Solution Manual For Situation Assessment in Aviation Bayesian Network and Fuzzy Logic-based Approach

SOLUTION MANUAL FOR THE EXERCISES in THE CRC BOOK: Situation Assessment in Aviation: Bayesian Networks and Fuzzy Logic Approaches. (By Jitendra, R, Raol, Sudesh K. Kashyap, and Lakshmi, S.) Chapter 2 2.1 Definition of SAW: Within the given volume of space and time, perception of environmental elements, understanding the meaning of them and projecting the status of them in the near future.

The four qualitative methods of describing the SAW are: • • • •

Using a model for processing information or data to define and explain SAW; Using cycle of actions-perceptions to define and explain SAW; Comparison of SAW with the expertise; Using SAW to describe the phenomena of behaviour.

2.2 The perception-Action cycle It contains three major components: i) object – assessable information in external environments, ii) schema – internal knowledge, which is structured theoretically in an organized way and is developed by experience or training; when it is not used, it will be stored in the long term memories, and iii) exploration – observer’s search in the environment.

2.3 SAW measurement - Categories and the subcategories Category

Subcategories • Retrospective Measurements • Concurrent Measurements • Measurements that use Freeze approach • Global Measurements • External Tasks Measurements •• Embedded Measurements Self-RatingTasks - Directly

Explicit Measurements Implicit Measurements

• Self-Rating - Comparatively • Observer Rating

Subjective Measurements

Factors contributing towards SAW measurement are Sensitivity, Selectivity, Diagnosticity, Obtrusiveness, Reliability.

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2.4 Four relevant Components of SAW related to Surveillance are: Spatial awareness, temporal awareness, environmental awareness and navigation awareness.

2.5 The SAWRT (elaborated as Situation Awareness Rating Technique) is used for self-rating in test situation awareness. Taylor group provided overall 3 dimensions and named it as 3D-SAWRT. He developed by electing the knowledge of pilot and Aircrew. The content of 3 dimensional SAWRT are:

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a. Demand on attention resources: It consists of complexity of situation, variability of situation and stability of situation. b. Supply of attentional resources: It is made up of spare mental capacity, division of attention, concentration of attention, and arousal of situation. c. Understanding of situation: This is a combination of information quality, familiarity and information quantity.

Chapter 3 3.1 A situation assessment is carried out in order to understand the needs and conditions of a given scenario or mission.

3.2 Steps are: i) discuss with the expert of the field, ii) gather the details that are required, iii) make relevant observations, iv) collect the findings, v) analyse the collected information, and vi) share the results of the analysis and determine what next to do.

3.3 These are: diagnostic, formative, and summative.

3.4 Some situation analysis tools are: SWOT, PESTLE, 5C, VRIO, and Porter's Five Forces.

SWOT: Strengths, Weaknesses, Opportunities, and Threats analysis.

PESTLE: Political, Economic, Social, Technological, Legal and Environmental factors.

5C Analysis is a framework to analyze the environment in which a company operates, and provides insight into the key drivers of success, as well as the risk-exposure to various factors. The 5Cs are Company, Collaborators, Customers, Competitors, and Context.

VRIO is an internal analysis framework that helps businesses identify the advantages and resources that give them a competitive edge. VRIO: value, rarity, imitability, and organization Porter's Five Forces are: i) Competitive Rivalry, ii) Supplier Power, iii) Buyer Power, iv) Threat of Substitution, and v) Threat of New Entry.

3.5 Threat assessment is the process of determining the credibility and seriousness of a potential threat, as well as the probability that the threat will happen.

Chapter 4 4.1 The domain expert knowledge is very essential because he/she has learnt many operating aspects of the system he/she is monitoring for several years. He/she as an operator and/or the designer has a good feel of the performance of the system and if this knowledge is wired in the design and operation of FL/S then we get good design of the FL/S. Another way is to learn the required rules from the empirical data of the system.

4.2 In DM/DSS we need to take some decision based on the acquired/processed information regarding the system. If… then rule base approach of FL is naturally suitable for implementation of such systems.

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4.3 After the FIS and aggregation process the end result is an area (or so) and hence we need to get the result as a crisp value, since this crisp value is further processed in a control system and/or FS. 4.4 The inner system is Type-I FL and the output is Type-0 FL system. In fact the Type-0 system is deterministic and not really fuzzy system.

4.5 One can define membership function for the input/output data and then use some portion of the data to see to what extent the data belong to what type/portion of the FLMF. Then one can use the predicted data to identify a parameter by using delta rule as is done in application of FFNN for system identification [28].

4.6 The main reason is that for the intersection the severity of the intersection increases from the standard one to the drastic one, and hence the norm of the relational matrix is reduced. Whereas in the case of the union operation, the severity of union (more and more union is expected) increases and hence the norm increases.

This can be ascertained from equations (4.18) to (4.21) and equations (4.35) to (4.38).

4.7 The Gaussian, trapezoidal, and triangular are very common functions.

4.8 The two methods are: 1) experts, and 2) based on the empirical data.

4.9 The experts can easily provide the rules due to their experience, and operational knowledge of the conventional (control) systems. However, they may not give out their total experience and hence the number of rules provided might be limited. The experts also have to specify the membership functions. The rules can be learned from the (training) data using ANNs. In this case the designer of the FL/S has to specify the membership functions.

Chapter 5 5.1 These are propositional, predicate, temporal, modal, constraint, and probabilistic.

5.2 In Propositional logic the variables are True or False (Boolean). The operators used are AND, OR, and NOT. A rule would be: If A is True, then B is False. In Predicate logic the variables are True, False, unknown, probabilities. A typical rule might be: If z(k+1) – z(k) > ‘a’ Then the controller = Type 1 (number 1, etc.); If –‘a’ < z(k+1) – z(k) < ‘a’ Then the controller = Type 2. In temporal logic the truth variable is function of time. A rule could be: ‘A’ is True until time t, thereafter it is False. In modal logic some statements are always True, some are possibly True, etc. In constraint logic the variable would be both a logical variable and a numerical variable.

The probabilistic logic incorporates uncertainty into the knowledge base. A rule could be: If the day temperature is > 40 deg., then with probability 0.9, there would be evening rains.

5.3 In a FFS some (or all) rules are fed back to each other and to themselves.

5.4 A FFS can model a dynamic system, e.g. x = f (x) .

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5.5 The data from a nonlinear dynamic system (NLDS) are fed to FFS and its parameters adjusted as per the usual system identification procedure. When convergence is reached the FFS models the NLDS. 5.6 Sometimes while learning these rules from empirical data/methods (like using ANNs) one needs to use some pruning approach to control the total number of rules so that the FL/S (FFS) does not become very complex, especially for real-time implementation.

5.7 The main reason is that the fuzzy variables have ‘vagueness’ or they represent ‘vagueness’, and the depending on how the variables interact and what is their degree of their involvement the fuzzy ‘patches’ are obtained, and hence several ‘internal’ operations within ‘min’ and ‘max’ can be defined. This in itself is the richness of FL/S. 5.8 In data fusion the information from two (or more) sensor is fused (may be after some preprocessing). Then further analysis is made to obtain the intended inference.

In the decision fusion, first the required analysis is made from the individual information (already some decision is made) and then the results are fused.

5.9 Voting method is widely used. The predictions obtained from the (say fault) classifiers are counted as votes. And a majority or plurality decision rule is used for fusion. Bayesian approach is used for evidence gathering. Posterior probabilities (of faults) can be computed by an agent for decision support system based on the Bayesian rule. Dempster-Shafer approach is a mathematical theory of evidence and is based on belief functions - uses degrees of belief collected from predictions to merge information from two sources.

5.10 Yes, it is. The raw data are processed and then filtered. Then the information is fused or integrated to some final identity or decision. This involves reduction of the original raw data to a great extent.

5.11 It can be considered as an approach to decision fusion. The fuzzy logic allows us to use more connectives than possible with the classical/conventional/crisp logic/set theory, thus the FL based aggregation and fusion can be used as decision fusion approach.

5.12 The FLKF is essentially a type of nonlinear/adaptive filter and not a linear filter like the KF. Hence, the numerical value of the normalized cost function may not meet the consistency check very accurately, however, it still will not be very far from the theoretical value. This is the price one pays for achieving the adaptive feature of the FLKF. 5.13 There is no binary logic used in KF gain updating that computes the Kalman gain by using information on previous state-error covariance and measurement error covariance matrices.

5.14 It is also soft decision but now it is based on FL/FIS.

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5.15 One can use state vector fusion, feature level or decision level fusion depending upon the situation.

Chapter 6 6.1 In the forward chaining the goal/conclusion is derived from the fundamental premise. In the backward chaining the proposition itself is defined as a goal.

6.2 Yes. One can represent the sensor measurements as providing the fuzzy non-crisp memberships. This then can be integrated using Bayesian inference rule for obtaining the predicted probabilities for inferring a decision as is done in situation assessment scenario.

6.3 In such a system the optimal decision can be viewed as some weighted sum of the individual local decisions. The weights in this sum could be related to the probabilities of the sensor’s signal detection and false alarm.

6.4 One can define fuzzy membership functions and the fuzzy connective operators, the parameters of which can be estimated and determined by using genetic algorithm.

The multiple sensor fusion can be accomplished by using any architecture of ANNs. 6.5 The strong point is that the expert’s heuristic knowledge can be easily incorporated in the FIS and the weak point is that it takes some time to design the FL/FMF for FIS and decision making.

6.6 One can use fuzzy logic for monitoring the tuning parameters of the KF and use ANN to estimate the target’s maneuver parameters. KF will eventually provide the track/trajectory of the target.

6.7 Fuzzy logic uses the (expert-) rules for mapping inputs to outputs. The knowledge of an expert designer is captured in the If…Then rules. This fuzzy rule base provides the mapping from input to outputs. This mapping is usually non-linear.

6.8 FL as such represents the imprecision that is associated with noisy sensors. This is modeled with fuzzy membership functions.

6.9 The clustering technique used for data classification identifies subsets of data points from large data set (e.g. measured I/O data) with each set making one group.

Arrangement of these data points into natural groups enables precise definition of system’s behavior. The other techniques are: i) maximum likelihood classifier (based on normal distribution assumption - may not be suitable for remote sensing image classification because geographical phenomena do not occur randomly in nature), and ii) ANN-based classification which totally depends upon training data and learning algorithms and is difficult to interpreted by human language.

Chapter 7 7.1 Fuzzy logic is used. In decision fusion, fuzzy logic (FL) method is used in making a definite real time crucial decisions based on inferences derived from a certain

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scenario. Fuzzy logic method handles uncertainties in real time data and is based on rules from domain experts; gives the output decision; typically to assist the fighter aircraft pilots to make crucial decisions.

7.2 Pair formation, attack model, threat assessment and flying along the airlane.

Modified pair formation model has new inputs ‘altitudes’ for both the aircrafts; which are added along with the ‘aspect’ (angle) between them considered. This in conjunction with the distance input helps in providing an accurate view of the sideways movement between the two aircrafts and enhances the accuracy in decision of the two aircrafts whether they form a pair or not. New altitude level information provides more accurate and robust decision result.

7.3 Threat assessment model gives the decision about whether the unknown aircraft is friend, enemy or transport aircraft. The set of inputs for the system are velocity, aspect angle, deviation angle (angle off), elevation, RWR sensor reading, class, ID and range in a systematic manner to compute various intermediary parameters like combat geometry, energy driven positional geometry, sensor driven positional geometry and situational geometry to determine the action of the unknown aircraft. The output of the system is, action, which has four membership functions: Offensive, Evasive, Defensive and Passive.

7.4 Attack model. This attack model system has the FIS (Fuzzy inference System) to determine speed advantage, the implication method used is PORFI and aggregation method is bounded sum (BS), while MORFI and standard union (SU) are used as the implication and aggregation methods in Closing and Attack FISs. All three FISs use centre of area (COA) for defuzzification.

7.5 To carry out the testing, the inputs are generated with the noises added; and then the reliability and robustness of the system have been validated. Based on the quantity level of SNR, the performance of the system is evaluated. Two models are: Threat assessment model and Flying along the airlane.

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Chapter 8 8.1 Since, these provide additional design degrees of freedom in Mamdani and TSK fuzzy logic systems (FLS), the T2 and IT2 fuzzy logics are very useful where lots of uncertainties are present.

8.2 Yes, these new FLs have a potential to provide better performance, wherein the performance of T1 FL is poor.

8.3 All of the results that are needed to implement an IT2 FLS can be obtained using T1 FS mathematics.

8.4 Since, ultimately we need crisp set results to interface with our usual deterministic system that is type 0, first we need to go to T1 by using type reducer from IT2FL, and then from T1 to T0, i.e. doing the defuzzification.

8.5 There are two MFs: i) primary, and ii) the secondary.

Chapter 9 9.1 These are for i) tracking of kinematics of the two aircrafts, and ii) recognition of the formation flying.

9.2 These are realized by adding Gaussian noise in the input sensor data.

9.3 In the pilot’s SA, there are several systems involved and they also form a closed loop. Hence, it is necessary to ascertain that such an overall system would be stable, especially since, some subsystems like FLS would be nonlinear. Lyapunov stability analysis help evaluate the conditions to achieve this stability.

9.4 The underlying control-theoretic condition that the input to the composite Pilot’s SA, aircraft’s dynamics, the scenario logistics, and the FL system should be bounded.

9.5 Use of more rules gives more flexibility to incorporate more possibilities of the scenario and would improve the performance of the FL based decision fusion system.

Chapter 10 10.1. The decision making is more critical in BVR. In BVR we have enough time to prepare our ownships by knowing the intent/threat using system like SAS/pilot support system like pilot associate.

10.2. The Multi-Sensor Data Fusion plays a very important role in development of pilot expert system because all information about enemy position/identity etc. are used to infer their intent and compute the threat level. The high priority threats are then engaged by weapon control system.

10.3. The CPT indicates pilot mental model. It can be developed with the help of expert pilots by getting the probability numbers for some event can happen due to

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occurrence of some apriory events. The accuracy in decision making depends how fine-tuned are CPT from past evidences.

10.4. The sensors that can be used in modern warfare are AESA Radar, Infrared search and track system (IRST), Missile approach warning system (MAWS), Radar waring receiver (RWR), IFF or CIT, fused sensor/track data from friendly aircraft/ data from AWACS through data link, etc. These sensors are fused to complement each other. For e.g. Radar is very accurate in range detection but not so accurate in angle detections whereas IRST is accurate in angle detections but derived range (termed as passive ranging) is not so accurate. So, when detections from Radar and IRST are fused we get accurate range and accurate angles. Hence, we need to have multiple sensors to increase the accuracy, spatial coverage etc.

10.5. Selection of sensors, Track to track Fusion of more than two sensors, handling of asynchronous sensors, handling of latency from subsystems, Track to track fusion across the multiple moving platforms, solving ghosting issues in passive ranging etc are the few problems need to be addressed.

Chapter 11 11.1. The situation assessment system (SAS) reduces the pilot work load otherwise pilot has to see the visuals cues on each sensor pages of PFD and has to fuse the information in mind to get some inferences on the situation developed around him/her. The pilot work load can increase exponentially if a greater number of targets appears in scenarios. The SAS can take the load from the pilot to assess the situation by using automated MSDF and Inference engine developed using Fuzzy logic/Bayesian network.

11.2. The decision logic for data fusion is to decide which of the contributing sensors shall be fused at any given time to get best results in terms of estimated states and covariances of the targets being tracked by contributing sensors. In general, the parameters considered to develop decision logics are sensors accuracy and ranges.

11.3. Radar works on radio frequency wherein radio signals are transmitted in certain direction and reflected signals from object/target in field of view of radar are used to determine the position of that object/target. In other hand IR works based sensing the thermal signature of object/target in field of view of IR. The IR sensor is imaging sensor. The capture image is used to find the angular position of the object/target. IR is preferred over radar in closed combat because radar is an active sensor and can be locked by enemy radar whereas IR is passive sensor and can be detected by enemy.

11.4. RWR stands for Radar Warning Receiver. RWR provides the angular position of target and is identity. RWR is passive sensor so enemy cannot detect ownship equipped with RWR. That is why RWR never switched off during combat mission.

11.5. The various sub-systems of SAS are MSDF, Event detector, Bayesian network (or equivalent inference engine) to assess the situation/threat from opponents, Tactical/Mission planner and Pilot control & Display.

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