[특허]N-best feasible hypotheses multitarget tracking system for space-based early warning systems

N-best feasible hypotheses multitarget tracking system for space-based early warning systems 원문보기

IPC분류정보
국가/구분	United States(US) Patent 등록
국제특허분류(IPC7판)	G01S-013/00
출원번호	US-0224024 (1994-04-05)
발명자 / 주소	Danchick Roy Newnam George E. Brooks Jack E.
출원인 / 주소	TRW Inc.
대리인 / 주소	Keller
인용정보	피인용 횟수 : 40 인용 특허 : 0

초록 ▼

An N-best feasible hypotheses multitarget tracking system for accurately detecting and tracking large numbers of closely spaced objects in a dense target environment in real time. The multitarget tracking system includes a two-dimensional focal-plane multitarget tracker for performing accurate single-sensor target track assembly of individual target data by directly generating N-best feasible hypotheses, a target typer for performing target typing and providing an initial estimate of each target's launch parameters, a launch parameter estimator for generating a target launch state vector and state error covariance matrix for each target from the assembled target track data, an optimal target data associator for performing multisensor state-to-state association of the target launch states, an optimal target data combiner for performing multisensor state-to-state fusion of the target launch states, a target state initializer for initializing an inertial target state vector and state error covariance matrix, and a target state estimator for recursively updating the inertial target track state vector and state error covariance matrix for each target.

대표청구항 ▼

[ We claim:] [24.] An N-best feasible hypotheses multitarget tracking system for accurately tracking large numbers of closely spaced objects in a dense target environment in real time, comprising:a first sensor for generating target line-of-sight (LOS) measurements during multiple scans of a target environment;means for generating initial track state vectors and state error covariance matrices from an initial set of first sensor scan measurements and subsequent track state vectors and state error covariance matrices from subsequent sets of first sensor scan measurements, subsequent measurements being assigned to previous track states and covariance matrices or new track states and covariance matrices;means for partitioning the initial and subsequent first sensor scan measurements into clusters, each cluster being assigned a number of hypotheses for associating measurements to track states and covariance matrices;means for propagating existing track states and covariance matrices associated with a first sensor scan to a successive first sensor scan to provide prediction of the location of the next measurement;means for setting gates about the predicted location for the first sensor;means for constructing a negative-log-likelihood matrix relating predicted to actual measurement locations for each hypothesis in each cluster for the first sensor;means for generating directly, non-combinatorially and in real time N-best feasible hypotheses from the negative-log-likelihood matrix for the first sensor;means for updating the track state vectors and covariance matrices after each subsequent first sensor scan with the associated subsequent measurements of the N-best hypotheses;means for generating an initial estimate of a target's launch position, launch direction, launch time, and pitchover angle using assembled track data from the first sensor to determine the type of target;means for comparing observed intensity measurements to intensity values based on database profiles to determine the type of target using data from the first sensor;means for generating a target launch state vector and state error covariance matrix from the assembled track data from the first sensor;a second sensor for generating target line-of-sight (LOS) measurements during multiple scans of a target environment;means for generating initial track state vectors and state error covariance matrices from an initial set of second sensor scan measurements and subsequent track state vectors and state error covariance matrices from subsequent sets of second sensor scan measurements, subsequent measurements being assigned to previous track states and covariance matrices or new track states and covariance matrices;means for partitioning the initial and subsequent second sensor scan measurements into clusters, each cluster being assigned a number of hypotheses for associating measurements to track states and covariance matrices;means for propagating existing track states and covariance matrices associated with a second sensor scan to a successive second sensor scan to provide prediction of the location of the next measurement;means for setting gates about the predicted location for the second sensor;means for constructing a negative-log-likelihood matrix relating predicted to actual measurement locations for each hypothesis in each cluster for the second sensor;means for generating directly, non-combinatorially and in real time N-best feasible hypotheses from the negative-log-likelihood matrix for the second sensor;means for updating the track state vectors and covariance matrices after each subsequent second sensor scan with the associated subsequent measurements of the N-best hypotheses;means for generating an initial estimate of a target's launch position, launch direction, launch time, and pitchover angle using assembled track data from the second sensor to determine the type of target;means for comparing observed intensity measurements to intensity values based on database profiles to determine the type of target using data from the second sensor;means for generating a target launch state vector and state error covariance matrix from the assembled track data from the second sensor; andoptimal target data association means for identifying which target launch locations are in a common field of view between the two sensors and comparing a number of the target launch states generated from the assembled track data from the two sensors using state-to-state association to produce a set of optimally matched pairs of target states and error covariance matrices and a set of unmatched target states and covariance matrices.

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N-best feasible hypotheses multitarget tracking system for space-based early warning systems 원문보기

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