United States Patent (19)
`Harris
`
`|||||||||||||||
`USOO5307289A
`5,307,289
`11
`Patent Number:
`Apr. 26, 1994
`45) Date of Patent:
`
`(56)
`
`54 METHOD AND SYSTEM FOR RELATIVE
`GEOMETRY TRACKING UTILIZNG
`MULTIPLE DISTRIBUTED
`EMITTER/DETECTOR LOCAL NODES AND
`MUTUAL LOCAL NODE TRACKING
`75 Inventor:
`James C. Harris, Vienna, Va.
`73 Assignee: Sesco Corporation, Vienna, Va.
`(21) Appl. No.: 758,782
`22 Filed:
`Sep. 12, 1991
`51
`Int. C. .............................................. G01S 13/06
`52 U.S. Cl. ...............
`... 364/516; 364/460
`58 Field of Search ....................... 364/460, 559, 516;
`342/352,457, 191, 356
`References Cited
`U.S. PATENT DOCUMENTS
`3,630,079 12/1971 Hughes et al. .
`3,742,498 6/1973 Dunn .
`3,836,970 9/1974 Reitzig ................................ 342/352
`3,866,229 2/1975 Hammack .
`3,953,856 4/1976 Hammack .
`3,996,590 12/1976 Hammack.
`4,347,996 9/1982 Grosso.
`4,560,120 12/1985 Crawford et al. .
`4,596,988 6/1986 Wanka ................................. 342/457
`4,651,156 3/1987 Martinez ............................. 342/457
`4,713,768 12/1987 Kosaka et al. .
`4,853,863 8/1989 Cohen et al. .
`4,884,208 1/1989 Mariuelli et al. .................., 364/460
`4,916,455 4/1990 Bent et al. .
`4,976,619 12/1990 Carlson .
`5,02,424 4/1991 Dodson .
`5,014,006 5/1991 Counselman, III ................. 342/352
`5,017,925 5/1991 Bertiger et al. ..................... 342/352
`5,019,827 5/1991 Wilson ................................ 364/460
`5,148,179 9/1992 Allison .
`5,150,310 9/1992 Greenspun et al. .
`OTHER PUBLICATIONS
`"Multiple Site Radar Tracking System", B. H. Cantrell
`and A. Grindlay, IEEE International Radar Confer
`ence, pp. 348-354 (1980) Apr.
`"Decentralized Processing in Sensor Arrays', Mati
`Wax and Thomas Kailath, IEEE Transactions on
`
`
`
`Acoustics, Speech, and Signal Processing, vol. AS
`SP-33, No. 4, Oct. 1985, pp. 1123-1128.
`Primary Examiner-Jack B. Harvey
`Assistant Examiner-Thomas Pesso
`Attorney, Agent, or Firm-Hoffman, Wasson & Gitler
`
`ABSTRACT
`57
`A method and system for tracking various objects utiliz
`ing a plurality of sensors. Separate locations or plat
`forms are provided with a number of sensors collocated
`with an energy generation/reflection device, and also a
`communication device. Each of the platforms is termed
`local nodes of a multi-sensor fusion system, and possibly
`can experience relative translational and/or rotational
`motion in as many as three dimensions with respect to
`itself and with respect to similar local nodes. Each local
`node is capable of measuring some combination of bear
`ing angles and/or range and/or respective derivatives
`from the local node to cooperative local nodes by gen
`erating or reflecting energy such that cooperative local
`nodes may obtain mutual sensor measurements. Infor
`mation obtained or processed by each local node, in
`cluding track data or track estimates, are possibly trans
`mitted to one or more central nodes denoted as fusion
`centers provided with processing capabilities. In addi
`tion, when an object or multiple objects which are not
`local nodes are being tracked, at least one cooperative
`local node can measure bearing angles and/or range
`and/or respective derivatives from the local node to the
`other object. After undergoing a series of processes,
`sensor data from multiple local nodes are combined at
`the fusion centers to provide estimates of both the rela
`tive geometry and relative orientation of each coopera
`tive local node with respect to other cooperative local
`nodes and the relative geometry of other sensed objects
`with respect to each cooperative local node. Estimated
`relative geometries are either range normalized or
`scaled with actual ranges depending upon sensor capa
`bilities.
`
`27 Claims, 11 Drawing Sheets
`
`Communication Fath
`
`Sensor Capability
`
`Communication Capability
`
`Data Fusion Capability
`Energy Emission Capability
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 1 of 11
`
`5,307,289
`
`FIG. 1 (Prior Art)
`
`X
`
`K-ry
`
`i.
`I. I.
`X I.
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 2 of 11
`
`5,307,289
`
`FIG. 2 (Prior Art)
`
`
`
`Sensor Alignment and
`Geometry Calibration
`
`
`
`Measurement
`
`
`
`Communication
`
`V
`Object
`Association/Tracking
`
`V
`Earth Coordinate
`Mapping and Data Fusion
`
`VI
`Application Interface
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 3 of 11
`
`5,307,289
`
`FG. 3
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 4 of 11
`
`5,307,289
`
`FIG. 4
`
`
`
`LEGEND
`-VA
`
`s
`A
`
`EEE
`
`Communication Path
`
`Sensor Capability
`
`Communication Capability
`
`Data Fusion Capability
`
`Energy Emission Capability
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 5 of 11
`
`5,307,289
`
`
`
`LEGEND
`-W-
`
`s
`A
`
`Communication Path
`
`Sensor Capability
`
`Communication Capability
`
`Data Fusion Capability
`
`Energy Emission Capability
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 6 of 11
`
`5,307,289
`
`FIG. 6
`
`
`
`-VA-
`
`Communication Path
`
`s
`Sensor Capability
`A Communication Capability
`
`Data Fusion Capability
`Energy Emission Capability
`
`&
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 7 of 11
`
`5,307,289
`
`Measurement
`
`
`
`12:
`
`16:
`
`FIG. 7
`
`8
`Elimination
`
`III
`Object
`Association/Tracking
`
`13
`
`VI
`Mutual Orientation
`
`14
`
`VII
`Perspective Mapping
`
`VIII
`Application Interface
`
`1.
`
`
`
`
`
`
`
`
`
`
`
`
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 8 of 11
`
`5,307,289
`
`FIG. 8
`
`to
`
`u up to o m e o so o so
`
`up to us up up up to
`
`a
`
`1.
`
`Own Body Motion
`Elimination
`
`Measurement
`a
`a
`
`:2
`
`
`
`... 3
`
`Local Node
`Processes
`-1
`
`Local Object
`Association/Tracking
`4.
`11,
`
`7
`A
`
`6
`
`to
`
`ps
`
`a
`
`in on
`
`IV
`Communication
`
`5
`
`1 - -
`
`- - - - - -
`
`V
`Communication
`
`8
`
`9
`
`
`
`
`
`
`
`V
`Object
`Association/Tracking
`12
`
`VII
`Relative Geometry
`13
`
`V
`Mutual Orientation
`14
`
`u-
`Fusion Center
`Processes
`
`X
`Application Interface
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 9 of 11
`
`5,307,289
`
`FIG. 9
`
`
`
`Measurement
`a
`
`Communication
`- - - to as a o no d
`
`Processes
`1.
`
`I
`
`
`
`5.
`V
`
`8
`
`
`
`D 3
`4
`
`C
`
`6
`
`- - - - - - -
`15
`
`Object
`Association/Tracking
`
`- 7. Own Body Motion
`Elimination
`
`is
`
`O
`
`C - - - - - - - -
`14
`
`13
`
`- - - - - - - - -
`
`
`
`N
`
`Fusion Center
`Processes
`
`12
`
`X
`Application Interface
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 10 of 11
`
`5,307,289
`
`FIG 10
`
`
`
`META 1011
`META V. THALES
`
`

`

`U.S. Patent
`
`Apr. 26, 1994
`
`Sheet 11 of 11
`
`5,307,289
`
`FIG 11
`
`CA
`
`CB
`
`META 1011
`META V. THALES
`
`

`

`1
`
`METHOD AND SYSTEM FOR RELATIVE
`GEOMETRY TRACKING UTILIZING MULTIPLE
`DISTRIBUTED EMITTER/DETECTOR LOCAL
`NODES AND MUTUAL LOCAL NODE TRACKING
`CROSS-REFERENCED DISCLOSURE
`DOCUMENT
`This invention relates in part to subject matter de
`10
`scribed in Disclosure Document No. 235417.
`FIELD OF THE INVENTION
`The present invention pertains to the general field of
`object state tracking, and more specifically to the field
`15
`of spatially distributed multi-sensor object state track
`ing. Related fields are those which require some subset
`of estimates of the relative geometries and relative ori
`entations between multiple sensor platforms and the
`relative geometries between multiple sensor platforms
`and other objects. Related fields include navigation,
`guidance, surveillance, landing aids, fire control, and
`robotic motion control.
`BACKGROUND OF THE INVENTION
`The process of object state tracking has been accom
`25
`plished for many years in a myriad of different ways.
`State tracking implies that some qualities of an object's
`geometry relative to a sensing device are being fol
`lowed and estimated. These qualities are estimated by
`sensing the generated/reflected energy emissions of the
`30
`object. Qualities of relative geometry which are tracked
`include range and/or bearing and/or the respective
`derivatives from a sensor to the energy source. The
`expressions state tracking and tracking are often used
`synonymously in the literature and will be used as such
`35
`within this document. Early tracking methods often
`utilized single sensors. To achieve improved tracking
`accuracy these single sensors were upgraded or re
`placed with sensors having improved accuracy. Cur
`rent methods continue to primarily utilize single sen
`40
`sors, although a trend is developing toward mixed mode
`and multi-sensor systems to overcome the limitations of
`single sensor systems.
`Mixed mode systems utilize different types of sensors
`such as combined Radio Frequency (RF) and optic
`45
`sensors collocated upon the same platform. Mixed mode
`systems are generally utilized when one sensor type
`complements the capabilities of another sensor type.
`One sensor type, for example, might have long range
`detection capability for initial tracking, and another
`50
`collocated sensor type which has better but range lim
`ited accuracy is utilized to provide improved short
`range tracking. An example of a mixed mode multiple
`sensor system is U.S. Pat. No. 3,630,079, issued to
`Hughes.
`Multi-sensor systems are utilized to overcome several
`limitations of single sensor systems. Multiple sensors
`provide an increasing quantity of available measure
`ments as additional sensors are utilized. A greater num
`ber of measurements from multiple collocated sensors,
`60
`for example, is combined to improve the statistics of
`tracking system estimates. Additionally, single sensor
`systerns encounter significantly decreased accuracy
`when tracked objects are in poor relative geometry
`with the sensor. Multiple geometrically distributed sen
`65
`sors can significantly relieve this problem by viewing
`the object from different geometric perspectives. An
`other limitation of single sensor systems is that they are
`
`5,307,289
`2
`unable to provide information about the relative orien
`tation of multiple bodies, whereas multiple sensor sys
`tems have this capability.
`The field of spatially distributed multi-sensor tracking
`is an emerging one, having its major roots beginning
`around 1980 with developments sponsored by MIT
`Lincoln Labs. The problems addressed in this field are
`typically so highly constrained that results are usually
`not reusable in different multi-sensor tracking situations.
`Prior art systems, for example, are typically constrained
`with sensor array formations whereby sensors are per
`manently fixed at well known (a priori) relative loca
`tions and/or orientations. FIG. 1 depicts a typical prior
`art sensor platform arrangement. Sensors are arranged
`in an array grid having well known and often equal
`spacings between sensor elements, i.e. randry known.
`Position vectors between sensor platforms are typically
`either directly measured with distance measuring equip
`ment, or inferred through the use of an external absolute
`coordinate determination system such as any navigation
`system or Global Positioning System (GPS). The rela
`tive orientation between the coordinate frames in which
`pairs of sensor elements function is also typically well
`known, often identical, and not allowed to change dy
`namically. Sensors utilized in prior art multi-sensor
`systems, for example, are very often located upon the
`same rigid body. Additionally, these sensor arrays are
`not allowed to experience Own-Body motion or rela
`tive motion, and three dimensional problems are often
`approximated with substantially inaccurate two dimen
`sional models. Prior art multi-sensor tracking methods
`also typically do not have the flexibility to utilize any
`combination of range, bearing, and respective deriva
`tive information as such information is available. Most
`major prior art developments are related to either dis
`tributed acoustic sensors or distributed ground based
`radars. Examples include Mati Wax and Thomas
`Kailath, "Decentralized Processing in Sensor Arrays'
`published in IEEE Trans. Acous. Speech Sig. Proc.,
`ASSP-33, October 1985 pp. 1123-1128 and Cantrell,
`B.H., and A. Grindley, "Multiple Site Radar Tracking
`System” published in Proc. IEEE Int. Conf., April 1980
`pp. 348-354.
`A typical prior art distributed multi-sensor data fu
`sion information flow diagram is shown in FIG. 2. The
`first process represented by Block I is to estimate rela
`tive sensor positions and alignments. A common prior
`art example is to align cooperative ground based radars
`with magnetic or true north. Alignment information is
`passed to Block V where it is stored for future use. The
`Measurement process, Block II, provides sensor data
`measurements of various sensed objects from the radar
`sites (local nodes) to a central processing agent via
`Block III, the Communication process. At the central
`node, the Object Association and Tracking process,
`Block IV, associates sensor data with common targets
`and updates object track filters as required. Results are
`passed to Block V, the Earth Coordinate Mapping and
`Fusing process, whereby fusion estimates are generated
`in a common coordinate frame, such a coordinate frame
`typically being earth coordinates. The fusion estimates
`are then passed to the Application Interface process,
`Block VI, which makes the estimates available to the
`application.
`There are many different fusion system architectures
`which can be implemented to optimize performance
`under the given multi-sensor tracking system con
`
`5
`
`55
`
`META 1011
`META V. THALES
`
`

`

`5,307,289
`3
`4.
`having no relative motion at precalibrated relative posi
`straints. Examples of fusion system architectures in
`tions. The patents to Bent etal and Carlson accomplish
`clude hierarchical, centralized tracking, and sensor
`position tracking utilizing range-only triangulation
`level tracking. Sensor level tracking systems form ob
`whereby the sensor platform orientation is not required
`ject tracks at the sensor level. Centralized tracking
`or estimated. The patent to Cohen et al uses arrays
`systems gather sensor data at a single node and all track
`composed of three non-colinear sensors having known
`ing and fusion processing takes place at the central
`relative positions and orientations which are located
`level, or central node. Hierarchical architectures com
`upon a 6 Degree of Freedom (6DOF) platform. A dif
`bine the sensor data from groups of local nodes at an
`ferent 6DOF platform has three non-colinear emitters at
`intermediate level. Intermediate level nodes feed higher
`known positions. Geometric relationships are utilized to
`level nodes until possibly reaching a central level. Any
`10
`node where data processing takes place is generally
`determine the relative orientations of the two platforms.
`referred to in the literature as an agent node. Any node
`OBJECTS OF THE INVENTION
`where data from multiple sensors is combined (fused) is
`The primary object of this invention is to provide a
`termed a fusion agent node or fusion node. The combi
`novel method for relative geometry and relative orien
`nation of a sensing device and a capability for communi
`15
`tation state tracking which can obtain much greater
`cations with agent nodes is generally referred to as a
`accuracies than the prior art. An additional object is to
`local node. A local node which is also an agent node is
`provide a modular method such that any fusion system
`sometimes additionally referred to as a local agent node.
`architecture can be implemented by simply rearranging
`A closely related area is that of multiple object track
`the basic processing blocks. Additional objects of the
`association. Developments in this area are concerned
`20
`with associating a set of multiple objects tracked by a
`invention include elimination of the following prior art
`restrictions: that tracking sensor platforms are in well
`sensor with the set of objects tracked by another sensor.
`Objects appearing to have identical trajectories and
`known (a priori) permanently fixed locations; that
`tracking sensor platforms require utilization of an exter
`falling within a confidence contour (gate) are deter
`mined to be common to each set of tracked objects.
`nal absolute coordinate determination system such as
`25
`any navigation system or GPS to estimate relative plat
`Early work in this area was concerned the problem of
`handing off a tracked object from one ground based
`form geometries; that the relative orientation between
`radar to another. Multiple object track association has
`the coordinate frames in which pairs of sensor elements
`more recently received amplified attention due to pro
`function is well known and not allowed to change dy
`grams sponsored by the U.S. Army and the Strategic
`namically; that tracking sensor platforms do not experi
`30
`Defense Initiative Organization (SDIO) for analysis of
`ence their own dynamic motion; that tracking sensor
`platforms do not experience relative dynamic motion;
`extended threat clouds. Much of the scholastic research
`in this area is occurring at the University of Connecticut
`that there is no flexibility for utilization of any combina
`Department of Electrical and Systems Engineering.
`tion of range, bearing, and respective derivative infor
`Examples of work in this area include Blackman, S.S.,
`mation as available.
`"Multiple Target Tracking with Radar Applications'
`published by Artech House, Dedham, Ma 1986 and
`Bar-Shalom, Y., and T.E. Fortmann, "Tracking and
`Data Association' published by Academic Press, New
`York, 1988.
`A research area just now receiving attention is con
`cerned with a process termed registration. Registration
`is the process of determining the relative orientation of
`one sensor to that of cooperating sensors. The prior art
`typically does not consider the case of dynamic relative
`sensor orientations. Cooperative sensors in prior art
`multi-sensor systems, for example, are typically not
`located upon different platforms having relative De
`grees of Freedom. A representative example of the
`prior art is one that determines the relative orientation
`50
`of earth fixed cooperative sensors, a specific example
`being multiple cooperative ground based radar sites.
`Prior art techniques for accomplishing the registration
`process are typically restricted to determining bias off
`sets about only a single coordinate axis, such as deter
`mining the azimuth offset of cooperative ground based
`radar sites. This is accomplished through various forms
`of stochastic filtering, including a model of the geome
`try of multiple radar sites and the tracks of mutually
`tracked aircraft. An example of efforts in the area of
`60
`sensor registration is Fischer, W.L., C.E. Muehe, and
`A.G. Cameron, "Registration Errors in a Netted Air
`Surveillance System', MIT Lincoln Laboratory Tech
`nical Note 1980-40, Sep. 2, 1980 AD-AO93691.
`Examples of other patented multi-sensor tracking
`systems are U.S. Pat. Nos. 4,916,455 issued to Bent et al,
`4,976,619 issued to Carlson, and 4,853,863 issued to
`Cohen et al. These systems utilize cooperative sensors
`
`SUMMARY OF THE INVENTION
`The method of the present invention and the system
`utilizing this method will be referred to as The Smart
`Weapon Adjustable Aspect and Ranging Munition
`(SW&RM) Tracking Method which has numerous ad
`vantages over the prior art, whereby a collection of
`several distinct processes are utilized to accomplish
`mutual local node relative geometry and relative orien
`tation state tracking, and relative geometry tracking of
`other energy emission sources. The invention addresses
`significant deficiencies in several areas of the prior art of
`multi-sensor tracking, including most notably, the areas
`of sensor platform motion, relative geometry determi
`nation, and multiple platform sensor orientation regis
`tration.
`The acronym SW&RM may be misleading since the
`SW&RM Tracking Method is capable of accomplishing
`angle-only multi-sensor tracking using range normal
`ized coordinates when only bearing information is avail
`able. There are applications where inferring or measur
`ing range is not necessary or required, an example being
`Line of Sight (LOS) guidance. Additionally, there are
`numerous applications where a local node is not a muni
`tion, examples being command guidance, fire control
`systems, landing aids, and pure tracking applications
`such as surveillance and airport air traffic control.
`The SW&RM Tracking Method requires platforms
`containing one or more sensing devices collocated with
`an energy generation/ reflection device, and also a
`communication system. These platforms are termed
`local nodes of a multi-sensor fusion system, and possibly
`experience relative translational and/or rotational mo
`
`55
`
`65
`
`35
`
`45
`
`META 1011
`META V. THALES
`
`

`

`O
`
`5,307,289
`5
`6
`tion in as many as three dimensions with respect to itself
`the fusion agents is what allows the SW&RM Tracking
`Method to accomplish the required processes at a high
`and with respect to similar local nodes. Each local node
`is capable of measuring some combination of bearing
`level of resolution even while local nodes experience
`angles and/or range and/or respective derivatives from
`Own-Body motion and relative motion. There are situa
`the local node to cooperative local nodes and can gener
`tions where some local node to local node tracks are not
`ate or reflect energy by which cooperative local nodes
`available, and in which the SW&RM Tracking Method
`is still applicable and makes sense. An example is the
`may obtain mutual sensor measurements. Information
`obtained or processed by each local node, including
`virtual object tracking problem where a local node to
`local node track is broken, or where a particular local
`track data or track estimates are transmitted to one or
`node is not capable of tracking another local node, but
`more central nodes denoted as fusion centers provided
`with processing capabilities. In addition, when an ob
`where the track can be inferred based on the observa
`ject or multiple objects which are not local nodes are
`tions of other local nodes. The SW&RM Tracking
`being tracked, at least one cooperative local node has a
`Method begins to break down and lose its utility when
`means for measuring bearing angles and/or range and
`fewer local node to local node tracks are available. The
`method approaches equivalency with the prior art in
`/or respective derivatives from the local node to the
`15
`other object.
`those situations where relative local node to local node
`The SW&RM Tracking Method executes a combina
`track information is not available, and relative local
`tion of various processes prior to obtaining a multiple
`node positions and orientations are well known.
`The Own-Body Motion Elimination process identi
`local node fusion track estimate, an example of which is
`fies the pertinent parameters of the Own-Body dynamic
`shown in FIG. 4. These processes include: a tracking
`20
`motion of a local node. A motion filter is employed
`sensor measurement process termed Measurement; an
`Own-Body motion identification and sensor data map
`which maps data obtained from the Measurement pro
`ping process termed Own-Body Motion Elimination; an
`cess within the local node body coordinates onto a
`object association and tracking process, termed Object
`synthetic coordinate frame whereby the effects of Own
`Association and Tracking, by which sensor data is re
`Body motion is removed from the data. Such a syn
`25
`ceived as input by a tracking filter and by which a track
`thetic coordinate frame of a local node is termed its
`Egocentric Coordinate Frame. This process allows a
`data estimate is formed; a communication process,
`multi-sensor tracking system local node to experience
`termed Communication, by which sensor data or track
`Own-Body motion with little statistical impact upon
`data estimates are communicated from local nodes to
`fusion agents; a process termed Relative Geometry, by
`fusion estimates. If no Own-Body motion occurs, then
`30
`which the relative geometry and dynamics thereof be
`the Egocentric Coordinate Frame is simply oriented at
`tween cooperating local nodes and other tracked ob
`some arbitrary constant offset from the sensor orienta
`jects is estimated; a process termed Mutual Orientation,
`tion.
`The Object Association and Tracking process per
`by which the relative orientation and dynamics thereof
`forms the tasks of data association and object tracking.
`between cooperating local nodes is estimated; and a
`35
`process termed Perspective Mapping, whereby the
`If only bearing information is available during the entire
`tracking timeline, then range information is not and
`tracks of cooperative local nodes to each other and to
`other objects is cast into a common coordinate frame by
`cannot be inferred and is not utilized. If, however, range
`fusion agents and whereby probabilistic weightings are
`rate between a tracked object and a local node is known
`together with bearing information, then "own ship ma
`applied to track data estimates and the estimates are
`40
`combined (fused). The probabilistic weightings are
`neuvers' allow the agent which processes the local
`based upon actual track data statistics or upon estimates
`node measurement data to estimate range from the local
`node to the tracked object. Data processing is accom
`of track data statistics such as estimates which are de
`plished at the local node level if the local node is also an
`rived from Geometric Dilution of Precision (GDOP)
`agent, or at some other fusion agent level depending
`factors.
`45
`Contrary to the prior art, the SW&RM Tracking
`upon design considerations such as communication load
`Method requires that sensor platforms (local nodes)
`and system architecture and complexity. Statistical the
`ory indicates that an optimal fusion solution can only be
`which provide measurement data or track data esti
`mates of any mutually tracked objects to a common
`accomplished if all local node sensor measurements are
`data fusion node are also required to provide some
`communicated to at least one common fusion agent
`50
`individually. Suboptimal solutions may be obtained if
`subset of measurement data or track data estimates of
`the Object Association and Tracking process occurs at
`tracks formed from each cooperative local node to the
`other. Clearly, an additional requirement of the
`some level other than a central level, and if each local
`SW&RM Tracking Method is that cooperative nodes
`node communicates sensor data or track data estimates
`to fusion agents at intervals greater than the individual
`emit or reflect energy that can be mutually detected.
`The Measurement process, in addition to providing
`measurement interval. The optimal fusion solution re
`quires a greater communication capability, results in
`sensor measurement data associated with target objects,
`greater fusion system complexity, and at a minimum
`is therefore also required to provide some combination
`only requires a single computational unit. The subopti
`of bearing angles and/or range and/or respective deriv
`ative measurements between some application depen
`mal fusion solution, however, places fewer demands
`dent subset of cooperative local nodes. Additionally,
`upon communication capability, and results in a less
`complex fusion system, but provides degraded perfor
`the Object Association and Tracking process is required
`mance and requires computational units for multiple
`to estimate the relative tracks between these coopera
`fusion agent nodes.
`tive local nodes.
`Local node to local node tracking is a unique key
`The Communication process is an information trans
`65
`mission pipeline between local nodes and fusion agents.
`element of the SW&RM Tracking Method which sepa
`rates the instant method and system from the prior art.
`The actual type of data transmitted and received de
`pends upon system design considerations such as fusion
`The availability of local node-to-local node track data at
`
`55
`
`META 1011
`META V. THALES
`
`

`

`5,307,289
`8
`7
`FIG. 7 is an information flow diagram of a possible
`system structure, available communication bandwidth,
`sensor level fusion architecture implementation of the
`and desired accuracy.
`present invention;
`The Relative Geometry process estimates the relative
`FIG. 8 is an information flow diagram of a possible
`geometry and dynamics thereof between the various
`hierarchical fusion architecture implementation of the
`triplets of local node pairs and mutually tracked objects
`present invention;
`including other local nodes. By relative geometry is
`FIG. 9 is an information flow diagram of a possible
`meant the shape of the triangle connecting the various
`hierarchical fusion architecture implementation of the
`combinations of local node pairs and mutually tracked
`present invention;
`third objects. This process is equivalent to determina
`FIG. 10 is a diagram depicting the Euler angles uti
`O
`tion of the ratio of the distance from each local node to
`lized in the formulation of Euler's Equations of Motion;
`the third object to the distance between the local node
`and
`pair. If range information is available, relative geometry
`FIG. 11 depicts a triangle having vertices defined by
`also means determination of the triangle leg sizes. Com
`the position of two SW&RM local nodes and a third
`binations of range and bearing are utilized as the infor
`object according to the present invention.
`15
`mation is available to estimate each triangle shape. The
`DETAILED DESCRIPTION OF THE PRESENT
`Relative Geometry process may make use of, but does
`not require position information from any external navi
`INVENTION
`gation system or GPS. Although structures having
`The SW&RM Tracking Method is a multi-sensor
`more legs than three may be estimated through a com
`tracking method which requires local nodes and fusion
`20
`plex stochastic filter, these structures decompose into
`centers having special capabilities. Each local node at a
`statistically equivalent combinations of local node and
`minimum includes: a device for measuring some combi
`third object triplets.
`nation of bearing angles and/or range and/or respective
`The Mutual Orientation process solves simultaneous
`derivatives from the local node to cooperative local
`equations to estimate the relative orientations and dy
`nodes; a device for generating energy or a capability to
`25
`namics thereof between pairs of local node Egocentric
`reflect energy by which cooperative local nodes may
`Coordinate Frames. A stochastic filter tracks the bias
`obtain mutual sensor measurements; and a device for
`angles such that pairs of Egocentric Coordinate Frames
`communicating sensor data or track data estimates from
`may undergo relative rotational motion. The relative
`the local node to fusion agents. In addition, when an
`object or multiple objects which are not local nodes are
`orientation between the coordinate frames in which
`30
`being tracked, at least one cooperative local node has a
`pairs of sensor elements function, therefore, is not nec
`means for measuring bearing angles and/or range and
`essarily known a priori, and may be dynamic.
`/or respective derivatives from the local node to the
`The Perspective Mapping process utilizes the results
`other object. Each local node which forms object mo
`of previous processes to map track data provided by
`tion tracks locally additionally has a processor. Each
`each local node onto a common coordinate frame.
`35
`fusion center is located within the system according to
`Choice of the common coordinate frame depends upon
`a selected fusion system architecture, and minimally
`the required use of the fusion estimate. The data is fused
`includes a processor and a communication system.
`utilizing weightings based on actual or estimated data
`In contrast to the typical prior art distributed multi
`statistics and forwarded to the Application Interface
`sensor tracking system, SW&RM local nodes may be at
`process which makes fusion estimates available to the
`any arbitrary position and orientation as depicted by
`application.
`FIG. 3. Additionally, SW&RM local nodes may experi
`The sequence in which the SW&RM Tracking
`ence their own dynamic motion and relative transla
`Method processes are executed depends upon the fusion
`tional motion with respect to each other and with re
`system architecture. Examples of fusion system archi
`spect to any other sensed objects.
`45
`tectures include hierarchical, centralized tracking, and
`C