18 results about "Probability hypothesis density filter" patented technology

The invention discloses a multi-unmanned aerial vehicle cooperative search and coverage optimization method based on an improved Lloyd algorithm, and is suitable for a target search task in a dynamic environment. Firstly, target distribution is perceived and estimated in real time by using a probability hypothesis density filter, and a target probability map is dynamically updated, so that the response capability and search precision of a system to target state change are improved; secondly, a multi-graph fusion mechanism is adopted, a target probability map, an uncertainty map and a search pheromone map are subjected to dynamic weighted integration, priority sharing and updating of key area information are achieved, and the cooperation efficiency and the communication resource utilization rate among multiple unmanned aerial vehicles are improved; and finally, based on an improved Lloyd algorithm, introducing a multi-step prediction mechanism and a direction-guided centroid updating strategy, optimizing search path generation, and preventing the path from being converged to a low-value region. Through the method of the invention, the system can realize adaptive and efficient search and coverage control in a complex dynamic environment, and stability, real-time performance and overall robustness of task execution of multiple unmanned aerial vehicles are significantly improved.

The invention discloses a multi-target tracking method based on asynchronous weighted time sequence optimization, a program, equipment and a storage medium, and belongs to the field of underwater multi-UUV cooperative detection of multiple targets. The method comprises the following steps: firstly, setting a tolerance time threshold value related to a target speed and an allowable error, approximating asynchronous data at a short time interval as synchronous data, identifying and screening active and passive sonar information from the same target by virtue of an association algorithm, and eliminating random clutters; then, the target detection precision is improved through a minimum mean square error weighted fusion method; and finally, inputting the optimized detection data into a probability hypothesis density filter to complete updating estimation of the multi-target state and number. According to the algorithm, the multi-target state estimation precision is improved, the excessive estimation phenomenon of the number of the targets is effectively reduced, the problem of time sequence asynchronization of active and passive sonar detection information in a multi-target detection environment can be solved, and the challenges of high-density clutter aliasing on multi-target state estimation precision reduction and excessive estimation of the number of the targets can be solved.

The invention discloses an improved trajectory extraction multi-maneuvering target tracking method, which comprises the following steps of: firstly, finishing long-time and continuous trajectory extraction of various targets including derivative targets, new targets and the like by adopting a labeled nearest neighbor trajectory management algorithm; then, introducing a multi-model thought, and proposing a multi-model Gaussian mixture probability hypothesis density filtering algorithm based on a jump Markov process; based on a tagged'nearest neighbor 'trajectory management algorithm and a multi-model Gaussian mixture probability hypothesis density filtering algorithm, a tagged jump Markov Gaussian mixture probability hypothesis density filtering algorithm is designed, and effective tracking and trajectory extraction of multiple maneuvering targets in a complex environment are realized. According to the method, the application scene of the Gaussian mixture probability hypothesis density filtering algorithm is expanded, the target trajectory can be accurately and effectively extracted in the face of a multi-target tracking scene including derivative target hypothesis, and the tracking precision under the complex condition is improved.

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.

This invention provides a maritime target tracking algorithm based on a Gaussian mixture potential probability hypothesis density filter (GMDP). The algorithm addresses the performance degradation issues of the GMDP when facing low, slow, and small targets with strong clutter and complex maneuvers at sea, such as false alarms and missed detections. First, a multi-model algorithm is introduced to match various target motion states based on the GMDP. Second, a joint position-Doppler gate is established by utilizing the correlation between the target's Doppler and position measurements to jointly filter the measurement information. Finally, the target is sequentially updated based on the position update. Comparative simulation results show that the proposed algorithm can improve the stability of target number estimation and enhance tracking accuracy.

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 present invention discloses a radar robust tracking method for maneuvering flight targets. It can achieve high-precision tracking of maneuvering targets in high-clutter environments and improve track quality. Under the framework of the Gaussian mixture probability hypothesis density filtering algorithm, multi-target state filtering results are obtained based on measurement information. First, the target state filtering results are input into the residual correction network, and the feature extraction of the time series is realized from the forward and reverse dimensions based on the bidirectional long short-term memory network; then, the outputs of different bidirectional long short-term memory network layers are weighted based on the multi-level attention mechanism. At the same time, the importance of the state of the input time series at different times to the network model training is captured based on temporal attention, thereby improving the feature extraction capability of the network and enabling it to adapt to the residual correction processing requirements of complex maneuvering targets. After processing by the residual correction network, the filter trajectory can be further smoothed, thereby improving the tracking accuracy of maneuvering targets in clutter environments.

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 provides an air maneuvering multi-target three-dimensional tracking method based on volume mean square Kalman filtering, and relates to the technical field of radar signal processing, and the method comprises the following steps: building an air maneuvering multi-target three-dimensional cooperative turning motion model and a three-coordinate radar measurement model; performing prediction filtering on a Gaussian item in the multi-target probability hypothesis density function through a volume mean square Kalman filter; updating a mean value and a covariance matrix of Gaussian items through a volume-mean-square Kalman filter based on the measurement position and Doppler information, and obtaining a multi-target track Gaussian component estimation set; performing pruning and merging operation on the Gaussian component to reduce the complexity of the system; and performing multi-target number estimation and state extraction according to the pruned and merged Gaussian components. According to the method disclosed by the invention, the high-precision advantage of the volume mean square Kalman filter and the multi-target tracking capability of the probability hypothesis density filter are utilized, so that the air maneuvering multi-target tracking and multi-target number estimation performance superior to that of a traditional method is realized.

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 invention provides an intra-group target number estimation method based on a potential probability hypothesis density filter, which utilizes the characteristic that the potential probability hypothesis density filter can track unknown targets in a complex environment to construct multiple hypotheses of the unknown target number corresponding to indistinguishable measurement through amplitude information. The method is introduced into a potential probability hypothesis density updating step so as to improve the accuracy of intra-group target number estimation, real targets and clutters are further distinguished through an elliptical gating technology, and the calculation amount is reduced. When the resolution of the sensor is limited, accurate estimation of the number of the intra-group targets of the dense group targets can be realized, and the estimation precision of the number of the group targets is obviously improved; the method is suitable for a dense clutter environment. When the number of the targets in the group is accurately estimated, the motion state of the group targets can be effectively estimated.

The invention discloses a multi-target tracking method based on Gaussian mixture probability hypothesis density filtering, and belongs to the technical field of multi-target tracking. The problems of target tracking missing and track mistaken deletion in a high-density and near multi-target scene are solved. The method comprises the following steps: acquiring measurement data corresponding to each sensor, and distributing a measurement identifier corresponding to the measurement data; updating the Gaussian component of each sensor, and setting a measurement source label of the Gaussian component as a measurement identifier corresponding to the measurement data of the sensor; establishing an adaptive pruning and merging decision model; inputting all Gaussian components into an adaptive pruning and merging decision-making model, and carrying out merging or pruning processing to obtain retained Gaussian components; and setting a state extraction weight threshold, comparing the retained Gaussian components, and outputting a mean value of the Gaussian components of which the weights are higher than the state extraction weight threshold as a finally extracted target state. According to the method, the multi-target tracking precision, robustness and continuity are remarkably improved.

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 particularly relates to a Gaussian mixture distribution homomorphic encryption fusion method based on key information packaging. The method comprises the following steps: establishing a state equation of target motion and a sensor measurement model; predicting and updating a target state by using a Gaussian mixture probability hypothesis density filter to obtain posterior probability density distribution of each sensor node, and performing pruning and merging processing to obtain a target posterior estimation Gaussian set; converting a mean value and a covariance in the posteriori estimation Gaussian set of each sensor node into an information vector and a precision matrix; uniformly quantizing the information vector and the precision matrix, and encrypting quantized data through a Paillier homomorphic encryption method; and transmitting the encrypted data to a fusion center for geometric mean fusion, and carrying out decryption and inverse quantization on the encrypted data by the fusion center to obtain a state estimation result of the target. According to the method, under the condition that the original fusion precision is not lost, the leakage risk of key information in the fusion process is further reduced.

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.