100 results about "Cubature kalman filter" patented technology

The invention relates to a self-adaptive fault-tolerant cubature Kalman filtering method applied to target tracking. The present invention generally includes three parts of content. Wherein the firstpart performs system modeling according to an actual moving target; Secondly, estimating noise statistical characteristics according to an improved adaptive filtering method; And a third part, detecting a fault according to a chi-square test, and carrying out weighting processing on the information part during state estimation according to a detection result. The method not only can dynamically estimate the process noise covariance and the measurement noise covariance at the same time, but also can cope with the condition that data measured by the radar break down, and achieves the effective tracking of a target.

The invention discloses an initial alignment method based on an iterated strong tracking spherical simplex radial cubature Kalman filter (ISTSSRCKF). The method comprises the following steps: selecting the cubature point of a CKF by adopting an SSR rule; introducing a fading factor in a strong tracking filter into the time and measurement update equation of the CKF; and introducing a Gauss-Newton iterative algorithm and improving the corresponding information variance and covariance in the iterative process. According to the initial alignment problem under the conditions of large misalignment angle and base sway, the excessive sampling points in the high-order CKF are reduced during sampling by adopting the SSR rule, and the problem that the performance of the CKF is reduced when a model is inaccurate is solved by introducing a strong tracking algorithm. Latest measurement information is fully utilized in using the iterative process, so that the state estimation error is effectively reduced, and the initial alignment precision superior to standard CKF is obtained.

The invention discloses a GPS/SINS/CNS integrated navigation method based on a five-order cubature kalman filter (CKF).The method is characterized by comprising the steps of 1, establishing an integrated navigation system non-linear error equation and a linear measurement equation based on a SINS error equation; 2, establishing the five-order CKF by means of the five-order spherical surface radial cubature rule; 3, filtering and fusing information output by a SINS, a GPS and a CNS by means of the five-order CKF to obtain the optimal estimation of navigation parameters.

The invention discloses a tightly coupled INS/UWB integrated navigation system and method adopting fixed-interval CRTS smoothing. The navigation system comprises an inertial navigation system (INS), a UWB radio tag, UWB wireless reference nodes, a reference system and a data processing system, wherein the inertial navigation system (INS) and the UWB radio tag are arranged on shoes of a pedestrian; the UWB wireless reference nodes and the reference system are arranged at a set position; the inertial navigation system (INS), the UWB radio tag and the reference system are connected with the data processing system respectively; the data processing system comprises a local data fusion filter, a Cubature Kalman Filter (CKF), a pseudo-distance data processing module, an RTS smoothing module and an average filtering module. The tightly coupled INS/UWB integrated navigation system and method has the benefits that the possibility of introducing truncation errors due to omitting of higher terms of Taylor expansion in a conventional tightly coupled integrated navigation model is effectively decreased.

The invention discloses a GPS/INS integrated navigation method based on self-learning cubature Kalman filter. Running stages of a GPS/INS integrated navigation system are divided into a training stagand an error compensation stage. At the training stage being a GPS signal effective stage, a self-learning Kalman filter device formed by two cyclic filtering subsystems carries out optimal estimationon a speed error and a position error of an INS by using the speed difference and position difference of the INS and the GPS as observed quantities, thereby realizing the self-learning function. At the error compensation stage being a GPS signal out-of-lock stage, the Kalman filter device has the function of predicting the observation quantities through self-learning, the prediction result of theLSTM network can be trusted fully, seamless navigation under the out-of-lock condition of the GPS signal is realized, and the optimal estimation error value of the Kalman filter device is compensated, thereby improving the positioning accuracy of intelligent vehicle navigation in complex environments. The GPS/INS integrated navigation method can be applied occasions like vehicle navigation positioning in a complex urban environment; and the autonomy of navigation positioning is improved effectively and thus the intelligence degree of the vehicle is increased.

The invention discloses a multi-robot co-location algorithm based on square root cubature Kalman filtering (SR-CKF), and belongs to the field of the co-location of robots. The whole algorithm is divided into two steps: prediction and updating. First, motion equations and observation equations of robots are established, a relative azimuth is utilized as a measured value, and a multi-robot co-location dynamic model is further obtained. A prediction stage comprises the following steps of calculating a volume point set; propagating volume points through a state equation; pre-estimating pose states of the robots and predicting a square root factor. An updating stage comprises the following steps of calculating the volume point set; calculating a Kalman gain; calculating pose information and the square root factor; updating a state vector, a covariance matrix and the pose information. According to the multi-robot co-location algorithm based on the SR-CKF, which is provided by the embodiment of the invention, a target state mean value and the square root factor of the covariance matrix are directly transmitted in an updating process; the calculation complexity is decreased; the symmetrical characteristic and the positive semi-definite characteristic of the covariance matrix are ensured; the precision and the stability of a numerical value are improved.

The invention provides a method for estimating a battery through strong tracking cubature Kalman filtering based on noise interference, based on noise interference of the battery and strong tracking Cubature Kalman Filter (CKF), estimate of the remaining electric quantity of the battery is performed, and the method includes the following characteristics: 1) analyzing the law of charge and discharge of battery charges, and obtaining an estimation theory of the SOC (State Of Charge) of the battery; 2) establishing a battery SOC model considering internal influencing factors such as battery temperature and a charge and discharge rate, and adopting a first-order RC equivalent model to establish a battery state equation; 3) performing online estimation of the SOC of the battery by a strong tracking cubature Kalman filtering algorithm; and 4) judging whether noise interference exists, if yes, performing self-adaptive square root cubature Kalman filtering to filter out noise, and if not, directly estimating the SOC of the battery. The method for estimating a battery through strong tracking cubature Kalman filtering based on noise interference has a relatively good estimation effect of estimating the remaining electric quantity of a battery model, and overcomes the defect that an error of a battery circuit accumulates and occurs over time, and the algorithm is applied to a practical situation where noise is not in ideal Gaussian distribution.

The invention discloses a cubature Kalman filtering (CKF) method applied to high-dimensional GNSS/INS deep coupling. The method comprises steps: S1, a high-dimensional GNSS/INS deep coupling filtering model is built; S2, a standard cubature rule is adopted for the built filtering model to generate an initial cubature point; and S3, the novel cubature point is adopted to update the rule for CKF. The method of the invention is applied to high-dimensional GNSS/INS deep coupling, and has the advantages of high stability and high precision.

The invention discloses an initial alignment method of a large azimuth misalignment angle of a strapdown inertial navigation system based on an RBCKF (rao-black-wellised cubature kalman filter), relates to the initial alignment method of the large azimuth misalignment angle of the strapdown inertial navigation system, and aims at solving the problems that the accuracy of the filtering method is low and easily diffused when the nonlinearity of the system is strong, and even EKF (exend kalman filter) filtering cannot be applied when the system is not continuous. The method comprises the following steps of: 1, building an error model for initial alignment of the large azimuth misalignment angle; 2, selecting an initial filtering value; 3, calculating a Cubature point set; 4, updating the time of a state variable and a measurement stable; and 5, updating a measurement equation. The initial alignment method is applied to the field of initial alignment of strapdown inertial navigation under the large azimuth misalignment angle.

The invention provides a target tracking method based on maximum correntropy cubature particle filter (MCCPF). The target tracking method based on MCCPF is characterized by completing state estimationduring the target tracking process by using the MCCPF algorithm; during the target tracking process, reconstructing a state equation and a measurement equation of target tracking into a non-linear recursion model, and performing processing by means of the maximum correntropy criterion; and in a framework of standard Particle Filter (PF), using MCCKF (Maximum Correntropy Cubature Kalman Filter) togenerate an importance probability density function required by PF, and according to the algorithm process of PF, acquiring estimation of the tracking target state so as to realize real-time trackingof the target. The invention provides a target tracking method based on maximum correntropy cubature particle filter can obtain better performance than the current particle filter, improved particlefilter and robust filter in the target tracking process in which outliers appear in measurement of noise.

The invention discloses an initial alignment method for micro electro mechanical system (MEMS) strap-down inertial navigation adaptive square-root cubature Kalman filtering (SCKF). The method comprises the following steps: determining initial position information of a carrier through a global positioning system (GPS), acquiring data which is output by an MEMS accelerometer and a magnetometer, and completing coarse alignment through an analytical method to roughly obtain a strap-down matrix; establishing a nonlinear model which is initially aligned by an MEMS-based strap-down inertial navigation system, and performing filtering initialization; performing state estimation through SCKF, and updating a system noise variance matrix according to the difference between state estimation quantity and one-time prediction state quantity during iteration at each time to improve filtering adaptive capability to indeterminacy of system noise statistical characteristics; and acquiring an attitude matrix of an accurate platform misalignment angle correction system so as to complete initial alignment. According to the initial alignment method, the initial alignment can be well completed when the constant drift of a gyroscope is large, and the filtering adaptive capability to the indeterminacy of the system noise statistical characteristics is improved.

The invention discloses an inertia/vision integrated navigation method adopting a PSO (particle swarm optimization)-based CKF (cubature kalman filter). The method comprises the following main steps: Step 1, when a visual signal is effective, acquiring a dynamic video with a camera carried by a mobile robot and determining the speed of the camera by an image feature extraction and nearest neighbor matching method; Step 2, calculating the speed of the mobile robot according to a course angle measured by an inertia navigation system and in combination with the speed of the camera and estimating the speed and the accelerated speed of the mobile robot with the CKF; Step 3, updating time and measurement with the CKF according to measurement values and status values at all time of the system and selected filter parameter values, so as to obtain an estimation value of a status of the system; Step 4, optimizing filter parameters Q and R by using a particle swarm algorithm according to current objective function values and filter parameter values at all time and taking an obtained modification value as an input parameter of the CKF until an optimal status estimation value is obtained.

The invention discloses an automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation. The method comprises the following stepsthat: firstly, setting a system initial value, and calculating a cubature point value in a time update stage; spreading the cubature point; estimating a one-step prediction state and an error covariance square root factor; in a measurement update stage, importing a Gauss-Newton nonlinear iteration method to carry out iteration update, and calculating the cubature point during each-time iteration;spreading the cubature point; calculating measurement estimation; calcauting the square root factor of an innovation covariance and a cross covariance matrix; calculating Kalman gain; updating a current iteration state, and estimating the square root factor of an error covariance; judging whether an iteration termination condition is achieved or not; updating a current state, and estimating the error covariance square root; and in the measurement update process, regulating the noise compensation factor to optimize state estimation. By use of the method, accuracy and stability in an automotiveradar target tracking process can be effectively improved.

The invention discloses a norm constraint strong tracking cubature kalman filter method for satellite attitude estimation. The norm constraint strong tracking cubature kalman filter method comprises the following steps: 1, acquiring output data of a gyro and a star sensor; 2, determining state variables and measuration amount of a satellite attitude estimation system; 3, performing cubature kalman filter time updating and measuration updating at the moment of k to obtain one-step state prediction variance, one-step measuration prediction variance and cross covariance; 4, using a multiple gradual-fading factor for correction of the one-step prediction variance; 5, performing cubature kalman filter measuration updating again, acquiring state variance and state estimation variance at the moment of k +1; 6 if K + 1 = M (M is end moment of an attitude estimation nonlinear discrete system), outputting attitude quaternion and gyroscopic drift of state estimation of the moment of k +1 to complete the attitude estimation, if K + 1 < M, and k = K + 1, repeating steps 3, 4,and 5. The a norm constraint strong tracking cubature kalman filter method has the advantages of high estimation accuracy and strong robustness.

The invention discloses a Cubature Kalman Filter (CKF) based IMU/Wi-Fi (Inertial Measurement Unit/Wireless Fidelity) signal tightly-coupled indoor navigation method. The method comprises the steps of building RSS (Really Simple Syndication) fingerprinting database of Wi-Fi signals; locating by use of an IMU to obtain the PoIMU of the pedestrian position, the speed, the acceleration, the posture and other information; correcting the drifting of an IMU accelerometer through zero-speed correction to obtain a corrected speed; locating by use of the Wi-Fi signals to obtain the PoWi-Fi of the pedestrian position; finding n reference points nearby the PoIMU and n reference points nearby the PoWi-Fi, obtaining the distances diIMU and diWi-Fi from the measurement point to the reference points according to the positions of the given reference points, and then obtaining a distance difference delta d; and performing information fusion by CKF to obtain the position, the acceleration, the speed and the posture of a pedestrian. The method adopts the indoor Wi-Fi signals and the IMU information in an integrated manner, realizes information fusion by the CKF, and has the advantages of high filtering precision, high rate of convergence, strong robustness and high navigation precision.

The invention discloses a multi-robot cooperative location algorithm based on square root cubature Kalman filtering (SR-CKF) and belongs to the field of robot cooperative location. The whole algorithm includes two steps: predication and updating. Firstly, a robot motion equation and a predication equation are established and a relative azimuth is used as a measured value; furthermore, a multi-robot cooperative location dynamic model is obtained. A predication phase comprises: calculating a cubature point set; transmitting cubature points through a state equation; pre-estimating a robot pose state and predicating a square root factor. An updating phase comprises: calculating a cubature point set; calculating Kalman gains; calculating pose information and a square root factor; updating a state vector, a covariance matrix and the pose information. According to the embodiment of the invention, a target state mean value and the square root factor of the covariance matrix are directly transmitted in an updating process and the complexity of calculation is reduced; the symmetry and the semi-positive definitiveness of the covariance matrix are ensured, and the numerical value precision and the stability are improved.

The invention provides an improved strong tracking square-root cubature Kalman filtering method. A mechanism for increasing the robustness of a strong tracking algorithm through an attenuation factor and an SCKF algorithm flow are analyzed; therefore, ISTCKF reselects the introduction position of the attenuation factor; and thus, the extra calculation amount due to introduction of the attenuation factor is reduced.

The invention discloses a spacecraft attitude estimation method based on norm-constrained cubature Kalman filter. The method comprises the following steps: a state equation of a spacecraft navigation system is established; a non-linear measurement equation of the spacecraft navigation system is established; a discretized state equation and a discretized measurement equation are obtained; a norm-constrained cubature Kalman filtering algorithm is used for the discretized state equation and the discretized measurement equation, and an attitude of the spacecraft is outputted. The norm-constrained cubature Kalman filter is used, norm constraint of quaternion for describing the spacecraft attitude is introduced into the attitude estimation, so that the problem of singular covariance existed in the process of attitude estimation without considering norm constraint is effectively solved, the influences of norm constraint conditions of quaternion on the spacecraft attitude estimation are effectively corrected by only adding small computational complexity, precision of attitude estimation is improved, navigation precision of the spacecraft is furthermore improved, and stability of the navigation process is enhanced.

A mobile robot self-localization method assisted by a wireless sensor network achieves self-localization of a mobile robot in an environment covered by the wireless sensor network by adopting a sequential square root cubature Kalman filtering algorithm. The mobile robot continuously performs state prediction through internal sensor information, and continuously requests location services from peripheral nodes at the same time. After that, the mobile robot corrects a prediction state in real time according to sequence of received response location data packets. The mobile robot self-localization method provided by the present invention avoids natural environment feature extraction and data association, and improves adaptive capacity to environment of the mobile robot.

The invention discloses a nonlinear asynchronous multi-sensor information merging method based on a CKF (cubature Kalman filter). The respective state variables of each sensor are respectively estimated by using the CKF, then, a time breakdown method is adopted for setting the time interval of the information merging center as the highest precision time unit among all sensors, the estimation results of the asynchronous multi-sensor is judged and merged in the corresponding moment, and the more precise state variable estimation results can be obtained. The nonlinear asynchronous multi-sensor information merging method has the advantages that the utilization rate on the asynchronous multi-sensor information can be enhanced, the estimation precision of the state variable in the multi-sensor system is greatly improved, and the survival capability of the system is enhanced.

The invention discloses a method for estimating the dynamic state of a generator based on an adaptive H infinite cubature Kalman filter, and the method is used for enhancing the robustness of a dynamic state estimator under the condition of model parameter uncertainty, and the accurate estimation of the state of a power system generator is realized. According to the method, based on the H infinitecubature Kalman filter (HCKF), an adaptive H infinite cubature Kalman filter dynamic estimation method is established by introducing an adaptive method, the method can define the influence of systemmodel parameter uncertainty on a state estimation result and avoids the problem that a HCKF method has uncertain upper bound parameters and the selection is difficult. According to the method of the invention, the state estimation precision can be effectively improved, the robustness of a state estimator is enhanced, the realization process is clear, and the method has a high engineering application value.

The invention discloses an AUV cooperative navigation method of maximum correntropy adaptive cubature particle filter (MCACPF). The MCACPF is used for completing the state estimation problem in the AUV cooperative navigation process. During AUV cooperative navigation, a state equation and a measurement equation of the AUV cooperative navigation are reconstructed into a nonlinear recursive model, and then maximum correntropy cubature Kalman filter (MCCKF) is adopted in the framework of cubature particle filter (CPF) to generate an importance probability density function required in particle filter (PF), generated particles are then re-sampled by using a Kullback-Leibler distance (KLD) resampling method, and finally the estimation of an AUV state is obtained according to the algorithm flow of the CPF so as to realize the positioning of an AUV and complete the cooperative navigation. The MCACPF method is applied to the AUV cooperative navigation with outliers in measurement noise, higheraccuracy than existing PF, improved PF and robust filtering is obtained, and the computational complexity is lower than an existing improved particle filtering algorithm.