Decision Model - Group 4 - 2018/2019, Semester B, Quartile 3

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Introduction

In this section, we will describe our decision model. First, a description of what a decision model actually is will be given, to give a basic understanding of the concept. After that, we will explain what our decision model in fact does on a higher level, without all the details inside the decision model. After that, we will explain how the decision model is derived, and how our decision model works on a lower level.

What is a decision model?

A decision model is an intellectual template for perceiving, organizing, and managing the business logic behind a business decision[1]. An informal definition of business logic is it is a set of business rules represented as atomic elements of conditions leading to conclusions. A decision model is not simply a list of business rules or business statements. Rather, it is a model representing a structural design of the logic embodied by those statements. In our case, we modify the decision model such that it proposes questions and uses the answers given to those questions in order to label certain solutions with a certain score based on how well they fit to the answer of the question. If a certain solution fits better for a certain answer on a specific question, this solution gets a higher score than a solution that does not fit that answer at all. We elaborate more on this later. Then, when all attributes are scored, they are combined and the solution that has the most attribute scores in common often has the highest score. A list that contains the three best fitting solutions based on what solutions have the highest score are displayed to user of the decision model.


As described before, our decision model gives as output the best solution for anti-UAV systems based on the input of the user. This user can be, for example, an airport seeking to improve on its anti-UAV systems. Due to the enormous growing list of solutions for this, airports may find it difficult to decide for themselves. After our thorough analysis on solutions and types of airport, we have seen that some solutions fit certain airports better than others, and thus we decide to give a systemized model to consult users in this difficult choice.

How does our decision model work?

Our model is a so-called attributed-based decision model. This means that we deconstruct the concept of anti-UAV systems into a set of attributes. Each of these attributes will be quantified by a score between 1 and 10. Then, by asking our user certain question about each attribute, we can get the ideal score of the anti-UAV system that fits this user best.

Attributes

As described above, we will create a decision model that airports can use to decide on which type of anti-UAV system to deploy. For this decision model, we have deconstructed the needs of the airports into concrete attributes, which we have quantified by giving a score between 1 and 10. These attributes are based on the recommendation report. Here, we distinguished between three different types of airports and identified all the USE-stakeholders for each type. Furthermore, we did a risk analysis for each type of airport and a stakeholder analysis. Using this stakeholder analysis, we were able to set up a set of requirements, from which we have derived these core attributes. We will first summarise a list of these attributes to get a clear overview of what attributes are all taken into account when creating the decision model.

Airport specific attributes

These attributes are attributes that are intrinsic to an airport. They are attributes that cannot be changed based on preference. Although the airport cannot pick a preference here, they are important to keep into account when advising the best solution, since, e.g. the size of an airport can have a big influence on which type of solution fits the user's preferences best.

List of airport specific attributes:

  • Type of the airport (Commercial, Military, Recreational)
  • Size of the airport

Preference specific attributes

These attributes are not necessarily only dependent on the airport/user. For two airports with comparable sizes and type, one airport might decide to prioritise certain attributes over others. Since we want to centralize the user and give the user as much freedom of choice as possible, these preference specific attributes were added. Some preference attributes, like the safety of the solution for bystanders, may seem like an open door. However, our main goal of these attributes is to get a value to the priority of this attribute; some airports might prioritize safety moreover costs than other airports. To consult the airports, while giving these airports as much freedom in setting their own preferences, these open door attributes are included.

List of preference specific attributes:

  • Cost of the solution, this can be split up into two sub-attributes:
    • Initial costs (purchase)
    • Long term cost (maintenance)
  • Range of the solution
  • Deployment speed of the solution
  • Safety of the solution for bystanders
  • Reliability of the solution
  • Hindrance to the immediate environment of the solution
  • Types of drones that the solution can be used for
  • Scalability of the solution in terms of a growing airport

Scoring the attributes

The next step is for the decision model to rank or score these attributes, so that the decision model can link the final outcome of the attributes to actual solutions. To score these solutions, multiple choice questions were used. As is usual in a decision model, the questions are dependent of what the user has answered to the previous questions. An example of scoring the attributes based on the questions is as follows:

Q: "How many people are living / working within 1km of the border of the airport?"

A:

[1]: 0-50 people, [2]: 50-100 people, [3]: 100-250 people, [4]: 250-500 people, [5]: 500-1.000 people,
[6]: 1.000-1.500 people, [7]: 1.500-2.000 people, [8]: 2.000-2.500 people, [9]: 2.500-3.000 people, [10]: >3.000 people.

Based on this question, we can score the attribute "Hindrance to immediate environment of solution" with a score ranging from 1 (picking 0-50) to 10 (picking 3000+). All these questions are justified and all questions will be explained in greater detail (see section questions), so that each attribute can get a justified and well-calculated score. The main point of this example is to show how we are going to score attributes based on the questions that we ask.

Weighing the attributes

Now that our decision model has calculated the score of each attribute with respect to the preferences of the user, we must also appropriately weigh the attributes. In most cases, the emmision does not contribute equally to the choice in solution as the safety of the solution, to give an example. We will weigh these attributes as follows: We ask the user to rank the attributes based on what they find most important. Again, it is expected that some attributes will always receive a higher ranking with respect to importance (such as safety compared to emission), but as mentioned before, we want to give the user as much freedom in selecting the preferences to give a decision model that is as user-centered as possible. So, the user ranks all preference specific attributes from 1 to TODO, after which we can translate this to actual solutions.

Translating the attributes to advised solutions

After the questions of the decision model have been answered by the user, an actual solution can now be proposed. This will be done by scoring all current solutions that we currently have gathered from the state of the art literature, which we have grouped together under the section solutions. These scoring of the actual solutions will be done such that these scores match the questions of the decision model as close as possible. In relation to the example question, a solution that does not cause any hindrance at all to the surroundings will be given a score of 10, whereas a solution that causes an enormous amount of hindrance to the surroundings will receive a much lower score. We will give adequate and well-funded scores to all attributes of all solutions in this manner.

So, we have now a way of scoring and weighing the demands of an airports based on the questions given below. We also have a way to score the given solutions based on the attributes that we have deconstructed from the needs of the stakeholders. What now remains is linking the solutions and the outcome of the decision model together. We do this by minimizing a cost function that we have defined. The cost function is defined as follows: For each attribute, we calculate the absolute difference between the score of the attribute, computed by the decision model, and the score of the attribute of the solution. Then, we multiply this difference by the weight of the attribute, which is also computed by the decision model. The sum of all absolute differences of all attributes between one solution and the decision model, multiplied by their respective weights, is the cost of that specific solution. What then remains is to sort the solutions in increasing order of the cost function, since the solution with the smallest cost value fits this airport or user best.

Determining the outcome of the decision model

At the end of the model, we thus get an outcome for each attribute. We then assign weights to each attribute, and this is the final result that will uniquely determine the solution proposed by the decision model.

Questions

In this section, we consider questions regarding each of the attributes proposed earlier.

Cost of the solution

Initial costs (purchase)

Q: "How many euros would you be willing to spend on anti-drone mechanisms?"

A:

  • [1]: <100 euros,
  • [2]: 100-1.000 euros,
  • [3]: 1.000-5.000 euros,
  • [4]: 5.000-10.000 euros,
  • [5]: 10.000-100.000 euros,
  • [6]: >100.000 euros.

We propose this question as it captivates the main ideas regarding the costs that a company would be willing to spend on anti-drone mechanisms. It is important what the budget of the company is in order to provide an adequate solution that considers the right price class. We can specify a few different categories when we consider the price ranges. All of these price ranges are based on the solutions that have been proposed.

Long term cost (maintenance)

Q: "How many euros would you be willing to spend on the anti-drone mechanisms after the initial purchase? (Think about future updates)"

A:

  • [1]: <100 euros,
  • [2]: 100-1.000 euros,
  • [3]: 1.000-5.000 euros,
  • [4]: 5.000-10.000 euros,
  • [5]: 10.000-100.000 euros,
  • [6]: >100.000 euros.

We propose this question as it captivates the ideas regarding the costs that a company would be willing to spend on anti-drone mechanisms after the initial purchase has been made. This is important to consider as new technologies release at unfixed times. These price ranges are equal to the price ranges proposed for the initial purchase and can be seen as a form of extension.

Range of the solution

Q: "What diameter regarding the area consisting of the airport should the anti-drone mechanism cover?"

A:

  • [1] <2 km,
  • [2]: 2-5 km,
  • [3]: 5-10 km,
  • [4]: 10-20 km,
  • [5]: >20 km.

This question is proposed when it comes to the area that the anti-drone mechanism should cover. Of course, when one purchases an anti-drone mechanism, one wants to make sure that the whole airport is covered by this mechanism such that any illegal drone activity can be detected and dealt with appropriately.

Deployment speed of the solution

Q: "In what time should the anti-drone mechanisms be active?"

A:

  • [1] <10 seconds.
  • [2]: 1/6 - 1 minute,
  • [3]: 1-5 minutes,
  • [4]: 5-10 minutes,
  • [5]: 10-30 minutes,
  • [6]: 1/2 - 1 hour,
  • [7]: 1-2 hours,
  • [8]: 2-5 hours,
  • [9]: >5 hours.

This question is proposed when it comes to how fast the anti-drone mechanism can be deployed. For some airports, it is more important that mechanisms can be deployed immediately, and for others, it might be a bit less important.


Q: "Within what time should the illegal drone activity be neutralised (if it has to be neutralised)?"

Safety of the solution for bystanders

Q: "To what extent should bystanders be protected from any illegal drone activity?"

  • [1]: Not at all,
  • [2]: To a minimum level,
  • [3]: If possible,
  • [4]: To a good extent,
  • [5]: At all cost.

For some airports, it is important that all bystanders are protected. For other airports, however, there might not really be any bystanders or only a very few. Then, it might be less important for the anti-drone mechanism to protect bystanders to a large extent.

Reliability of the solution

Q: "To what extent should the anti-drone mechanism be reliable?"

A:

[1]: Not at all, [2]: To a minimum level, [3]: If possible, [4]: To a good extent, [5]: At all cost.

When it comes to some airport, it is important that the anti-drone mechanism should be reliable. Under normal conditions, the mechanism should, of course, be reliable as things should simply work when deploying them, but we can still make a few distinctions based on the solutions offered.

Hindrance to the immediate environment of the solution

Q: "How many people are living/working within 1km of the border of the airport?"

A:

[1]: 0-50 people, [2]: 50-100 people, [3]: 100-250 people, [4]: 250-500 people, [5]: 500-1.000 people,
[6]: 1.000-1.500 people, [7]: 1.500-2.000 people, [8]: 2.000-2.500 people, [9]: 2.500-3.000 people, [10]: >3.000 people.

Certain solutions might cause annoyance to their surroundings. Therefore, it is important to consider the number of people that either live or work within 1km of the border of the airport.


Q: "To what extent should the CO2 output be minimised?"

A:

[1]: Not at all, [2]: To a minimum level, [3]: If possible, [4]: To a good extent, [5]: At all cost.

It might be important for some types of airports to minimise their CO2 output. For other airports, however, this output might not matter at all. This can be taken into account when offering a solution.

Types of drones that the solution can be used for

Q: "What type of drone operations should the anti-drone mechanism be able to handle?"

A:

[1]: All operations under the open category (C1,C2,C3, and C4), [2]: All operations under the specific category, [3]: All operations under the certified category, [4]: All operations under the open and specific category, [5]: All operations.

Some airports expect more types of a certain drone to appear than others. It could be possible that some airports only wants to protect against certain types of drones due to a various number of reasons. Then, it is possible to offer different solutions to this airport.

Scalability of the solution in terms of a growing airport

Q: "Does the anti-drone mechanism have to scale when the airport grows?"

A:

[1]: Not at all, [2]: To a minimum level, [3]: Preferably, [4]: At all cost.

Some airports are already quite established, while others are still growing every day. When these growing airports invest in an anti-drone mechanism, it could be important that this solution can scale with the size of the airport, such that no new anti-drone mechanism is needed once the airports have grown exponentially.

Solution attribute scores

UAV Detection

Radar system

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Echodyne's radar

Table 1: Echodyne's radar attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

WiFi receiver

Table 1: WiFi receiver attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Listening on communication between drone and ground

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Detecting drones with other drones

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

UAV Identification

Identification by coded signal

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

3D radar system with machine learning

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

X-band radar system

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

UAV Neutralisation

Missiles

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Lasers

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Interfering with GPS

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

GPS spoofing

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Capturing UAVs using nets underneath other UAVs

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Bazooka net system

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Geo-fence coordinates

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Eagles

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

Radio interference

Table 1: Radar system attribute scores
Attribute 1 2 3 4 5
Initial costs
Long term cost
Range of the solution
Deployment speed
Safety for bystanders
Reliability
Hindrance
Types of drones
Scalability

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