Communications: directive radio wave systems and devices (e.g. – With particular circuit – Digital processing
Reexamination Certificate
1999-11-30
2001-08-21
Sotomayor, John B. (Department: 3662)
Communications: directive radio wave systems and devices (e.g.,
With particular circuit
Digital processing
C342S090000, C342S095000, C342S096000, C342S108000, C342S113000, C342S115000, C342S140000, C342S189000
Reexamination Certificate
active
06278401
ABSTRACT:
TECHNICAL FIELD OF THE INVENTION
The present invention relates in general to tracking of targets by means of measurements from various sensors and in particular to target type estimation by using discrete, target type related information.
PRIOR ART
In target tracking, data from sensors are used to determine a target track. This target track, or entities involved in the process of creating it, is associated with a certain target. It is useful if this target could be identified for supporting the classification, e.g. friendly aircraft against hostile ones, helping the operator of the tracking system to initiate relevant measures. The target type identification may use many types of different information, for instance, discrete information associated with e.g. ESM (Electronic Support Measures) data, IRST (Infra-Red Search and Track) measurements and direct target type observations. There exist many different types of targets that may need to be classified, which requires a method using efficient algorithms to reduce the computational needs.
Many multi target tracking systems of known art utilise algorithms which are based on a probabilistic approach, why it would be advantageous if extra functionality such as target type estimation would retain a probabilistic framework.
It is, e.g. from S. S. Blackman “Multiple-Target Tracking with Radar Applications”, Artech House, MA, USA 1986, p. 368-380 known in prior art to use a Bayesian probabilistic framework for estimation of discrete quantities. Target type probabilities are suitable estimates, which are easily integrated within the probabilistic framework of the total multi target tracking system. Such methods have the disadvantages of requiring more and more data during the calculations when the measurement history grows, and this amount of data will eventually prohibit a real time treatment of the problem. Another disadvantage with the method is that it exhibits an inherent difficulty in treating ignorance or uncertainty.
It is further known to use e.g. the Dempsters-Shafer method (S.S. Blackman “Multiple-Target Tracking with Radar Applications”, Artech House, MA, USA 1986, p. 380-391) in similar applications. One of the disadvantages with such methods is that the true probabilistic interpretation is lost. As a consequence, the integration of this type of method in a target tracking system of probabilistic type becomes troublesome.
Other approaches, such as neural networks and fuzzy logic are known in the art. However, these methods are not easily integrated with other parts of the probabilistically based tracking system.
In the U.S. Pat. No. 5,392,225 a method and apparatus for correlating target data is disclosed. This method is time static, i.e. uses only information from the last measurements. The method computes the likelihood for measurements from different sensors to originate from the same target. Disadvantages with this method is that neither filtering over time, nor correlation to tracks, is used, which significantly limits the degree of accuracy.
In the U.S. Pat. No. 5,392,050 a method of recognising the type of a radar target object is disclosed. This method uses a time-frequency analysis of the RCS (Radar Cross Section). The method is not applicable to e.g. passive sensors and discrete information sources.
In the U.S. Pat. No. 5,282,013 a passive ranging technique for infrared search and track systems is disclosed, in which a library of emission vs. contrast is available. By measuring the atmospheric background, a type match may be found, which eventually gives the target distance. Earlier distance estimates are also used in the calculations. However, this system is integrated in a special type of target tracking system and may not be used in a general manner.
SUMMARY OF THE INVENTION
An object of the present invention is to provide a method for target type estimation for target tracking purposes using discrete information, which method is possible to execute in real time and that is able to handle ambiguities and uncertainties
Another object of the present invention is to provide a method for target type estimation using discrete information from different types of measurements.
Yet another object of the present invention is to provide a method for target type estimation which easily is integratable in a probabilistic framework for various target tracking purposes.
A further object of the present invention is to provide a method for calculation of crosses between strobe tracks including target type information and the quality of crosses, in target type space.
Still a further object of the present invention is to provide a method for multi-sensor multi-target tracking including target type information.
The object of the present invention is achieved by a process exhibiting the features set forth in the claims. The process of the invention uses time recursive Bayesian methods for calculating probabilities for different target types, using different types of discrete information, such as ESM data, IRST information and direct observations. In order to keep a low complexity in the calculation, approximations are introduced in the likelihood calculations, and an ambiguity restoring procedure is introduced for removing inherent abnormalities in Bayesian methods.
Target type estimates can be used during different stages in a target tracking process, e.g. in the strobe tracks and in the target tracks, particularly for improving the data to track association, cross calculations, cross quality calculations, track quality calculations and for supporting a MHT (Multiple Hypothesis Tracking) functionality. When crosses between strobe tracks are calculated, the target type information can improve the quality evaluation of the crosses, helping suppressing “ghost targets”, i.e. false crosses. The inclusion of target type infor mation in the track state can also improve the data to track association. When the association of data to tracks is ambiguous, several alternative hypotheses may be retained and the decision postponed until further data become available, which MHT functionality can be improved if also target type information is available.
REFERENCES:
patent: 5170440 (1992-12-01), Cox
patent: 5282013 (1994-01-01), Gregoris
patent: 5317319 (1994-05-01), Fagarasan et al.
patent: 5355325 (1994-10-01), Uhlmann
patent: 5365236 (1994-11-01), Fagarasan et al.
patent: 5390133 (1995-02-01), Sohie
patent: 5392050 (1995-02-01), Guerci et al.
patent: 5392225 (1995-02-01), Ward
patent: 5400264 (1995-03-01), Phillis et al.
patent: 5414643 (1995-05-01), Blackman et al.
patent: 5469374 (1995-11-01), Graham et al.
patent: 5491645 (1996-02-01), Kennedy et al.
patent: 5537118 (1996-07-01), Appriou
patent: 5631653 (1997-05-01), Reedy
patent: 9838521 (1998-09-01), None
S. S. Blackman “Multiple-Target Tracking With Radar Applications”, Artech House, MA, USA, 1986, pp. 249-300; pp. 368-391; pp. 397-401.
Y. Bar-Shalom and X. -R.Li, “Estimation and Tracking: Principles Techniques and Software”, Artech House, MA, USA, 1993, Ch. 1 and pp. 382-399.
SAAB AB
Sotomayor John B.
Young & Thompson
LandOfFree
Target type estimation in target tracking does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Target type estimation in target tracking, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Target type estimation in target tracking will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2541965