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Multiple Target Tracking In The Adaptive Cruise Control Environment Using Multiple Models And Probabilistic Data Association
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Book Synopsis Multiple Target Tracking in the Adaptive Cruise Control Environment Using Multiple Models and Probabilistic Data Association by : Derek Stanley Caveney
Download or read book Multiple Target Tracking in the Adaptive Cruise Control Environment Using Multiple Models and Probabilistic Data Association written by Derek Stanley Caveney and published by . This book was released on 2001 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Group-target Tracking by : Wen-dong Geng
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.
Author :Derek Stanley Caveney Publisher :Ann Arbor, Mich. : University Microfilms International ISBN 13 : Total Pages :380 pages Book Rating :4.:/5 (34 download)
Book Synopsis Multiple Model Techniques in Automotive Estimation and Control by : Derek Stanley Caveney
Download or read book Multiple Model Techniques in Automotive Estimation and Control written by Derek Stanley Caveney and published by Ann Arbor, Mich. : University Microfilms International. This book was released on 2004 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Feature-Based Probabilistic Data Association for Video-Based Multi-Object Tracking by : Grinberg, Michael
Download or read book Feature-Based Probabilistic Data Association for Video-Based Multi-Object Tracking written by Grinberg, Michael and published by KIT Scientific Publishing. This book was released on 2018-08-10 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Association Algorithms for Multiple Target Tracking by : J. C. McMillan
Download or read book Data Association Algorithms for Multiple Target Tracking written by J. C. McMillan and published by . This book was released on 1990 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER. by :
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.
Book Synopsis Algorithms for Tracking Single Maneuvering and Multiple Closely-Spaced Targets by : Mohamed Nabil Abdelghaffar Eltoukhy
Download or read book Algorithms for Tracking Single Maneuvering and Multiple Closely-Spaced Targets written by Mohamed Nabil Abdelghaffar Eltoukhy and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Target tracking is crucial in monitoring and controlling air traffic in civilian and military applications. Target tracking is a process of estimating the current position and predict the future position of one or more targets using the measurements received by a radar system. One of the major challenges in tracking a single target is when it performs a maneuver and the angle of maneuver is not known. The interacting multiple model (IMM) algorithm is the most commonly-used algorithm for tracking a maneuvering target with an a priori knowledge of the target turn rate, since it provides a very good tracking performance with moderate complexity. However, the tracking performance of such an algorithm deteriorates or may even fail when the target performs a maneuver with a turn rate larger than that assumed in the design of the algorithm. A few methods have been reported to overcome this limitation of an assumed turn rate by actually estimating it adaptively. Two of such algorithms use nonlinear filters that leads to a large complexity, and one of them uses linear filters and models providing good tracking performance, but only for mild maneuvers. For tracking multiple targets, several algorithms have been proposed, among which the joint probability data association (JPDA) algorithm is considered to be the best algorithm, since it provides good tracking performance when the targets are widely spaced. However, the tracking performance of this algorithm deteriorates, and coalescence of the tracks may occur, when the targets are closely spaced. Some efforts have been made to overcome the problem of tracking closely spaced targets by ignoring the target identity, but at the expense of very large complexity. The work of this thesis is carried out in two parts. In the first part, two algorithms within the IMM framework are proposed to track a single maneuvering target, when the target turn rate is not known a priori. In both the algorithms, the turn rate is dynamically estimated using noisy measurements. In the first algorithm, the turn rate at each time instant k is estimated based on the target speed and the radius of the circle formed by the measurement at that instant and the two previous consecutive noisy measurements, (k-1) and (k-2). The segment of this circle covered by these three noisy measurements is used to model the true track of the target at the instant time k. In the second algorithm, the accuracy of the turn rate estimated in the first algorithm is improved using the information on the level of the measurement noise. In the second part of the thesis, a systematic study on the impact of the spacing between the targets as well as when the targets make abrupt turns with sharp angles on the tracking performance of the JPDA algorithm is conducted. Then, a new algorithm for tracking multiple targets based on the spatial distribution of the measurements for determining the weights for measurement-target association is proposed within the JPDA framework. The proposed algorithm for multiple target tracking is designed to deal with the problems of closely spaced targets and their abrupt sharp turns more effectively. Effectiveness and superiority of the algorithms proposed for tracking single and multiple targets are demonstrated through extensive experiments with a wide variety of different scenarios for target motions.
Book Synopsis Tracking of Maneuvering Targets by : Hongren Zhou
Download or read book Tracking of Maneuvering Targets written by Hongren Zhou and published by . This book was released on 1984 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiple-target Tracking in Complex Scenarios by : Srinivas Phani Kumar Chavali
Download or read book Multiple-target Tracking in Complex Scenarios written by Srinivas Phani Kumar Chavali and published by . This book was released on 2013 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we develop computationally efficient algorithms for multiple-target tracking (MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm (and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter (MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound (PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter (HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection (SS), resource allocation (RA) and data fusion (DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently.
Book Synopsis Multisensor Data Association and Resource Management for Target Tracking by : Thiagalingam Kirubarajan
Download or read book Multisensor Data Association and Resource Management for Target Tracking written by Thiagalingam Kirubarajan and published by . This book was released on 1998 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Single and multiple target tracking by : Mohamed El-Ghoboushi
Download or read book Single and multiple target tracking written by Mohamed El-Ghoboushi and published by GRIN Verlag. This book was released on 2015-12-08 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract from the year 2015 in the subject Computer Science - Applied, grade: excellent, Suez Canal University (Faculty of engineering), course: Manoeuvering Target tracking, language: English, abstract: A comprehensive review of the literature on manoeuvring target tracking for both uncluttered and cluttered measurements is presented. Various discrete-time dynamical models including nonrandom input, random-input and switching or hybrid system manoeuvre models are presented. The problem of manoeuvre detection is covered.We are going to discuss single target tracking using single model and multiple models. Further more we are going to describe multiple target tracking using multiple models.
Book Synopsis Some Statistical Models and Approaches to Target Tracking and Data Association by : Yanhua Ruan
Download or read book Some Statistical Models and Approaches to Target Tracking and Data Association written by Yanhua Ruan and published by . This book was released on 2003 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Association and Adaptive Filtering in Multiple Target Tracking Using Phased Arrays by : Mokhtar Keche
Download or read book Data Association and Adaptive Filtering in Multiple Target Tracking Using Phased Arrays written by Mokhtar Keche and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements Using IMMPDA Filtering and Parallel Detection Fusion by :
Download or read book Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements Using IMMPDA Filtering and Parallel Detection Fusion written by and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors dealing with possibly asynchronous (time delayed) measurements. The filtering algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach, the Probabilistic Data Association (PDA) technique, and asynchronous measurement updating for state-augmented system estimation for the target. A state augmented approach is developed to estimate the time delay between local and remote sensors. A multi- sensor probabilistic data association filter is developed for parallel sensor processing for target tracking under clutter. The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used. Compared with an existing IMMPDA filtering algorithm with the assumption of synchronous (no delay) measurements sensor processing, the proposed algorithm achieves considerable improvement (especially in the case of larger delays) in the accuracy of track estimation.
Book Synopsis Multiple-Target Tracking and Data Fusion Via Probabilistic Mapping by :
Download or read book Multiple-Target Tracking and Data Fusion Via Probabilistic Mapping written by and published by . This book was released on 2000 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new approach is taken to address the various aspects of the multi-sensor, multi-target tracking (MTT) problem in dense and noisy environments. Instead of fixing the trackers on the potential targets as the conventional tracking algorithms do, this new approach is fundamentally different in that an array of parallel-distributed trackers is laid in the search space. The difficult data-track association problem that has challenged the conventional trackers becomes a nonissue with this new approach. By partitioning the search space into cells, this new approach, called PMAP (probabilistic mapping), dynamically calculates the spatial probability distribution of targets in the search space via Bayesian updates. The distribution is spread at each time step, following a fairly general Markov-chain target motion model, to become the prior probabilities of the next scan. This framework can effectively handle data from multiple sensors and incorporate contextual information, such as terrain and weather, by performing a form of evidential reasoning. Used as a pre-filtering device, the PMAP is shown to remove noiselike false alarms effectively, while keeping the target dropout rate very low. This gives the downstream track linker a much easier job to perform. A related benefit is that with PMAP it is now possible to lower the detection threshold and to enjoy high probability of detection and low probability of false alarm at the same time, thereby improving overall tracking performance. The feasibility of using PMAP to track specific targets in an end-game scenario is also demonstrated. Both real and simulated data are used to illustrate the PMAP performance. The PMAP algorithm is parallel distributed in nature; for serial computer implementation, fast algorithms have been developed. Some related applications based on the PMAP approach, including a spatial-temporal sensor data fusion application and a gray-scale video sequence stacking application, are also discussed.
Book Synopsis Tracking of Multiple Maneuvering Targets Using Multiscan JPDA and IMM Filtering by :
Download or read book Tracking of Multiple Maneuvering Targets Using Multiscan JPDA and IMM Filtering written by and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and joint probability data association technique. But unlike the standard single scan joint probabilistic data association (JPDA) approach, we exploit a multiscan joint probabilistic data association (Mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving training of three maneuvering targets and a multiscan data window of length two.
Book Synopsis Final Report on Exploratory Study on Intelligent Multi-radar Multi-target Tracking by :
Download or read book Final Report on Exploratory Study on Intelligent Multi-radar Multi-target Tracking written by and published by . This book was released on 1994 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple target tracking (MTT) is an essential requirement for surveillance systems employing one or more radars to interpret target activity. A difficult problem in MTT is to make correct association of the same target between consecutive time frames of the radar system. This report describes a target association methodology known as constellation matching (CM) and its application to MTT. CM is different from traditional data association methods in that it makes full use of the target spatial configuration information for the plot-to-target assignment. It incorporates spatial constraints to eliminate impossible assignments and maximum entropy partitioning to increase the matching speed & reduce computation complexity. The CM method is then combined with Kalman filter techniques for target tracking as well as further data association. To demonstrate & evaluate the capability of the CM-based MTT system developed, real-world radar data are used in experiments presented to demonstrate the effectiveness of the CM-based MTT system, even in noisy environments. The results are compared to tests with an MTT system based on multiple hypothesis testing, using the same data set.
