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The wide-sense auto-regressive moving-average (ARMA) model is widely applied into varieties of fields. The unknown bounded parameter estimation of an ARMA model is an extremely vital research subject. Up to recent, most research is conducted with the known disturbing environment noise or the model of the known noise with the unknown variance. Actually the disturbing noise in the modern control system...
Tracking single or multiple maneuvering targets is an urgent need for defense. In order to meet the military requirement, we propose a modified clustering-based Rao-Blackwellized particle filter (CBRBPF) to track single or multiple maneuvering targets with observations received by single or multiple sensors. The modified RBPF is basing on the clustering-based data association method. We partition...
A loosely coupled INS/GPS integrated navigation system is a nonlinear dynamic system. A particle filter (PF) is a particular tool for the nonlinear and non-Gaussian problems. However typical bootstrap particle filters (BPFs) cannot solve the mismatch between the importance function and the likelihood function very well so that they are invalid to some extent in the application of the INS/GPS integrated...
Device-free localization (DFL) is an emerging wireless network target localization technique that does not need to attach any electronic device with the target. It is remaining as a challenging research problem due to the weak wireless signals and the uncertain wireless communication environment. In this paper, a novel Gaussian Process (GP) based wireless propagation model is proposed to describe...
This paper compares the tracking performance that can be achieved when using a nonlinear drag model for a helicopter, a constant drag motion model, and a baseline constant acceleration model. A particle filter is used for state estimation to address problems associated with nonlinear drag and nonlinear measurements of helicopter pose. We demonstrate that the inclusion of this nonlinear kinematic effect...
Particle filters are a widely used tool to perform Bayesian filtering under nonlinear dynamic and measurement models or non-Gaussian distributions. However, the performance of particle filters plummets when dealing with high-dimensional state spaces. In this paper, we propose a method that makes use of multiple particle filtering to circumvent this difficulty. Multiple particle filters partition the...
This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The...
This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these...
This paper proposes a method of searching for missing people in mountains by UAVs (Unmanned Aerial Vehicles) based on beacon signals. This method alternately updates the distribution of estimated target position and unknown parameters by particle filtering, and determines the next best observation location based on the idea of the uncertainty sampling. We will show how this method can be integrated...
This paper proposes an improvement to FastSLAM. The approach is applicable when the dynamic model describing the motion of the camera has linear sub-structure. The core novelty of the proposed algorithm is to separate the consideration of the camera's dynamic model into two sub-models without constraining the two sub-models to have independent noise processes. In contrast to commonly-used FastSLAM...
Particle Filter (PF) is a popular sequential Monte Carlo method to deal with non-linear non-Gaussian filtering problems. However, it suffers from the so-called curse of dimensionality in the sense that the required number of particle (needed for a reasonable performance) grows exponentially with the dimension of the system. One of the techniques found in the literature to tackle this is to split the...
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