47 results about "Probability hypothesis density filter" patented technology

The invention discloses an extended target probability hypothesis density filtering method based on cubature Kalman filtering. The method comprises the steps of (1) pre-setting the Gaussian mixture form of posterior strength of the moment k-1 and obtaining the mean value and covariance of the ith Gaussian item, (2) conducting one-step prediction on the weight, mean value and covariance of the ith Gaussian item obtained from the first step, and (3) conducting measurement updating on the prediction result obtained from the second step to obtain the estimated value of each Gaussian component (as stated in the specification) of the moment k. According to the extended target probability hypothesis density filtering method based on cubature Kalman filtering, extended target tracking is achieved under a nonlinear system, extended target tracking is achieved when the Jacobian matrix of a nonlinear function does not exist or is hard to solve, and a new realization approach is provided for extended target tracking under nonlinear conditions.

The invention discloses a flight track extraction method based on a probability hypothesis density filter associated with global time and space. The flight track extraction method comprises the following steps: S1, extracting a target state by using the probability hypothesis density filter; S2, measuring consistency and calculating consistency confidence; and S3, obtaining a global flight track extraction strategy. By adopting the scheme, the target state is firstly obtained by using the probability hypothesis density filter; then the consistency between a predicted peak value and an estimated peak value is measured by using global time-space information and the consistency confidence is calculated; simultaneously four decision rules based on expert knowledge of the flight track extraction are shown, namely a rule for judging inseparable targets, a rule for judging homology of the targets, a rule for judging disappearance of the targets and a rule for judging novel targets; a global flight track extraction strategy is shown based on the consistency confidence and four decision rules, thereby extracting flight tracks of a plurality of targets, improving the flight track extraction effect, improving the flight track extraction accuracy and playing an important role in multi-target tracking engineering application.

The invention belongs to the field of radar target tracking, and particularly relates to a multi-maneuvering-target Doppler radar tracking method based on Gaussian mixture probability density hypothesis. The method comprises the steps: firstly, introducing pseudo measurement to replace target radial velocity measurement obtained by a Doppler radar, then, introducing a measurement conversion methodbased on predicted value information to process position measurement and pseudo measurement, and meanwhile, carrying out decorrelation on the pseudo measurement and the position measurement; by adopting a Gaussian mixture probability hypothesis density filtering method and by means of a multi-model framework, aiming at the correlation between Gaussian components and models, carrying out differentprocessing on the Gaussian components of surviving, newly-born and derivative targets; for Gaussian components irrelevant to the models, namely, the newly-born and derivative Gaussian components, directly estimating the states thereof; for the Gaussian components related to the models, that is, the surviving Gaussian components, obtaining the model probability of each model filter and the model condition distribution of the updated components, and then, fusing the models and the condition distribution of the updated components to obtain state estimation, wherein introducing sequential filtering during filtering of the weight, the mean value, the covariance and the like of the Gaussian components, and obtaining position estimation according to position measurement; and performing sequential processing on the position estimation by using pseudo measurement to obtain final state estimation.

The invention discloses a weak target detection and tracking method based on box particle probability hypothesis density filter; the method comprises the following steps: using a box particle PHD filter method to process a weak target under low signal to noise ratio conditions, using mean value filtering to weaken single point edge noises so as to highlight a target located area, and taking the target located area as section measurement according to the maximum uncrossed principle; using dozens of box particles to replace several hundreds of point particles, thus effectively reducing the calculating complexity, improving the calculating efficiency, and obtaining a more accurate target state in the same time.

The present invention discloses a probability hypothesis density filtering radar spatial error registering method under an ECEF (earth-centered Earth-fixed coordinate) coordinate system. According to the method, the observation data of a target in two observation radars are transformed to rectangular coordinate systems with the radars adopted as centers respectively; the data are transformed to the ECEF coordinate system, so that the common-observation relationship of the target between the two radars is built; on the basis of the common-observation relationship, a radar system error observation equation and a state equation under the ECEF coordinate system are established; a probability hypothesis density filter is used to perform fusion estimation on radar system errors under the ECEF coordinate system; and therefore, spatial error registration of the two radars is realized. The method of the invention has the advantages of no need for data association processing, high estimation accuracy, good estimation performance and the like.

The invention is suitable for the field of multi-sensor information fusion and provides a probability hypothesis density filter target information maintaining method. The probability hypothesis density filter target information maintaining method includes: step 1, forecasting posterior moments and Gaussian items at the current moment according to posterior moments and Gaussian items of the last moment; step 2, updating the posterior moments and the Gaussian items according to the posterior moments and the Gaussian items of the current moment and a measurement set of the current moment; 3, cutting down or combining the updated Gaussian items; step 4, extracting a weight Gaussian item as output of a filter according to the cut down and combined Gaussian items, wherein means and variances in the corresponding Gaussian items are state estimation and error estimation of a survival target. By means of the hypothesis density filter target information maintaining method, information of a target with detection leaked is retained in a posteriori updating moment by amending an updating function of a probability hypothesis density filter, so that information of the target with detection leaked cannot be missing, effectiveness in target number estimation and target state extraction is improved, and further a multi-target tracking capability of the Gaussian probability hypothesis density filter is improved.

The invention discloses a multi-sensor multi-target tracking method based on posterior track estimation, and the method comprises the steps: obtaining the posterior track probability hypothesis density of each sensor and a corresponding posterior track estimation set through a track probability hypothesis density filtering method, and constructing an association cost matrix between different sensors; dividing posterior tracks in the posterior track estimation sets of different sensors into associated tracks and non-associated tracks; fusing the probability hypothesis densities of the associated tracks among different sensors to obtain a fused associated track probability hypothesis density; error tracks in the non-associated tracks among different sensors are removed, the probability hypothesis density of the non-associated tracks after the error tracks are removed is fused with the probability hypothesis density of the fused associated tracks, and the probability hypothesis density of the fused tracks among the different sensors is obtained; and according to the fusion track probability hypothesis density among different sensors, extracting to obtain a multi-sensor estimated track state. According to the invention, the communication load is obviously reduced, and the problem of missing detection in GCI fusion is solved.

The invention discloses a PHD filtering method for tracking non-rigid multiple extended targets, which comprises the following steps of: firstly, modeling key points of target extension by using a space-time Gaussian process, and constructing states of the extended targets by using internal reference points of kinematics; employing a probability hypothesis density filter to propagate a first moment of the non-rigid multi-extension target random finite set; establishing a measurement tracking gate to select targets and corresponding measurement, then grouping the targets and the measurement to construct the suitable prediction likelihood of optimal division of the non-rigid multi-extension targets, and providing filter recursion and necessary approximation and hypothesis; and finally, converting the posterior density is converted into a Gaussian mixture form, so that the closed-form solution of the non-rigid target and the closed-form solution of the corresponding smoothing filter are obtained, and the number, the motion state and the shape of the non-rigid target can be accurately estimated in the presence of clutter, leak detection and noise.

The invention proposes an unknown clutter passive cooperative positioning method based on sparseness order optimization. This method embeds the sparsity order optimized clutter density estimator into the Gaussian mixture probability hypothesis density filter to estimate the states and numbers of multiple targets. First, the target measurement is eliminated by threshold technology and target state feedback to obtain clutter measurement; secondly, samples are selected from the clutter measurement, and the samples are fitted by GA-SVR; finally, the extreme point of the fitting curve is obtained by the gradient method. The abscissa of the value point is rounded down to the optimal sparsity order n. The invention can effectively improve the passive cooperative positioning multi-target tracking performance under unknown clutter, and solve the difficult problem of multi-target tracking.