The Gaussian mixture probability hypothesis density (GM-PHD) smoother proposed recently can yield better state estimates than the GM-PHD filter. However, there are two major problems with it. First, the smoothed PHD distribution can not provide a more accurate target number estimate due to the target number estimation bias becoming larger by smoothing. Second, the computational complexity of co...

Biomedical signal monitoring and recording are an integral part of medical diagnosis treatment control mechanisms. For this, enhanced signals with appropriate peak preservation required. The OWA (OrderedWeighted Aggregation) Filter used in this paper helps non-linear filtering peaks for accurate diagnosis. Weights important aspect the filter, Gaussian method KDE (Kernel Density Estimation) func...

Radio-based vehicular simultaneous localization and mapping (SLAM) aims to localize vehicles while the landmarks in environment. We propose a sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters, which handle entire problem theoretically optimal manner. The complexity proposed filters is progressively reduced sustaining high accuracy by deriving density approximation with...

Particle filters are fully non-linear data assimilation techniques that aim to represent the probability distribution of the model state given the observations (the posterior) by a number of particles. In high-dimensional geophysical applications the number of particles required by the sequential importance resampling (SIR) particle filter in order to capture the high probability region of the ...

Unmanned aerial vehicles (UAVs) will become an indispensable part of future sixth-generation (6G)-based mobile networks that can provide flexible deposition, strong adaptability, and high service quality. Under the guarantee blockchain, UAVs efficient communication or computing services for ground intelligence devices promote development wireless communication. However, as number increases, iss...