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.

Download or read book Tracking of Multiple Maneuvering Targets in Clutter Using Multiple Sensors IMM and JPDA Coupled 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 clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach, the joint probabilistic data association (JPDA) technique and coupled target state estimation to a Markovian switching system. The algorithm is illustrated via a simulation example involving tracking of two highly maneuvering, at times closely spaced, targets.

Download or read book Integrated Tracking Classification and Sensor Management written by Mahendra Mallick and published by John Wiley & Sons. This book was released on 2012-12-03 with total page 738 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique guide to the state of the art of tracking, classification, and sensor management This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications. Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques. Features include: An accessible coverage of random finite set based multi-target filtering algorithms such as the Probability Hypothesis Density filters and multi-Bernoulli filters with focus on problem solving A succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and tracking A state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification models New structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and management Coverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor management Insight into cutting-edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR) With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.

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.

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 Recent Developments in Mechatronics and Intelligent Robotics written by Srikanta Patnaik and published by Springer Nature. This book was released on 2020-03-04 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at the Third International Conference on Mechatronics and Intelligent Robotics (ICMIR 2019), held in Kunming, China, on May 25–26, 2019. The proceedings cover new findings in the following areas of research: mechatronics, intelligent mechatronics, robotics and biomimetics; novel and unconventional mechatronic systems; modeling and control of mechatronic systems; elements, structures and mechanisms of micro- and nano-systems; sensors, wireless sensor networks and multi-sensor data fusion; biomedical and rehabilitation engineering, prosthetics and artificial organs; artificial intelligence (AI), neural networks and fuzzy logic in mechatronics and robotics; industrial automation, process control and networked control systems; telerobotics and human–computer interaction; human–robot interaction; robotics and artificial intelligence; bio-inspired robotics; control algorithms and control systems; design theories and principles; evolutional robotics; field robotics; force sensors, accelerometers and other measuring devices; healthcare robotics; kinematics and dynamics analysis; manufacturing robotics; mathematical and computational methodologies in robotics; medical robotics; parallel robots and manipulators; robotic cognition and emotion; robotic perception and decisions; sensor integration, fusion and perception; and social robotics.

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.

Download or read book Proceedings of the 2015 Chinese Intelligent Systems Conference written by Yingmin Jia and published by Springer. This book was released on 2015-11-21 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected research papers from the 2015 Chinese Intelligent Systems Conference (CISC’15), held in Yangzhou, China. The topics covered include multi-agent systems, evolutionary computation, artificial intelligence, complex systems, computation intelligence and soft computing, intelligent control, advanced control technology, robotics and applications, intelligent information processing, iterative learning control, and machine learning. Engineers and researchers from academia, industry and the government can gain valuable insights into solutions combining ideas from multiple disciplines in the field of intelligent systems.

Download or read book UAV Based Remote Sensing Volume 1 written by Felipe Gonzalez Toro and published by MDPI. This book was released on 2018-04-27 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "UAV-Based Remote Sensing" that was published in Sensors

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.

Download or read book Parallel Detection Fusion for Multisensor Tracking of a Maneuvering Target in Clutter Using IMMPDA Filtering written by and published by . This book was released on 2003 with total page 14 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. The filtering algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach and the Probabilistic Data Association (PDA) technique to a two sensor (radar and infrared, for instance) problem for state estimation for the target. A detection fusion approach is followed where the raw sensor measurements are passed to a fusion node and fed directly to the target tracker. A multisensor probabilistic data association filter is developed for parallel sensor processing for target tracking under clutter. A past approach using parallel sensor processing has ignored certain data association probabilities leading to an erroneous derivation. Another existing approach applies only to non-maneuvering targets. 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 sequential sensor processing, the proposed algorithm achieves significant improvement in the accuracy of track estimation.

Download or read book A Comparative Analysis of QADA KF with JPDAF for Multitarget Tracking in Clutter written by Jean Dezert and published by Infinite Study. This book was released on with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a comparative analysis of performances of two types of multi-target tracking algorithms: 1) the Joint Probabilistic Data Association Filter (JPDAF), and 2) classical Kalman Filter based algorithms for multi-target tracking improved with Quality Assessment of Data Association (QADA) method using optimal data association. The evaluation is based on Monte Carlo simulations for difficult maneuvering multiple-target tracking (MTT) problems in clutter.

Download or read book Target Tracking Filter Study for Command All The Way Intercepts written by and published by . This book was released on 1995 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: In command-all-the-way (CAW) interceptor guidance, an interceptor missile receives all acceleration commands from guidance laws in the weapons system at the launch platform, where a radar is used to track both the missile and the target. Since the missile uses no terminal homing in CAW, highly accurate state estimates of both the interceptor missile and the target are needed for the guidance algorithms to provide acceleration commands to the interceptor missile that are sufficiently accurate to ensure intercept of the target An Interacting Multiple Model (IMM) algorithm is considered for tracking highly maneuvering targets for support of CAW intercepts. The IMM algorithm uses multiple models with model switching governed by an underlying Markov chain to better represent the target dynamics than a single model filter. Thus, using the output state estimate from the IMM algorithm will result in better acceleration commands from the guidance algorithm to the interceptor missile than that of a single model filter. The performance of the IMM algorithm and a single model Kalman filter are compared for both maneuvering and non-maneuvering targets. The simulation was executed for varying intercept ranges. The impact of track filtering on the radar resources required for an intercept and adjusting the proportional navigation gain were also studied through simulation studies and the results are summarized. The IMM algorithm provides for lower miss distances while using less radar resources than that of single model filters.

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:

Download or read book Target Tracking with Random Finite Sets written by Weihua Wu and published by Springer Nature. This book was released on 2023-08-02 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.

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 Improvement of multiple ground targets tracking with fusion of identi cation attributes written by Benjamin Pannetier and published by Infinite Study. This book was released on with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple ground targets (MGT) tracking is a challenging problem in real environment. Advanced algorithms include exogeneous information like road network and terrain topography. In this chapter, we develop a new improved VS-IMM (Variable Structure Interacting Multiple Model) algorithm for GMTI (Ground Moving Target Indicator) and IMINT (IMagery INTelligence) tracking which includes the stop-move target maneuvering model, contextual information (on-off road model, road network constraints), and ID (IDentification) information arising from classifiers coupled with the GMTI sensor