Superpositional sensor model can characterize the observations in many different applications such as radio frequency tomography, acoustic sensor network based tracking and wireless communications. In this paper we present two filters based on the random finite set (RFS) theory the multi-Bernoulli filter and its variant the hybrid multi-Bernoulli CPHD filter for superpositional sensors. We prov...

To maintain custody of the increasing number of detectable objects in Earth orbit, tracking systems require robust methods of multi-target state estimation and prediction. One alternative to the classic multiple hypothesis and probabilistic data association methods uses a random finite set for modeling the multi-target state. The common forms of such filters sacrifice knowledge of specific targ...

Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended ...

An extended CPHD (Cardinalised Probability Hypothesis Density) filter for combining multi-target tracking with sensor alignment is proposed. The augmented state is established by appending the sensor biases to the single-target state. The cardinality distribution of the targets and the intensity of the augmented state are propagated by employing Gaussian mixtures. The target states and the sens...

The probability hypothesis density (PHD) methodology is widely used by the research community for the purposes of multiple object tracking. This problem consists in the recursive state estimation of several targets by using the information coming from an observation process. The purpose of this paper is to investigate the potential of the PHD filters for real-time traffic state estimation. This...

A finite set statistics (FISST)-based method is proposed for multi-target tracking in the image plane of optical sensors. The method involves using signal amplitude information in probability hypothesis density (PHD) filter which is derived from FISST to improve multi-target tracking performance. The amplitude of signals generated by the optical sensor is modeled first, from which the amplitude...

In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches in finite set statistics (FISST). Our solution is based on the modeling of the measurements and the landmark map as RFSs and factorizing the MVSLAM posterior into a produc...

Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) lter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-t...

Random processes of bounded variation are generated by using of randomized sinusoidal model and nonlinear filter model. In the randomized sinusoidal model, random noises ar\e introduced in phase angles; while in the nonlinear filter model, a set of nonlinear Itô differential equations are employed. In both methods, the spectral density of a modeled random process can be matched by adjusting mod...