Exemplifying Objective Information: Air Traffic Control System

Abstract

Air Traffic Control System (ATCS) is a large-scale information system that spreads all over the world and is widely used (Zhang et al., Modern Air Traffic Management (in Chinese). Beijing University of Aeronautics and Astronautics Press, Beijing, 2005). Although it cannot represent the entire information system, its structure, functional composition and application methods can already reflect many basic characteristics of today’s information systems, especially large-scale information systems. In this chapter, OIT is applied to explain the behavior of ATCS for demonstrating the feasibility and practicality to investigate information systems.

5.1 The Air Traffic Control System (ATCS)

ATCS is the core system for implementing airspace management, ensuring flight safety and maintaining the order of air transport, and also an information system of colossal volume, great complexity and typical significance [1]. The navigation intelligence, dynamic meteorological information, flight rules and regulations as well as static materials that are acquired, processed or generated by ATCS are the sources of operation for the whole system [2]. Thereinto, the flight status, meteorological and geographical information are the variations of the objective world, while flight regulations are subjectively stipulated by men. Once being imported and documented, all this information will become the objective reflection of the related objects and their motion states in ATCS, supporting the operation and management of ATCS [3].

5.2 The Sextuple of ATCS

For ATCS, its information set includes flight data, airport information, navigation and regional charts, national and international NOTAM, navigation chart information, route information, airport live weather reports, runway visual range, airport weather forecasts, significant weather alerts, weather images, air traffic control regulations, agreements with surrounding areas, procedures for handling special circumstances, airplane performance parameters and other data codes, etc. Table 5.1 characterizes the main elements of the aforesaid information in ATCS.

Table 5.1 The main elements of the information set in ATCS

In Table 5.1, the noumenon of flight information include both objective objects, i.e. flights and other flying objects, and subjective consciousness of the pilots, since flight data reflects not only the motion parameters of flying objects, but also the pilots’ operational intentions. Take the 9.11 attack for example, after the terrorists’ hijacking of the plane, the most significant information in the air traffic control system is nothing but the subjective intention of the pilot, which is reflected through flight data. Thus, the state set of flight data contains not only the position, speed and direction of the flying object, but also subjective factors such as the pilot’s operational intention. However, the reflection set only includes the motion parameters of the flying object, but not the subjective intention of the pilot.

The carriers of each information item are put into different subsystems of ATCS according to their features in retention and application. In fact, ATCS itself is the carrier of all these information items, while the subsystems in ATCS, such as communication gateway subsystem, information distribution subsystem and system data backup subsystem etc., may become the carriers of relevant information at the corresponding moments. For the sake of conciseness, many trivial details are omitted here.

The reflection time of all information items in Table 5.1 is “from acquisition & import to the outage of ATCS”, because even when the aforesaid information items become outdated, they may still play a part in supporting and assisting the operation control in the future, and therefore, the advanced ATCS would save these information items in case of need.

In Table 5.1, the air traffic control regulations, the agreements with neighboring areas and the procedures for handling special circumstances etc. are all products of subjective will, but once issued, signed or stipulated, they will become important objective information stored in ATCS, supporting the air traffic control institutions and ATCS in the management and control of flying objects.

The sextuple model of OIT in ATCS indicates the specific components of an information item.

$$ I=\left\langle o,{T}_h,f,c,{T}_m,g\right\rangle . $$

where, o, T_h, f, c, T_m, g denote the noumenon, state occurrence time, state set, carrier, reflection time and reflection set of information I in ATCS respectively. Table 5.1 indicates 16 information items in ATCS. From the third row on Table 5.1, $o\supseteq \bigcup \limits_{i=1}^{16}{o}_i,\kern0.75em {T}_h\supseteq \bigcup \limits_{i=1}^{16}{T}_{hi},\kern0.75em f\supseteq \bigcup \limits_{i=1}^{16}{f}_i,\kern0.5em c\supseteq \bigcup \limits_{i=1}^{16}{c}_i,\kern0.5em {T}_m\supseteq \bigcup \limits_{i=1}^{16}{T}_{mi}$ and $g\supseteq \bigcup \limits_{i=1}^{16}{g}_i$ denote the noumenon, state occurrence time, state set, carrier, reflection time and reflection set of information I in ATCS respectively.

$$ {I}_i=\left\langle {o}_i,{T}_{hi},{f}_i,{c}_i,{T}_{mi},{g}_i\right\rangle \kern1.25em \left(i=1,2,\cdots, 16\right) $$

Here, the relation between the former and the latter items in the equations is “⊇” but not “=” because Table 5.1 only lists the typical information of ATCS but not all.

5.3 The Information Metrics for ATCS

5.3.1 ATCS Volume

In ATCS, the airspace capacity is a significant technical indicator which describes the traffic volume that ATCS is able to process. ATCS has to decide the means to process new tracks, including overload fault-tolerance processing, according to the number of tracks that have to be processed by the system at any moment. The track number is the traffic volume and it is in fact an indicator of the scope of the flight information in ATCS. Specifically, for the flight information I₁ in ATCS, the noumenon o₁ is consisted of the flights, other flying objects and the subjective consciousness of all pilots.

It is obvious that in ATCS, all kinds of information will take up some storage capacity of their carriers. For example, flight information I₁ will take up the storage capacity of radar, secondary radar and the integrated display subsystem, while airport information I₂ will take up that of the data maintenance subsystem. The demand for storage capacity, σ(c₁) and σ(c₂), is the volume of the corresponding information, with bit as its unit of measurement. It has to be particularly emphasized that communication system, as a special carrier of ATCS, can only function normally when it fulfills the transmission of information from one subsystem to another, i.e. from one carrier to another, in a certain time period. In this case, the information volume will impose a clear requirement on the capability of communication system.

Zhang et al. [1] conducts a simulation experiment of the transmission of flight information I₁ between airplanes and ground stations based on the VDL Model 2 communication system in the aeronautical telecommunication network. It finds out how throughput varies along with the number of airplanes and the message length (Fig. 5.1). Here the throughput is the ratio of effective information amount transmitted by communication system while the different message lengths, namely 512 Byte, 220 Byte and 96 Byte, are the volumes of one piece of flight information sent by one plane under different conditions. Figure 5.1 shows that the message lengths have clear influences over the system’s throughput ratio, which means that information volume is the key element affecting the effectiveness of information transmission when the transmission ways and the communication system capabilities are certain.

Fig. 5.1

Throughput changes with the number of airplanes and the message length [3]

We can further assume that there is a piece of state information X in the message sent from the airplane to the ground station, with a value range of a_i(i = 1, ⋯, 7), representing 7 different kinds of working states respectively. Commonly, we can set a 3-bit code in the message for X to indicate its specific state. If the probabilities of occurrence for each state are the same, according to its Shannon entropy $H(X)={\sum}_{i=1}^7\frac{1}{7}{\mathit{\log}}_27={\mathit{\log}}_27=2.808 bit/\mathrm{symbol}$, the actual information volume of X is only 2.808 bits. If the probabilities of occurrence for each state are not the same, the actual information volume of X will drop further. Take Table 5.2 as an example, according to its Shannon entropy, $H(X)=-{\sum}_{i=1}^7p\left({a}_i\right){\mathit{\log}}_2p\left({a}_i\right)=2.61\ bit\slash \mathrm{symbol}$, the actual information volume of X is only 2.61 bits. Its average code length is

$$ \overline{K}={\sum}_{i=1}^7p\left({a}_i\right){K}_i=2.72\ bit\slash symbol, $$

according to the Huffman encoding format.

Table 5.2 Occurrence probabilities of airplane state and their code formats

It is thus clear that, according to Shannon entropy, the message length of state information X doesn’t have to be 3 bits, but 2.72 bits is enough in statistical sense if Huffman encoding is adopted. When the message of flight information contains large amount of such information, we could apply Shannon entropy equation to designing the specific code format of information, so as to shorten the message length and reduce the volume of information to a large extent. Thus it is of great significance for improving communication quality.

5.3.2 ATCS Delay

As it takes time to collect, transmit and process information, there will be a certain degree of delay from state occurrence to reflection in the carrier for all kinds of information. For every piece of atomic information I_λ = (o_λ, T_hλ, f_λ, c_λ, T_mλ, g_λ, ) of the flight information I₁ = 〈o₁, T_h1, f₁, b₁, T_m1, g₁〉 in ATCS, o_λ is a specific flying object and T_hλ is its state occurrence time, then we can get that supT_hλ = T_hλ. As T_mλ is actually the time when I_λ stays on Carrier o_λ, infT_mλ is the first moment when ATCS captures I_λ. So infT_mλ −  sup T_hλ is the delayed time that it takes for the state information of flying object o_λ at T_hλ to reach ATCS, and

$$ delay\left({I}_1\right)={\mathit{\max}}_{\lambda \in \varLambda}\left\{\mathit{\operatorname{inf}}{T}_{m\lambda}-\mathit{\sup}{T}_{h\lambda}\right\} $$

is the maximum delay that it takes for the state information of all flying objects to reach ATCS.

Figure 5.2 shows how the delay of the flight information sent from airplane to ground station in ATCS is influenced by the number of airplanes and the data updating rate [1]. When the number of airplanes reaches 120 and the message updates at a rate of 2s ∕ time, the delay exceeds 3s—the ceiling system requirement and other means of communication should be adopted in order to meet the requirement.

Fig. 5.2

Delay changes with the number of planes and the rate of data update [3]

5.3.3 ATCS Scope

Assuming that ${o}_1^{\prime }:=\left\{a| any\ flying\ object\ processed\ by\ ATCS\ at\ time\ {T}_{h1}\right\}$ and ${o}_2^{\prime }:=\left\{p| the\ subjective\ consciousness\ of\ all\ pilots\ at\ time\ {T}_{h1}\right\}$, then

$$ {o}_1={o}_1^{\prime}\cup {o}_2^{\prime }. $$

Here, we can define the measure of o₁, denoted by μ₁, as the number of flying objects processed by ATCS, namely ${\mu}_1\left({o}_1\right)=\left|{o}_1^{\prime}\right|$, where $\left|{o}_1^{\prime}\right|$ is the potential of the set ${o}_1^{\prime }$. So one of the indicators of the scope of the flight information in ATCS is

$$ {scope}_{\mu_1}\left({I}_1\Big({f}_1\left({o}_1,{T}_{h1}\right)\right)=\left|{o}_1^{\prime}\right|, $$

which is also the traffic volume of ATCS at time T_h1, whose upper limit is the airspace capacity of ATCS.

On the other hand, statistics indicate that 70% of flight accidents are caused by human factors, of which psychological factors form a major part [2]. It is generally considered that the pilot’s psychology can be influenced by such factors as his/her age, flying hours, degree of education, duties during flight and whether he/she smokes, as well as the age and type of the airplane. To have a timely understanding of all pilots’ consciousness status before or during the flight is critical for improving flight quality and avoiding or reducing flight accidents. So it is important for ATCS to collect the pilots’ subjective consciousness as much as possible, in order to ensure flight safety. In fact, the flight current intent and the trajectory change point, etc. in ATCS are all judgments and predictions of the pilots’ subjective consciousness. Now we can define another measure of o₁, denoted by μ₂, as the number of pilots whose subjective consciousness is understood by ATCS at time T_h1, namely ${\mu}_2\left({o}_1\right)=\left|{b}_2^{\prime}\right|$, where $\left|{o}_2^{\prime}\right|$ refers to the number of pilots in the set ${o}_2^{\prime }$. Then another indicator of the scope of information I₁ in ATCS is

$$ {scope}_{\mu_2}\left({I}_1\Big({f}_1\left({o}_1,{T}_{h1}\right)\right)=\left|{o}_2^{\prime}\right| $$

It reflects ATCS’s understanding of the subjective consciousness of all pilots of the flying objects.

The technical attribute of the airspace refers to the Information Field formed through various technical methods [2]. The airspace range, within which flight information service could be provided, is also a significant indicator of ATCS. For any flying object a in ${o}_1^{\prime }$, there is a perception space s_a for ATCS, namely when a is in the space s_a, it can be perceived and processed by ATCS. Thus, if we define $\bigcup \limits_{a\in {o}_1^{\prime }}{s}_a$ as the perception space of ${o}_1^{\prime }$ and ${P}_{\perp}\left(\bigcup \limits_{a\in {o}_1^{\prime }}{s}_a\right)$ as the orthogonal projection of $\bigcup \limits_{a\in {o}_1^{\prime }}{s}_a$ on the ground, we can get another measure of o₁, namely ${\mu}_3\left({o}_1\right)=S\left({P}_{\perp}\left(\bigcup \limits_{a\in {o}_1^{\prime }}{s}_a\right)\right)$, where $S\left({P}_{\perp}\left(\bigcup \limits_{a\in {o}_1^{\prime }}{s}_a\right)\right)$ refers to the area of ${P}_{\perp}\left(\bigcup \limits_{a\in {o}_1^{\prime }}{s}_a\right)$. Then, another indicator of the scope of flight information I₁ is

$$ {scope}_{\mu_3}\left({I}_1\Big({f}_1\left({o}_1,{T}_{h1}\right)\right)=S\left({P}_{\perp}\left(\bigcup \limits_{a\in {o}_1^{\prime }}{s}_a\right)\right) $$

which reflects the airspace range within which ATCS is able to provide flight information service.

5.3.4 ATCS Granularity

The navigation chart information I₅ in ATCS is consisted of various navigation charts and different charts have different measuring scales. For example, generally speaking, the measuring scale for a worldwide navigation chart is 1 ∶ 1000000, that for a regional one is 1 ∶ 500000, and that for an airport chart is 1 ∶ 10000. In fact, these measuring scales reflect the different granularity of the navigation chart information.

Assuming that a certain navigation chart ${I}_5^{\prime}\left({f}_5^{\prime}\left({o}_5^{\prime },{T}_{h5}^{\prime}\right)\right)\subseteq {I}_5\left({f}_5\left({o}_5,{T}_{h5}\right)\right)$, the Noumenon o_λ of its atomic information I_λ(f_λ(o_λ, T_hλ)) is the area that corresponds to a single pixel. We can define the measure of o_λ, denoted by ${\mu}_5^{\prime }$, as its area, namely ${\mu}_5^{\prime}\left({o}_{\lambda}\right)=S(o)$ (S(o_λ) refers to the area of o_λ), and then get the granularity of information ${I}_5^{\prime}\left({f}_5^{\prime}\left({o}_5^{\prime },{T}_{h5}^{\prime}\right)\right)$

$$ {granularity}_{\mu_5^{\prime }}\left({I}_5^{\prime}\left({f}_5^{\prime}\left({o}_5^{\prime },{T}_{h5}^{\prime}\right)\right)\right)={\mathit{\max}}_{\lambda \in \varLambda}\left\{S\left({o}_{\lambda}\right)\right\} $$

When the measuring scales used in the navigation chart are identical, max_λ ∈ Λ{S(o_λ)} is no different from any specific S(o_λ), and its reciprocal is exactly the measuring scale of the chart. However, if the navigation chart is composed of several parts with different measuring scales, then the reciprocal of max_λ ∈ Λ{S(o_λ)} is the coarsest measuring scale of the whole chart.

On the other hand, ATCS must make correlations among different radar tracks so as to exclude false targets. For primary tracks, only location and speed correlation could be conducted; for secondary and primary-secondary tracks, code correlation must be conducted before location and speed correlation. These processing demonstrate the resolving power of ATCS and in fact also reflect the detailedness of the flight information. Namely, for flight information I₁, I_λ(f_λ(o_λ, T_hλ)) which reflects the flight status of any flying object o_λ at time T_hλ is the atomic information of I₁.

For o_λ, there exists a neighborhood space ${\delta}_{o_{\lambda }}$which centers around o_λ at the radius of the distance resolution threshold d_λ, and we can define the measure of o_λ, denoted by $\overset{\sim }{\mu_1}$, as the volume of ${\delta}_{o_{\lambda }}$, namely, $\overset{\sim }{\upmu_1}\left({o}_{\uplambda}\right)=\mathrm{V}\left({\delta}_{o_{\lambda }}\right)$ ($\mathrm{V}\left({\delta}_{o_{\lambda }}\right)$ refers to the volume of ${\delta}_{o_{\lambda }}$), then the granularity of information I₁ can be obtained

$$ {granularity}_{\overset{\sim }{\mu_1}}\left({I}_1\Big({f}_1\left({o}_1,{T}_{h1}\right)\right)={\mathit{\max}}_{\lambda \in \varLambda}\left\{V\left({\delta}_{o_{\lambda }}\right)\right\}, $$

which in fact reflects the maximum distance resolution of ATCS for different flying objects. Similarly, we can define the granularity of the flight information based on the speed resolution threshold to reflect the speed resolution of ATCS. These are all significant indicators of the information processing of ATCS.

5.3.5 ATCS Variety

The specific contents of the navigation chart information in ATCS is listed in Table 5.3 and there are all together 26 items of them. That is to say, the State Set of information I₅, denoted by f₅(o₅, T_h5), contains 26 items of graphic information, namely f₅(o₅, T_h5)={f_5 − i| i = 1, 2, ⋯, 26}. Here, the potential of f₅(o₅, T_h5), denoted by |f(o, T_h)|, equals the number of the graphic information categories in it, and its measure is defined as

$$ \rho \left(f\left(o,{T}_h\right)\right)=\left|f\left(o,{T}_h\right)\right|. $$

Then we can get the definition of the variety of I₅(f₅(o₅, T_h5)):

$$ {\mathrm{variety}}_R\left({I}_5\Big)\right)=\left|{\left[f\left({o}_5,{T}_{h5}\right)\right]}_R\right| $$

which reflects the variety of the navigation chart information in ATCS.

Table 5.3 Navigation chart information

Similarly, we can get the State Set of the information set I(f(o, T_h)) in ATCS from Table 5.3, i.e. f(o, T_h) = {f(o_i, T_hi)| i = 1, 2, ⋯, 16}. The measure of its variety, denoted by variety_R(I) = |[f(o_i, T_hi)]_R|, also reflects the variety of the information in ATCS.

5.3.6 ATCS Duration

For any flying object o_λ, there is track data information I_λ(f_λ(o_λ, T_hλ)) in the flight information I₁ of ATCS that reflects its track in the airspace range of ATCS, satisfying that ${I}_1\left({f}_1\left({o}_1,{T}_{h1}\right)\right)=\bigcup \limits_{\lambda \in \varLambda }{I}_{\lambda}\left({f}_{\lambda}\left({o}_{\lambda },{T}_{h\lambda}\right)\right)$. A flight track is the motion trail of a flying object, which is formed by a series of discrete location reporting points. T_hλ is the time set that corresponds to the discrete location reporting points of o_λ, which constitutes a finite time sequence, namely T_hλ = {t| t = t₁, t₂, ⋯, t_n}, n is the number of disperse location reporting points. The measure of T_hλ, denoted by τ, can be defined as the number of disperse location reporting points, denoted by n, and then we can get a definition of the duration of I_λ(f_λ(o_λ, T_hλ))

$$ {\mathrm{duration}}_{\tau}\left({I}_{\lambda}\left({f}_{\lambda}\left({o}_{\lambda },{T}_{h\lambda}\right)\right)\right)=n. $$

Its significance lies in that, generally speaking, when the time span of T_hλ is certain, namely when t_n − t₁ is certain, the higher the duration and the sampling rate is, the more assuring the track quality of o_λ will be; when the average sampling interval in T_hλ is certain, namely when (t_n − t₁)/(n − 1) is certain, the higher the duration is and the longer the maintenance time of the track is, the more assuring the control of ATCS over the track will be. At this point, a concise definition of the duration of I₁ can be given:

$$ {\mathrm{duration}}_{\tau}\left({I}_1\left({f}_1\left({b}_1,{T}_{h1}\right),{o}_1,{T}_{m1}\right)\right)=\sum \limits_{\lambda \in \varLambda }{\mathrm{duration}}_{\tau}\left({I}_{\lambda}\left({f}_{\lambda}\left({b}_{\lambda },{T}_{h\lambda}\right),{o}_{\lambda },{T}_{m\lambda}\right)\right). $$

In ATCS, for specific objects and time slots, it is possible to propose other definitions of duration under specific restraining conditions under research needs, such as the duration of the flight information during special periods like taking off and landing, so as to demonstrate the time-enriched requirements of system applications and control for different information.

5.3.7 ATCS Sampling Rate

In ATCS, the operation of the flight trajectory consists of a series of discrete location with spatiotemporal information. The report with the point of trajectory information reflects the parameters on object motion such as position, speed, heading. The parameter value are acquired with various sensors, and the process of the flight data acquisition is sampling actually. The sampling rate of ATCS reflects the sampling interval of information and is closely related to the reducibility of information. The higher the degree of detailed track information, the more timely and accurate the monitoring of flight status.

Specifically, the flight information I₁ in ATCS has a track information I_λ(f_λ(o_λ, T_hλ)) reflecting the flight object o_λ in the airspace range of ATCS. T_hλ is the time set corresponding to the discrete position reporting points of o_λ, which constitutes a finite time series. That is, T_hλ = {t| t = t₁, t₂, ⋯, t_n}, n is the number of reporting points in discrete positions. For any λ ∈ Λ, there are U_λ ⊆ [infT_h ，  sup T_h] ， T_h ∩ U_λ = ∅, where Λ is an index set of ATCS track data. Then, the sampling rate of I_λ is the ratio of the cardinality of Λ to the Lebesgue measure ∣U∣ of $U=\bigcup \limits_{\lambda \in \varLambda }{U}_{\lambda }$.

$$ \mathrm{sampling}\_\mathrm{rate}(I)=\frac{\left|\varLambda \right|}{\mid U\mid }. $$

5.3.8 ATCS Aggregation

In ATCS, flight track is important data. The aggregation degree of ATCS reflects the correlation degree of flight information content. By measuring the similarity between different voyage information, the same voyage information is gathered, different voyage information is separated, and the voyage trajectory is divided into different clusters. With the clustering algorithm, the trajectory of flight history is grouped, and the normal flight trajectory model of aircraft is constructed. It may lay the foundation for real-time detection of abnormal flight trajectory, and then provide a new method to improve the intelligent level of air traffic supervision.

Specifically, for ATCS, let $\mathcal{F}$ be the set of all flight trajectory of flight o on time T_h, and $\left(\mathcal{F},{2}^{\mathcal{F}},l\right)$ be the flight trajectory measure space, l is the navigation trajectory model measure on the navigation trajectory measurable set ${2}^{\mathcal{F}}$, The navigation trajectory information I = (o, T_h, f, c, T_m, g), and the clustering degree of navigation trajectory model measure l clustering_l(I) is the navigation trajectory model measure l(f(o, T_h)) of f(o, T_h) on the measurable set ${2}^{\mathcal{F}}$, i.e

$$ {clustering}_l(I)=l\left(f\left(o,{T}_h\right)\right) $$

where, the $l\left(f\left(o,{T}_h\right)\right)=\frac{\sum_{i,j\in clu}d\left(f\left({o}_i,{T}_h\right),f\left({o}_j,{T}_h\right)\right)}{\sum_{i,j\notin clu}d\left(f\left({o}_i,{T}_h\right),f\left({o}_j,{T}_h\right)\right)}$, clu is a cluster, d for similarity measure.

5.3.9 ATCS Coverage

The value of a network system is equal to the product of the maximum scope and the maximum coverage of all contained information. In ATCS, information such as airport real weather I₇, airport weather forecast I₉ and significant weather alert I₁₀ is all carried by the subsystem of meteorological information query. The subsystem is further comprised of World Area Forecast System (WAFS), central database cluster and numerous clients, which are all carriers of meteorological information. Assuming that ATCS consists of WAFS, central database cluster and 498 clients, and the updated region distribution of lightning I₀ has got reflected in WAFS, central database cluster and 448 clients but not in the rest 50 clients, then the coverage of this information in ATCS is

$$ {coverage}_{I_0}(I)=\frac{450}{500} $$

The 50 clients that haven’t got the updated regional distribution of lightning are obviously restricted in terms of their ability to dispatch and control flights although they still have the old information. Therefore, the coverage of information exerts important influence on the overall running of ATCS.

5.3.10 ATCS Distortion

In ATCS, flight information I₁ reflects such parameters of the flying object as location, speed and course, which might bear some differences from the real status due to errors in the measurement, transmission and processing sessions. We use f₁(o₁, T_h1) to denote the real motion state of the airplane. According to g₁(c₁, T_m1), no matter it is graph or data, we could infer the motion state of the flying object, denoted by ${\hat{f}}_1^{\prime}\left({\hat{o}}_1^{\prime },{\hat{T}}_{h1}^{\prime}\right)$, based on common sense. The difference between f₁(o₁, T_h1) and ${\hat{f}}_1^{\prime}\left({\hat{o}}_1^{\prime },{\hat{T}}_{h1}^{\prime}\right)$ reflects the system error of ATCS, which is the validity of I₁. Figure 5.3 displays the error correction results of flight information before and after fusion of data from ADS and 3 SSR systems in ATCS, from which we can see that different Carriers of flight information differ greatly from each other and that data fusion could reduce errors and improve the validity of information. The root-mean-square error after data fusion as listed in Table 5.4 is the validity of flight information.

Fig. 5.3

Simulation results of ADS-SSR fusion [3]

Table 5.4 Root-mean-square errors of ATCS sensors and systems

5.3.11 ATCS Mismatch

Different users have different demands for information and the suitability of information also differs from person to person. Take the information in ATCS as an example, for a governor of the overall situation and a specific user focusing only on one specific flight, the connotation and measure of information suitability will be hugely different.

For governors of the overall situation, all the items of information and their component elements listed in Table 5.1 are essential to the information set of ATCS. There is a specific target set for the elements of each information item and the distance spaces $\left\langle \left({\mathcal{P}}_o,{\mathcal{P}}_{T_h},{\mathcal{P}}_f,{\mathcal{P}}_c,{\mathcal{P}}_{T_m},{\mathcal{P}}_g\right),d\right\rangle$ should ascertain the distance through the coverage of target set. Thus the suitability of the information in ATCS, is able to support the design, exploration and assessment of ATCS.

For specific users who focus only on specific flights, we can get the information suitability for individual users with the specific flight, flying time, flying status, information system, display time and display mode as focus, and with the degree of matchability between information elements and the focus as measure. Future ATCS should be able to not only provide comprehensive information support to governors of the overall situation but also offer personalized information service to single users.

Therefore, for all eleven metrics of information, we could find a series of actual indexes in ATCS that correspond to them, such as airspace capacity, target resolution ratio, data sampling rate, message length, system delay and data precision, most of which reflect the key capabilities of ATCS. Out of the need for concise exposition, usually we only choose certain proper sub-information I^‘ ⊆ I = 〈o, T_h, f, c, T_m, g〉 in ATCS for analysis instead of analyzing I directly. However, this does not affect the rationality of the exposition, since the measure of its sub-information I^‘ can also be considered a measure of I itself.

5.4 Chapter Summary

ATCS is a comprehensive system that integrates control staff, control automation equipment, control operation environment, and various operation management mechanisms and rules. It aims to ensure the safe flight of aircraft and accelerate and maintain an orderly air traffic flow. This chapter analyzes the objective information in ATCS, establishes an objective information model, and analyzes the information measurement model of ATCS based on the measurement system of objective information theory, which can assist system construction and control departments to clearly grasp the operating status and trend of ATCS, and facilitate their implementation of control In the process, the operation of the control system is coordinated and managed to help the control department equationte long-term and effective control measures and control system operation plans, thereby increasing the control capacity, ensuring the safety and normality of flights, and accelerating the flow of air traffic.