The resampling process continues until MPD satisfies the given condition. After resampling, the particle set consists of two subsets, one contains new born particles, and another contains unresampled particles.
Over the past decade, there has been a continued interest in the design of schemes for the implementation of particle filtering algorithms using parallel or distributed hardware of various types, including general purpose devices such as multi-core CPUs or graphical processing units (GPUs) [1] and...
Research on Receiver Autonomous Integrity Monitoring Algorithm Using Genetic Algorithm Resampling Particle FilterGlobal navigation satellite systemReceiver autonomous integrity monitoringParticle filterGenetic algorithmsWith the rapid development of global navigation satellite system, Receiver Autonomous Integrity ...
Because of this reason, sub-optimal filters are required in order to at least approximate the solution. One of the most used sub-optimal filters is the Particle Filter (PF), which is based on the Sequential Monte Carlo (SMC) method. The main idea of this filter is to exploit recursively...
To test the proposed DQPF, several groups of experiments on the target tracking problem are compared to the other four improved particle filters. Two state-of-the-art classical particle filters: standard Systematic Resampling particle filter (SR) [26] and Compressed Monte Carlo Resampling particle ...
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This...
The primary filtering algorithms used for SOC estimation are the Kalman filters [11–13] and the particle filters [14–16]. For example, a hierarchical adaptive extended Kalman filter algorithm for estimating battery SOC is presented [12]; An improved firefly optimized particle filter is studied ...
We also describe how, when the analytic solution is intractable, extended Kalman filters, ap- proximate grid-based filters, and particle filters approximate the optimal Bayesian solution. III. OPTIMAL ALGORITHMS A. Kalman Filter The Kalman filter assumes that the post...
A novel deep learning-based approach is proposed under the encoder-separator-decoder architecture, which is first applied to solve the SCBS problem for co-frequency modulated communication signals as far as we know. Distinguishing from traditional algorithms, the proposed deep network no longer requires...
Before delving into the use of Bayesian filters for tracking the mobile robot, it is important to assess the degree of Gaussianity of the measures in the database. The aforementioned database serves to test positioning algorithms, which sometimes resort to the Gaussian assumption, and thus their ...