33 results about "Sequential estimation" patented technology

A method for constructing a model representative of a heterogeneous medium from data expressed in different time scales with application to hydrocarbon characterization. The method first estimates sequentially, from the data expressed in each different time scale, parameters or physical quantities of the model, described for each different time scale. Second, a scale factor allowing conversion of a model described in a time scale into a model described in another time scale is determined. This determination is carried out by minimizing the dissimilarity between a parameter estimated in a time scale and this parameter estimated in the other time scale. Finally, estimation of a single model is performed, by taking simultaneously an account of the data expressed in the various time scales, and using the scale factor found previously for the time scale conversions.

The invention discloses a modeled clutter Doppler parameter estimation method of airborne radar. The clutter model provided by said invention is Doppler distribited clutter (DDC) model. The clutter covariance matrix described by said model is analyzed and expressed by four specific parameters of Doppler centre, Doppler spectrum-width extending coefficient, clutter roughness parameter and noise roughness parameter. The clutter parameter estimation method includes the following steps: (1) airborne radar can be used for transmitting pulse signal, and collecting echo data; 2). utilizing clutter data in collected signals to form itnerference covariance matrix of sample; and 3). according to the itnerference covariance matrix of sample combinatively estimating the unknown parameter in DDC clutter model. Besides, said invention also provides the advantage and application of said estimation method.

The invention discloses a coding method comprising executing maximum likelihood ratio sequential estimation, obtaining estimated sequence, computing reliability of each bit of the sequence; initializing LDPC coding information according to reliability of each bit and received sequence; after completing initialization, using information generated by initialization as initial value of LDPC coding, executing LDPC iteration coding, obtaining coding result. The invention also discloses a coder. The inventive coder and coding method can effectively reduce communication system error rate of combining error correcting and coding for MLSE and LDPC code.

Provided are a method, medium, and apparatus for docking a mobile robot. The method includes: (a) receiving a docking signal, which is transmitted from a signal transmitting unit, by using a signal receiving unit loaded in the mobile robot while the mobile robot is moving; (b) estimating a range of poses of the signal transmitting unit based on the received docking signal; (c) updating estimated poses of the signal transmitting unit by reflecting the estimated range of poses in a pose estimation algorithm when the pose of the signal transmitting unit, which was estimated using the pose estimation algorithm, exists within the estimated range of poses and adding a new pose of the signal transmitting unit, which is to be estimated using the pose estimation algorithm by reflecting the estimated range of poses, when the pose of the signal transmitting unit, which was estimated using the pose estimation algorithm, does not exist within the estimated range of poses; and (d) attempting to return the mobile robot to a charging station by using a plurality of poses of the signal transmitting unit, which were estimated by repeating operations (a) through (c) and using the pose estimation algorithm, in order of accuracy, until the mobile robot is docked in the charging station.

The invention provides a combined authorized user perception and link state estimation method and device in order to resolve the problem of spectrum sensing in the cognitive radio technology. The method takes link state information between a main user and a secondary user into account, and takes the link state information as another hidden state in spectrum sensing, and then a universal dynamic state space model is built. A Bernoulli smoothing mechanism is designed based on the Bayesian sequential estimation framework and the Bernoulli random finite set theory, and combined blind estimation of the state information of a main link and a secondary link can be obtained while the work state of an authorized frequency band is perceived and detected (the algorithm process is shown in the picture). The combined authorized user perception and link state estimation method makes full use of the state information of the main link and the secondary link, thereby obviously improving the performance of spectrum sensing. Meanwhile, the obtained state information of the main link and the secondary link can be used for subsequent resource optimization. In addition, the combined authorized user perception and link state estimation method and device have universality and can be expanded to other state information of the main link and the secondary link.

The invention relates to a robust positioning method with non-visual-range error elimination function, which can estimate motion state of a mobile station (MS) in mixed environment of LOS / NLOS, effectively increases positioning precision, reduces positioning error and has robutness to LOS / NLOS transition probability models in different environments. The method comprises the following steps: firstly, establishing motion equations of the mobile station, and establishing a mutually conversed first-order Markov model of the LOS environment and the NLOS environment; secondly, establishing measuring equations containing target motion states and LOS / NLOS environment variables on the basis of the first step, and estimating visual-range condition posterior probability distribution according to measuring signals from each base station; and thirdly, adopting a method of distributed extended Kalman filter, estimating the current motion states on the basis of particle set of the second step , and repeating the second step and the third step and obtaining sequential estimation results of motion target states.

The invention provides an RSSI (received signal strength indicator) distance measuring method based on a cost-reference particle filter. The method comprises the following steps of: arranging anchor nodes in an indoor environment, and planning a moving path of a mobile node; enabling the mobile node to move according to the planned path, and acquiring data packs sent by different anchor nodes at each predetermined position; performing Gaussian filter preprocessing on the data packs acquired at each position; and performing cost-reference particle filter processing on the data after preprocessing so as to estimate the distance between the anchor nodes and the mobile node. The method provided by the invention improves the present situation that distance measurement requires a lot of priori experiments to fit models, is suitable for a situation with unknown noise in a wireless signal transmission attenuation process, and has the characteristics that a latest observed value is used for sequential estimation, and real-time adaptive adjustment is performed according to environmental data; adaptive fading factors are introduced to dynamically adjust the influence degree of history observed values on an estimation process; a loop-locked adjustment strategy is adopted to calculate feedback coefficients, enhance system robustness, and improve the precision of distance measurement.

A system for estimating gas concentrations in a mixed atmosphere includes (a) a plurality of sensors for providing a set of measurements, at least one of the sensors sensitive to an internal concentration of hydrogen; and (b) a processor for receiving the set of measurements and for executing a sequential estimation filter that includes a plurality of states having a corresponding set of values. The processor responsively adjusts at least a portion of the set of values in response to the set of measurements. The plurality of sensors can include a resistive sensor and a capacitive gas sensor, both of which are sensitive to hydrogen concentration. The plurality of states can include states representative of hydrogen pressure in the mixed atmosphere, hydrogen concentration in a bulk material of at least one of the sensors, and hydrogen concentration in an interface layer of at least one of the sensors.

The invention relates to an implementation method of a particle swarm resampling particle filter based on Metropolis-Hastings variation. The invention provides the implementation method of the particle swarm resampling particle filter based on the Metropolis-Hastings variation so as to solve the problem that estimated accuracy of a particle filter is not high when the number of particles is small. The implementation method enables Metropolis-Hastings (MH) movement to be used as a variation operator of particle swarm optimization, an MH variation rule is combined with a speed-position search process of a particle swarm, a resampled particle swarm is more approximate to a real posterior probability density distribution, the problem that a common variation particle swarm algorithm diverges easily is effectively solved, a convergence speed of the particle filter in a sequential estimation process is accelerated, and estimation accuracy of the particle filter is improved. Proved by a simulation test, the particle swarm optimization particle filter based on the Metropolis-Hastings variation can effectively overcome the phenomenon of particle depletion and improve tracking and estimating effects of a nonlinear system.

A navigation system has: a candidate input device 14 for inputting a search key; an input vocabulary storage 11 that stores words to become candidates of a search key input from the candidate input device; a vehicle information storage 12 for storing vehicle information relating to a vehicle; a preference information storage 13 for storing preference information representing user' preference in connection with the words stored in the input vocabulary storage; an input word estimating module 17 for searching the input vocabulary storage for words corresponding to the search key input from the candidate input device, and for estimating the words obtained by the search in accordance with an order of priority based on the vehicle information read out of the vehicle condition storage and the preference information read out of the preference information storage; and a candidate output device 15 for outputting the words delivered from the input word estimating module as candidates.

A sequential estimation method for real-time positioning or navigation systems is performed by, at each iteration: propagating a first state estimate into a state prediction, and then forming a second state estimate, by updating the state prediction using state measurements. The first state estimate is valid at a first time and comprises estimated values of a plurality of state parameters, the plurality of state parameters including at least one state parameter referring to a time earlier than the first time. The second state estimate is valid at a second time and comprises estimated values of said plurality of state parameters, the plurality of state parameters including at least one state parameter referring to a time earlier than the second time. The state measurements comprise measurements relating to state parameters at the second time and measurements relating to state parameters at the time earlier than the second time.

A wireless data reception method for receiving orthogonal frequency division multiplexing (OFDM) signal includes determining that an interfering signal is present, along with a serving cell signal, in the OFDM signal on the shared downlink channel, determining an estimate of a transmission structure of the received interfering signal, wherein the transmission structure is defined by a plurality of transmission parameters, classifying the estimate of the transmission structure as belonging to a subgroup from a plurality of possible subgroups in a dynamic order, estimating, in a priority order, at least some of the plurality of transmission parameters of the estimate of the transmission structure, and performing further receiver-side processing of the serving cell signal by subtracting contribution of the interfering signal from the OFDM signal based on the estimate of the transmission structure and a result of the estimating step.

The invention discloses a passive acoustic positioning method of a single beacon for an underwater glider under the influence of the ocean current. The method only needs the single beacon to provide positioning information for the glider, and includes the structure design of a positioning signal frame sent by the beacon, a signal processing method at the receiving end of the glider, implementationsteps of the positioning method, a sequential estimation algorithm of the position of the glider and a positioning algorithm of the glider when the acoustic positioning signal is temporarily missing.The passive acoustic positioning method can be used with no time synchronization between the glider and a sound source and no active signals sent by the glider. The passive acoustic positioning method can improve the positioning performance of the underwater glider under the influence of the ocean current, and has the characteristics of low power consumption, simple implementation, and large improvement range of positioning accuracy.

The invention concerns a method for source decoding a variable-length soft-input codewords sequence (y [1: T]) into a soft-out-put bit sequence (Lv [1: T]), the variable-length soft-input input code-words sequence (y [1: T]) encoded in accordance with a VLC codewords table. It comprises—a first stage (100) of implementing a stack decoding algorithm for a sequential estimation of an hard-output bit sequence of said variable length soft-input codewords sequence, including storage of intermediate data contained in the stack and generated by the stack decoding algorithm; and—a second subsequent stage (102) of post-processing the stored intermediate data for generating the soft-output bit sequence (Lv [1: T]), a soft-output (L(x [t])) being provided for each bit.

The invention provides a combined spectrum sensing and authorized user position estimating scheme for solving the problem of mobile authorized user spectrum sensing and mobile authorized user geographic position information estimating. A self-adaptation view expanding method is provided for the first time, the negative effect caused by the nondeterminacy and silence state of the authorized user position is effectively solved, and the spectrum sensing performance is improved. A brand new dynamic state-space model is established based on Bernoulli random finite sets and a Bayes sequential estimation frame, a three-section type Bernoulli smoothing mechanism is designed, the mobile authorized user geographic position is estimated while the authorized user working state is detected, and accordingly the spectrum sensing performance is obviously improved. Meanwhile, spectrum multiplexing of time, frequency and space is achieved, and accordingly the following spectrum resource allocation is further optimized.

The invention provides a Gaussian particle filtering data processing method based on error quaternion three-dimensional vector distribution, which belongs to the technical field of digital filtering and multi-sensor data fusion, and is mainly used for solving the problem of huge calculation burden of a particle filter in attitude estimation. The method takesGaussian particle filtering as a frame,data of a gyroscope, an accelerometer and a magnetometer are fused, and tGaussian distribution of an error quaternion three-dimensional vector on a three-dimensional Euclidean space is taken as posteriori distribution of an attitude estimation error. According to the indirect filtering method taking quaternion as a global attitude parameter and the multiplicative error quaternion as a local attitude error description, normalization of the quaternion in the filter can be guaranteed, sequential estimation of the filter can be achieved, parallel computing can be achieved, the computing speed is increased, the filtering precision is stable, and the method is suitable for attitude estimation, data fusion and other application occasions.

The method for estimating parameters of a navigation signal received by a receiver which receives navigation signals through a plurality of paths wherein the parameters include data modulated on the navigation signal and complex amplitudes i.e. amplitude and phase shift, and time delays of the individual paths, comprises receiving a navigation signal and sampling the received navigation signal. Moreover, the parameters are sequentially estimated in terms of a posterior probability density function. For facilitating the sequential estimation, the received vector is transformed into a compressed vector without loss of information by using a correlator bank having a plurality of correlator reference signals. The sets of samples representing the posterior probability function of the parameters are sequentially calculated by nonlinearly, recursively filtering the compressed vector and optionally predicting the complex amplitude and time delays by performing equivalent time delay shifts and phase shifts of the correlator signals of the correlator bank.

Aiming at the problem of spectrum sensing in cognitive radio, the present invention designs a joint spectrum sensing and link state estimation method device. This method for the first time incorporates a link state information between the primary user and the secondary user as another hidden state in spectrum sensing, and thus establishes a general dynamic state space model; based on Bayesian Based on the sequential estimation framework and Bernoulli random finite set theory, a Bernoulli filtering mechanism is designed, which can obtain the joint blind estimation of the primary-secondary link state information while sensing and detecting the working status of the licensed frequency band (algorithm process has been Attached). The method makes full use of the primary-secondary link state information, thereby significantly improving spectrum sensing performance; at the same time, the obtained primary-secondary link state information can also be used for subsequent resource optimization; in addition, the joint user perception and The method and device for estimating link state information is versatile and can be extended to other primary-secondary link state information.

The invention provides a Doppler radar sequential smooth variable structure filtering method and device, belongs to the technical field of radar data processing, and can solve the problem of a nonlinear underdetermined observation model of a Doppler radar and the problem of robust tracking under the condition that a target motion model is uncertain at the same time. According to the method, target position measurement and radial speed measurement decoupling processing of a Doppler radar are adopted, and the problem of a nonlinear underdetermined observation model is solved through a sequential estimation strategy. Obtaining first-stage target state estimation by using a nonlinear target position measurement and generalized smooth variable structure filtering method; and in the second-stage measurement conversion module and the second state estimator, constructing pseudo measurement by using radial speed measurement, and updating the first-stage target state estimation to obtain the final posterior state estimation of the current frame target. According to the generalized smooth variable structure filtering method, the robustness of the model under the uncertain condition is ensured. Therefore, the target tracking precision of the Doppler radar can be improved, and the robust estimation performance can be maintained under the condition that the target motion model is uncertain.

The invention relates to an implementation method of a particle swarm resampling particle filter based on Metropolis-Hastings variation. The invention provides the implementation method of the particle swarm resampling particle filter based on the Metropolis-Hastings variation so as to solve the problem that estimated accuracy of a particle filter is not high when the number of particles is small. The implementation method enables Metropolis-Hastings (MH) movement to be used as a variation operator of particle swarm optimization, an MH variation rule is combined with a speed-position search process of a particle swarm, a resampled particle swarm is more approximate to a real posterior probability density distribution, the problem that a common variation particle swarm algorithm diverges easily is effectively solved, a convergence speed of the particle filter in a sequential estimation process is accelerated, and estimation accuracy of the particle filter is improved. Proved by a simulation test, the particle swarm optimization particle filter based on the Metropolis-Hastings variation can effectively overcome the phenomenon of particle depletion and improve tracking and estimating effects of a nonlinear system.

The invention discloses a coding method comprising executing maximum likelihood ratio sequential estimation, obtaining estimated sequence, computing reliability of each bit of the sequence; initializing LDPC coding information according to reliability of each bit and received sequence; after completing initialization, using information generated by initialization as initial value of LDPC coding,executing LDPC iteration coding, obtaining coding result. The invention also discloses a coder. The inventive coder and coding method can effectively reduce communication system error rate of combining error correcting and coding for MLSE and LDPC code.

The invention relates to a multi-sensor multi-target space-time deviation calibration and fusion method and a computer readable storage medium. The method comprises the steps: calculating dimension expansion state prediction and dimension expansion state prediction covariance of a target i at the k moment and dimension expansion observation prediction of a sensor s on the target i; defining a mapping p = p (m) for indicating the number of a corresponding target; calculating a cross covariance between the dimension expansion state estimation error of the target i and the dimension expansion state estimation error of the target p (m) at the moment k when m is equal to i + 1,..., i + N1 by adopting a recursive processing strategy; calculating a cross covariance between the dimension expansionstate prediction and the dimension expansion observation prediction and an auto-covariance of the dimension expansion observation prediction; and updating the dimension expansion state estimation andthe dimension expansion state estimation covariance of the target i at the moment k. According to the invention, sequential processing strategies are executed among different targets and different sensors at the same moment, so that the precision of multi-sensor space-time deviation estimation is improved while sequential estimation of each target state is realized.

The invention discloses an all-in-one machine with a chip data protection function, and the all-in-one machine comprises the steps: collecting interaction type cloud data, carrying out the preprocessing of the data, adding a mark, and obtaining a sample label; taking a timestamp effective value and a data exception basic interval in the sample label as an input quantity and a constraint parameter of a data processing objective function respectively; according to a distributed minimum mean square error sequential estimation quantization factor, outputting a quantization value of the data processing target function; and inputting the quantized value into a chip controller for adjustment, and updating the current value of the to-be-corrected parameter into an output result adjusted by the chip controller. Parameter regulation and control are carried out on the chip controller through the designed quantized value, safe operation of data is guaranteed, when parameters are unreasonable or data are abnormal, the chip controller can carry out parameter regulation and control in a self-adaptive mode, the situations of data abnormity, data leakage and data tampering by people are reduced or avoided, and the reliability of data processing is improved. And the data privacy protection reliability of the love and lingering interaction device in different places is improved.