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[FIG10] Resulting priorities for three targets with different probabilities of being enemy, moving on the same trajectory. Figure 10(a) shows the first test trajectory where a target moves towards the radar platform on a straight line, having a constant velocity of 300 m/s. The red circle indicates the origin of the trajectory. Three targets are assumed in the analysis. They have the same dynamics and flight height; however, their proba- bilities of being enemy are different as follows: 1 (enemy), 0.5 (unknown), and 0.1 (friendly), corresponding to the red, blue, and green curves, respectively. The evolution of the resulting priorities is shown in Figure 10(b), which shows that, in gener- al, all priorities increase as the targets move towards the radar platform; and the greater the probability of being enemy, the greater the resulting priority. Figure 10 also suggests that priori- ties of targets that have unknown identity present a similar behavior to friendly targets in the early stages of the trajectory. This may be explained by the fact that during that period, the range of the targets is longer than the tactical range of the plat- form weapon systems. This happens until around 60-80 s. From a= Figure 11 presents the results of a simulation where targets are assumed to move on a straight line trajectory with 800 m/s of velocity. The same probabilities of being enemy of the previ- ous analysis are considered. Due to the high velocity and short ranges, the evolution of the priorities is rather different from the previous case. During the first few seconds of simulation, both unknown and enemy targets have slightly higher priorities than in the first example. This may be explained by their high velocities. All target priorities remain fixed until about 30 s,

Fig10 10 [FIG10] Resulting priorities for three targets with different probabilities of being enemy, moving on the same trajectory. Figure 10(a) shows the first test trajectory where a target moves towards the radar platform on a straight line, having a constant velocity of 300 m/s. The red circle indicates the origin of the trajectory. Three targets are assumed in the analysis. They have the same dynamics and flight height; however, their proba- bilities of being enemy are different as follows: 1 (enemy), 0.5 (unknown), and 0.1 (friendly), corresponding to the red, blue, and green curves, respectively. The evolution of the resulting priorities is shown in Figure 10(b), which shows that, in gener- al, all priorities increase as the targets move towards the radar platform; and the greater the probability of being enemy, the greater the resulting priority. Figure 10 also suggests that priori- ties of targets that have unknown identity present a similar behavior to friendly targets in the early stages of the trajectory. This may be explained by the fact that during that period, the range of the targets is longer than the tactical range of the plat- form weapon systems. This happens until around 60-80 s. From a= Figure 11 presents the results of a simulation where targets are assumed to move on a straight line trajectory with 800 m/s of velocity. The same probabilities of being enemy of the previ- ous analysis are considered. Due to the high velocity and short ranges, the evolution of the priorities is rather different from the previous case. During the first few seconds of simulation, both unknown and enemy targets have slightly higher priorities than in the first example. This may be explained by their high velocities. All target priorities remain fixed until about 30 s,

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n recent years, p hased array antenna technology has been maturing rapidly, and this form of transduction is set to become the norm in complex and advanced radar systems. Instantaneous, adaptive beam pointing enables the combi- nation of functions such as tracking, surveillance, and weapons guidance, w hich previously were performed by sepa- rate, dedicated individual radar systems. However, while being able to instantaneous y and adaptively position and control the beam has clear advantages, it also brings about a new set of challenges. In particu parameters according ar, this form of radar is able to adapt its to the way it perceives its environment. Thus, the interpretation of radar backscatter and the subsequent decisions as to what and how radar resources are to be allocated becomes vital to unleashing the full potential of a phased array system. This dynamic and interactive interplay between the set- ting of radar parameters to optimize the tasks to be carried out and perception of environment highlights the role in which knowledge and intelligence will be central in multifunction radar performance. For example, the system has to share its time between all the different types of functions that it has to perform. This means that in certain operating conditions, it may not have enough resources to perform all required tasks. In this instance, a control system has to evaluate the allocation of resources to provide the best solution. The radar resource
n recent years, p hased array antenna technology has been maturing rapidly, and this form of transduction is set to become the norm in complex and advanced radar systems. Instantaneous, adaptive beam pointing enables the combi- nation of functions such as tracking, surveillance, and weapons guidance, w hich previously were performed by sepa- rate, dedicated individual radar systems. However, while being able to instantaneous y and adaptively position and control the beam has clear advantages, it also brings about a new set of challenges. In particu parameters according ar, this form of radar is able to adapt its to the way it perceives its environment. Thus, the interpretation of radar backscatter and the subsequent decisions as to what and how radar resources are to be allocated becomes vital to unleashing the full potential of a phased array system. This dynamic and interactive interplay between the set- ting of radar parameters to optimize the tasks to be carried out and perception of environment highlights the role in which knowledge and intelligence will be central in multifunction radar performance. For example, the system has to share its time between all the different types of functions that it has to perform. This means that in certain operating conditions, it may not have enough resources to perform all required tasks. In this instance, a control system has to evaluate the allocation of resources to provide the best solution. The radar resource
[FIG2] Results from the Butler type scheduling of surveillance and tracking tasks in three sectors of coverage. Required surveillance occupancy = 80%. scheduled), and provides the scheduler with a smaller queue of requests that are c ose to their due time of execution. Likewise, the track function calculates the update times of the targets under track and feeds the track manager with a queue of tasks to be schedu ist of inactive track tasks that are to be sent when close o their due time of execution. ed. The track manager also maintains a to the scheduler Both surveillance ind track manager select from the waveform database the darameters into consid to be used in the transmission of associated with each radar job. Several aspec eration in the criteria of selection, including the radar pulses ts are to be taken ooundaries of the regions of coverage, expected targets, target range, and ire to be al target speed. Decisions relating o how resources ocated according to the relative importance of the tasks and how to assess this relative importance are made by the block called Priority Assignment. The scheduler is fed by
[FIG2] Results from the Butler type scheduling of surveillance and tracking tasks in three sectors of coverage. Required surveillance occupancy = 80%. scheduled), and provides the scheduler with a smaller queue of requests that are c ose to their due time of execution. Likewise, the track function calculates the update times of the targets under track and feeds the track manager with a queue of tasks to be schedu ist of inactive track tasks that are to be sent when close o their due time of execution. ed. The track manager also maintains a to the scheduler Both surveillance ind track manager select from the waveform database the darameters into consid to be used in the transmission of associated with each radar job. Several aspec eration in the criteria of selection, including the radar pulses ts are to be taken ooundaries of the regions of coverage, expected targets, target range, and ire to be al target speed. Decisions relating o how resources ocated according to the relative importance of the tasks and how to assess this relative importance are made by the block called Priority Assignment. The scheduler is fed by
[FIG3] Results from the Orman type scheduling of surveillance and tracking tasks in three sectors of coverage. Required surveillance occupancy = 80%.
[FIG3] Results from the Orman type scheduling of surveillance and tracking tasks in three sectors of coverage. Required surveillance occupancy = 80%.
[FIG4] Results from the Butler type scheduling of surveillanc and tracking tasks in three sectors of coverage. Required surveillance occupancy = 100%.
[FIG4] Results from the Butler type scheduling of surveillanc and tracking tasks in three sectors of coverage. Required surveillance occupancy = 100%.
[FIG5] Results from the Orman type scheduling of surveillanc and tracking tasks in three sectors of coverage. Required surveillance occupancy = 100%.
[FIG5] Results from the Orman type scheduling of surveillanc and tracking tasks in three sectors of coverage. Required surveillance occupancy = 100%.
[TABLE 1] FUZZY VALUES RELATED TO THE MAIN VARIABLES USED IN THE PRIORITY ASSIGNMENT.
[TABLE 1] FUZZY VALUES RELATED TO THE MAIN VARIABLES USED IN THE PRIORITY ASSIGNMENT.
[TABLE 2] FUZZY VALUES RELATED TO THE MAIN VARIABLES USED IN THE PRIORITY ASSIGNMENT.
[TABLE 2] FUZZY VALUES RELATED TO THE MAIN VARIABLES USED IN THE PRIORITY ASSIGNMENT.
[FIG6] Decision tree for sectors of surveillance priority assessment.
[FIG6] Decision tree for sectors of surveillance priority assessment.
[FIG7] Decision tree for targets priority assessment.
[FIG7] Decision tree for targets priority assessment.
[FIG8] Fuzzy membership functions related to the fuzzy variables representing the degree of hostility and the position of a target.
[FIG8] Fuzzy membership functions related to the fuzzy variables representing the degree of hostility and the position of a target.
[FIG9] Graphic representations of the fuzzy rules, considering fixed three variables: Position, track quality, and weapon systems. It is observed that, as might be expected, the priority increases as a consequence of increases in the degrees of threat and hostil- ity of a target. Conversely, low degrees of hostility and threat maintain the resulting priorities at low levels. Two other areas may also be identified on this surface. The first is related to the degree of hostility varying 0.5-1 (medium to very high) and the degree of threat varying 0-0.5 (very low to medium). The result- ing priority increases as a result of rises in the degree of threat or hostility. However, the sensitivity to rises in degree of hostili- ty is greater than the sensitivity to the degree of threat. This behavior is explained by examining situations in which targets with medium and high probability of being the enemy are mov- ing away from the radar platform, in this case, the higher the dif- ferences in the way the target is moving away from the radar. Thus, the high degree of threat produces a larger effect in the final target priority than the lower value of hostility. The second
[FIG9] Graphic representations of the fuzzy rules, considering fixed three variables: Position, track quality, and weapon systems. It is observed that, as might be expected, the priority increases as a consequence of increases in the degrees of threat and hostil- ity of a target. Conversely, low degrees of hostility and threat maintain the resulting priorities at low levels. Two other areas may also be identified on this surface. The first is related to the degree of hostility varying 0.5-1 (medium to very high) and the degree of threat varying 0-0.5 (very low to medium). The result- ing priority increases as a result of rises in the degree of threat or hostility. However, the sensitivity to rises in degree of hostili- ty is greater than the sensitivity to the degree of threat. This behavior is explained by examining situations in which targets with medium and high probability of being the enemy are mov- ing away from the radar platform, in this case, the higher the dif- ferences in the way the target is moving away from the radar. Thus, the high degree of threat produces a larger effect in the final target priority than the lower value of hostility. The second
[FIG11] Resulting priorities for three targets with different probabilities of being enemy, moving on the same trajectory. Target velocity 800 m/s.
[FIG11] Resulting priorities for three targets with different probabilities of being enemy, moving on the same trajectory. Target velocity 800 m/s.
[FIG12] Resulting priorities for three targets with different probabilities of being enemy, moving on the same trajectory. The prioritization method called hard logic can be described by a set of rules similar to the ones proposed for the fuzzy logic approach. However, for each operational con- dition, only one rule is fired, determining the priority of the radar task. In this case, the input variables are classified in sets using the same labels which described the fuzzy vari- ables. These variables are crisp numbers, which means that at any time they will only belong to one labeled set. In prac- tice, this method works like the fuzzy logic approach but using sharp edge membership functions. The main advantage of this method is the reduced computational burden in assigning the priorities of the radar tasks. [TABLE 3] FIXED PRIORITIZATION ORDER FOR RADAR TASKS.
[FIG12] Resulting priorities for three targets with different probabilities of being enemy, moving on the same trajectory. The prioritization method called hard logic can be described by a set of rules similar to the ones proposed for the fuzzy logic approach. However, for each operational con- dition, only one rule is fired, determining the priority of the radar task. In this case, the input variables are classified in sets using the same labels which described the fuzzy vari- ables. These variables are crisp numbers, which means that at any time they will only belong to one labeled set. In prac- tice, this method works like the fuzzy logic approach but using sharp edge membership functions. The main advantage of this method is the reduced computational burden in assigning the priorities of the radar tasks. [TABLE 3] FIXED PRIORITIZATION ORDER FOR RADAR TASKS.
[TABLE 4] REQUIREMENTS FOR THE EXAMPLE SURVEILLANCE VOLUME.
[TABLE 4] REQUIREMENTS FOR THE EXAMPLE SURVEILLANCE VOLUME.
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