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Under the common state space model for tracking a maneuvering target, the tracker needs to adapt its state transition model timely to match the target maneuver, which is usually carried out by finding the best one from a bank of candidate Markov models or employing all of them simultaneously but assigning different probabilities. Both methods suffer from time delay for confirming the target maneuver...
In this paper we present a method for the tracking of interacting targets disregarding whether or not the targets are close to each other. The method relies on parametric modeling of assumptions about targets interactive motion. Our filtering solution incorporates the parameters of the model in the state vector to perform on-line parameter estimation and exploitation. The proposed method is applied...
This paper investigates the box-particle filter for multi-target tracking, and proposes a clustering based box-particle implementation of PHD filter. A subdivision step is added before the estimation of states. Each box is divided into several sub-box based on the estimated number of targets. An equivalent set of particles can be extracted from the set of subdivided boxes. Then, clustering technique...
A Cubature Kalman Filter with noise estimator is proposed to solve the problem that the selection of the statistic property parameter is not reasonable, which leads to filtering algorithm declining in accuracy and even diverging, when the noise statistic property is unknown in the bearing only target tracking of Unmanned Underwater Vehicle. This algorithm can estimate the noise statistic property...
In this paper, sparsity-promoting sensor selection algorithms for target tracking with quantized data are developed. We formulate sensor selection as an optimization problem that aims to strike a balance between estimation accuracy and the number of selected sensors. To cope with sensor selection problems in large-scale wireless sensor networks (WSNs), we propose a fast centralized optimization algorithm...
We study the problem of distributed state estimation over adaptive networks, where agents collaborate to estimate a common state parameter vector. If the sensing target area is too large or we want to improve the convergence speed of a large adaptive network, single-level diffusion algorithms do not have better performance, so we study the multi-level diffusion Kalman filter algorithm where a network...
Direction-of-arrival (DOA) estimation and tracking of signals using passive sensor arrays is a classic problem that becomes challenging when the number of sources varies over time and the signal-to-noise ratio is low. In this paper, we pose this problem as minimum mean OSPA (MMOSPA) estimation, which minimizes the the optimal sub-pattern assignment (OSPA) metric of the posterior random finite set...
Target tracking is a hot topic for unmanned aerial vehicle surveillance. Recently, the novel random sample consensus (RANSAC) algorithm shows a good tracking performance in dense clutter environment. However, the heavy computational burden limits the usage for unmanned aerial vehicle (UAV). In this paper, a density-based recursive random sample consensus (DBR-RANSAC) algorithm is proposed, which utilizes...
Purpose of the paper is to present a solution for the target height estimation problem for multi-frequency multi-static passive radar. Aim of the proposed estimation strategy is to provide 2D passive sensors, which measure only bistatic range and Doppler, with target height estimation capability via the simultaneous exploitation of multiple FM transmitters (88–108 MHz). Results achieved with simulated...
This paper presents two approaches considering a distributed framework for joint optimization of sensor coverage for target detection and target tracking for maximizing estimation performance for multi-agent systems. The first algorithm is based on the Lloyd algorithm, which uses a centroid of Voronoi partitions, one of the workarounds of sensor coverage problems. The other algorithm is based on the...
Due to the limited bandwidth of underwater communication links, underwater cooperative localization usually adopts a distributed processing architecture. Members of the team estimate positions using their local sensor data, and fuse the information communicated by other members for cooperation. It is common practice to naïvely assume independency during information fusion between cooperative members...
Aiming at the tracking failure in dynamic scene with fixed template and drift problem caused by using dynamic model in visual tracking, an improved target tracking algorithm with two-phrase is proposed. Dimension reduction on positive and negative samples is made in high dimension feature space using partial least squares (PLS), and then the appearance model of target is constructed in learned low...
The intelligent measuring system must provide for changing the parameters of the operation algorithm in order to increase the measurements accuracy. In addition it must have a self-monitoring function realized, for example, on the basis of intelligent sensors. One of the common problem of intelligent systems and intelligent sensors is the automatic error correction as a result of external factors...
Relative state estimation is an important component in multi-vehicle applications in GPS-denied environments. In this paper, we consider relative heading estimation between two Unmanned Aerial Systems (UAS). We examine observability properties of relative states between the two UAS in the presence of constant disturbances. We assume that UAS 2 measures UAS 1's position in its own frame. We first present...
In order to increase the tracking performance of ballistic targets, various estimation algorithms have been implemented in the literature. Extended Kalman Filter is one of the most widely used estimation algorithm which uses the nonlinear system and measurement models and linearization methods to estimate the state and state covariances. In the first part of this study, a ballistic coefficient state...
This paper proposes a system for the motion characteristics estimation of multiple objects with uncertain quantity. The system employs background subtraction method based on Gaussian Mixture Model to detect objects and tracks them through an improved algorithm combining Camshift with Kalman filtering. By analyzing the directions and trails of targets, the system can figure out their pixel distance...
Traditional kernelized correlation filter tracking methods use the target position in the current frame to estimate the moving target initial position in the next frame. For fast moving target, these methods lose the target easily. To cope with this problem, a novel scale-adaptive regression position prediction tracking approach is proposed. This algorithm employs regression prediction method to predict...
Target tracking in a network of wireless cameras may fail if data are captured or exchanged asynchronously. Unlike traditional sensor networks, video processing may generate significant delays that also vary from camera to camera. Moreover, the continuous and rapid change of the dynamics of the consensus variable (the target state) makes tracking even more challenging under these conditions. To address...
One of the most important challenges in target tracking is the modeling of correlated and non-Gaussian random processes. In this paper, a new target tracking approach by means of particle filtering in environments with highly correlated sensors, is discussed. The goal is to provide an accurate model of dependency structure in multivariate observation likelihood function, with non-Gaussian marginals...
In this paper, single-target tracking using radar measurements is addressed. Recently, algorithms based on Bernoulli random finite sets have proved efficient in a cluttered environment. However, in Bayesian approaches, the choice of the motion model impacts the trajectory estimation accuracy. To select an appropriate set of motion models, a joint tracking and classification (JTC) algorithm can be...
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