The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementations of this technique have been developed. The first of which, called the Particle PHD filter, req...

When the extension state of non-ellipsoidal extended target (NET) changes, performance traditional multiple tracking algorithms based on constant number sub-objects will decrease. To solve this problem, paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for targets with varying sub-objects, called VN-NET-GGIW-PHD filter. In proposed filter, each NET is conside...

The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Stateof-the-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules to control the growth of hypotheses. T...

When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a model-data association problem. Another approach to solve the problem with computational complexity is to track only the first...

When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a model-data association problem. Another approach to solve the problem with computational complexity is to track only the first...

The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) algorithm is a parametric track-before-detect method. It locates targets in imagery by fitting a mixture of probability densities. In recent work, an unknown target shape was accommodated through the use of a Wishart prior on the measurement probability density, this version of the algorithm is referred to as H-PMHT-RM. This paper ap...

We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account the general lack of knowledge that target-tracking practitioner must deal with when working real data. also introduce study variants notions point process intensity function, which lead derivation an analogue probability hypothesis density (PHD) filter. The gains provided...

This paper considers the object detection and tracking problem in a road traffic situation from participant’s perspective. The information source is an automotive radar which attached to ego vehicle. scenario characteristics are varying visibility due occlusion multiple detections of vehicle during scanning interval. goal maintain report state undetected though possibly present objects. propose...