Fuzzy Reasoning For Multisensor Management J.M. Molina López*, F.J. Jiménez Rodríguez, Student Member, IEEE, J. R. Casar Corredera, Member, IEEE *Dpto. Informática, Universidad Carlos III de Madrid. GPSS-DSSR- ETSIT, Univ. Politécnica de Madrid, Ciudad Universitaria 28040-MADRID, SPAIN Tel:(91)3367225, FAX:(91)3367350, e-mail:
[email protected] ABSTRACT A multisensor management scheme is proposed in this contribution, intended to be applicable to a generalized suite of several multitask sensors, deployed for multitarget surveillance purposes. The objective of the multisensor management function is to optimize, in a coordinated fashion, the measurement process of each sensor using overall system performance information. The coordination manager will provide each sensor with a list of sensor-level tasks, ranked according to their priorities, which are in turn obtained from the inferred global system-level necessity of fulfilling the task. Each sensor-level task will be also accompanied with an indication of the sensing performance objective desired to achieve with it, expressed in different terms depending on the type of task. The management process will be performed by means of a symbolic reasoning process starting from the output information of the Data Fusion and Situation Assessment functions. Fuzzy reasoning strategies and possibility theory conceps, in conjunction with heuristic search techniques, are applied to the processes of system-level task necessity evaluation, sensorlevel task priority computation, especification of the sensing performance objectives (desired to guarantee with each task) and sensor(s)-to-task(s) assignment.
manager include: effective assignment of the limited sensor resources to existing tracks (targets) according to their individual needs, quality and nature; optimal waveform selection in multimode radars; and resource assignment to specific tasks (such as non-expected operator’s requests, for example). On the one side, instead of keeping the operator aware of any aspects of sensor operation, the sensor management function should monitor system performance, at different levels, in order to provide an adaptive feedback control of sensor operation based on much more information that possibly could be displayed. Most traditional criteria used for this dynamic feedback scheduling have reduced to specific detection and tracking performance figures. However, it is agreed that these should be suplemented by additional criteria, seldom considered in previous works, that address overall performance of the surveillance system, such as identification needs or situation assessment.
Increasing data collecting capabilities of modern surveillance sensors, which are required to cope with ever increasing complex tactical environments (either military or civil), place enormous additional burden on the system´s operator (fighter pilot or ATC controller). As a result, the capabilities of many modern sensors, amenable to dynamic scheduling, e.g. multifunction radars, cannot be efficiently exploited without the development of intelligent automatic resource allocation schemes.
On the other side, multisensor integration[1] is becoming an essential aspect of modern surveillance systems, which are being designed to use a network of multiple geographically dispersed sensors, applying sensor fusion concepts to improve their performance. Therefore, the sensor management function should also be responsible for achieving a synergistic operation of all the sensors deployed in the network. Multisensor coordination issues have been treated in some previous works[2], but effective solutions are still sought for problems such as: multiple assignment (allocation of sensor(s) to target(s) or search volume(s)); cooperative reinforcement (assignment of additional sensors to support a target track that was primarily maintained by a certain sensor); or intersensor cueing (providing one sensor with information derived from another sensor to improve its response time, e.g. for track initiation).
This is the core of the sensor management function. It is required not only to reduce human’s workload (alleviating the need for the operator to specify, based on the displayed information, each task the sensor should perform next), but also to optimize the sensor measurement process (controlling sensor operation parameters to yield much faster adaptation to the changing environment and/or to mantain prespecified performance criteria). Thus, the main functions of a sensor
Considering both aspects, a multisensor management scheme, integrated into the data processing functions of a fusion center, is proposed. The coordinated management process will proceed based on the output information of the Data Fusion (DF) function, (kinematic state vector and identification of individual targets, both stored in the central track file), and on the output of the Situation Assessment (SA) function (environment interpretation).
1. INTRODUCTION
The multisensor manager will, firstly, take decisions about the whole set of tasks that the sensor network should perform during the next management cycle (system-level tasks), by evaluating, depending on the current/predicted situation of the perceived environment, their degree of necessity for surveillance purposes. Based on this system-level necessity of performing a certain task, the multisensor manager will secondly determine which sensors should perform which tasks, distributing the system-level tasks among them. As a result, it will provide each sensor with a list of tasks (sensor-level tasks) ranked according to the priority assigned to its execution during the next management cycle. This priority will be obtained based on the necessity of performing the system-level task. Each task is also given an indication of the sensing required performance objectives expressed, depending on the type of task, as a required approximate value of a certain figure of merit. The structure of the paper is as follows. In Section 2 the proposed functional architecture that implements the above management philosophy is presented. The fuzzy decision trees and inference procedures employed for system-level task necessity evaluation and sensing performance objectives specification are detailed in Section 3. In Section 4 are describe the heuristic search mechanism applied to obtain the distribution of the system-level tasks among the sensors, and the translation of the system-level sensing performance objectives into their sensor-level equivalents. Finally, main conclusions and some future works are summarized in the Section 5.
targets or achieve all of the sensing performance objectives desired by the system operator, but the optimal compromise among conflicting demands is sought. The amount of time spent by a particular sensor searching in each spatial sector or dwelling on targets under track or identification, and the final signal-level characteristics of the task (transmitted signal waveform, power balance, etc..) are under the responsability of the local managers of each sensor, but they should be managed to balance, on one side, several global-system performance criteria (detection probability of new targets, tracking&ID quality measures, own-ship detection probability due to emissions in the direction of possible hostile sensors/target, etc). To formalize the proposed management methodology, let us consider a particular tactical scenario, composed of N moving targets and a network of M sensors able to be allocated to three types of tasks: Search, Track Update and Identification. Not every sensor may perform all types of tasks. Therefore a set of system-level tasks composed of: N Track Update tasks, N Identification tasks and K Search tasks (K is the number of sectors in which the joined multisensor coverage has been divided), can be defined. This system-level tasks set could be expanded by simply defining additional task types. The functional diagram depicted in Fig. 1 represents the two main activities performed by the multisensor manager at the beginning of each management cycle (of length Tm secs., in general, much longer than the scheduling interval of each sensor): HUMAN OPERATOR
2. MULTISENSOR MANAGEMENT ARCHITECTURE Virtually every C2 application employs a variety of surveillance sensors and sources (humint, data-linked reports, etc.) to collect the information necessary to develop, by means of data fusion tecniques, a perception of the scenario situation. Sensors usually deployed are land-based or airborne, mechanically or phased-array radars, IFF to interrogate aircrafts for identification, (omni)directional ESM passive sensors or IR Search&Track, some of them able to perform several different kinds of tasks, which we will name sensor-level tasks. For example, modern multifunction radars can switch between Search, Track Update, Detection Confirmation, Backscan tasks, etc, controlled by several types of time-scheduling and management schemes [3]. The multisensor management function, whose objective is to achieve a coordinated operation among all the deployed sensors to accomplish several types of missions (maintenance of tracks for all aircrafts whithin the combined surveillance volume, detection&identification of hostile targets, location of possibly hostile emitters, sector search for early-warning, etc), refers the ability to allocate sensor resources to targets, that is, to the ability to control the pointing and dwell time of sensors on detected targets or regions of the surveillance volume. In general, the sensor suite may not be able to service all the
DF
Tacti cal Envi ron ment
sensor SCHEDULER SENSOR MANAGER
SA
sensor SCHEDULER SENSOR MANAGER
MULTISENSOR MANAGER
SYSTEM-LEVEL TASKS EVALUATION
System-level tasks list Task : Necessity + sensing perform. objec.
SENSORS-TASKS DISTRIBUTION
Sensor-level tasks lists Task : Necessity + sensing perform. objec.
Fig. 1: Multisensor Manager Functional Architecture
1) System-Level Tasks Evaluation. The aim of this first activity is to obtain the list of system-level tasks the surveillance system has to cope with during the next management cycle, each accompanied by its necessity and its system-level performance objective, expressed as a certain demanded value of a certain figure of merit.
The system-level task necessity reflects the need of fullfilling a certain task considering global system-level safety/ performance/strategic aspects and the currently perceived situation of the whole environment.
value, the sensor manager will recommend its time scheduler an order in the execution of the tasks. Thus, over certain sensor load, a low priority value may cause a high delay in the task execution.
The system-level sensing performance objetive for a certain system-level task refers to a demanded performance level that is desired to guarantee on a certain figure of merit related to the output results of a task. For example, in our reference tactical scenario the defined sensing performance objectives for each system-level task will be expressed in the following terms: Accuracy for Track Update, measure of the size of the filtering error for the track; Track Initiation Delay for Search tasks, maximum time interval from first detection to track confirmation; and ID Certainty Degree for Identification tasks, that represents the probability of correct identification of a target as Enemy.
Each node in the tree will be labelled with an heuristic value (estimation of the proximity to the goal node) which will weight two concepts: the suitability of the assignment of the system-level task to the set of sensors in the node and the load increase that this particular assignment represents to each sensor. (The definition of both suitability and load will be explained in detail in Section 4). The searched best sensor-totask distribution is defined to be that which, considering these estimated values, maximizes the minimum suitability for all sensor-level tasks and the minimum global sensor load (better worst case assignment in terms of suitability and load). 3. SYSTEM-LEVEL TASKS GENERATION
This first activity will be performed by a knowledge-based system applying inexact reasoning as inference paradigm and fuzzy sets and possibility theory[4][5] as its knowledge representation scheme. Both offer a unified framework for dealing with uncertain knowledge such as the conclusions obtained from the DF and SA functions, and have been chosen as the formal method to represent the variables involved in the inference process of necessity evaluation and sensig performance objective definition. 2) Tasks to Sensors Assignment. In this second activity, it is decided which sensor(s) should perform each system-level task, and the sensor-level sensing performance objectives required to achieve with each sensor-level task to guarantee the demanded sensor-level sensing performance objetives. The determination of the system-level tasks that each sensor should execute next (sensor-level tasks) will be performed by means of an heuristic search process[7], trying to find the bestsuited set of sensors that should perform each system-level task. A tree of potential task-to-sensor assignments will be built. The number of levels of the tree will be equal to the number of system-level tasks to be performed. Each level of the tree will be compossed of up to 2M nodes (M sensors), each node containing a candidate set of sensors that could perform the system-level task. As an example, for a Track Update system-level task, if the tracked aircraft enters a region covered by two sensors (namely S1 and S2), the tree level associated to this task will include three nodes: S1 performs alone the Track Update task; S2 performs it alone; S1 and S2 both perform the task. Each sensor-task pair in the node will be accompanied by 2 values: the sensor-level sensing performance objective that the sensor is required to provide, in cooperation with the other sensors in the node, to guarantee the demanded system-level sensing performance objective; and the task priority calculated from the necessity of the system-level task. Considering this
As has been stated before, the aim of the system-level tasks generation process (see Fig. 1) is to determine, for the whole surveillance system, the necessity and the system-level sensing performance objectives of performing each system-level task, according to the perceived state of the environment (assessed situation). Instead of defining a numerical objective function whose optimization would provide with the most needed system-level task to perform next, the proposed management scheme obtains the necessity of each system-level task by means of an inference process which mimics that of a human decision-maker. This approach allows high-level, nonnumerical, imprecise or even subjetive information to be included, in a natural way, into the management function. Thus, to obtain the necessity of each system-level task, a forward-chaining inexact reasoning process takes place on a fuzzy decision tree, whose nodes are the intermediate linguistic variables that a human expert operator would take into account, during his/her decision-making process of evaluation of the task necessity. The trees shown in Fig. 2, 3 and 4 result from several knowledge engineering sessions with a team of experts in surveillance systems design, during which they were faced with several representative tactical situations. They sinthesize the relevants aspects that should be considered when defining the necessity of each type of task under those conditions. The particular fuzzy decision trees related to each type of tasks derived for the reference tactical environment are now explained. 3.1 Track Update tasks The tree adopted to calculate the degree of necessity of a Track Update task (Fig. 2) results from the combined consideration of several possible relevant situations: the target is approaching a friend possition being its identity relatively ambiguous; the target is developing a trajectory considered as threatening such
as a low altitude flight (potential missile), high diving angle (potential attack profile) or group flight; the target is near to a friend position or at a position that converts it in dangerous. Situations such as if the target is a guided missile, or an enemy under target, are not considered by the reasoning process because they have a clear predefined necessity. In the particular examples, it is obliged to give a HIGH necessity to this task in any situation.
Absolute Position
Relative Position
Approach
Friend Degree
Declaration Uncertainty
Missile Heading Data Base of FriendPositions
Targetsize
Threatening Degree
Threatening Trajectory Diving
Group flight
Flight Altitude
Absolute Velocity
Manoeuver
Fig 2 : Fuzzy Tree to infer the Necessity of Track Update Tasks
The values that can take the linguistic variables naming each box in the tree are labels of fuzzy sets (characterized by a membership function) in the universe of discourse (domain) of the represented concept. For example, the linguistic variable “Track Update Necessity”, whose universe of discourse ranges from 0 to 1 (numerical degree of necessity), will have 4 linguistic values: CRITICAL, VERY NECESSARY, NECESSARY, and UNNECESSARY, with equally spaced same-width trapezoidal membership functions. 3.2 Identification tasks The situations considered influential when defining the necessity of an Identification task are, mainly, if recently detected targets have unknown identity, are approaching a friend location or are developing a suspicious trajectory. As can be seen in Fig. 3, the evaluation of the necessity of an Identification task relies on some intermediate conclusions obtained from the Track Update necessity tree. All targets without a clear identification, measured as its certainty degree, should also cause the generation of an Identification task with HIGH necessity. NECESSITYofIDENTIFICATIONtask DeclarationIncertity
Hostile
NECESSITY of SEARCH task Static Priority
Threat Sector
New - target rate
NECESSITY of TRACK UPDATE task
Hostile
interest sector; high density of potential spliting targets; jammed sector, which possibly implies that undetected targets are covered by the noise source; and static priorities assigned to each sector, reflecting its potential degree of threat.
Threatening
Fig3:FuzzyTreetoInfertheNecessityof IdentificationTasks
3.3 Search tasks In the Search tasks case, the surveillance focus changes from targets to sectors. The fuzzy reasoning process (see Fig. 4), applied to obtain the Search task necessity for a particular sector takes into account the following situations, pointed by the experts: high rate of appearance of new targets in the
Density of Potential Splitting Targets
Jamming
Fig 4 : Fuzzy Tree to Infer the Necessity of Search Tasks
The leaf nodes of the three trees represent the linguistic variables associated with information (uncertain in nature) directly supplied by the DF and SA functions. Those related to the kinematic variables provided by the DF are: Absolute Position, Relative Position, Altitude, Target Size, Absolute Velocity, Heading, Manoeuver. Their domain of numerical values is covered by a set of membership functions which give a linguistic interpretation of particular ranges. For example, “Relative Position” will be fuzzified into VERY NEAR, NEAR and FAR. The following information sholud be obtained from the SA function: Friend Degree, ID Uncertainty, Groups in the environment, New-target Rate, Density of Potential Splitting Targets, and Jammed Sectors. Additional required information such as friend forces location, guided missiles in flight and enemies on target, own weapon systems capacity, and static priority of each surveillance sector, will be obtained from the ancillary data bases accessed by the Situation Assessment function. Starting from this initial fuzzified information, the inference engine of the knowledge-based sensor manager proceeds up (data-driven) using forward chaining through the trees generating the intermediate conclusions until a linguistic value is assigned to all system-level tasks of each type, informing about its subjective necessity. The set of rules which are graphically represented by these fuzzy decision trees constitute the knowledge base of the multisensor manager, whose fuzzy antecedents and consequents establish the relationships among the linguistic variables representing the nodes of each tree. An example of one of such rules is, taken from the ID task tree: IF (Target ID Certainty is LOW) OR (Threatening is HIGH) THEN (ID Necessity is VERY NECESSARY)
3.4 System-level operational requirements As has been introduced above, each system-level task has an associated system-level sensing performance objective, equally obtained through a fuzzy reasoning process based on a fuzzy
tree similar to the previous ones (Fig. 5). The performance figure of each task (Accuracy for Track Update, Track Initiation Delay for Search tasks, and ID Certainty Degree for Identification tasks) are obtained using the following trees: ACCURACY of TRACK UPDATE task Threatening
Vulnerability
Hostile
Capabilities of own Weapon Systems
Fig 5.a: Fuzzy tree to obtain the Accuracy
IDENTIFICATION DEGREE of an Identification Task NECESSITY of Identification task Fig. 5.b: Fuzzy tree to obtain the Identification Certainty Degree ACCURACY of TRACK UPDATE task NECESSITY of Search Task
merit, representing the performance objective.
pursued
system-level
sensing
4. SENSOR-LEVEL TASKS GENERATION The second activity of the multisensor manager implies the assignment of each system-level task to some sensors. The sensor-level task lists that are to be generated, one for each sensor, will be ordered by the sensor-level task priority and each sensor-level task will be characterized by its sensor-level performance objetives. The details are now explained. 4.1 Sensor-level operational requirements As was explained in section 3, the execution of a system-level task could be accomplished by different sets of sensors. Before obtaining the nodes of a level of the tree, the set of sensors which are capable of performing each system-level task is obtained from the SA function.
Relative Position
Fig 5.c: Fuzzy tree to obtain the Initiation Track Delay
The knowledge represented in these trees include the factors that, according to the experts opinion, influence and are to be considered when specifying a certain value of the figure of merit representing the system-level sensing performance objective of a certain system-level task, whose necessity has already been evaluated. The required accuracy of tracking is considered to be essentially dependent on the capability of the tracked target to destroy any friend position. This capacity has been represented as the target threatening degree, balanced by its vulnerability, as can be seen in Fig 5.a. The concept vulnerability depends on two other concepts: the defensive systems capabilities and the enemy offensive capabilities (through the hostility evaluation). The other two trees (Fig 5.b, 5.c) show the almost direct dependence of the required sensing performance objectives for Search and Indentification tasks on the already available necessity of the task. The linguistic values of the figures of merit that quantify the sensing performance objectives for each type of task are the following: • The domain of values of the Accuracy is fuzzified into HIGH ACCURACY, MEDIUM ACCURACY and LOW ACCURACY. • The ID certainty degree will be fuzzified into three linguistic terms: HIGH CERTAINTY, MEDIUM CERTAINTY and LOW CERTAINTY. • The Track Initiation Delay could be: SHORT DELAY, MEDIUM DELAY, and LARGE DELAY. whose associated membership functions have the same aspect that those defined for the necessities, with distinct domains. Thus, the output of this activity will be a list of system-level tasks, ordered according to its defuzzified necessity, and each one accompanied by an aproximate value of a desired figure of
Then, each alternative node (see Fig. 4) generated in the searched tree, contains alternative sets of sensors that can perform a certain system-level task. Each node will be further subdivided into different configurations that represent the different sensor-level sensing performance objetives to be impossed to the each sensor in the node, in such a way that the system-level sensing performance objetives are guaranteed.
Alternative1 : S1
Alternative2 : S2
Configuration1
Configuration1
Alternative3 : S1S2 Configuration1
Configuration2
Taski Configuration3
Fig. 4: Different Configurations for a Track Update Task and two sensors For example, if the demanded system-level sensing performance objetive for a Track Update Task is HIGH ACCURACY, the first and second alternatives in Fig. 4 may only have one possible configuration, HIGH ACCURACY for the sensor, but the third alternative could be performed with several different pairs of sensor-level objectives imposed to each sensor manager, depending on the sensor characteristics. The different configurations, for a certain sensors-task assignment, are obtained from predefined tables available for the multisensor manager which are clearly dependent on the characteristics of the deployed sensors. For example, the part of the table from which the configurations for S1 and S2 of Fig. 4 could have been extracted, can be seen in Table 1: System Accuracy HIGH ACCURACY
Sensor 1 Accuracy
Sensor 2 Accuracy
HIGH ACCURACY
HIGH ACCURACY
HIGH ACCURACY MED. ACCURACY MED. ACCURACY HIGH ACCURACY
MED. ACCURACY MED. ACCURACY MED. ACCURACY HIGH ACCURACY MED. ACCURACY MED. ACCURACY MED. ACCURACY MED. ACCURACY
Table 1 : Translation of system-level sensing performance objectives into sensor-level sensing performance objectives.
Similar translation tables for the other types of tasks should be available for the multisensor manager, also reflecting the characteristic of the sensors in the network. These tables will be the base for the load distribution process, guided by the sensing performance objectives, which will serve as a guide for the sensor manager to decide how the sensor ought to perform each sensor-level task, that is, to obtain the signal-level characterization of a task. 4.2 Priority computation In parallel, the necessity is traslated into another concept, the priority. This priority will be used by the time scheduler of the sensor to decide when the task execution should begin. The priority value of each sensor-level task is obtained, directly from the necessity of its system-level equivalent task, using a fuzzy relational algorith (FRA)[5], where the rules are: IF Task Necessity is CRITICAL THEN Priority is VERY HIGH IF Task Necessity is VERY NECESSARY THEN Priority is HIGH IF Task Necessity is NECESSARY THEN Priority is MEDIUM IF Task Necessity is UNNECESSARY THEN Priority is LOW
possibility of the sensor to perform it, considering potentially appearing risk situations and the (cap)abilities of the sensor. For example, if a group of enemy aircraft attack a certain friend position located in a certain sector, employing CM to cover each other, and if the sensors in the network are geographically dispersed, only some of them may be able to perform the required search task, in particular, those free of jamming. The multisensor manager should be able, through the suitability, to assign very necessary tasks (such as a Search Task on that sector) to the more appropiated sensors (those not jammed). The required information about the possibility of a certain sensor-task assigment is obtained from the SA function. Following with the example of tables 1 and 2, the information contained in the node after the load and suitability have been calculated is shown in Table 3: (Notice that if task 1 to task y-1 had been assigned to S1 and/or S2 the computed load and suitability for that task are also stored with the node.) Sensor 1
Sensor 2
Task 1
Load(T1,S1) Suitability(T1,S1)
Load(T1,S2) Suitability(T1,S2)
Task 2
Load(T2,S1) Suitability(T2,S1)
Load(T2,S2) Suitability(T2,S2)
....
.... .....
Task i For example, a possible configuration of Alternative 3 in Fig 4, using Table 1 and the FRA, can be seen in Table 2: Track Update Task Sensor 1 Sensor 2
NECESSARY
Necessity
System-level sens. perfor. object.
MEDIUM
HIGH ACCURACY MED. ACCURACY
MEDIUM
HIGH ACCURACY
Priority
Sensor-level sens. perfor. object.
Table 2: An example of Priority and sensor-level sensing performance objectives computation.
4.3 Sensor-to-task assignment
.... ....
Load(Ti,S1) Suitability(Ti,S1)
Load(Ti,S2) Suitability(Ti,S2)
Table 3 : Node Information for heuristic computation
At this point, an integration of the information in the node is necessary to evaluate its associated heuristic. On one side, the total sensor load (T.LOAD) is evaluated as the accumulation of the estimated individual loads of each sensor. On the other side, the worst case sensor-task assignment is evaluated as the minimum of all suitabilities (M.SUIT) of a sensor (in the same column in Table 3). In the case of our example, this information is shown in the following table:
Total
Sensor 1
Sensor 2
T.LOAD(S1) M.SUIT(S1)
T.LOAD(S2) M.SUIT(S2)
The heuristic associated to each node containing a configuration is a function of two concepts, both related to each the tasksensor pairs: the sensor load and the sensor suitability.
where:
The sensor load constitutes an estimation of the sensor resources needed to perform the task. Its value is essentially a function of the sensor-level sensing performance objetives demanded to the task and the sensor characteristics, which are made available to the multisensor manager by means of tables provided by the ancillary Situation Assessment data bases.
The search objective is to find the node where the sensors resources are better employed and whose suitability, in the worst case assignment, is maximum. Therefore, the heuristic for a node containing a configuration of j=1,2,...,P sensors performing simultaneously a certain task will be defined as:
The sensor suitability to perform a certain task represents a balance between the necessity of performing the task and the
with n the number of tasks of the node. As the search progresses down the tree (assigning new system-level tasks to
T.LOAD(S1) is ∑ i Load(Ti,S1) M.SUIT(S1) is mini { Suitability(Ti,S1) }
H = ( minj { T.LOAD(Sj ) } + maxj { M .SUIT(Sj ) } ) /n
sensors), the new generated nodes are ordered according to the decreasing value of the heuristic H. Thus, the tree grows through the nodes having a maximum value of H, which means that the goal distribution of tasks among sensors has maximum minimun total load and maximum worst case suitability. 5. CONCLUSIONS A coordinated management scheme for multisensor surveillance purposes which accomplish its functions using knowledgebased reasoning based on fuzzy decision trees has been presented. It has the possibility of achieving a fast reaction to unpredicted tactical situations that may occur in the environment, determining the most necessary tasks and the most suitable sensors that should execute them to cope with the inferred situation. Sensors deployed in the network are, generally, of heterogeneous nature and have only limited awareness of the whole environment. Therefore, the multisensor manager has to decide first, in a centralized way, the whole set of tasks to perform next , at a global system level, using the information coming from all the sensors, and then proceed with the distribution of each task to the most appropriate subset of sensors. To get this, the multisnsor manager has to preevaluate the performance that can be expected from each sensor under the determined situation, estimating its present load and its suitability under the operation conditions. Another perspective that could have been adopted in the design of the coordination manager is a distributed one. Each local manager in each sensor should determine in each management cycle the set of tasks that, according to its own criteria, are more necessary. After a negotiation process with the other sensor managers in the network, some task may migrate from one sensor to the other in order to maintain certain preestablished performance criteria. REFERENCES [1] Waltz, E., J. Llinas, Multisensor Data Fusion, Artech House Inc., Norwood, MA, 1990. [2] Bar-Shalom, Y., Multitarget Multisensor Tracking. Vols. III, Artech House Inc., Norwood, MA, 1992. [3] Bogler, P. L., Radar Principles with Applications to Tracking Systems, John Wiley&Sons, Columbia, MI, 1990. [4] Zimmermann, H. J., Fuzzy Set Theory and its Applications, Kluwer Academic Publishers, 1990. [5] Klir, Folger, Fuzzy Sets, Uncertainty and Information, Prentice-Hall, 1992. [6] Leon, B. D., Heller, P. R., “An Expert System and Simulation Approach for Sensor Management & Control in a Distributed Surveillance Network”, SPIE Vol. 786 Applications of Artificial Intelligence V (1987). [7] Luger G. F., Stubblefield, W. A., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, The Benjamin/Cummings Publ., 1993.
