theory-based multi-sensor detecting and tracking combined processing method has the advantage of good detection performance and can be used for detecting and tracking the target on the basis of observation data.

The invention relates to an improved double-threshold cooperative spectrum sensing method, which comprises the following steps that: a cognitive user in a cognitive radio system senses a local spectrum, compares and classifies sensed main user energy values, if the energy values are beyond two thresholds, can directly perform local judgment to obtain a hard judgment result whether a main user exists and send the result to a fusion center, if the energy values are between the two thresholds, the cognitive user needs to send the sensed main user energy values and signal to noise ratios to the fusion center, and the fusion center calculates a weight factor of each cognitive user according to the received signal to noise ratios and performs fusion judgment according to the received energy values to obtain a soft judgment result whether the main user exists; and the fusion center comprehensively processes the hard judgment result and the soft judgment result, and makes a final judgment whether the main user exists by using an 'or' rule. According to the method, the advantage of double-threshold cooperative spectrum sensing can be fully realized; and the spectrum sensing performance is improved.

The invention provides a single-frequency network-based passive radar system and a signal processing method for the same. A plurality of illumination sources for the system are used for simultaneously transmitting the same signals at the same frequency, and namely work in a single-frequency network system. A plurality of transmission stations and one or more receiving stations form a MISO (multiple-input single-output) or MIMO (multiple-input multiple-output) detection system, wherein the MIMO system is provided with a fusion center for performing communication and data transmission with each receiving station through a communication link and fusing processing results of each receiving station, and works at the same frequency; each transmission station is in the same form, and each receiving station is in the same form. A one-stop space-domain processing and single-frequency network defuzzification method is adopted for the signal processing method. According to the system and the method, a conventional broadcasting single-frequency network is used under the condition of no interference on broadcasting, so that the problems of severe clutter caused by the single-frequency network and fuzziness of the single-frequency network are solved, and the advantages of single-frequency network detection in frequency power reduction, hardware saving, detection stability and tracking continuity are fully exerted.

The invention applies for the protection of an SNR comparison-based algorithm for cooperative frequency spectrum sensing in cognitive radio and relates to a wireless communication system. The method comprises: SNR estimation technology is applied to cognitive radio; SNRs of signals received by sensing nodes are used as reliable references for the sensing nodes; the sensing nodes transmit local sensing results and estimated SNR values to a fusion center; and in the fusion center, the SNRs from the sensing nodes are compared and sensing nodes with good SNR are selected according to a selection algorithm provided by the invention for judgment and fusion. The algorithm of the can effectively improve frequency spectrum sensing performance and reduce the nodes for judgment and fusion.

A distributed collaborative signal identification method based on blind estimation of higher order statistics and signal to noise ratio includes the following steps: establishing feature space; enabling collected features and estimated signal to noise ratio to be input as smart volume management (SVM) aiming at modulating signals of special types, and establishing a modulation signal recognition classifier; and the third step is that each sensor node makes blind estimation for signal to noise ratio of received signals and transmits characteristic parameters together with the signal to noise ratio to a fusion center, and the fusion center distributes weights for each sensor node by utilizing the signal to noise ratio, obtains fused characteristic parameters and thus performs modulation type identification. Compared with the prior art, as for a single sensor node, the algorithm is capable of maintaining high identification probability on the condition of low signal to noise ratio, identified signals are rich in variety, and by making blind estimation for the signal to noise ratio and performing fusion at a feature level, identification accuracy rate of target signals still can be maintained on the premise that a plurality of sensor node channels have extremely poor conditions.

The invention provides a cooperative frequency spectrum perception method applied in a cognitive radio system. The method comprises the following steps: configuring the cognitive radio system comprising N secondary users and at least one information fusion center in a network of an existing primary user; based on an energy detection method, acquiring a detection error probability of a single link, wherein the detection error probability of the single link is related with a detection probability, a false alarm probability and a false dismissal probability; when the detection error probability of the single link is a minimum, based on that, acquiring a relation of a threshold value and a signal to noise ratio, therefore, determining an optimal threshold value under the different signal to noise ratios; based on the detection probability of the single link, the false alarm probability and the false dismissal probability, acquiring the detection probability, the false alarm probability, the false dismissal probability and a detection error probability of the cognitive radio system. Compared to the prior art, by using the method of the invention, the detection probability can be greatly increased and the detection error probability of the cognitive radio system can be reduced so that the primary user can be guaranteed not to be disturbed during a process of transmitting data.

The invention belongs to the technical field of information processing in fire-to-air management, and particularly relates to an ADS and radar information system error calibration algorithm used in fire-to-air management. The algorithm aims to both a single target in the air and multiple targets in the air. When the algorithm aims to a single target in the air, the algorithm comprises the steps that (a) a distributed multisensory system is used for receiving data and processing the data; (b) the data are sent to a fusion center, data conversion, time alignment, space alignment, track correlation and data synthesis are achieved according to track data of all sensor nodes, and then the optimal estimation of system errors is obtained. When the algorithm aims to multiple targets in the air, multi-target system error estimation and measurement data integration are achieved through recognition of the multiple targets in the air. The algorithm has the advantages that system errors can be effectively calibrated, monitoring precision is high, tracks which are detected by different radar devices and ADSs and belong to the same target are correlated, multiple radar data are fused and ADS data are further fused, accurate recognition and high-precision positioning and tracking of targets in the air are achieved, and unified and accurate management is realized.

The invention discloses a cooperative frequency spectrum sensing method for a cognitive radio network. The method comprises the following steps that: (1) the cognitive radio network which comprises N cognitive users SU and an information fusion center FC is configured in a network with an existing privileged user PU; (2) the cognitive user SU uses energy detection to independently judge whether aprivileged user PU signal is present, and transmits a binary hard decision result to the information fusion center FC, and the transmitting power of the cognitive user is adaptively adjusted according to the condition of a channel from the privileged user PU to the cognitive user SU; and (3) the information fusion center FC fuses the results of a plurality of cognitive users SU by adopting a maximum likelihood detector, and finally determines whether the privileged user PU is occupying the frequency spectrum. The cognitive user SU can independently process the signal without transmitting softinformation or interchanging information; and a detector in the information fusion center FC has a simple structure and is easy to implement. Meanwhile, the cooperative frequency spectrum sensing method has good performance such as low communication overhead, high diversity gain, low false alarm rate and lost detection rate and the like, and has high practical value.

The invention provides a cooperative modulation identification method based on multi-class characteristic parameters and an evidence theory. The method is used for realizing correct identification on 13 modulation modes of 2ASK, 4ASK, 8ASK, 2PSK, QPSK, 8PSK, 2FSK, 4FSK, 8FSK, 16QAM, 32QAM, 64QAM and OFDM13. The cooperative modulation identification method comprises the following steps that: cooperative nodes extract different classes of characteristic parameters respectively based on characteristics of instantaneous information, characteristics of the combination of detail coefficients of wavelet decomposition and high-order cumulants, and characteristics of a signal cepstral coefficient, wherein the characteristic parameters can represent a target signal from different angles; each node inputs the characteristic parameters to a trained BP (back propagation) neural network for testing; and then the output of the neural network is directly used as an evidence and is conveyed to a fusion centre to be fused, and a fusion rule adopts a D-S (Dempster-Shafer) evidence theory combined rule.

The invention provides a collaboration spectrum sensing method based on duplicate credibility which includes steps as follows: each second user i processes frequency spectrum independently of each other locally for obtaining frequency spectrum detection amount XEi; each second user i processes creditableness expression to the frequency spectrum detection amount XEi in the m time by using the formulation as follows; each second user SUi sends the detection result creditableness degree mi (H1m) and mi (H0M) to a fusion center AP, the fusion center AP uses a self-adapted iterative algorithm to estimate a priori information P1m, P0m, PDim and PFim firstly, then uses the follow formulation for obtaining the i-th second user SUi sensing creditableness cim in the m-th time according with a data optimizing fusion theory; the mi(HIm), mi(HOm) and ci(m) is obtained by the above steps, the fusion center AP uses the follow formulation for judging whether a main user PU existing in the m-th time, and outputs the final judged result u0m. The method can improve reliability of each second user, and improve the detection performance of the system.

The invention discloses a data aggregation method based on compressed sensing in a wireless sensor network. The method includes: uniform clustering of the sensor network is performed, a node with the most residual energy is selected as a cluster head node, member nodes independently select whether to participate in sampling with the probability ptx, and the cluster head node always participate in sampling; then sampling nodes obtain original signals f and obtain sparse representations x thereof through transform of sparse transform bases, x are projected in a measuring matrix phi, sparse measuring signals y are obtained and sent to the cluster head node, and the cluster head node merges the collected measuring signals to a signal Y by employing vectorization operators and sends the signal Y to a fusion center; and finally the fusion center performs reconstruction on the signals one by one by employing an adaptive weight GPSR algorithm and recovers the sparse representations X thereof. According to the method, the characteristics of noise-containing signals, large data bulk, and high timeliness requirement of the wireless sensor network are completely achieved, the adaptive weight GPSR algorithm does not need to know the signal sparsity in advance, and all high-dimensional signals can be accurately reconstructed in a short period.

A method for transmitting signals based on dual sensing in a wireless communication system is disclosed. One or more sensor nodes receive Gaussian codes corresponding respectively to the one or more sensor nodes, allocated from a fusion center. The one or more sensor nodes determine whether to operate at a specific time. At least one sensor node that has determined to operate among the one or more sensor nodes multiplies the Gaussian codes by a transmission signal and transmits the multiplied signal to the fusion center.

The invention discloses a collaborative spectrum sensing vague fusion method based on clustering. The collaborative sensing performance in the collaborative spectrum sensing technology can be improved based on the clustering. In addition, a linear weighting soft fusion algorithm based on the vague comprehensive judgment is designed at a fusion center end, and therefore the collaborative spectrum sensing of the cognitive wireless network is achieved. A clustering submodule and a soft fusion submodule are achieved through the vague clustering and a vague comprehensive judgment method, the intelligent cognitive function of the cognitive wireless network can be achieved, and meanwhile the operation complexity is reduced.

The invention discloses a pseudo-measurement-based asynchronous track fusion algorithm with a feedback maneuvering target. Firstly, input interaction is carried out on a model set, and the filtering initial value of each model is calculated according to the model probability and the model transfer probability; secondly, a fusion center calculates one-step prediction values on the basis of the Kalman filtering algorithm, after new sensor measurement information in the filtering period is obtained, the one-step prediction values are distributed in a time shaft sequence, recurrence is conducted on a fusion moment, information such as sensor observation matrixes, noise and model prediction are added, and asynchronous track fusion is conducted; thirdly, secondary filtering is carried out for calculating model output, output interaction is performed in the fusion center to obtain a fusion center estimated value and an estimation error matrix, and the fusion center estimated value and the estimation error matrix are fed back to a sensor according with feedback conditions. The overall precision of the algorithm is improved by introducing a fusion structure with feedback so that a better effect can be achieved in multi-sensor maneuvering target tracking.

The invention relates to the field of optimization of a cognitive wireless network spectrum and provides a cognitive wireless network spectrum sensing and access decision combined optimization method. Secondary users decide whether to participate in sensing according to the possibly obtained handling capacity; the secondary users deciding to participate in sensing are subjected to spectrum sensing, a local sensing result is obtained, and the local sensing result is uploaded to a fusion center according to the sequence of linklihood ratios; the fusion center decides whether a primary user occupies a channel or not according to a decision criterion; the secondary users decide whether to have access to the channel according to the decision criterion and the possibly obtained handling capacity; when the proportionate growth rate of the secondary users participating in sensing and the proportionate growth rate of the secondary users having access to the channel tend to be stable, the proportion of the secondary users participating in sensing and the proportion of the secondary users having access to the channel are obtained. On the basis of evolutionary game, the spectrum sensing and access are combined, the sensing and access proportions of the secondary users are dynamically adjusted, the balanced state is achieved finally, and the system performance of the system is optimized.

The invention discloses a selecting type cooperation spectrum sensing method based on double-threshold energy detection. The selecting type cooperation spectrum sensing method comprises the following steps: (1) in a master user signal detection stage, adopting a double-threshold energy detection method by various cognitive users in a cognitive radio system to carry out local spectrum sensing, if an energy value exceeds two thresholds, carrying out local judgment 'H0 or H1'by the cognitive users, and if the energy value is between the two thresholds, reserving the energy value as a primary energy detection value by the cognitive users; (2) in an initial detection result reporting stage, evenly distributing a master user frequency band by the cognitive users, adopting a selecting type strategy by the cognitive users to report initial detection results respectively to a fusion center, preventing cognitive detection results with unreliable parameters from being introduced, meanwhile, saving traditional exclusive control channel resources, adopting the equal gain criterion by the fusion center to carry out master user judgment on the received primary energy detection value, allowing the judgment result to be equivalent to a node strategy by the fusion center, and then adopting the 'or' criterion by the fusion center to make the final judgment whether a master user exists. The simulation results show that according to the selecting type cooperation spectrum sensing method based on the double-threshold energy detection, and on the premise that ROC properties are not lost, the exclusive control channel resources are effectively saved, and meanwhile higher detection efficiency can be obtained.

The invention discloses a cooperative spectrum sensing parameter optimizing method utilizing an improved energy detector. The cooperative spectrum sensing parameter optimizing method comprises the following specific steps of: (1) configuring a CRN (Cognitive Radio Network) including an SU (Secondary User) and an information FC (Fusion Center) in a network having a PU (Primary User); (2) locally receiving, detecting, judging and transmitting signals by each SU; (3) receiving and judging signals of the side of the information FC; and (4) respectively optimizing each parameter sensed by cooperative spectrum applying the improved energy detector by miniaturizing false detection probability. According to the cooperative spectrum sensing parameter optimizing method utilizing the improved energy detector, disclosed by the invention, an optimal energy detector is obtained, a cooperative spectrum sensing performance of the optimized energy detector is better than the cooperative spectrum sensing performance utilizing a traditional energy detector (keeping p equal to 2), the number of optimal SUs involving cooperative spectrum sensing under minimum false detection probability is theoretically deduced, and expense of an acknowledge radio network is reduced by selection of a number of the optimal SUs involving cooperation.

The invention discloses a device and a method for monitoring tensioning states of scraper conveyor chains and relates to the device and method for monitoring the tensioning states of scraper conveyor chains in coal mines. The device is characterized in that the device comprises scraper conveyor head and scraper conveyor tail motor power converters, chain suspension quantity transducers, scraper conveyor tail telescopic cylinder pressure sensors, signal conditioning units, primary fusion centers and a chain state display controller; the scraper conveyer head and tail are provided with the power converters respectively; the two telescopic cylinders disposed on the scraper conveyer tail in a parallel manner are provided with the pressure transducers respectively; and separation points of scraper conveyor head chain wheels and two chains are provided with the suspension quantity transducers. The signal conditioning units completes the preprocessing, such as amplification and filtering of the output signals of the transducers, the primary fusion centers perform primary processing of the transducer data of same types, the chain state display controller completes a second-step fusion of the chain tension characteristic information of different types and indicator lights show the chain tensioning states. The device and method improve accuracy and reliability of the monitoring, protect safety of the chains and accordingly prolong the service lifetimes of the chains.

The invention discloses a power and bandwidth combined distribution method used for target tracking with a networking radar system. The method comprises steps of establishing the networking radar system, wherein the networking radar system comprises a fusion center and N radar stations and targets exist in detection regions of the N radar stations; initializing k, where k belongs to {1, 2, ..., K}, calculating sampling echo data of the N radar stations in the networking radar system at the moment of k, and sending the sampling echo data to the fusion center; after receiving echo data waveforms reflected by the targets received by the N radar stations in the networking radar system at the moment of k through the fusion center, calculating target state vector estimation values at the moment of k, calculating cost functions about Pk+1 and betak+1 of resource distribution of the networking radar system at the moment of k+1, and calculating emission signal power output values of the N radar stations at the moment of k+1 and emission signal bandwidth output values of the networking radar system at the moment of k+1; and stopping tracking of the targets until the emission signal power output values of the N radar stations at the moment of k+1 and the emission signal bandwidth output values of the networking radar system at the moment of k+1 are obtained.

The invention discloses a double-threshold frequency spectrum perception method based on a trust degree. The method has both of cognitive-radio-system reliability and low load. The method includes: a system uses preferentially cognitive nodes which meet a double-threshold demand to participate in collaborative perception; when the number of cognitive nodes which meet the double-threshold demand is not sufficient, cognitive nodes which meet trust degree parameter demands are added to participate in the collaborative perception; and a fusion center stores a detection record of the cognitive nodes and uses the detection record as a local detection result to set a fusion weight. In the method, perception parameters which need to be transmitted are reduced and an occupied channel bandwidth is reduced. At the same time, because of the reduction of unreliable cognitive nodes, detection performance of the method is improved further. Moreover, compared with most methods which only improve disadvantages of a specific performance index of the system, the system is capable of adapting to different types of wireless services through adjustment of the value of a parameter n<t> and has a specific flexibility.

The invention provides a network data compressing method, a network system and synthesis center equipment. The method comprises the steps: sampling in a sensor network comprising J network nodes and obtaining node data of the J network nodes, wherein the node data of the J network nodes are relevant, and the node data of each network node have a pubic component and a special component with sparseness under a same base; carrying out compression processing on the node data of each network node in the sensor network by applying a random compression matrix, obtaining node compression data corresponding to the network node, wherein the random compression matrix is generated by a physical address of the network node; and sending the node compression data in each network node in the sensor network and the physical address corresponding to the node compression data to the synthesis center equipment. The invention can effectively and jointly compress the information of a plurality of nodes, avoid that node compunction takes up high bandwidth while calculating the relativity and satisfy the double requirements on efficiency and precision.

The invention discloses a multi-sensor non-sequential measurement asynchronous fusion method based on GM-PHD filtering. The multi-sensor non-sequential measurement asynchronous fusion method can be used for solving the multi-target tracking problem of multi-sensor asynchronous non-sequential measurement based on radar, sonar and the like in a clutter environment. According to the method, a centralized feature level fusion strategy is adopted, a fusion center judges the measurement received in real time, and a fusion algorithm based on a GM-PHD filter is designed for two types of asynchronous measurement of sequential measurement and arrival lag measurement. Especially for arrival lag measurement, the GM-PHD filter is reasonably improved, the problems of reverse state prediction and negative time measurement updating under a random set framework are solved, and the secondary estimation of the target state is achieved. By means of the advantages of the stochastic set theory, the complexdata association problem in the asynchronous fusion problem is avoided, the method is simple in structure and is easy to achieve iteration updating, and the method has the important practical significance for solving the actual multi-sensor multi-target tracking problem.