Bayesian Multi-sensor Data Fusion for Target Identification

Applications in Naval and Ground based Command and Control Systems

Albert Bodenmüller

Airbus Defence and Space GmbH, D-89077 Ulm, Germany

Keywords: Military Target Identification, Situation Awareness, Bayes Decision Theory, AIS, GMTI.

Abstract: Military Command and Control Systems have to deal with a wide range of different sensors and sources.

Besides traditional information sources like IFF, Tactical Data Links and ESM sensors additional sources

like AIS, Blue Force Tracking and GMTI Radar become important sources for target identification and

classification. A correct identification is an important prerequisite to prevent fratricide and civilian collateral

damages and to complete the Situational Awareness. This paper gives an overview of our solution for the

extension of the Bayesian identification process in order to establish a tactical picture for naval but also for

air and ground targets. For some sensors and important identification source like Automatic Identification

System (AIS), Automatic Target Recognition (ATR) and GMTI Radar our solution approach will be

detailed.

1 INTRODUCTION

In the first section of this paper the current existing

military standard of target identification and

classification will be described. This standard fusion

process uses Bayes decision theory as described by

(Desbois, 2009) and (Stroscher, 2000). It has already

been implemented in airborne reconnaissance

systems and different naval and ground based Air

Defense Systems, but it is not limited to military

systems; it may be used for any identification and

categorization problem.

Future systems will use the principle also for

renegade detection and more granular rating of

various kinds of suspicious behaviour. The

implementation of this standardized fusion process

ensures the comparability of results and the

exchange of source data in future.

Section II will give an overview of the principles

of Bayesian Fusion for target identification and

classification, section III will detail the proposed

processing of some non-standardised sensors and

sources in a Command and Control (C2) system.

The paper describes our approach for some

additional sensors which were not yet considered in

the identification standard. For each of the described

sources the sensor’s provided source information

and the required data for the processing is indicated.

2 PRINCIPLES OF BAYES

FUSION

2.1 Source Processing

The identification process consists of two main

processing parts: The first step is a source

processing component, which provides the source

specific processing, which is unique for each source

type (Figure 1), and the second step is the fusion

component, which has the task to combine and fuse

all contributing sources of information and to assign

the final decision for the identification and

classification.

Figure 1: Identification Source Processing.

Following the flow of information the initial step

ID Source

Association

Pre-Conversion

Combining

Conversion

Post-

Conversion

Combining

Sensor Data

Kinematic Data

Track data

Mapping

LV in SDOC

CLV in SDOC

LV in

EBIOC/POC

Declaration

LV in SDOC

to external

IDCP nodes

LV in SDOC

from external

IDCP nodes

Standard

Interface

for IDCP

data exchange

p(D1|O1) p(D1|O2) … p(D1|On)

… O

…

… … … …

p(Dm|O1) p(Dm|O2) … p(Dm|On)

SPM

p(O

) p(O

) … p(O

)

… B

…

… … … …

p(O

) p(O

) … p(O

)

ID Source

Association

Pre-Conversion

Combining

Conversion

Post-

Conversion

Combining

Sensor Data

Kinematic Data

Track data

Mapping

LV in SDOC

CLV in SDOC

LV in

EBIOC/POC

Declaration

LV in SDOC

to external

IDCP nodes

LV in SDOC

from external

IDCP nodes

Standard

Interface

for IDCP

data exchange

p(D1|O1) p(D1|O2) … p(D1|On)

… O

…

… … … …

p(Dm|O1) p(Dm|O2) … p(Dm|On)

SPM

p(D1|O1) p(D1|O2) … p(D1|On)

… O

…

… … … …

p(Dm|O1) p(Dm|O2) … p(Dm|On)

SPM

p(O

) p(O

) … p(O

)

… B

…

… … … …

p(O

) p(O

) … p(O

)

p(O

) p(O

) … p(O

)

… B

…

… … … …

p(O

) p(O

) … p(O

)

Bodenmüller, A.

Bayesian Multi-sensor Data Fusion for Target Identiﬁcation - Applications in Naval and Ground based Command and Control Systems.

DOI: 10.5220/0006629401070114

In Proceedings of the 7th International Conference on Sensor Networks (SENSORNETS 2018), pages 107-114

ISBN: 978-989-758-284-4

107

is to establish an unique association between a

sensor or source information and a system track.

When no related existing system track can be found,

a new track based on the kinematic data of the

sensor will be established. This will be performed

for those sensors or sources, which provide

positional and or kinematic data, e.g. a Blue Force

Tracking / Friend Force Information system will

normally provide the actual own position

information. During this process the results of

sensors like Electronic Support Measure (ESM) or

Ground Moving Target Indicator (GMTI) Radar

including the contributing collateral data is assigned

to a track. For many sensors the association process

and the pre-conversion combining are an integrated

process making a final hard decision, if a source

declaration is made or not.

In some cases the periodical association match

analysis is input into a pre-conversion combining

step, which uses a hysteresis or stochastic mean of

several association attempts to make the final

declaration hard decision.

The source processing is specific for each kind of

sensor and such the determined declarations are not

in a form which is appropriate for fusion. Hence

they are converted into a Likelihood Vector (LV),

which is a set of probabilities related to appropriate

types of object classes. The standard proposes for

this conversion the application of a Source

Probability Matrix, which represents the probability

of the source to make these source specific

declarations given a known object type. The Source

Probability Matrix (SPM) contains for each possible

declaration, which can be made by a source, the

related likelihoods. Different qualities or

confidences related to the association process are

considered by different SPMs.

Given a determined source declaration and a

priori determined source probabilities in the SPM

the conversion step is performed by selection of the

related row of the source specific SPM. The result of

the conversion step is a Likelihood Vector (LV) in

the Source Discrimination Object Class (named LV

in SDOC) to which additional collateral information,

which is required for the mapping stage, is attached.

The result of this conversion of a declaration D

is a source specific Likelihood Vector LV

which

can be written in the following way:

= (p(D

), p(D

), …, p(D

))

(1)

where p(D

) denotes the probability of declaration

given Object property O

The LV in SDOC expresses the performance of

that particular source to make this declaration.

There are different possibilities to exchange

identification information between different

identifying and classifying systems or nodes. One

possibility is to exchange final identification and

classification results as this is performed via Tactical

Data Links e.g. Link-16 or Link-22. The

disadvantage is that only the final result is available

such that receiving nodes are not able to assess what

the basis of this assessment had been. So the

comparability of final results is often a problem

when different systems interact in a joint combined

mission. Therefore the exchange of identification

source data is preferred. The exchange of Likelihood

Vectors or references on harmonized pre-defined

LVs enables a standardized identity information

exchange between fusion nodes. By this way the

source information and the confidence of the

information is transferred, but the information has

not yet been interpreted, i.e. the allegiance, the

distinction of civil/military targets or the platform

data has not been derived.

When more than one sensor or source of the

same type (i.e. using the identical SDOC) of either

several own sensors or by receiving data from other

identification nodes contribute to one track, the

combination of these LVs is performed by column

wise multiplication in the Post Conversion

Combination step according the following formula:

















(2)

with CLV = (p(D

1,… ,

), …, p(D

1,… ,

)).

The Combined Likelihood Vector (CLV) is

determined by a column multiplication of the single

contributing LVs, and is still in form of the source

specific SDOC. Such a CLV in SDOC contains the

complete information of one source type which

contributes to the final result of the

identification/classification.

2.2 Mapping Processing

A Likelihood Vector or Combined Likelihood

Vector in SDOC is a source specific representation

of information and such different LVs in SDOC

cannot be fused directly without a conversion into a

common format. In the Mapping stage the

LVs/CLVs in SDOC are mapped in such a common

information representation which allows for fusion.

This common information format is called

Output Object Class (OOC). The OOC shall be

defined according the operational needs to

distinguish object categories, e.g. when only a

SENSORNETS 2018 - 7th International Conference on Sensor Networks

108

distinction of civil and military targets is needed, the

OOC may contain only the members:

▪ Military Target;

▪ Civil Target.

When a distinction of basic allegiances is needed the

OOC contains for example the members:

▪ Own Forces (OF);

▪ Enemy Forces (EF);

▪ Non-Aligned (NA).

And a basic distinction of air platform categories can

be defined for example as:

▪ FIGHTER;

▪ BOMBER;

▪ HELICOPTER;

▪ UAV;

▪ AEW AIRCRAFT;

▪ SAR AIRCRAFT;

▪ PATROL AIRCRAFT;

▪ FREIGHT AIRCRAFT;

▪ GLIDER;

▪ BALLOON;

▪ MISSILE;

▪ OTHER AIR TARGET.

Also combinations of basic OOC for certain

applications are reasonable and hence an OOC using

members like friendly fighter, hostile fighter, own

forces civil helicopter etc. may be used. A very

common composite OOC is the Extended Basic

Object Class (EBIOC) using the combinations of

basic allegiances and civil/military targets, e.g. Own

Forces Civil (OFC) and Own Forces Military

(OFM). Depending on the discriminating

capabilities of the contributing sensors/sources and

the user’s operational requirements any kind of

Platform Object Class (POC) can be defined as OOC

for target classification applications. In any case the

OOC members shall be mutually exclusive and the

OOC has to be exhaustive.

The mapping is calculated according the

formula:

 

| | |

OOC i j i k MM k j

p D B p D O P O B







(3)

where p(D

| O

) denotes the CLV in SDOC and

| B

) denotes the Mapping Matrix (MM).

The mapping values are stored in a source

specific Mapping Matrix, which is defined

specifically for each corresponding source type and

SDOC. In cases where different operational facts or

constraints have to be considered (e.g. a radar may

be currently jammed) different MMs can consider

such circumstances by different mapping values.

After the mapping stage the LV in OOC is

normalized and then passed to the conflict detection

and fusion process.

2.3 Conflict Recognition on Basis of

Source Information

The next step now is to check if there exist source

inconsistencies and contradictions. The

identification source information after the mapping

step is available in a common normalized format

which enables the recognition of potentially

contradicting information. The

inconsistency/conflict recognition is performed in

the following way:

When an element of a LV in OOC indicates that

one object class is very likely and the same element

of the compared second LV in OOC indicates that

this object class is very unlikely, this test indicates a

possible information inconsistency/conflict.

When an element of a LV in OOC indicates that

one object class is very likely and another element of

the compared second LV in OOC indicates that this

different object class is very likely, this test indicates

a further possible information inconsistency/conflict.

Finally an information content distance measure

between two LVs indicates a possible information

inconsistency when the distance exceeds a certain

threshold:







yxd

(4)

where x and y represent the two LVs to be tested.

This test makes sense particular for large LVs with

many elements. The statistical information distance

between two LVs is a measure for inconsistency.

The inconsistency/conflict recognition tests are

performed for each combination of two contributing

LVs in OOC and the results are summarized for

display purposes to the operational user.

Figure 2 illustrates the following processing

steps including conflict detection, fusion and final

category decision.

Figure 2: Bayesian identification fusion and decision.

Bayesian Multi-sensor Data Fusion for Target Identiﬁcation - Applications in Naval and Ground based Command and Control Systems

109

2.4 Fusion

In the first step of the fusion process a combination

of all determined contributing LVs/CLVs in OOC is

calculated by a component wise multiplication of all

contributing LVs, building the Joint Likelihood

Vector (JLV). The JLV is a probability distribution

over all members of the OOC. The elements of the

calculated JLV contain the probability that a target

may have these associated declarations given that

the target belongs to that respective OOC:

iOOC

BDpJLV

,...,1

)|(



















(5)

In the second step the Posterior Likelihood Vector

(PLV) is calculated from the JLV by application of

Bayes’ Theorem according the following formula:

 

   









jji

BpBDp

DBp

(6)

where p(B

| D

) denotes the PLV, p (D

| B

) denotes

the JLV and p(B

) denotes the required a priori

information called Force Mix Ratio (FMR).

The FMR is a priori information and it quantifies

the relative expectation that a member of that object

class could be found in the area of interest. When

using this processing for target classification

analogously a Platform Mix Ratio is required. The

elements of the calculated PLV contain the posterior

probability that the target belongs to that respective

OOC given the considered declarations.

2.5 Conflict Recognition on Basis of

Combination/Fusion Result

The declaration combination result JLV can be used

additionally to detect possible information

inconsistencies.

When an element of the JLV indicates that one

object class is very likely and the same element of

the a priori FMR indicates that this object class is

very unlikely this test indicates a possible

information inconsistency.

When an element of a JLV indicates that one

object class is very likely and another element of the

a priori FMR indicates that this different object class

is very likely this test indicates a further possible

information inconsistency.

The inconsistency/conflict recognition is

performed on each update of the JLV and the result

is used for display purposes or alerting the

operational user.

2.6 Final Identity Decision Process

The PLV contains the fusion result and such it can

be displayed to operators to support the further

decision process. A final identity decision could be

realized by a simple thresholding function based on

the most likely element. But usually this result is

translated into a recommendation, which regards the

user's needs and operational aspects (Krüger, 2009).

In the domain of target identification the operational

user expects an identity category according to

NATO STANAG 1241 or MIL-STD 6016 and a

civil/military target assessment. In the case of target

classification a platform type or platform specific

type according to military Data Link standards

STANAG 5516 or STANAG 5522 is required.

The decision process is based on a loss function

which uses a set of loss values (see Figure 3), which

define the operational risk when making a wrong

decision.

Figure 3: Identification Loss Table.

The decision process determines for each

decision alternative a specific risk value by

weighting the loss values of that category (decision

alternative) by the posterior probabilities of the

fusion result:

Risk = p(OOC

)*L

ID,1

+ p(OOC

)*L

ID,2

+ …

+ p(OOC

)*L

ID,N

(7)

where p(OOC

) represents the n

element of the

PLV, L

ID,m

the loss value related to that evaluated

identity (ID) and OOC element m.

The decision alternative comprising the lowest

risk is proposed as final decision result. In those

cases were ambiguous risk values prohibit a decision

based on the risk values a final decision applying a

rule based approach is advised.

If during the identification process additional

operationally important information is attained,

which is not suitable for fusion but relevant for the

M,6

M,5

M,4

M,3

M,2

M,1

KILO

…………………

6,3

5,3

4,3

3,3

2,3

1,3

EFC

6,2

5,2

4,2

3,2

2,2

1,2

OFM

6,4

5,4

4,4

3,4

2,4

1,4

EFM

6,6

6,5

6,1

HOSTILE

5,6

5,5

5,1

SUSPECT

4,6

4,5

4,1

NEUTRAL

3,6

3,5

3,1

FRIEND

2,6

2,5

2,1

ASSUMED FRIEND

1,6

1,5

1,1

UNKNOWN

NAMNACOFCLoss Table

M,6

M,5

M,4

M,3

M,2

M,1

KILO

…………………

6,3

5,3

4,3

3,3

2,3

1,3

EFC

6,2

5,2

4,2

3,2

2,2

1,2

OFM

6,4

5,4

4,4

3,4

2,4

1,4

EFM

6,6

6,5

6,1

HOSTILE

5,6

5,5

5,1

SUSPECT

4,6

4,5

4,1

NEUTRAL

3,6

3,5

3,1

FRIEND

2,6

2,5

2,1

ASSUMED FRIEND

1,6

1,5

1,1

UNKNOWN

NAMNACOFCLoss Table

SENSORNETS 2018 - 7th International Conference on Sensor Networks

110

decision, this information is incorporated in the

decision process. For instance when the operational

alert state changes from peace to tension or a target

violates a self-defence safety zone this has to be

considered for the identity decision. For all these

cases a set of dedicated loss tables has to be

provided, which contain modified loss values

regarding operational facts and target relevant

criteria.

3 NON-STANDARDISED

SOURCE TYPES FOR

IDENTIFICATION AND

CLASSIFICATION

The following section describes our solution for

some additional sources and sensors which were not

yet covered by the identification standard. Hence we

enhanced the standard and introduced capabilities

like Automatic Identification System (AIS),

Automatic Target Recognition (ATR) and Ground

Moving Target Indicator (GMTI) Radar. For some

of these the implemented solution is presented in the

following sections.

3.1 Automatic Identification System

The Automatic Identification System (AIS) is

originally a radio-based collision avoidance system

for ships. AIS has the main requirements to

▪ Support the avoidance of collisions by enabling

an efficient navigation of vessels;

▪ Support the protection of the environment by

providing information about the ship’s cargo;

▪ Actively support Vessel Traffic Systems (VTS)

by providing static, dynamic and voyage data.

Besides that port authorities use AIS to warn ships

about hazards, low tides and shoals that are

commonly found at sea. In open sea AIS-enabled

distress beacons are used to signal and locate men

who have fallen overboard (Balduzzi, 2014).

Several state-of-the-art surveillance satellites are

now equipped with AIS (Høye, 2007), thus the fused

information from dual sensors Radar and AIS

contributes to global maritime surveillance. But also

naval ships like corvettes and frigates are going to

exploit received AIS data for the improvement of the

maritime picture and tactical situation in real-time.

The information extracted from AIS radio broadcast

data includes:

▪ Static ship data: Maritime Mobile Service

Identity (MMSI), i.e. the vessels unique

identification number, International Maritime

Organization (IMO) ship identification number,

radio call sign, name of the vessel, type of ship;

▪ Dynamic ship data: navigation status, position of

the vessel, time of position, course over ground,

speed over ground, true heading, rate of turn;

▪ Further voyage data: current maximum draught

of ship, hazardous cargo, destination, estimated

time of arrival (ETA) at destination.

In a first step the received positional data of a vessel

are used for the association of the AIS data with

existing system tracks, which is part of the source

data association. If no matching system track is

available a new AIS based system track will be

initiated and the track is updated with the AIS

position data.

For the evaluation of AIS data for military target

purposes it is important to recognize that AIS

message content can be spoofed easily, so that the

manipulated result of the data association process or

from the information exploitation may be erroneous.

Besides the intentional manipulation also any kinds

of intentional and unintentional interference of the

AIS signals or the improper setup of AIS devices

may cause problems in the evaluation.

The AIS is a civilian system, hence no primary

military information is transmitted by default. For

military purposes also dedicated variants (NATO

STANAG 4668 WARSHIP - AUTOMATIC

IDENTIFICATION SYSTEM (W-AIS) and NATO

STANAG 4669 - AUTOMATIC

IDENTIFICATION SYSTEM (AIS) ON

WARSHIPS) exists, which are not handled here in

this paper. In order to use the civilian AIS data for

military identification and classification purposes a

further processing is necessary. In the optimal case a

database providing military and intelligence

information is available, such that the received AIS

data can be compared with it and the stored

(military) information can be retrieved to support the

tactical interpretation. The database content provides

information like ship type, specific type, platform

class and platform name, allegiance, civil/military

information and of course data like sensor

equipment, weapon systems and further tactical

intelligence information.

But usually on board of a ship this intelligence

database is not available and such a more pragmatic

solution was additionally necessary. In this case the

broadcasted MMSI number is exploited, because the

MMSI number uniquely identifies a vessel. The

MMSI is not an identity in the military sense, where

a distinction between civil and military objects and

Bayesian Multi-sensor Data Fusion for Target Identiﬁcation - Applications in Naval and Ground based Command and Control Systems

111

the membership to either a friendly, neutral or

hostile allegiance is required. Thus the Identification

Digit (MID), which is part of the MMSI number, is

extracted from the MMSI. The MID is a 3 digit

number and defines uniquely the country, where the

vessel is registered.

A simple repository then is used to determine the

allegiance of the country. A civilian/military

distinction is determined from a simple MMSI

repository. When this repository information is not

available for a received MMSI, the civilian/military

distinction is derived from the AIS message content

"type of ship".

AIS is handled as a new source type and hence a

new AIS specific SDOC definition and related SPM

and MMs were introduced:

▪ Surface vessel with an operating AIS transponder

is sending data ´x´;

▪ Surface vessel with an operating AIS transponder

is sending data different from ´x´;

▪ Surface vessel is not fitted with a transponder or

the surface vessel is fitted with an AIS

transponder and the transponder is not operating.

The source type AIS provides the following

declarations:

▪ AIS (data) received;

▪ AIS (data) not received.

The related SPM has therefore the following format

as given in Table 1.

Table 1: AIS Source Probability Matrix.

AIS

SPM

AIS SDOC

Fitted and

operating

sending

data x

Fitted and

operating,

sending data

different x

Fitted and NOT

operating

or NOT Fitted

AIS received

AIS not received

1-A

1-B

1-C

Such the related Mapping matrices have the

following format as indicated in Figure 4.

Figure 4: AIS Mapping Matrix.

One problem in the military identification using

AIS data arises from the ability to manipulate the

transmitted AIS data easily. Additional threats arise

from triggering SAR alerts to lure ships into

navigating to hostile, attacker-controlled sea space

or spoofing collisions to possibly bring a ship off

course. Hence a possibility to detect spoofing targets

is required (Katsilieris, 2013).

In our system we implemented a multitude of

consistency checks for the AIS data, were we

compare the received data with repository and

intelligence information for plausibility. When this

check indicates a sufficient discrepancy the operator

is alerted and he has the possibility either to suppress

the generation of a declaration and the usage of the

AIS data or to declare this vessel as a spoofing

target. This knowledge is then used in the mapping

process for the selection of dedicated mapping

values for the spoofing case or in the final identity

decision processing to assign special identity

categories respectively.

3.2 Automatic Target Recognition

For our naval and ground based Command and

Control Systems (C2 Systems) we are using

(different types of) Daylight/Infra-Red cameras with

Automatic Target Recognition (ATR).

ATR has become increasingly important in

modern defense systems, because it permits

precision strikes against certain tactical targets with

reduced risk and increased efficiency (Dudgeon,

1993). ATR helps to minimize collateral damages to

civilian persons and objects (like cars, vessels,

planes and buildings). The main advantage is that

ATR systems connected and fed by sensors can

detect and recognize targets automatically so that the

workload of an operator can be reduced and the

accuracy and efficiency of the complete C2 System

can be improved.

For the detection and recognition of tactical

relevant objects and their more or less coarse

classification different algorithms are known, e.g.:

▪ Pattern recognition;

▪ Detection theory;

▪ Artificial Neural Network;

▪ Model-based target recognition;

▪ Artificial intelligence and model-based methods.

In our system we implemented a combination of

model-based target recognition and Artificial Neural

Network for detection and classification of target

objects. The result of this processing is already in

the form of a probability distribution over the

discriminated object attributes (object classes), so it

can be processed and fused directly in our

identification and classification processing.

1-A

-B

1-A

-B

1-A

-B

1-A

-B

1-A

-B

1-A

-B

Fitted and NOT operating

or NOT Fitted

AIS MM

SDOC  EBIOC

OFC OFM EFC EFM NAC NAM

Fitted and operating

sending data x

Fitted and operating,

sending data different x

1-A

-B

1-A

-B

1-A

-B

1-A

-B

1-A

-B

1-A

-B

Fitted and NOT operating

or NOT Fitted

AIS MM

SDOC  EBIOC

OFC OFM EFC EFM NAC NAM

Fitted and operating

sending data x

Fitted and operating,

sending data different x

SENSORNETS 2018 - 7th International Conference on Sensor Networks

112

An interface for sensors and sources, which

provide results in a form which is suitable for fusion,

has been introduced and allows for the fusion of the

image processing result, because the ATR result is

already a probability distribution over platform

categories, which correspond to a LV in POC (see

Figure 5). The detection of conflicts with other

sensor results, the combining, Bayes’ processing and

final category decision are performed as described in

section II.

Figure 5: Extension of Bayesian Classification Fusion

with ATR Interface.

3.3 Ground Moving Target Indicator

(GMTI) Radar

Usually GMTI Radars are mounted on

reconnaissance aircrafts and UAV which operate in

high altitudes above the normal height of civil

aircrafts. The observed area has a large extend and

allows for the observation of many ground and

maritime moving targets (Austin, 2010).

The NATO Standard Agreement (NATO

STANAG 4607, 2013) provides a generic and

complex GMTI radar interface standard which

describes the data encoding. Sometimes problems

occur by different interpretation and implementation

of the format description and such a robust interface

connection is necessary (Dästner, 2016).

STANAG 4607 GMTI target reports provide an

enumeration field denoting the classification of the

target. The classification types include e.g. wheeled

vehicles, non-wheeled vehicles, helicopters, fixed-

wing air targets, rotating antenna, maritime etc., for

both live and simulated targets. Additionally an

optional Target Classification Probability (TCP)

may be transmitted.

The classification result set is relative coarsely,

but it is sufficient to perform a target classification

based on it. In order to achieve a good classification

result the interpretation of STANAG 4607 GMTI

target report classification and probability results

shall be clarified with the vendor such that the

GMTI source processing can be optimized for that

sensor and the related mapping values can be

adapted accordingly.

A pre-conversion is not necessary when the

GMTI radar provides the result of the most actual

integrated assessment. Otherwise a temporal

integration using adequate methods like a hysteresis

function or a probabilistic logic using a running

mean p = Σ TCP

/n, where TCP

is the received

Target Classification Probability, and a threshold

function are used to make a declaration.

The integrated assessment is converted into a

proper related normalized LV in OOC using the

Target Classification Probability for the proper OOC

element, the residual R (R = 1-TCP) is equally

distributed on the remaining OOC components. The

normalized vector is then input into the fusion

process analogously to ATR (Figure 5). In cases

where no TCP is transmitted a proxy LV in OOC is

determined using experience or analytic measures.

4 CONCLUSIONS

In section II a short overview on the principles of

military target identification and classification

applying Bayes reasoning has been given.

In section III this paper addressed some sensors

and source types, which typically contribute to the

Situation Awareness of Combat Management

Systems. The paper presented how data received

from vessel based AIS are evaluated for an enhanced

identification. Also the processing of daylight or

infra-red video streams using ATR algorithms and

processing of GMTI Radar results for target

classification has been presented in this paper.

Due to the military aspects of this paper no

significant simulation or just real results can be

published without disclosure of restricted

information. Nevertheless this paper demonstrates

the application of Bayesian Multi-Sensor Data

Fusion for military target identification and

classification. In our implemented systems we could

prove that the identification results were complying

with the expectations of the military operators and

the adherence of identification doctrines and

operational rules succeeds very well.

REFERENCES

M. Desbois, 2009. Sensor Data Fusion & Sensor

Management in the Nato Air C2 SYSTEM (ACCS).

Conflict

Detection

Fusion I

(Combining)

Fusion II

(Bayes´)