[특허]Interacting multiple bias model filter system for tracking maneuvering targets

[미국특허] Interacting multiple bias model filter system for tracking maneuvering targets 원문보기

IPC분류정보
국가/구분	United States(US) Patent 등록
국제특허분류(IPC7판)	G01S-013/66
출원번호	US-0069818 (1993-06-01)
발명자 / 주소	Blair William D. (Fredericksburg VA) Watson Gregory A. (Fredericksburg VA) Rice Theodore R. (Fredericksburg VA)
출원인 / 주소	The United States of America as represented by the Secretary of the Navy (Washington DC 06)
인용정보	피인용 횟수 : 29 인용 특허 : 0

초록 ▼

A filtering system used in the tracking of a maneuvering target is provided. A first filter estimates a partial system state at a time k in terms of target position measurements. A plurality of second filters are each provided with an acceleration model hypothesis from a prior time (k-1) free of position and velocity constraints. Each second filter generates an acceleration estimate at time k and a likelihood at time k that the acceleration model hypothesis is correct. The likelihoods from the second filters are summed to generate a probability vector at time k. A third interaction mixing filter generates the acceleration model hypotheses from prior time (k-1) using the probability vector from prior time (k-1) and the acceleration estimates from prior time (k-1). The third filter also provides an error covariance to the first filter to reflect the uncertainty in the acceleration model hypotheses from prior time (k-1). A probabilistic acceleration estimate for time k is formed as a sum of each of the acceleration estimates associated with each of the acceleration model hypotheses multiplied by a corresponding probability from the probability vector. The partial system state estimate and the probabilistic acceleration estimate are summed to generate a complete system state estimate at time k in terms of position, velocity and acceleration of the maneuvering target.

대표청구항 ▼

A filtering system used in tracking of a maneuvering target comprising: a target tracking sensor providing target position measurements; first filter means for determining a partial system state estimate at a time K in terms of said measurements; means for generating an error difference at time K between said partial system state estimate and said measurements; bias filter means provided with prior acceleration model hypotheses from a time (K-1) free of position and velocity constraints and said error difference for generating acceleration estimates at time K and likelihoods at time K that said prior model hypotheses are correct; updating means summing said likelihoods from said bias filter means for generating a probability vector at time K defining probability associated with each said prior acceleration model hypothesis; interaction mixing filter means for generating each said prior acceleration model hypothesis using said probability vector from prior time (K-1) and said acceleration estimates from said prior time (K-1) as a probabilistic sum of said acceleration estimates from prior time (K-1) multiplied by said probability vector from prior time (K-1), means associated with said interaction mixing filter means for supply of an error covariance to said first filter means to reflect therein an uncertainty with respect to each said prior acceleration model hypothesis; bias estimator means for generating a probabilistic acceleration estimate from each of said acceleration estimates and said probability vector at time K as a sum of each of said acceleration estimates associated with each of said prior acceleration model hypotheses multiplied by a corresponding probability from said probability vector; and means summing said partial system state estimate and said probabilistic acceleration estimate for generating a complete system state estimate at time K defining position, velocity and acceleration of said maneuvering target.

이 특허를 인용한 특허 (29) 인용/피인용 타임라인 분석

Friesel, Mark A., Active optimal reduced state estimator.
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Malakian, Kourken; Salvatore, Stephen J., Adjustment of radar parameters to maintain accelerating target in track.
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Gaylor, David; Bamford, William; Goodzeit, Neil E.; Weigl, Harald J., Geosynchronous spacecraft autonomous navigation.
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Luu, Thu-Van T.; Boka, Jeffrey B., Kinematic algorithm for rocket motor apperception.
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Li,Luo Wen; Lam,Chian Poh, Maneuvering target tracking method via modifying the interacting multiple model (IMM) and the interacting acceleration compensation (IAC) algorithms.
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Olson, Teresa Lorae Pace; Lee, Harry; Slaski, James Joseph; Sefcik, Jason; Cannon, Jamie; Stanfill, Robert, Method and apparatus for tracking an object.
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Sefcik,Jason, Method and system for estimating the position of moving objects in images.
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Thomson David,SEX ; Bentland Benny,SEX ; Kronhamn Thomas,SEX, Method for association.
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van Dorp, Philip, Method for estimating an object motion characteristic from a radar signal, a computer system and a computer program product.
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Pavlovic , Vladimir; Rehg, James Matthew, Method for learning switching linear dynamic system models from data.
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Pavlovic, Vladimir; Rehg, James Matthew, Method for motion classification using switching linear dynamic system models.
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Pavlovi?, Vladimir; Rehg, James Matthew, Method for motion classification using switching linear dynamic systems models.
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Pavlović,Vladimir; Rehg,James Matthew, Method for motion synthesis and interpolation using switching linear dynamic system models.
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Nguyen Quan Bach ; Landau Herbert, Method for tracking a target having substantially constrained movement.
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Engel Stephen Joseph, Method for tracking and detecting turns of maneuvering targets.
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Pavlovic , Vladimir; Rehg, James Matthew, Method for visual tracking using switching linear dynamic system models.
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Pavlovic,Vladimir; Rehg,James Matthew, Method for visual tracking using switching linear dynamic systems models.
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McCabe,D. Hugh; Smith Carroll,A. Sunshine, Multiple model radar tracking filter and systems and methods employing same.
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David, George Thomas, Noncoherent gain enhancement technique for non-stationary targets.
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Collins,Noel; Moore,Kirk D.; McMillan,Donald R., Process for sensor resources management.
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Mookerjee, Purusottam; Reifler, Frank J., Rapid determination of model transitions for interacting models with bounded parameters.
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Olson,Teresa Lorae Pace; Slaski,James Joseph; Sanford,Carl William; Han,Ruey Yuan; Contini,Casey Leonard; Reinig,Robert Russell, Real-time multi-stage infrared image-based tracking system.
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Mookerjee,Purusottam; Reifler,Frank J., Reduced state estimation with biased measurements.
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Nettleton, Eric William; Lloyd, Christopher Mark, Relating target tracking.
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Hovorka, Roman, Substance monitoring and control in human or animal bodies.
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Friesel, Mark A., System and method for covariance fidelity assessment.
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Pickle, Catherine; Blackman, Samuel S.; Tyler, Douglas B., Systems and methods for tracking targets by a through-the-wall radar using multiple hypothesis tracking.
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Mookerjee, Purusottam; Reifler, Frank J., Track fusion by optimal reduced state estimation in multi-sensor environment with limited-bandwidth communication path.
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Gotfried Roberta L. ; Kubler Douglas C. ; Ledford G. David ; Zahavi Joseph S. ; Oxford Stephen C., Use of symmetric multiprocessors for multiple hypothesis tracking.
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[미국특허] Interacting multiple bias model filter system for tracking maneuvering targets 원문보기

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