Download or read book Implementation of Target Tracking Algorithms for a Cluttered Environment Using Transputers written by Sohail Rabbani Khan and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi target Tracking Algorithms for Cluttered Environments written by Marco Antonio Mayor and published by . This book was released on 1987 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Target Tracking in Environments of Rapidly Changing Clutter written by Karl Dutson and published by . This book was released on 2015 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking targets in the presence of clutter is inevitable, and presents many challenges. Additionally, rapid, drastic changes in clutter density between different environments or scenarios can make it even more difficult for tracking algorithms to adapt. A novel approach to target tracking in such dynamic clutter environments is proposed using a particle filter (PF) integrated with Interacting Multiple Models (IMMs) to compensate and adapt to the transition between different clutter densities. This model was implemented for the case of a monostatic sensor tracking a single target moving with constant velocity along a two-dimensional trajectory, which crossed between regions of drastically different clutter densities. Multiple combinations of clutter density transitions were considered, using up to three different clutter densities. It was shown that the integrated IMM PF algorithm outperforms traditional approaches such as the PF in terms of tracking results and performance. The minimal additional computational expense of including the IMM more than warrants the benefits of having it supplement and amplify the advantages of the PF.

Download or read book Some Implementations of Multiple Target Tracking Algorithms on Transputers written by and published by . This book was released on 1992 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Generalized Data Association for Multitarget Tracking in Clutter written by A. Tchamova and published by Infinite Study. This book was released on with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this chapter is to present an approach for target track ing in cluttered environment, which incorporates the advanced concept of generalized data (kinematics and attribute) association (GDA) to improve track maintenance performance in complicated situations (closely spaced and/or crossing targets), when kinematics data are insufficient for correct decision making.

Download or read book Some Implementations of Multiple Target Tracking Algorithms on Transputers written by D. M. A. Hussain and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Implementation and Testing of a Target Tracking Algorithm Under Real Environment written by Aziza Merzouki and published by . This book was released on 2010 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Group target Tracking written by Wen-dong Geng and published by Springer. This book was released on 2016-10-01 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes grouping detection and initiation; group initiation algorithm based on geometry center; data association and track continuity; as well as separate-detection and situation cognition for group-target. It specifies the tracking of the target in different quantities and densities. At the same time, it integrates cognition into the application. Group-target Tracking is designed as a book for advanced-level students and researchers in the area of radar systems, information fusion of multi-sensors and electronic countermeasures. It is also a valuable reference resource for professionals working in this field.

Download or read book EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER Güner, Onur M.S., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. Mustafa Kuzuoðlu August 2005, 88 Pages This thesis describes the theoretical bases, implementation and testing of a multi target tracking approach in radar applications. The main concern in this thesis is the evaluation of the performance of tracking algorithms in the presence of false alarms due to clutter. Multi target tracking algorithms are composed of three main parts: track initiation, data association and estimation. Two methods are proposed for track initiation in this work. First one is the track score function followed by a threshold comparison and the second one is the 2/2 & M/N method which is based on the number of detections. For data association problem, several algorithms are developed according to the environment and number of tracks that are of interest. The simplest method for data association is the nearest-neighbor data association technique. In addition, the methods that use multiple hypotheses like probabilistic data association and joint probabilistic data association are introduced and investigated. Moreover, in the observation to track assignment, gating is an important issue since it reduces the complexity of the computations. Generally, ellipsoidal gates are used for this purpose. For estimation, Kalman filters are used for state prediction and measurement update. In filtering, target kinematics is an important point for the modeling. Therefore, Kalman filters based on different target kinematic models are run in parallel and the outputs of filters are combined to yield a single solution. This method is developed for maneuvering targets and is called interactive multiple modeling (IMM). All these algorithms are integrated to form a multi target tracker that works in the presence (or absence) of clutter. Track score function, joint probabilistic data association (JPD.

Download or read book Algorithms for Tracking in Clutter and for Sensor Registration written by David Frederic Crouse and published by . This book was released on 2011 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Tracking Multiple Targets in Cluttered Environments with the Probabilistic Multi Hypothesis Tracking Filter written by Darin T. Dunham and published by . This book was released on 1997-03-01 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking multiple targets in a cluttered environment is extremely difficult. Traditional approaches generally use simple techniques that combine gating with some form of nearest neighbor association to reduce the effects of clutter. When clutter densities increase, these traditional algorithms fail to perform well. To counter this problem, the multi-hypothesis tracking (MHT) algorithm was developed. This approach enumerates almost every conceivable combination of measurements to determine the most likely tracks. This process quickly becomes very complex and requires vast amounts of memory in order to store all of the possible tracks. To avoid this complexity, more sophisticated single hypothesis data association techniques have been developed, such as the probabilistic data association filter (PDAF). These algorithms have enjoyed some success, but do not take advantage of any future data to help clarify ambiguous situations. On the other hand, the probabilistic multi-hypothesis tracking (PMHT) algorithm, proposed by Streit and Luginbuhl in 1995, attempts to use the best aspects of the MHT and the PDAF. In the PMHT algorithm, data is processed in batches, thereby using information from before and after each measurement to determine the likelihood of each measurement-to-track association. Furthermore, like the PDAF, it does not attempt to make hard assignments or enumerate all possible combinations, but instead associates each measurement with each track based upon its probability of association. Actual performance and initialization of the PMHT algorithm in the presence of significant clutter has not been adequately researched. This study focuses on the performance of the PMHT algorithm in dense clutter and the initialization thereof.

Download or read book Application of Smoothing Techniques for Tracking Maneuvering Targets Multiple Target Tracking in Clutter New Approaches written by and published by . This book was released on 1992 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: The project was concerned with development of fast algorithms for clustering and data association and then applying fixed-lag smoothers for tracking maneuvering and non-maneuvering multitargets in clutter. The data association was done by a depth-first search (DFS) technique which extends naturally to three dimensional validation matrices which occur in Markov models of system parameters for maneuvering targets and also in multiscan correlation. A faster algorithm is then suggested for computing approximately the a posteriori probabilities without generating the data association hypotheses. This algorithm is suitable for implementation with multiprocessors in mobile airborne or seaborne distributed tracking facilities, while the DFS-based approach is suitable for ground-based trackers. Simulation studies have been conducted to demonstrate the advantages of the algorithms developed in this project over comparable ones existing in the literature.

Download or read book Advanced Data Association Techniques in Multi target Tracking System written by Negm Eldin Mohamed Shawky and published by LAP Lambert Academic Publishing. This book was released on 2012 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: In multi-target tracking system, data association and tracking filter are two basic parts of tracking objects. The choosing of data association technique to associate the track to the true target in noisy received measurements is an important key to overcome the issues of the tracking process. Many data association algorithms have been developed to be the most powerful techniques for these issues, but still there are disadvantages in their restricting assumptions, complexity and in the resulting performance. For these reasons, some of data association algorithms that are widely used have been studied. These algorithms have some issues during tracking in dense clutter environment, tracking a highly maneuvering targets and swapping in the presence of more background clutter and false signal. Then, these algorithms have been updated to overcome the issues, improve the performance, decrease the burden of the computational cost, decrease the probability of error and to give the targets the ability to continue tracking without failing.

Download or read book Resources in Parallel and Concurrent Systems written by and published by . This book was released on 1991 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Systems Organization -- Parallel architecture.

Download or read book Maneuvering Target Tracking in a Cluttered Environment with Variable State Dimension written by Kailash Birmiwal and published by . This book was released on 1981 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development and Implementation of a Real time Multi target Tracking Algorithm written by Thomas Henry Wendeln and published by . This book was released on 1986 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Suboptimal Target Tracking in Clutter Using a Generalized Probabilistic Data Association Algorithm written by Wai Ying Kan and published by . This book was released on 1996 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Simple tracking algorithms based upon nearest neighbor filtering do not correctly consider measurement origin uncertainty and, therefore, fail to perform well in situations of high target density and clutter. The optimal tracking algorithm for commonly used target-clutter models computes the posterior density of the target state conditioned on the past history of observations. This posterior density is a Gaussian mixture with the number of terms equal to the number of possible ways to associate observations and targets. Though a recursive algorithm may be developed for the optimal estimator, it requires exponentially growing memory and computation and is, therefore, unimplementable. In this paper a new suboptimal algorithm is proposed where approximation is done by naturally partitioning and grouping the target state estimates into a set of approximate sufficient statistics. A new criterion function is introduced in this approximation process. The well-known Probabilistic Data Association filter (PDAF) turns out to be a special case of the new algorithm. Comparisons are made for the proposed estimator versus the PDAF."