CA2130737A1 - Process for processing the signals of a passive infra-red detector and an infra-red detector for implementing the process - Google Patents
Process for processing the signals of a passive infra-red detector and an infra-red detector for implementing the processInfo
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- CA2130737A1 CA2130737A1 CA002130737A CA2130737A CA2130737A1 CA 2130737 A1 CA2130737 A1 CA 2130737A1 CA 002130737 A CA002130737 A CA 002130737A CA 2130737 A CA2130737 A CA 2130737A CA 2130737 A1 CA2130737 A1 CA 2130737A1
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- 238000012545 processing Methods 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 title claims description 17
- 230000008569 process Effects 0.000 title claims description 16
- 238000011156 evaluation Methods 0.000 claims abstract description 16
- 230000005855 radiation Effects 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 10
- 238000001514 detection method Methods 0.000 description 7
- 230000009471 action Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
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- 238000004364 calculation method Methods 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241000202252 Cerberus Species 0.000 description 1
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Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/19—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using infrared-radiation detection systems
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- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Photometry And Measurement Of Optical Pulse Characteristics (AREA)
- Burglar Alarm Systems (AREA)
- Feedback Control In General (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
Abstract The infra-red detector contains a sensor element (1) and, connected downstream of it, an evaluation circuit with an amplifier (3), an analogue/digital converter (4) and a microcontroller (6). The microcontroller (6) contains a pulse processing stage (5), in which the digitized sensor signals are converted into pulses, and a fuzzy controller (7), to which these pulses are fed. In the fuzzy controller (7) each of several successive pulses are compared to rules stored in the form of linguistic variables.
(Fig. 1)
(Fig. 1)
Description
~ ^ 2 ~ 3 ~
;
Cerberus AG, 8708 Mannedorf . . ' : .
Process for processing the signals of a passive infra red detector and an infra-red detector for implementing the process-: . , ~ ' :
This invention concerns a process for processing the signals of a passive infra-red detector, which generates electrical signals, described as sensor signals, in relation to incident infra-red radiation, then evaluates these -electrical signals.
. , :
In known processes of this kind, in the simplest case either the sensor signal is examined to ascertain whether it exceeds positive or negative thresholds, or the number of threshold crossings is counted. It is also known for an alarm to then be triggered if a positive threshold is ~;
exceeded before a negative thr~shold, and vice versa.
. - ~
All detectors based on simple thresholds are in principle -very susceptible to faults, since a single glitch of -~ -sufficiently large amplitude can trigger a false alarm. On the other hand, those detectors in which several pulses are counted, whether or not this depends on polarity, lose sensitivity relatively quickly, especially when an intruder is positioned at the boundary of the detection area, or moves only through one zone of the coverage pattern.
Detection systems are also known in which the sensor signal is continuously compared to a set of stored reference patterns and triggers an alarm when there is sufficient correlation. These systems are certainly very reliable and sensitive, but they require a large number of components.
This also means that a large and, consequently, expensive processor must be provided in the detector to supply the necessary storage capacity and power.
`
J) 3 rS~
Due to the invention a process of the type stated in the preamble shall now be specified, in which there is good discrimination between the widely overlapping classes of interference signals on the one hand and intruder signals on the other hand, certainly with high detection power and especially also in the peripheral monitoring area.
Furthermore, a simple evaluation with low numbers of -~
components should be possible and achievable with a simple microcontroller.
, This object is achieved in that the sensor signals are digitized and processed in the form of pulses, that the pulses are characterised hy data, and that the evaluation of these pulses is effected by means of fuzzy logic, whereby in each case the data are compared to a series of several pulses in the form of linguistically variable, stored rules.
The invention furthermore concerns an infra-red detector for implementing the said process, with at least one sensor element for generating the sensor signals and with an evaluation circuit for their processing and evaluation.
The infra-red detector according to the invention is characterised in that the evaluation circuit contains a fuzzy controller to which the pulse data are fed.
Due to the processing of the sensor signals in pulse form, an appreciable reduction in the data rate takes place, which fulfils an important requirement for simple evaluation. As defined, a pulse then starts when the signal departs from the normal position in the positive or-negative direction, and ends on return to the normal position. The data characterising the pulse, such as amplitude, duration, polarity, spacing and the like are stored and for the evaluation, a series of pulse data, i.e. the data of a series of successive pulses, is always used.
~ ~3~7 The use of fuzzy logic, instead of strictly classical logic, in the signal evaluation has the advantage that the rules for the evaluation can be based on an empirical scientific base that could be converted into conventional, analytical algorithms only in a considerably more laborious and cumbersome manner.
. . ~.
For examination of alarm pla~sibility, the pulse data stream ~ -~
of a number of preceding pulses is available, whereby the - criteria for triggering an alarm are formulated in the form of fuzzy logic operations on the previous fuzzy pulse data, that is, transformed into linguistic variables. The criteria -~
thus include in linguistic form a scientific base for the i-allocation of a pulse stream to the intrusion or fault class, whereby the contents of the scientific base represent all the experiences gained from the observation of innumerable walk tests and with interference signals.
The indistinct fuzzy sets as defined, deliver an eq~ally indistinct result, the defuzzification of which supplies a distinct decision for or against an alarm~ The rules in linguisti~ form in the microcontroller of the evaluation circuit require only a minimal memory requirement.
Furthermore, the fuzzyfication and defuzzification, that is the conversion of distinct numbers into indistinct areas, or the extraction of clear or distinct statements from indistinct areas, are considerably less numerically demanding than the processing of classical rules.
- :
The invention is explained in more detail bélow with the aid of an embodiment and the drawings, of which:
. ,, Fig. 1 shows a block diagram of an embodiment of an IR
detector according to the invention, Fig. 2 shows a detail of the circuit diagram of Fig. 1;
and Fig 3 shows a diagram for explaining the opsration.
3 ~ U;'37 In Fig. 1, an IR detector according to the invention contains a sensor element 1, which receives IR radiation from the space being monitored via an associated optical system 2 of a certain focal length and, dependent upon the --incident radiation, outputs an électrical signal referred to below as the sensor signal. The use of a single sensor element 1 is understood to be non-limiting; naturally, two or more sensor elements can also be provided. The sensor signal is amplified by an amplifier 3 and its output signal is fed to an analogue/digital converter 4 and after digitizing reaches a pulse processing stage 5 which forms part of a microcontroller 6. The microcontroller 6 furthermore contalns a fuzzy controller 7.
The data rate of the digitized sensor signals is first sharply reduced in the pulse processing stage 5 by s~oring them as "pulses". As defined, such a pulse commences when the signal has moved suf~iciently far from the normal position in the positive or negative direction and ends on return to the normal position. Each pulse is described by data characterising it, such as amplitude, duration, polarity, spacing and the like, and these data are stored.
. . ..
In known manner, the optical system 2 contains a mirror system which corresponds to a number of optical focusing means and focuses the IR radiation from a number of fan-type radiation detection areas onto the sensor 1 (see for example GB~A-2`047 886 or EP-A-O 361 224~. These radiation detection areas are discrete zones, whereby, on entry, an object passing~such a zone produces a positive sensor signal and on leaving, a negative sensor signal, the two together producing a characteristic sign~l. Such a signal can, for example, be described within a certain time interval by the pulses: small positive, large positive, large negative and small negative. During evaluation, the pulses derived from the sensor signal can thereupon be examined to ascertain whether they have a type and configuration that is " 213D'!37 characteristic to the intrusion of a person, a group of -several successive pulses always being examined. ! .' . - , Practical experience has shown that within the observed time -window, three or a maximum of four such pulses can usually be obtained from the digitized sensor signal, so that it is not practical to examine more than four pulses. The procedure during this examination is that the last four pulses are always stored and examined, the examination taking place in the fuzzy controller 7.
. ~
As Fig. 2 shows, in the known manner the fuzzy controller 7 contains a rule base 8, an inference machine 9, a process interface 10, and an action interface 11, at the output of which an alarm signal AS is obtainable during detection of an undesirable intruder in the monitored space. With regard to fuzzy logic, in the meantime reference is made to extensive literature on this subject, for example the book, "Fuzzy Set Theory and its Applications" by H.-J. Zimmermann, Kluwer Academic Publishers, 1991.
, In the known manner, the rule base 8 contains a set of linguistic rules for the evaluation of the pulse. Based on these rules, an algorithm is sonstructed, by which the values are defined as so-Galled fuzzy sets, i.e. indisti~ct quantities. Linguistic variables are words and expressions of the colloquial language or a natural language. These variables should be able to accept natural language expressions (small, medium, large), these expressions being names for the said fuzzy sets.
.
Like classical logic, the rules of fuzzy l~gic consist of a condition or premise part and an inference part. In Fig. 2 r the condition part is symbolized by the process interface 10 and the in~erence par-t by the action interface 11. The inference machine 9 links the direction of influence and the : , ~ 2 ~ rl 3 7 magnitude of the instantaneous state in the fuzzy sets on the basis of empirical technological knowledge.
:
Fig. 3 shows with the aid of a graphical representation with a typical fuzzy rule the essential features of a fuzzy controller. The rule denoted rule 1~ in the Figure says: "if A = LARGE and B = NO~MAL, then X = SMALL". Rule 2 says: for example: "if B = NORMAL and C = SMALL, then X = NORMAL". A, B and C are input variables. The part of the sentence beginning with "if" is the condition part, the part beginning with "then" is the inference part.
The central term of fuzzy logic are the fuzzy sets or indistinct quantities, whereby the membership of elements in a fuzzy set is defined by the so-called membership function.
While in the case of distinct quantities a one means membership and a zero non-membership, in the case of fuzzy sets not only zero or one, but any values in between are permissible as values for the membership function.
The conversion from distinct numbers to indistinct quantities is termed fuzzyfication. With this~ each input variable, that is in practice, for example, a sensor signal, has at least one function represented as a matrix. The x scaling of this function has a numerical equivalence in the respective sensor signal, and the y scaling corresponds to the truth content or to the degree of approximation to the corresponding statement and can assume evéry value from O to 1. This degree of approximation is calculated via the membership function.
. ~ ~
For the statements present in the condition part a variable for the membership values is searched for with a suitabl-e operator; if this variable is the minimum value of the membership function, then the operator is the mini~um operator as in Fig. 3 and this is in turn the~mean of the two fuzzy sets for the input variables A and B. The result . ' , ,.
, ~ 213 ~ 7 ~ 7 of the inference of the two rules 1 and 2 is thus the average due to the fuzzy sets for A and B or B and C, respectively.
, ' A distinct output variable is now calculatec1 from these inferènces (action interface 11, Fig. 2). If, as in Fig. 3, inerences are available from several rules, then the membership values for the respective rules are synthesized.
This is achieved, for example, by a comparison between thé -~
inference parts of the rules to obtain the maximum value of ;
the membership values of the inference parts and to generate a new membership function. This process is called the -maximum operator; it represents the union of the inference parts. ~
From the indistinct result delivered by the inference machine 9 (Fig. 2), a distinct output variable is then calculated, which, for example, is achieved by calculation of the centre of mass of the synthetic membership function.
The design of the fuzzy controller 7 (Fig. 1, 2) is roughly implemented in the following steps: - `
- Definition of all input and output variables:
In the present case the input variables are the data characterising the pulses obtained ~rom the sensor signal, and a time window; the output variable is a value that states whether merely a fault or an unauthorised entry is involved.
- Definition of the indistinct quantities (fuzzy sets~
for the linguistic variables.
- Drawing up the rules:
A suitable rule, for example, is that unauthorised entry then applies if the condition of a pulse stream of three successive pulses with the amplitudes small positive, large negative and small positive is met or a prolonged period in the time interval.
- Setting the in~erence machine:
.
;
Cerberus AG, 8708 Mannedorf . . ' : .
Process for processing the signals of a passive infra red detector and an infra-red detector for implementing the process-: . , ~ ' :
This invention concerns a process for processing the signals of a passive infra-red detector, which generates electrical signals, described as sensor signals, in relation to incident infra-red radiation, then evaluates these -electrical signals.
. , :
In known processes of this kind, in the simplest case either the sensor signal is examined to ascertain whether it exceeds positive or negative thresholds, or the number of threshold crossings is counted. It is also known for an alarm to then be triggered if a positive threshold is ~;
exceeded before a negative thr~shold, and vice versa.
. - ~
All detectors based on simple thresholds are in principle -very susceptible to faults, since a single glitch of -~ -sufficiently large amplitude can trigger a false alarm. On the other hand, those detectors in which several pulses are counted, whether or not this depends on polarity, lose sensitivity relatively quickly, especially when an intruder is positioned at the boundary of the detection area, or moves only through one zone of the coverage pattern.
Detection systems are also known in which the sensor signal is continuously compared to a set of stored reference patterns and triggers an alarm when there is sufficient correlation. These systems are certainly very reliable and sensitive, but they require a large number of components.
This also means that a large and, consequently, expensive processor must be provided in the detector to supply the necessary storage capacity and power.
`
J) 3 rS~
Due to the invention a process of the type stated in the preamble shall now be specified, in which there is good discrimination between the widely overlapping classes of interference signals on the one hand and intruder signals on the other hand, certainly with high detection power and especially also in the peripheral monitoring area.
Furthermore, a simple evaluation with low numbers of -~
components should be possible and achievable with a simple microcontroller.
, This object is achieved in that the sensor signals are digitized and processed in the form of pulses, that the pulses are characterised hy data, and that the evaluation of these pulses is effected by means of fuzzy logic, whereby in each case the data are compared to a series of several pulses in the form of linguistically variable, stored rules.
The invention furthermore concerns an infra-red detector for implementing the said process, with at least one sensor element for generating the sensor signals and with an evaluation circuit for their processing and evaluation.
The infra-red detector according to the invention is characterised in that the evaluation circuit contains a fuzzy controller to which the pulse data are fed.
Due to the processing of the sensor signals in pulse form, an appreciable reduction in the data rate takes place, which fulfils an important requirement for simple evaluation. As defined, a pulse then starts when the signal departs from the normal position in the positive or-negative direction, and ends on return to the normal position. The data characterising the pulse, such as amplitude, duration, polarity, spacing and the like are stored and for the evaluation, a series of pulse data, i.e. the data of a series of successive pulses, is always used.
~ ~3~7 The use of fuzzy logic, instead of strictly classical logic, in the signal evaluation has the advantage that the rules for the evaluation can be based on an empirical scientific base that could be converted into conventional, analytical algorithms only in a considerably more laborious and cumbersome manner.
. . ~.
For examination of alarm pla~sibility, the pulse data stream ~ -~
of a number of preceding pulses is available, whereby the - criteria for triggering an alarm are formulated in the form of fuzzy logic operations on the previous fuzzy pulse data, that is, transformed into linguistic variables. The criteria -~
thus include in linguistic form a scientific base for the i-allocation of a pulse stream to the intrusion or fault class, whereby the contents of the scientific base represent all the experiences gained from the observation of innumerable walk tests and with interference signals.
The indistinct fuzzy sets as defined, deliver an eq~ally indistinct result, the defuzzification of which supplies a distinct decision for or against an alarm~ The rules in linguisti~ form in the microcontroller of the evaluation circuit require only a minimal memory requirement.
Furthermore, the fuzzyfication and defuzzification, that is the conversion of distinct numbers into indistinct areas, or the extraction of clear or distinct statements from indistinct areas, are considerably less numerically demanding than the processing of classical rules.
- :
The invention is explained in more detail bélow with the aid of an embodiment and the drawings, of which:
. ,, Fig. 1 shows a block diagram of an embodiment of an IR
detector according to the invention, Fig. 2 shows a detail of the circuit diagram of Fig. 1;
and Fig 3 shows a diagram for explaining the opsration.
3 ~ U;'37 In Fig. 1, an IR detector according to the invention contains a sensor element 1, which receives IR radiation from the space being monitored via an associated optical system 2 of a certain focal length and, dependent upon the --incident radiation, outputs an électrical signal referred to below as the sensor signal. The use of a single sensor element 1 is understood to be non-limiting; naturally, two or more sensor elements can also be provided. The sensor signal is amplified by an amplifier 3 and its output signal is fed to an analogue/digital converter 4 and after digitizing reaches a pulse processing stage 5 which forms part of a microcontroller 6. The microcontroller 6 furthermore contalns a fuzzy controller 7.
The data rate of the digitized sensor signals is first sharply reduced in the pulse processing stage 5 by s~oring them as "pulses". As defined, such a pulse commences when the signal has moved suf~iciently far from the normal position in the positive or negative direction and ends on return to the normal position. Each pulse is described by data characterising it, such as amplitude, duration, polarity, spacing and the like, and these data are stored.
. . ..
In known manner, the optical system 2 contains a mirror system which corresponds to a number of optical focusing means and focuses the IR radiation from a number of fan-type radiation detection areas onto the sensor 1 (see for example GB~A-2`047 886 or EP-A-O 361 224~. These radiation detection areas are discrete zones, whereby, on entry, an object passing~such a zone produces a positive sensor signal and on leaving, a negative sensor signal, the two together producing a characteristic sign~l. Such a signal can, for example, be described within a certain time interval by the pulses: small positive, large positive, large negative and small negative. During evaluation, the pulses derived from the sensor signal can thereupon be examined to ascertain whether they have a type and configuration that is " 213D'!37 characteristic to the intrusion of a person, a group of -several successive pulses always being examined. ! .' . - , Practical experience has shown that within the observed time -window, three or a maximum of four such pulses can usually be obtained from the digitized sensor signal, so that it is not practical to examine more than four pulses. The procedure during this examination is that the last four pulses are always stored and examined, the examination taking place in the fuzzy controller 7.
. ~
As Fig. 2 shows, in the known manner the fuzzy controller 7 contains a rule base 8, an inference machine 9, a process interface 10, and an action interface 11, at the output of which an alarm signal AS is obtainable during detection of an undesirable intruder in the monitored space. With regard to fuzzy logic, in the meantime reference is made to extensive literature on this subject, for example the book, "Fuzzy Set Theory and its Applications" by H.-J. Zimmermann, Kluwer Academic Publishers, 1991.
, In the known manner, the rule base 8 contains a set of linguistic rules for the evaluation of the pulse. Based on these rules, an algorithm is sonstructed, by which the values are defined as so-Galled fuzzy sets, i.e. indisti~ct quantities. Linguistic variables are words and expressions of the colloquial language or a natural language. These variables should be able to accept natural language expressions (small, medium, large), these expressions being names for the said fuzzy sets.
.
Like classical logic, the rules of fuzzy l~gic consist of a condition or premise part and an inference part. In Fig. 2 r the condition part is symbolized by the process interface 10 and the in~erence par-t by the action interface 11. The inference machine 9 links the direction of influence and the : , ~ 2 ~ rl 3 7 magnitude of the instantaneous state in the fuzzy sets on the basis of empirical technological knowledge.
:
Fig. 3 shows with the aid of a graphical representation with a typical fuzzy rule the essential features of a fuzzy controller. The rule denoted rule 1~ in the Figure says: "if A = LARGE and B = NO~MAL, then X = SMALL". Rule 2 says: for example: "if B = NORMAL and C = SMALL, then X = NORMAL". A, B and C are input variables. The part of the sentence beginning with "if" is the condition part, the part beginning with "then" is the inference part.
The central term of fuzzy logic are the fuzzy sets or indistinct quantities, whereby the membership of elements in a fuzzy set is defined by the so-called membership function.
While in the case of distinct quantities a one means membership and a zero non-membership, in the case of fuzzy sets not only zero or one, but any values in between are permissible as values for the membership function.
The conversion from distinct numbers to indistinct quantities is termed fuzzyfication. With this~ each input variable, that is in practice, for example, a sensor signal, has at least one function represented as a matrix. The x scaling of this function has a numerical equivalence in the respective sensor signal, and the y scaling corresponds to the truth content or to the degree of approximation to the corresponding statement and can assume evéry value from O to 1. This degree of approximation is calculated via the membership function.
. ~ ~
For the statements present in the condition part a variable for the membership values is searched for with a suitabl-e operator; if this variable is the minimum value of the membership function, then the operator is the mini~um operator as in Fig. 3 and this is in turn the~mean of the two fuzzy sets for the input variables A and B. The result . ' , ,.
, ~ 213 ~ 7 ~ 7 of the inference of the two rules 1 and 2 is thus the average due to the fuzzy sets for A and B or B and C, respectively.
, ' A distinct output variable is now calculatec1 from these inferènces (action interface 11, Fig. 2). If, as in Fig. 3, inerences are available from several rules, then the membership values for the respective rules are synthesized.
This is achieved, for example, by a comparison between thé -~
inference parts of the rules to obtain the maximum value of ;
the membership values of the inference parts and to generate a new membership function. This process is called the -maximum operator; it represents the union of the inference parts. ~
From the indistinct result delivered by the inference machine 9 (Fig. 2), a distinct output variable is then calculated, which, for example, is achieved by calculation of the centre of mass of the synthetic membership function.
The design of the fuzzy controller 7 (Fig. 1, 2) is roughly implemented in the following steps: - `
- Definition of all input and output variables:
In the present case the input variables are the data characterising the pulses obtained ~rom the sensor signal, and a time window; the output variable is a value that states whether merely a fault or an unauthorised entry is involved.
- Definition of the indistinct quantities (fuzzy sets~
for the linguistic variables.
- Drawing up the rules:
A suitable rule, for example, is that unauthorised entry then applies if the condition of a pulse stream of three successive pulses with the amplitudes small positive, large negative and small positive is met or a prolonged period in the time interval.
- Setting the in~erence machine:
.
2~3~737 ~:
For the operator, for example, a special AND function, the so-called FUZZY-AND of the form F = y * min(A,B) + 0.5 * (1-y) * (A~B) is chosen, where A and B are the input ~ariables and y a gamma factor. For the gamma factor y = 1 the FUZZY-AND operator becomes the minimum operator (Fig. 3).
- Definition of the calculation of the distinct output variables:
This operation, also termed defuzzyfication, in which a distinct variable is obtained from an indistinct quantity via an output membership function, is preferably implemented by generating the centre of mass as in Fig. 3.
The described signal processing in IR detectors facilitates good, accurate separation between intruder and interference signals, with high detection performance. In particular, extremely noisy sensor signals and signals from the peripheral monitoring area can also be unamhiguously evaluated. The storage of sensor signals in the form of pulses resuits in a large reduction in the memory requirement, especially also that reguired for the rules in linguistic form. Added to this, the fuzzyfication and the defuzzyfication are relatively less numerically demanding and require a less complicated outlay, which can be readily realised with a simple microcontroller.
Because of the indistinct formulations which typify the fuzy logic it is improbable that a signal will be rejected on grounds of barely missing a condition. The described processing rather corresponds to the very different and indistinct intruder signals. Due to the ~uzzy formulation, the algorithm in its heart is simple and transparent. When once been written it will be valid also ~or changing conclitions in which case only some constants have to be amended (so-called parametering). The constants are optimized on the basis of tests and simulations.
,
For the operator, for example, a special AND function, the so-called FUZZY-AND of the form F = y * min(A,B) + 0.5 * (1-y) * (A~B) is chosen, where A and B are the input ~ariables and y a gamma factor. For the gamma factor y = 1 the FUZZY-AND operator becomes the minimum operator (Fig. 3).
- Definition of the calculation of the distinct output variables:
This operation, also termed defuzzyfication, in which a distinct variable is obtained from an indistinct quantity via an output membership function, is preferably implemented by generating the centre of mass as in Fig. 3.
The described signal processing in IR detectors facilitates good, accurate separation between intruder and interference signals, with high detection performance. In particular, extremely noisy sensor signals and signals from the peripheral monitoring area can also be unamhiguously evaluated. The storage of sensor signals in the form of pulses resuits in a large reduction in the memory requirement, especially also that reguired for the rules in linguistic form. Added to this, the fuzzyfication and the defuzzyfication are relatively less numerically demanding and require a less complicated outlay, which can be readily realised with a simple microcontroller.
Because of the indistinct formulations which typify the fuzy logic it is improbable that a signal will be rejected on grounds of barely missing a condition. The described processing rather corresponds to the very different and indistinct intruder signals. Due to the ~uzzy formulation, the algorithm in its heart is simple and transparent. When once been written it will be valid also ~or changing conclitions in which case only some constants have to be amended (so-called parametering). The constants are optimized on the basis of tests and simulations.
,
Claims (9)
1. Process for processing the signals of a passive infra-red detector, which generates electrical signals, described below as sensor signals, in relation to incident infra-red radiation and then evaluates these electrical signals, characterised in that the sensor signals are digitized and processed in the form of pulses, that the pulses are characterised by data, and that the evaluation of these pulses is carried out by means of fuzzy logic (7), whereby in each case the data of a series of several pulses are compared to rules stored in the form of linguistic variables.
2. Process according to Claim 1, characterised in that, for the conversion of the digitized sensor signals into pulses the waveform of the signals is examined, and that at a certain excursion of the signal from its normal position a pulse start is set and on return to the normal position a pulse end is set.
3. Process according to Claim 2, characterised in that, as characterising data for describing the pulses, their amplitude and/or duration are used.
4. Process according to Claim 2 or 3, characterised in that, as characterising data for describing the pulses, their polarity and/or mutual spacing are used.
5. Process according to one of the Claims 1 to 3, characterised in that, in each case the data of the last n successive pulses are compared with the stored rules, whereby the value of n lies between 2 and 4, and is preferably 3.
6. Infra-red detector for implementing the process according to Claim 1, with at least one sensor element for generating the sensor signals and with an evaluation circuit for processing and evaluating them, characterised in that the evaluation circuit contains a fuzzy controller (7) to which the pulse data are fed.
7. Infra-red detector according to Claim 6, charactreised in that the fuzzy controller (7) contains a rule base (8) and an inference machine (9) and forms part of a microcontroller (6).
8. Infra-red detector according to Claim 7, characterised in that the rules stored in the rule base (8) of the fuzzy controller (7) are of the type that their condition part contains number and data of pulses and the time interval of the occurrence of the pulses.
9. Infra-red detector according to Claim 7 or 8, characterised in that the inference machine (9) of the fuzzy controller has a FUZZY-AND function as the operator.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CH02975/93A CH686805A5 (en) | 1993-10-04 | 1993-10-04 | A method for processing the signals of a passive infrared detector and infrared detector for implementing the method. |
| CH02975/93-6 | 1993-10-04 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2130737A1 true CA2130737A1 (en) | 1995-04-05 |
Family
ID=4245848
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002130737A Abandoned CA2130737A1 (en) | 1993-10-04 | 1994-08-23 | Process for processing the signals of a passive infra-red detector and an infra-red detector for implementing the process |
Country Status (7)
| Country | Link |
|---|---|
| EP (1) | EP0646901B1 (en) |
| JP (1) | JPH07159238A (en) |
| CA (1) | CA2130737A1 (en) |
| CH (1) | CH686805A5 (en) |
| DE (1) | DE59408859D1 (en) |
| ES (1) | ES2139696T3 (en) |
| IL (1) | IL110760A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8253563B2 (en) | 2005-05-18 | 2012-08-28 | Idteq As | System and method for intrusion detection |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0718814B1 (en) * | 1994-12-19 | 2001-07-11 | Siemens Building Technologies AG | Method and device for flame detection |
| FR2756401B1 (en) * | 1996-11-28 | 1999-02-19 | Valeo Electronique | METHOD AND DEVICE FOR DETECTING INTRUSION IN A MOTOR VEHICLE |
| DE19709805A1 (en) * | 1997-03-10 | 1998-09-24 | Stribel Gmbh | Room monitoring device |
| DE50103419D1 (en) | 2001-11-05 | 2004-09-30 | Siemens Building Tech Ag | Passive infrared detector |
| CN107016813A (en) * | 2017-06-16 | 2017-08-04 | 合肥讯邦网络科技有限公司 | A kind of intelligent information safety-protection system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0107042B1 (en) * | 1982-10-01 | 1987-01-07 | Cerberus Ag | Infrared detector for spotting an intruder in an area |
| JPH04256130A (en) * | 1991-02-08 | 1992-09-10 | Nissan Motor Co Ltd | Arithmetic circuit for fuzzy control |
| JPH0518827A (en) * | 1991-07-10 | 1993-01-26 | Matsushita Electric Ind Co Ltd | Human body detection device |
| JPH0552963A (en) * | 1991-08-22 | 1993-03-02 | Matsushita Electric Ind Co Ltd | Human body detection device and air-conditioning equipment with it |
-
1993
- 1993-10-04 CH CH02975/93A patent/CH686805A5/en not_active IP Right Cessation
-
1994
- 1994-08-23 IL IL11076094A patent/IL110760A/en not_active IP Right Cessation
- 1994-08-23 CA CA002130737A patent/CA2130737A1/en not_active Abandoned
- 1994-09-05 DE DE59408859T patent/DE59408859D1/en not_active Expired - Fee Related
- 1994-09-05 ES ES94113876T patent/ES2139696T3/en not_active Expired - Lifetime
- 1994-09-05 EP EP94113876A patent/EP0646901B1/en not_active Expired - Lifetime
- 1994-09-19 JP JP22341594A patent/JPH07159238A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8253563B2 (en) | 2005-05-18 | 2012-08-28 | Idteq As | System and method for intrusion detection |
Also Published As
| Publication number | Publication date |
|---|---|
| EP0646901B1 (en) | 1999-10-27 |
| DE59408859D1 (en) | 1999-12-02 |
| JPH07159238A (en) | 1995-06-23 |
| CH686805A5 (en) | 1996-06-28 |
| IL110760A (en) | 1997-02-18 |
| ES2139696T3 (en) | 2000-02-16 |
| IL110760A0 (en) | 1994-11-11 |
| EP0646901A1 (en) | 1995-04-05 |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FZDE | Discontinued |