H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. Automat. Contr., vol. 33, pp. 780--783, Aug. 1988. Home/Search Document Not in Database Summary Related Articles Check |
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Ladar-Based Detection and Tracking of Moving Objects from a .. - Wang, Thorpe, Suppe (2003) (Correct)
....and different types of moving vehicles in urban areas. 1 Introduction Detection and tracking of moving objects (DATMO) is one of the most important and challenging problems for driving assistance and autonomous driving. Although the DATMO problem has been extensively studied for several decades [1, 2, 3, 4, 9, 10], it is still very difficult to accomplish DATMO in crowded urban environments from a ground vehicle at high speeds. One of the most difficult issues is to separate moving objects and stationary objects. In indoor environments, the most important targets are people. If cameras are used to detect ....
....using a simple distance criterion. With the surrounding map and the pose estimate from SLAM, moving objects (groups) are detected by finding inconsistencies between the new scan and the map. Using the data associated with a moving object, the Interacting Multiple Model (IMM) estimation algorithm [2, 3, 4] tracks and predicts the motion of this moving object with the constant velocity model and the constant acceleration model. The multiple hypothesis tracking (MHT) 9, 3] method is applied to refine detection and data association. 3.1 Scan Segmentation (a) The robot pose, a moving object (in ....
H. A. P. Blom and Y. Bar-Shalom, The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients, IEEE Trans. On Automatic Control, Vol. 33, No. 8, Aug. 1988.
Hybrid Bayesian Networks for Reasoning about Complex Systems - Lerner (2002) (9 citations) (Correct)
....the cost of computing the M Gaussians at time t 1. Therefore the computational cost of the IMM algorithm is only slightly higher than the GPB1 algorithm, and is significantly lower than GPB2. In practice, it seems that IMM often performs significantly better than GPB1 and almost as well as GPB2 [BBS88] Thus, the IMM algorithm appears to be a good compromise between complexity and performance. 8.7 Particle Filters As we have seen in previous chapters, sampling techniques offer an alternative approach to other approximate inference algorithms for Bayesian networks. The situation is similar ....
H. A. P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control, 33(8):780--783, August 1988.
Adaptive Target State Estimation Using Neural Networks - Menon, Sharma (1999) (Correct)
....model appears to act like any one of these models. The resulting target state estimator consists of a bank of Kalman filters that is switched or blended using a hypothesis testing algorithm. Methods that employ such approaches are called Interacting Multiple Model (IMM) estimation techniques [13 19]. These methods will not be discussed in this work. In the present research, the target model is assumed to consist of three chains of three integrators driven by an adaptive maneuver strategy. The target equations of motion are assumed to be of the form: 3 2 1 , j z j y j x = 8 8 8 8 8 8 ....
Blom, H. and Bar-Shalom, Y., "The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients", IEEE Transactions on Automatic Control, Vol. 23, Aug. 1988, pp. 780-783.
Variable- and Fixed-Structure Augmented IMM Algorithms.. - Semerdjiev, Mihaylova.. (2000) (2 citations) (Correct)
.... 1 1 D and ( g x p p a i k i i k , 1 1 D are expanded in Taylor series up to first order terms around the filtered estimate x i k k a 1 1 ; the function ( h x p p a i k i i k , D is expanded up to first order terms around the predicted estimate x i k k a 1 [2, 3]. The equations of the i th EKF take the form: x x K i k k a i k k a i k a i k = 1 g , 7) x f x p p i k k a a i k k a i i k k = 1 1 1 1 1 D , 8) g i k k a i k k a i i k k z h x p p , 1 1 D , 9) P f P f Q ....
Blom H. A. P., Y. Bar-Shalom, The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients, IEEE Trans.on AC, Vol.33, No.8, pp.780-783, 1988.
An IMMPDAF Solution to Benchmark Problem for.. - Angelova.. (Correct)
....resources management. The tracking filter performance criterion is the minimization of a weighted combination of a radar time and energy at the cost of a maximum 4 tracks loss. The previous results devoted to this problem have shown that the Interacting Multiple Model (IMM) filtering algorithm [8] is the most efficient and cost effective tool for tracking highly maneuvering targets [3,9,10] Additionally the presence of FA and ECM requires sophisticated data association approaches such as Probabilistic Data Association (PDA) or Multiple Hypothesis Tracking [3] In the present paper a ....
H. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. on AC, Vol.33, No.8, pp.780-783, 1988.
Joint IMM and Coupled PDA to track closely spaced targets and - To Avoid Track Self-citation (Blom) (Correct)
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Blom, H. A. P., and Y. Bar-Shalom, "The Interacting Multiple Model algorithm for systems with Markovian switching coefficients," IEEE Tr. on Automatic Control, Vol. 33 (1988), pp. 780-783.
Online Bayesian Estimation of Transition - Probabilities For Markovian (Correct)
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H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. Automat. Contr., vol. 33, pp. 780--783, Aug. 1988.
A comparative study of multiple-model algorithms for.. - Ryan Pitre Vesselin (Correct)
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H. A. P. Blom and Y. Bar-Shalom. The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients. IEEE Trans. Automatic Control, AC-33(8):780--783, Aug. 1988.
Estimation of Markovian Jump Systems with Unknown.. - Jilkov, Li, Angelova (2003) (1 citation) (Correct)
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Blom, H.A.P., Bar-Shalom, Y.: The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients. IEEE Trans. Automatic ControlAC-33 (1988) 780--783
Target Perceivability and Its Applications - Ning Li And (Correct)
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H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," Automat. Contr., vol. AC-33, pp. 780--783, Aug. 1988.
General Model-Set Design Methods for Multiple-Model Approach - Li, Zhao, Li (2005) (Correct)
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H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. Autom. Control, vol. AC-33, no. 8, pp. 780--783, Aug. 1988.
Multiple-Model Estimation with Variable Structure - Part VI.. - Li, Jilkov, Ru (2005) (11 citations) (Correct)
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Blom, H. A. P., and Bar-Shalom, Y. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control, 33, 8 (Aug. 1988), 780---783.
Multiple-Model Estimation with Variable Structure - Part II.. - Li (2000) (11 citations) (Correct)
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H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. Automat. Contr., vol. 33, pp. 780--783, Aug. 1988.
Random Sets for Multitarget Tracking and Data Fusion - Examiners (Correct)
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H. A. P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coe#cients. IEEE Transactions on Automatic Control, 33(8), Aug. 1988.
Random Sets for Multitarget Tracking and Data Fusion - Licentiate Thesis Examiners (Correct)
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H. A. P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coe#cients. IEEE Transactions on Automatic Control, 33(8), Aug. 1988.
Variable- and Fixed-Structure Augmented IMM Algorithms.. - Emil Semerdjiev Ludmila (2000) (2 citations) (Correct)
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Blom H. A. P., Y. Bar-Shalom, The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients, IEEE Trans.on AC, Vol.33, No.8, pp.780-783, 1988.
Tracking Highly Maneuverable Targets With Unknown Behavior - Schell, Linder, Zeidler (2004) (Correct)
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H. A. P. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. Automat. Contr., vol. 33, pp. 780--783, Aug. 1988.
State Estimation of Probabilistic Hybrid Systems with Particle.. - Funiak (Correct)
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H.A.P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coe#cients. IEEE Transactions on Automatic Control, 33, 1988. 113
State Estimation and Prediction in a Class of.. - Cinquemani, Micheli.. (2004) (Correct)
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H. A. P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coe#cients. IEEE Transactions on Automatic Control, 33(8):780--783, Aug. 1988.
Multi Target Tracking of Ground Targets in Clutter with.. - Musicki, Suvorova, Challa (Correct)
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Henk Blom and Yaakov Bar-Shalom. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans. Automatic Control, 33(8):780-- 783, Aug 1988.
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H. A. P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Aerospace and Electronic Syst., 33(8):780--783, August 1988.
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Henk Blom and Yaakov Bar-Shalom. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans. Automatic Control, 33(8):780-- 783, Aug 1988.
Integrated Track Splitting Filter for Manoeuvring Targets - Musicki, Scala, Evans (Correct)
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H.A.P. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans. Automatic Control, 33(8):780--783, Aug 1988.
An Extended Set-valued Kalman Filter - Morrell Arizona State (Correct)
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Blom, H. A. P., and Bar-Shalom, Y. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans. on Automatic Control AC-33, 8 (1988), 780--783.
An Extended - Set-Valued Kalman Filter (Correct)
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BLOM, H. A. P., AND BAR-SHALOM, Y. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans. on Automatic Control AC-33, 8 (1988), 780--783.
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