44 results about "Doppler velocity log" patented technology

The invention discloses a nonlinear-model-based SINS/DVL (strapdown inertial navigation system/ doppler velocity log) integrated navigation method. The method comprises the following steps of: building a quaternion-based SINS nonlinear speed, posture and position error model according to the working principles of an SINS and a DVL, and confirming an error model of the DVL; building a state equation of the systems according to the error models of the two systems, measuring by taking the difference between the actually-measured speeds of the SINS and the DVL as the quantity, and building a measurement equation of the system; discretizing an actual continuous system to obtain a discrete nonlinear model which is convenient to compute; initializing the system, and computing sampling points and corresponding weight numbers by the discrete nonlinear model and unscented conversion; and sequentially carrying out time update and measurement update on unscented kalman filter on the basis of the discrete nonlinear model according to the constructed Sigma point. After the SINS/DVL integrated navigation system is used, information of each subsystem can be effectively used, and the best of each subsystem can be taken, so that the overall positioning accuracy can be greatly improved; the UKF (unscented kalman filter) estimation can be carried out by the nonlinear model of the SINS/DVL integrated navigation system, so that the positioning error of the system can be effectively reduced and the accurate positioning of the navigation system can be realized better.

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 neural network assisted integrated navigation method for an underwater vehicle. The neural network assisted integrated navigation method is implemented by use of strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), a magnetic compass pilot (MCP) and a terrain aided navigation system (TAN), wherein the integrated navigation is completed by use of a decentralized filter structure of Kalman filtering and a fault-tolerant method, assisted by a radial basis function neutral network (RBFNN). In a fault-free time period, RBFNN is in an online learning model, the observed quantity difference between the SINS and each auxiliary system is taken as the expected output of the RBFNN, and the output fb of an accelerometer after error compensation and the output shown in the specification of a gyroscope are taken as the inputs of the RBFNN; when a sub-system composed of the SINS serving as a reference system and each auxiliary system is out of order, an RBFNN prediction mode is immediately activated, and the predicted output is taken as the measurement input of a corresponding sub-filter. Compared with the SINS mode out of order, the RBFNN mode has the advantages that the navigation accuracy is improved; especially when the fault recovery time is relatively long, the improvement of the navigation accuracy of the RBFNN mode is particularly obvious.

The invention relates to a doppler velocity log (DVL) parameter calibration method used for an integrated navigation system of an underwater inertial navigation system (INS) and a DVL. The procedures includes the following steps: (1) calibrating a global navigation satellite system (GNSS) for the first time after initial alignment of an underwater aircraft is completed, (2) calibrating the GNSS for the second time after the underwater aircraft directly sails at a constant speed for a certain distance, (3) calculating the parameter of the DVL according to position information of the two calibrated GNSS and position information of the integrated navigation, (4) calibrating the GNSS for the third time after the aircraft pulls back and directly sails at a constant speed for a certain distance, calculating the parameter of the DVL, and iteratively calculating the total parameter of the DVL according to the former parameter of the DVL, (5) and calibrating the GNSS for the forth time after the aircraft pulls back and directly sails at a constant speed for a certain distance, calculating position error of the integrated navigation according to position information of the forth calibrated integrated navigation and the position information of the GNSS, and judging whether accuracy meets requirements, and if the accuracy meets requirements, calibration is completed, if the accuracy does not meet the requirements, the calibration is completed through iterative calculation. The DVL parameter calibration method used for the integrated navigation system of the underwater INS and the DVL has the advantages of being simple in calculation, high in accuracy, simple in operation and the like.

The invention provides a UUV (unmanned underwater vehicle) aided navigation method based on current profile. The method comprises the following steps: firstly setting up a Kalman filter model of current profile data, secondly determining the observation noise variance R and process noise variance Q in a Kalman equation according to variations of the environment and the UUV own velocity and finally setting up a UUV velocity-current profile relation database, utilizing the relation database and the current profile information measured by an ADCP (acoustic Doppler current profile) to reckon the UUV velocity at the time of DVL (Doppler velocity log) failure and further obtaining the navigation position of the UUV by deadreckoning. The method has the following beneficial effects: when the UUV performs a task under water, once the sonar data of the DVL have failure, the UUV velocity can be reckoned by using the current profile information measured by the ADCP, so that the UUV can continue performing the ocean exploration task in the complex ocean environments such as great depth, thus improving the environmental adaptation capability of the UUV.

The invention relates to a gain compensation self-adaptive filtering-based SINS (strap-down inertial navigation)/DVL (Doppler velocity log) combined positioning method and belongs to the technical field of high-precision SINS/DVL combined positioning. The invention aims to solve the problem of low positioning precision due to the influence of the insufficient flexibility of the filtering algorithmof conventional SINS/DVL combined navigation. The method of the invention includes the following steps that: corresponding state initial values and observation values are acquired based on the sensorinformation of a strap-down inertial navigation system and a Doppler velocity log; a corresponding system equation and observation equation based on a combined navigation error model are constructed,a gain compensation self-adaptive filtering algorithm is adopted to correct errors, so that the post-correction speed and position error information of a target is obtained; and finally, the obtainederror information and the observation information of the strap-down inertial navigation system and the Doppler velocity log are fused, so that a high-precision positioning result is obtained.

The invention provides an SINS (Strapdown Inertial Navigation System)-based method for restraining the speed measuring error of a DVL (Doppler Velocity Log). The method comprises the following steps of: starting the DVL and an SINS, comprehensively processing the output speeds of the DVL and the SINS, performing high pass digital filtering and outputting the optimized speed. By means of method provided by the invention, the high-frequency noise of the DVL can be filtered while the motion high-frequency information is not filtered; and speed and acceleration references are provided for initial alignment and navigation of other inertial systems.

The invention discloses a dynamics-based velocity model assisted underwater intelligent navigation method. The method includes the following steps that: (1) on-board sensor information is periodicallycollected; (2) the input variables and output variables of a velocity model to be established are determined; (3) the velocity model is constructed and trained; and (4) when it is detected that a DVL(Doppler Velocity Log) malfunctions or the data of the DVL fail, navigation analysis is performed with the output velocity of the trained model used as a bottom velocity, and therefore, model-assisted navigation can be performed. According to the method of the invention, a new AUV (Autonomous Underwater Vehicle) velocity model is put forward based on the idea of dynamic models; the influence of factors such as rudder angles of rudder blades, and heading angles is considered; as long as the fault data of the DVL are not detected during an operation process, training sets can be increased at any time to train the model; after the fault data of the DVL are detected, the output velocity of the model can be adopted instead, and therefore, the problem of navigation error increase caused by thefailure or fault of the DVL can be avoided; the redundancy method of velocity sensors is provided with no hardware costs increased; the method has good system robustness and can guarantee high-precision navigation.

The invention provides an integrated alignment method based on speed trial error estimation of a doppler velocity log on a geographical system under a constant-speed direct flight condition. The projection of a speed trial error of the doppler velocity log on the geographical system is expanded into a state variable, and a doppler velocity log/strapdown inertial navigation integrated alignment system is established on the basis. Compared with a method for only expanding the speed trial error of the doppler velocity log into the state variable, the method provided by the invention can be used for improving the observability level of the speed trial error of the doppler velocity log, effectively estimating the speed trial error and overcoming the influence on precision caused by the speed trial error of the doppler velocity log.

The invention discloses a DVL-assisted SINS robust on-moving initial alignment method. The DVL-assisted SINS robust on-moving initial alignment method comprises the following steps of step (1), acquiring real-time sensor data; step (2), establishing a vector apparent motion parameter equation; step (3), on the basis of establishing the vector apparent motion parameter equation, estimating a parameter matrix through adaptive robust Kalman filtering; step (4), establishing reconfiguration vectors based on estimated parameters; step (5), through an optimal base quaternion attitude determining method, computing the error angle between a determined attitude and a real attitude; step (6), setting an initial alignment moment as M and a real-time data acquiring moment as k, if k=M, outputting an initial alignment result and completing an initial alignment process, and if k<M, determining that the initial alignment process is not completed, and repeating the above steps of (1) to (5) until theinitial alignment process is completed. The DVL-assisted SINS robust on-moving initial alignment method solves the problems of poor alignment precision and divergent alignment processes when system measured noise is abnormal.

The invention discloses a combined velocity measuring system suitable for a micro UUV, and relates to the combined velocity measuring technology for a micro unmanned underwater vehicle (UUV). The invention aims to solve the problem that a conventional Doppler velocity log (DVL) is not suitable for velocity measurement when a micro underwater vehicle performs underwater navigation at a great depth. Two groups of water wheel velocity measuring devices are of the same structure, and used for measuring ocean current velocities in a length direction and in a width direction of the UUV respectively. Output ends of the two groups of water wheel velocity measuring devices are both connected with input ends of relative velocity calculators. Output ends of the relative velocity calculators are connected with relative velocity input ends of a data fusion module. An output end of a high-frequency acoustic DVL is connected with a ground velocity input end of the data fusion module. An output end of the data fusion module is connected with an input end of an ocean current information Kalman filter which outputs ocean current information. The invention has the characteristics of being simple in structure, low in cost, small in size, light in weight, high in reliability, and capable of real-time calculation of time-varying ocean current information. The invention is suitable for measuring ocean current information.

The invention discloses a deep-water intelligent navigation method based on water velocity assisted inertial navigation. Measures from aspects of acoustic information pretreatment, a water velocity assisted inertial navigation system and the like are taken so as to solve the related problems of poor reliability of acoustic information caused by acoustic measurement and a marine environment and large accumulated navigation errors when an autonomous underwater vehicle (AUV) is too far away from the seabed without Doppler velocity log (DVL) ground velocity assistance and the like. The deep-waterintelligent navigation method particularly comprises the steps that (1) based on a motion constraint idea, rudder, fin and paddle change information is introduced to preprocess the acoustic information to improve the credibility of the acoustic information; (2) a virtual speed log scheme is introduced, a delay-free HMM/KF filter is designed, and the velocities of a DVL and an inertial navigation system (INS) are comprehensively processed to reduce DVL velocity errors and INS long-period errors caused by AUV bumpy swing, acceleration and deceleration and turning; and (3) a graph optimization algorithm is adopted, nonlinear optimization is used for controlling the error level, and a recursive method is utilized to marginalize and optimize to realize real-time composition so as to improve navigation accuracy.

The invention provides a UUV deep sea integrated navigation device with dual-Doppler adaptive switching speed measurement and a navigation method. The device comprises a Doppler velocity log detectionmodule, a Doppler velocity log mode conversion module, a Kalman filtering module, a high-frequency convection Doppler velocity log, a low-frequency convection Doppler velocity log and an inertial navigation system. According to the device and the method, the stability of a UUV in a trench and during deep sea navigation can be improved, the high-precision navigation capability of an aircraft in alarge-depth sea area is improved, the water outlet correction frequency is reduced, and the navigation requirements of large-range navigation and hidden operation are met. The amount of information needed for judgement of switching of an operation state of the DVL is small, and a control command is simple.

The invention discloses a DVL (doppler velocity log) failure processing method for an SINS (strapdown inertial navigation system)/DVL integrated navigation system. When a DVL is effective, calculation information of an SINS and measurement information of the DVL are acquired and form a data table, and a prediction model is established by means of partial least squares regression; when the DVL is failed, measurement information of the DVL is predicted by means of the established model, a prediction result is merged with the calculation information of the SINS, and SINS/DVL integrated navigation is realized in the failure state of the DVL. The measurement information of the DVL is predicted with the calculation information of the SINS at the failure moment and before failure as input, the accuracy of the prediction result is improved, the model is established by means of partial least squares regression, the defect that robustness of the model is damaged due to multiple correlation of independent variables is overcome, and the reliability of the established model is guaranteed.

The invention belongs to the technical field of inertial navigation systems and in particular relates to a velocity aiding based gyroscopic drift correction method of an inertial navigation system, which is used for carrying out gyroscopic drift correction by utilizing velocity information aiding in long-time navigation. The velocity aiding based gyroscopic drift correction method comprises the following steps: pre-heating the inertial navigation system, starting to enter a navigation state after initial alignment early-stage work; after a period of time, recording a moment as t1 when the inertial navigation system reaches the condition of constant-speed direct navigation being kept within 10 minutes; obtaining referred external velocity information according to a ground-toward velocity measuring signal of an external aided measurement device, such as a Doppler velocity log; projecting the obtained external velocity information to a geographic coordinate system and the like. By establishing a relational expression between velocity error information and gyroscopic drift, the gyroscopic drift of the inertial navigation system can be estimated and revised by only utilizing the velocity information, so that the precision and the efficiency of the inertial navigation system are improved and the safety and the privacy of a carrier are guaranteed.

The invention discloses an on-line calibration method for a scalefactor error of a DVL (Doppler velocity log).A circular trace is adopted to enable an underwater vehicle to cyclically operate for 360 degrees, the underwater vehicle inevitably passes four positions including due east, due west, due south and due north during operation of a circle of circular trace, time periods of the vehicle passing the four positions are determined by a LBL (long base line) acoustic navigation system, decoupling of the scalefactor error of the DVL and a compass course error in a DR (dead reckoning) process is realized, and the scalefactor error of the DVL is calibrated on line according to navigation results of DR and LBL. The on-line calibration method for the scale factor error of the DVL has the advantages of simplicity, convenience, effectivity and only requirement for positional information and is suitable for on-line calibration of the DVL scalefactor error for the underwater vehicle.

The invention discloses a robust alignment method for a module value detection moving base. The robust alignment method for the module value detection moving base is characterized in comprising the following steps that: obtaining the real-time data of an inertial sensor, and carrying out attitude update; obtaining auxiliary sensor information, and constructing a vector observer; carrying out module value calculation on the vector observer, and carrying out exception detection by a standard module value; through the module value detection, weakening an exception noise influence, and constructing an adaptive vector attitude determining method; initializing alignment process operation time as M; if an attitude change frequency k is equal to M, outputting an alignment result, and finishing aninitial alignment process; and if the attitude change frequency k is smaller than M, proving that the initial alignment process is not finished, and repeating the above steps to finish the initial alignment process. The method has the beneficial effect that the module value detection method is adopted to have the advantage of being convenient in calculation. The invention designs an optimal weightcalculation method, and has an efficient DVL (Doppler Velocity Log) measurement exception detection effect.

The invention discloses an error calibration method of a model-free doppler velocity log (DVL). The error calibration method does not require a priori DVL error model to achieve high-precision error calibration. The error calibration method includes the following steps that during the underwater movement of an underwater vehicle (AUV), the two-dimensional horizontal velocity information and vertical velocity information of the AUV are obtained, and the horizontal measurement velocity and the vertical measurement velocity of the doppler velocity log (DVL) arranged above the AUV are obtained; ahorizontal regression predictor is constructed, and the two-dimensional horizontal velocity information is used as an output training sample, the horizontal measurement velocity of the DVL is used asan input training sample, and the horizontal regression predictor is trained to obtain a trained horizontal regression predictor; similarly, a trained vertical regression predictor is obtained; the trained horizontal regression predictor and the trained vertical regression predictor are combined to obtain a target predictor; and the DVL velocity measurement information is collected in real time tobe taken as the input of the target predictor, and then the output of the target predictor is the calibrated DVL measurement velocity.