106 results about "Posterior probability density function" patented technology

The invention discloses a multi-vehicle signal separation method of wireless sensor network based on particle filter, comprising the steps of: collecting and detecting a sound mixing signal sent by the multi-target motion vehicle in scale by the sensing node, then initializing parameters of sequence important sampling method including particle number, span and weight of each particle and determining signal mixing matrix and prior probability function according to the system parameters, then obtaining the posterior probability function by Bayesian theory, calculating weight of each particle and normalizing to obtain normalized weight of each particle, then re-sampling the particle according to threshold value set, calculating a sound signal value of present time by weighing of all particles, repeating, finally obtaining estimation value of the sound value of all times to separate the multi-vehicle sound signal. The method of the invention is capable of processing non-Gaussian, non-linear mode and sound signal of non-stationary vehicle, which is easy to accomplish.

The invention discloses a target tracking method and system and an expansion truncation no-trace Kalman filtering method and device. The expansion truncation no-trace Kalman filtering method comprises the steps that an original prior probability density function is obtained according to no-trace conversion; a first posterior probability density function is obtained according to the original prior probability density function; the original prior probability density function is corrected according to the statistics linear regression theory and a target observation vector of the current target observation moment so that the corrected prior probability density function can be obtained; a second posterior probability density function is obtained according to the corrected prior probability density function; a combined posterior probability density function is obtained according to the original prior probability density function and the second posterior probability density function. According to the Kalman filtering method, the problem that the observation function does not have the only inverse function can be solved, prior distribution variances of target states are effectively reduced, state upgrading is carried out in a self-adaptation mode according to the precision of the observation information, the filtering precision is effectively improved, and the practicality is high.

The invention discloses a hydrological simulation method based on Bayesian mode average fusion multi-source data. The method comprises the following steps of firstly, collecting meteorological data, hydrological series, satellite inversion rainfall and a reanalysis air temperature data set of a ground station in a scarce data area; respectively establishing correction models of ground observationdata and a simultaneous simulation meteorological data set in different months by adopting a quantile mapping-based daily deviation correction method, a regression correction method and an equal ratecorrection method; then adopting a seasonal Bayesian mode averaging method, optimizing the weight of each deviation correction scene through a posterior probability density function to acquire a corrected long-series meteorological data set; and calibrating a basin hydrological model and a long-short-term memory neural network model according to actual measurement data, and finally inputting the corrected long-series meteorological data set to realize runoff process simulation. Long-series runoff simulation of regions with scarce data can be realized, and an important reference basis with highoperability can be provided for basin water resource management and planning.

The invention provides a method and an arrangement for detecting moving point-targets within a large set of noisy measurements. The method is based on Bayesian model selection where the measurements containing targets are modeled with their physical trajectories and the non-target measurements are modeled with the statistical distribution of measurements containing no targets. An a posteriori probability density function is utilized together with a optimization algorithm specifically designed for this problem. Advantages of the invention involve a numerically efficient formulation of the a posteriori probability density, combined with the optimization algorithm. The main applications of the invention are in detecting moving targets within e.g., radar, sonar, lidar and telescopic measurements. The method is also applicable for multi-instrument data fusion.