A worked example is introduced in this section in order to be used throughout the remainder of the thesis for illustrating and clarifying the concepts implemented in the simulation environment. A hypothetical GBAD scenario is illustrated in Figure 4.9. In this figure the threat paths of three threats are indicated by the three lines, the FEC of the three threats are shown in Table 4.6. The positions of two DAs are depicted by black dots and their positions are listed in Table 4.7.
Figure 4.9: Hypothetical ground-based air defense scenario.
The differently coloured domes represent the SSHP distribution volumes of two ground-based WSs which are indicated by the two crosses; their properties are indicated in Table 4.8. The outside dome has a SSHP of 0.5, whereas the other coloured domes do not have particular SSHP values associated with them, irrespective of their colour. These domes were mainly used for verification purposes and to aid understanding. The SSHP functions, as described in§4.2.3, are used to populate the SSHP matrix.
Although the threat models are unaware of this, Threat 1 is executing a pitch-and-dive attack manoeuvre in respect of DA 2. Threat 2, on the other hand, is executing a toss-bomb attack manoeuvre in respect of DA 1. The basic principles on which these attack manoeuvres rely are illustrated in figures provided in Appendix B. Finally, Threat 3 is a passenger aircraft passing over the conflict zone and therefore poses no real threat. Threat 3 is only present to ascertain the response of the TE algorithms in the case where an aircraft is not attacking the DAs. This hypothetical GBAD scenario is used throughout the remainder of this thesis in order to demonstrate the working of the underlying TE and WA algorithms.
4.6. Illustrative Example 69
Table 4.6: Threat-related data for the illustrative example.
Information type Data
Threat 1
Weapon Deployment Profile {Pitch and Dive}
x-coordinates {6 320, 10 994, 15 845, 20 716, 25 043, 24 056} y-coordinates {49 556, 45 825, 42 344, 38 904, 36 174, 31 929} z-coordinates {1 524, 1 524, 1 523, 1 549, 3 833, 1 492} Speed points {180, 180, 240, 240, 300, 240} WRL [75, 100] Threat 2
Weapon Deployment Profile {Toss Bomb}
x-coordinates {19 409, 21 084, 22 785, 24 053, 21 044, 16 378} y-coordinates {11 355, 16 027, 20 762, 26 285, 27 254, 23 519} z-coordinates {1 091, 1 091, 1 092, 1 590, 1 075, 1 093} Speed points {200, 200, 240, 200, 200, 200} WRL [50, 70] Threat 3
Weapon Deployment Profile {None}
x-coordinates {36 190, 33 280, 29 940, 27 810, 25 360, 21 330} y-coordinates {17 550, 20 120, 22 920, 25 370, 28 050, 30 850} z-coordinates {11 987, 11 897, 11 887, 11 877, 11 877, 11 897}
Speed points {190, 200, 240, 240, 240, 240}
WRL N/A
Table 4.7: DA-related data for the illustrative example.
DA 1 DA 2
DA type Command Centre Hanger
x-coordinate 23 800 m 25 250 m
y-coordinate 31 200 m 28 940 m
z-coordinate 0 m 0 m
Table 4.8: WS-related data for the illustrative example.
WS 1 WS 2
WS type SHORAD VSHORAD
x-coordinate 28 800 m 16 300 m y-coordinate 29 200 m 29 200 m
z-coordinate 0 m 0 m
Setup time 4 s 4 s
4.7 Chapter Summary
In §4.1 the notion of M&S was introduced and the differences between military and commer- cial simulations were clarified. Several considerations were also mentioned for the evaluation of military systems. The rationale for the use of simulation as a test bed for a TEWA system was motivated and several difficulties in this respect were elucidated. The section not only formed the background for the rest of the chapter, but also serves as background for the performance evaluation chapter to follow in a later part of this thesis. The different classifications of sim- ulation models were described in §4.1.1. Simulations were first categorised according to the standard used to classify military simulations, after which two simulation modelling approaches — discrete-event simulation and systems dynamics — were compared. In §4.1.2, a framework was introduced to provide some structure to the simulation process. This section concluded with a clarification of three generally misused concepts: verification, validation and accreditation in §4.1.3.
The representation of each of the TEWA elements (sensor systems, DAs, Wss and threats) within the simulation environment was detailed in §4.2. The assumptions made in respect of sensor systems were described in §4.2.1, after which the representation (priority values) and assumptions related to the DAs were addressed (§4.2.2). In §4.2.3, the modelling of WSs was considered, with special emphasis on the construction of a SSHP function and the notion of vulnerability. The modelling of the threats was finally clarified in §4.2.4, with reference to the construction of an FEC, the generation of threat tracks and flight path prediction.
A motivation was provided in §4.3 for the simulation environment, Matlab, selected for the purpose of model development. In §4.4, a high-level overview was provided of the logic flow in the simulation developed in this thesis. Each of the stages in the simulation environment were also introduced. The chapter closed with the introduction of a hypothetical GBAD scenario, to be used for clarifying the WA-related and TE-related concepts throughout the remainder of this thesis in §4.6.
CHAPTER 5
Threat Evaluation Implementation
Although our intellect always longs for clarity and certainty, our nature often finds uncertainty fascinating.
— Carl von Clausewitz
Contents
5.1 Threat Evaluation Overview . . . 71 5.2 Implemented Threat Evaluation Models . . . 73 5.2.1 Slant Distance TE Model . . . 74 5.2.2 Course-related TE Model . . . 75 5.2.3 Closest Point of Approach TE Model . . . 77 5.2.4 Altitude-related TE Model . . . 78 5.3 Data Fusion Processes . . . 79 5.3.1 Computation of Threat-DA Pair Threat Values . . . 81 5.3.2 Threat-DA Threat Value Scaling . . . 87 5.3.3 Computation of System Threat Values . . . 88 5.4 Threat Evaluation Simulation Architecture . . . 92 5.5 Chapter Summary . . . 92
TE is essentially the ongoing process of identifying, accessing and prioritising aerial entities which pose a threat to the defended system. This chapter opens with an overview of TE so as to better clarify the purpose and aim of the TE subsystem. The core processes and models of the TE subsystem are also introduced. After a basic understanding of TE has been acquired, the working of its constituent TE models are explained in some detail. A newly developed TE model fusion output process is subsequently explained and demonstrated. The chapter closes with a logic flow diagram of the TE simulation architecture adopted for system evaluation purposes in this thesis.
5.1 Threat Evaluation Overview
The TE process is a crucial C2 function during any GBAD scenario [169]. TE consists of determining the level of threat posed to the defended system by aerial threats and, consequently, the priorities associated with these threats in terms of their engagement by WSs within the Area
of Responsibility (AOR) [91]. This task is a highly complex task that requires making useful interferences under tight time and serious uncertainty constraints.
Prior to executing the process of TE, input information is required from ground radars and associated sensors. These sensors are responsible for detecting, tracking and identifying potential threatening aerial vehicles [78]. The TE subsystem utilizes the kinematic, tracking and attribute data collected by the sensors, together with pre-deployment information, in order to estimate the level of threat posed by each aerial vehicle. The output of this process is ultimately a system threat value for each threat.
Different measured attributes are taken into consideration when determining these threat values. These attributes may be subdivided into three classes [91, 149, 190]:
Opportunity parameters quantify the extent to which certain preconditions in the envi- ronment are met in order for the threat to successfully launch an attack. Truter and Van Vuuren [206] refer to this class of parameters as “proximity parameters” in order to contextualise this parameter class in a TEWA environment, since opportunity is gener- ally measured in terms of the proximity of a threat in relation to a DA. Opportunity is nonetheless used here for the sake of generality. A threat that is far away from a DA will not be classified as an imminent threat to that DA, when compared to threats that are closer to the DA. A widely used example of such a parameter, is the range to the closest point of approach of a threat with respect to a DA [168] or distance to weapon release. Capability parameters attempt to quantify a threat’s ability to cause damage to a DA. In
order to calculate this value, it is required to know specific characteristics of the attacking aircraft. Examples of capability parameters include the threat type, its weapon envelope and its fuel capacity.
Intent parameters aim to quantify the will and determination of a threat to cause damage to a DA. Of the three parameter classes, intent is the most difficult type of parameter to estimate, but certain measured threat attributes may nevertheless be used to estimate a threat’s intent [169]. One method in which intent is estimated is through recognition of known attack manoeuvres from an aircraft’s measured track.
Examples of the above-mentioned classes of parameters are implemented in a number of different algorithms of varying complexity and sophistication that typically run concurrently within the TE subsystem, each assigning threat values to each threat with respect to each DA separately. This results in several threat values for each threat. In the case where certain necessary sensor data are not available, the TE system will select scaled-down TE models which are able to estimate threat values in the absence of very detailed threat data [120]. The different threat values for each threat-DA pair are then fused together to obtain a single prioritised list of system threat values (i.e. a threat value for each threat, typically found on a consensus basis, taking into account the results contributed by all the TE models) [120]. These threat values are used by the WA subsystem in a bid to optimise the utilisation of available resources (WSs and ammunition) when weapon assignment decisions are made for engaging the aerial threats.
Roux and Van Vuuren [168] proposed three levels of TE models of varied complexity. This multi-level model infrastructure is illustrated graphically in Figure 5.1. In order of increas- ing complexity, they are flagging models, Deterministic Models (DMs) and stochastic models. Flagging models are binary in nature and are activated when certain threshold violations occur (e.g. when sudden increases in altitude or the dropping of paratroopers are observed). Stochastic