9 results about "Probability hypothesis density filter" patented technology

The application belongs to the technical field of wireless positioning, and particularly relates to a synchronization positioning and mapping method based on millimeter wave communication multipath transmission signals. By establishing a multi-antenna virtual base station model, non-line-of-sight paths are equivalent to virtual line-of-sight paths transmitted by the multi-antenna virtual base station, the state of the virtual base station is described by position and azimuth angle, thereby simplifying the relationship between the geometric parameters of the non-line-of-sight transmission path and the state of the user, and the measurement information of the non-line-of-sight path transmission signals is more effectively utilized; secondly, the multi-antenna virtual base station is regarded as a map feature in a spatial radio frequency environment, a mobile user synchronization positioning and mapping algorithm based on millimeter wave multipath parameter measurement is proposed, the multipath parameter measurement set and the map feature set are modeled as a random finite set, and a probability hypothesis density filter is used to estimate the map, thereby significantly improving the synchronization positioning and mapping performance in the dense clutter and sensor missed detection scenarios.

The application relates to a Gaussian mixture probability hypothesis density filtering method based on clutter density estimation. First, potential target measurement and clutter measurement are estimated by combining multi-target Gaussian mixture intensity prediction value and measurement value, then the clutter density around each target is estimated and combined with a Gaussian mixture probability hypothesis density filter, the predicted Gaussian component is updated by the potential target measurement, and the multi-target state is estimated in combination. The method disclosed by the application does not depend on prior clutter distribution, can effectively estimate the distribution of the clutter, especially can effectively and continuously track multiple targets in a complex scene, improves the real-time performance of tracking, and improves the tracking precision of the target.

The invention belongs to the technical field of wireless positioning, and particularly relates to a synchronous positioning and mapping method based on millimeter wave communication multipath transmission signals. By establishing a multi-antenna virtual base station model, a non-line-of-sight path is equivalent to a virtual line-of-sight path transmitted by a multi-antenna virtual base station, and the state of the virtual base station is jointly described by a position and an azimuth angle, so that the relationship between geometric parameters of a non-line-of-sight transmission path and the state of a user is simplified; the measurement information of the non-line-of-sight path transmission signal is effectively utilized; secondly, taking a multi-antenna virtual base station as a map feature in a spatial radio frequency environment, providing a mobile user synchronous positioning and mapping algorithm based on millimeter wave multipath parameter measurement, modeling a multipath parameter measurement set and a map feature set into a random finite set, and estimating a map by adopting a probability hypothesis density filter, and the synchronous positioning and mapping performance in a dense clutter and sensor missing detection scene is remarkably improved.

The invention provides a distributed multi-target fusion tracking method and device based on time calibration, and relates to the technical field of sensors. The distributed multi-target fusion tracking method based on time calibration comprises the following steps: on the basis of a generalized covariance cross fusion criterion, constructing a posterior probability density expression of a time calibration parameter by using a Gaussian mixture approximation technology and a probability hypothesis density filter; selecting a mean value of the Gaussian component with the maximum weight from a plurality of Gaussian components of the posterior probability density expression as an estimated value of the time calibration parameter; and based on a generalized covariance cross fusion criterion, fusing the first multi-target probability density function and the calibrated second multi-target probability density function to obtain a multi-target state estimation value of the tracking target. According to the invention, data from a plurality of sensor nodes can be efficiently and accurately fused in a distributed multi-target tracking scene so as to realize accurate positioning of a tracking target.

The invention belongs to the technical field of mobile robot navigation, and particularly relates to a robot joint SLAM and multi-target tracking method based on a random finite set. A mobile robot, a plurality of static landmarks and dynamic targets exist in the scene, and the positions of the static landmarks and the dynamic targets are unknown, so that the positioning method comprises joint positioning of the robot, the static landmarks and the dynamic targets. Distance, angle and category measurement values acquired by a sensor can be combined, and a Gaussian mixture probability hypothesis density filtering and multi-model interaction method is used for solving, so that robot self-positioning, environment mapping and target tracking are realized. The positions of the maneuvering target and the static environment can be accurately estimated, the method is simple, and the effect is good.

The application discloses a target tracking method of PHD filtering with adaptive measurement noise and detection probability, and mainly solves the problem that the traditional multi-target tracking filtering algorithm cannot accurately track targets, resulting in the decline of the performance of the filter under the condition that the measurement noise covariance and the detection probability are both unknown in the multi-target tracking scene. The method models the measurement noise covariance and the detection probability with inverse gamma distribution and beta distribution respectively on the basis of the traditional Gaussian mixture probability hypothesis density filter (GM-PHD), updates the weight values of the PHD of the detected and missed targets in the posterior update step by using the properties of the beta function, and then decouples the state and the measurement noise covariance by using the variational Bayesian method, so that the variational iteration approximates the real posterior distribution, and the joint estimation of the target state, the measurement noise covariance and the detection probability is realized. Compared with the traditional tracking algorithm, the application has better tracking performance under the condition that the measurement noise covariance and the detection probability are unknown.

The invention discloses a multi-target tracking method and system based on Gaussian process interactive multiple models and adaptive noise, and belongs to the technical field of multi-target tracking. In order to solve the problems of serious model mismatch, low tracking precision and easy track loss in a sudden strong maneuvering complex scene, the invention constructs a heterogeneous motion model library comprising a standard motion model, a Gaussian process GP-based motion model and an adaptive noise maneuvering model; expanding and defining each Gaussian component in a GM-PHD (Gaussian Mixture Probability Hypothesis Density) filter as an IMM (Interactive Multi-Model) structure embedded with the heterogeneous motion model library; in the prediction and update cycle of a filter, in the IMM structure of each Gaussian component, probability interaction, parallel prediction and parallel update and fusion based on sensor measurement are carried out on the multiple motion models, an updated Gaussian component set is obtained, pruning and merging are carried out, and new target components are generated; and multi-target tracking is completed.

The present application belongs to the technical field of multi-target tracking application, and particularly relates to a nonlinear multi-target tracking method based on a conversion state probability hypothesis density filter. The method reconstructs a nonlinear system of multi-targets into a linear system by using a conversion state, and obtains a Gaussian mixture probability hypothesis density algorithm of the conversion state by combining with a GM-PHD filter. Compared with a traditional nonlinear multi-target tracking method, such as a GM-PHD of an extended Kalman filter and a GM-PHD of an unscented Kalman filter, the present application has higher tracking precision and lower execution time.

The invention discloses a multi-sensor fusion method and device based on a probability hypothesis density filter. The method comprises the following steps: constructing a one-transmitting three-receiving external radiation source sensor system, adjusting the measurement noise intensity of each receiving sensor through a noise coefficient, and simulating unevenness of data; each sensor independently executes SMC-PHD filtering to generate a target state particle set; and carrying out distance and speed DV fusion, dynamically matching a multi-sensor target based on a distance and speed direction dual judgment criterion, carrying out weighted summation on a particle set of the matched target according to a weight, and outputting a fused target state. The device comprises a system construction module, a particle set generation module and a fusion module, and is used for realizing the multi-sensor fusion method based on the probability hypothesis density filter. According to the invention, the low-quality sensor data can be automatically filtered, and the fusion efficiency and the fusion precision are improved without depending on a prior weight condition.