WO2011073241A1 - Procédé et système de détection d'intrusion - Google Patents

Procédé et système de détection d'intrusion Download PDF

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Publication number
WO2011073241A1
WO2011073241A1 PCT/EP2010/069724 EP2010069724W WO2011073241A1 WO 2011073241 A1 WO2011073241 A1 WO 2011073241A1 EP 2010069724 W EP2010069724 W EP 2010069724W WO 2011073241 A1 WO2011073241 A1 WO 2011073241A1
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WO
WIPO (PCT)
Prior art keywords
intrusion
impacts
determining
closed entity
detected
Prior art date
Application number
PCT/EP2010/069724
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English (en)
Inventor
Vincent Spruytte
Original Assignee
Eyasi Trading Group Lc
Verleye, Lormans & Co Cva
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Eyasi Trading Group Lc, Verleye, Lormans & Co Cva filed Critical Eyasi Trading Group Lc
Publication of WO2011073241A1 publication Critical patent/WO2011073241A1/fr

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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
    • G08B13/16—Actuation by interference with mechanical vibrations in air or other fluid
    • G08B13/1654—Actuation by interference with mechanical vibrations in air or other fluid using passive vibration detection systems
    • G08B13/1672—Actuation by interference with mechanical vibrations in air or other fluid using passive vibration detection systems using sonic detecting means, e.g. a microphone operating in the audio frequency range

Definitions

  • the present invention generally relates to the field of methods and systems for detecting intrusion from outside into a closed entity like the load compartment of a truck or trailer, a container, a building and the like.
  • Intruder systems are known in the art and offered by several vendors. Various types of intruder systems exist, e.g. based on infrared detection, possibly with photo-electric beams, subsonic or ultrasonic systems, radio systems, etc ...
  • Prior art systems typically apply a low frequency analysis in order to determine an intrusion.
  • One such solution is described in patent application EP1883910, where a system and method for intrusion detection is presented. It tackles the problem of false alarms. It is noted that alarm systems and detectors do not discern intruder activities from other activities, thereby causing frequent false alarms. In other words, the probable cause of the alarm signal is not discerned.
  • a detection system is proposed comprising a microphone to detect infrasound signals. Infrasound is subaudible sound with frequencies less than 20 Hz. The detected infrasound signals are (assumed to be) caused by the intrusion, e.g. by the movement of a door or a window.
  • Control parameters are adaptable so that the alarm responses may be maintained to a predetermined temporal rate of false alarms.
  • An element of this detection system employs the determination of the probability that particular information will occur within an ongoing temporal period. Such information correlates to desired detected activities and may include various signal characteristics, the source detecting the infrasound signals, and temporal relationships within and between detected signals.
  • WO2008/121041 is also concerned with vehicle intrusion detection. It proposes an improved surveillance system for trucks, trailers and the like.
  • the solution comprises a multi-function day-running side-light unit. A number of such side-light units are mounted on opposite sides of the vehicle.
  • Solutions based on low frequency analysis however suffer among other things from the important drawback that weather conditions (e.g. wind) may considerably affect the applied method. Consequently, there is a need for an intrusion detection solution wherein these drawbacks are avoided or overcome.
  • the present invention aims to provide a method and system for intrusion detection wherein the drawbacks of the prior art, specifically the influence of weather conditions, are avoided or overcome.
  • a further aim is to provide a method and system with improved detection capability.
  • the present invention relates to a method for determining intrusion in a closed entity.
  • the method comprises the steps of
  • impacts is meant a force or a shock applied on the closed entity over a short time period due to some collision, caused e.g. by an intrusion into the closed entity, the clicking of a lock, the cutting of a cable under tension, the impact of a step on the floor, a knock on the side, ... or by the meteorological conditions acting on the closed entity (mostly indirectly by loose parts moving by the wind, like the end of a cable, the wind playing with a soft sided truck, ).
  • the signal analysis can be performed on signals with considerably higher frequency content. In other words, there is no need anymore to measure a signal of, say, less than 20 Hz. It is in this low frequency range that weather conditions like wind have most effect on the measurements. In the proposed method it is irrelevant whether the measurements are performed inside or outside on the closed entity's structure.
  • the intrusion is manifested as a single peak, i.e. one (very) large impact.
  • the intrusion can appear as a series of impulses, most typically with smaller amplitude than in the case a single impulse is observed.
  • the actual appearance of the intrusion in the observed signals largely depends on the modus operandi of the intruder.
  • the density of peaks in a given time frame is preferably taken into account in the step of determining intrusion. This may provide valuable information in determining whether the observed impulses are caused by an intrusion or not.
  • the method further comprises a step of categorizing the detected impulses as being caused by a known source or as intrusions. This categorizing is preferably performed based on statistical analysis of impulses detected before, i.e. in an earlier time frame. Alternatively, the categorizing is performed based on predetermined knowledge of a physical system producing impulses different from intrusions, a measurement of a parameter of the system and a comparison of that parameter with the single peak or the plurality of peaks.
  • the method further comprises a step of filtering the detected impulses in order to reduce or even remove the effect of noise. This can also be achieved by applying a denoising algorithm.
  • the step of detecting impacts or impulses comprises performing a transformation on a set of digital samples obtained from the measurements to distinguish between samples indicative of a possible intrusion and other samples.
  • such transformation comprises a non-linear summation of digital samples of said set or a statistical processing of digital samples. Such transformation indeed has as effect on the sample set that differences between sample values are stretched and portions corresponding to intrusions become more easily discernible.
  • the transformation may be preceded by a preprocessing step, wherein a frequency analysis or a wavelet analysis is applied to said digital samples.
  • This preprocessing brings the sample set in an adequate form for subsequently applying the transformation.
  • the preprocessing step may yield a quantity derived from said samples, said quantity further being used in the step of performing said transformation. Doing so is advantageous in that the sensitivity is enhanced.
  • the method comprises in an embodiment an initial step of measuring the vibrations on the closed entity and digitizing the measurements resulting in the set of digital samples.
  • the invention relates to a program, executable on a programmable device containing instructions, which when executed, perform the method as previously described.
  • the invention in another aspect relates to a processing device for use in the method for determining intrusion in a closed entity as described above, said processing device being operable for receiving measurements of mechanical vibrations measured inside or outside said closed entity, for detecting one or more impacts in said received measurements and for determining whether there is intrusion or not based on the detected impacts.
  • the processing device is further arranged for outputting an alarm signal dependent on the outcome of the determining step.
  • the processing device comprises communication means for transmitting an output alarm signal.
  • the invention relates in another aspect to an intrusion detection system comprising a processing device as described and further comprising at least one sensing means for sensing mechanical vibrations.
  • FIG. 1 illustrates an embodiment of an intrusion detection system according to the invention.
  • FIG. 2 illustrates the application of the invention for a truck.
  • Fig. 3 represents a flow chart wherein various possible data analysis algorithms are shown. They can be applied separately or in combination.
  • Fig. 4 illustrates a normal distribution where the standard deviation is changed, so the exceptional values become more or less visible.
  • Fig. 5 illustrates the calculation of a decision parameter.
  • Fig. 6 illustrates a denoising operation.
  • Fig. 7 illustrates the relationship between (on the x-axis) the low frequency component defined by the wind on a truck, and (on the y-axis) the levels of the impulses.
  • Fig. 8 illustrates an algorithm for a tractor/trailer combination.
  • Fig. 9 illustrates an algorithm for analyzing the cause of a measured impulse.
  • Fig. 10 illustrates a full algorithm.
  • Fig. 1 1 illustrates the detection of many relatively small impulses.
  • the present invention presents a method for detecting intrusion in a vehicle cabin, container, building, ... as well as a processing device for use in said method and an intrusion detection system comprising such a processing device.
  • the invention exploits the observation that intrusion detection can be performed based on measuring mechanical vibrations on the structure of the closed entity and detection of impacts or impulses in the measured signals.
  • Such impulses are forces acting on the closed entity producing a finite change of momentum. They are characterised in the time domain as short discontinuities in the signal and in the frequency domain as broadband signals whereby they differentiate from other signals like resonances (that are only visible around one or a few frequencies, normally in the low frequency range) in the higher frequencies, e.g. above 100 Hz.
  • This is opposed to the prior art solutions wherein one relies on low frequency measurements as explained in the background section.
  • the invention further distinguishes from the prior art in that an advantageous data analysis approach is adopted that considerably improves the visibility of signal portions corresponding to an intrusion as compared to other portions of the measured signal, for example by applying a non-linear summation.
  • Fig.1 illustrates schematically an embodiment of an intrusion detection system comprising a processor device according to the invention.
  • the detection system contains at least one sensing means (sensor) adapted to detect mechanical vibrations.
  • This can be a vibration detector, an accelerometer, ...
  • the sensing means can be axis sensitive, i.e. arranged for sensing in one or more specific directions according to its position.
  • Each sensor defines a measurement channel.
  • digitizing means are provided, e.g. a conventional A/D converter to convert the measured analogue signal into a corresponding digital representation. This digitisation can be performed either inside the processing unit, in a separate A/D converter or in A/D conversion means integrated in the sensor.
  • a separate A/D converter may be in connection with the processor either via a wireless or wired network link.
  • the use of communication based on a protocol of the IEEE 802 standards family (e.g. ZigBee, Wi-Fi, etc) can be envisaged.
  • the processor according to this invention is advantageously also provided with memory for storing data related to the measurement or the processing thereof.
  • the intrusion detection system advantageously comprises a communication device, preferably arranged for short distance communication.
  • the communication can be performed according to an IEEE 802 standard.
  • a module comprising a detector, an A/D convertor and a radio device is provided, completed with a small processor to be able to organize the communication.
  • the analysis is carried out in another module provided with a more performing processing means.
  • every module also contains a radio unit (e.g. ZigBee or another communication standard of the IEEE 802 family) to be able to communicate with the neighbouring units to analyze if a measured intrusion is direct or indirect from these neighbouring units.
  • a radio unit e.g. ZigBee or another communication standard of the IEEE 802 family
  • Fig.2 shows an application for a truck.
  • the detection system is meant to protect the load, when the truck is parked, with or without somebody (driver, ...) in the cabin.
  • One or more modules are placed on strategic places on the frame of the trailer/load compartment. These units have one, two or three-dimensional accelerometers as sensors. They communicate with the main module, i.e. the module with the processor that performs the bulk of the data analysis, which is placed in the driver's compartment or cabin.
  • modules with reduced processing power are preferably used throughout the building. They communicate wirelessly or through "IP over 220V" with a main module comprising processor means suitable for carrying out the data analysis.
  • This module contains a more performing processor or may even be a multiprocessing system.
  • the detection can further be applied for protecting containers.
  • Each container is provided with a sensing module and the modules are provided with communication means for communication with the other modules.
  • At least one module is further arranged for communication with the outer world, e.g. for transmitting a silent alarm.
  • the signal processing is discussed more in detail.
  • the signal analysis can be performed in a statistical way, whereby an intrusion is defined as a non-expected signal. Alternatively, an analytical approach can be followed.
  • the transformation performed on the sample set can take various forms that each achieve the same goal.
  • One advantageous solution is based on a nonlinear summation (e.g. based on the power of 10) of samples of the sample set :
  • A Iog 10 ( ⁇ l0 ai/a0 )
  • a is a digital representation of a measured amplitude and a 0 a reference value that defines the amplification.
  • the transformation of the data sample set is performed with a continuous wavelet transformation. Further, an approach can be adopted wherein a transformation is performed on the probability of having a measurement falling within a predetermined range, as explained below in detail.
  • Fig.3 represents a flow chart illustrating in each branch a different possible way of statistically processing the data samples representative of the measured mechanical vibration. In more specific embodiments of the invention several of the proposed options can be combined.
  • a preprocessing frequency analysis is carried out.
  • An intrusion is mostly characterized as an impulse or impact and is in general independent of the characteristics of the truck, trailer, .... This means that in an FFT analysis an intrusion is detectable in the higher frequencies. Therefore a high pass filter is used to detect the impacts.
  • a noise cancelation for instance based on a wavelet denoising algorithm.
  • For the high frequency band for every sample, instead of the classical RMS value, a non-linear summation is calculated. In this way, one can amplify the pulses within the total sum and make the signals due to an intrusion, however short, more visible :
  • a is a measured amplitude of the sample and a 0 a reference value that defines the amplification.
  • the further analysis can be done either directly on the measured signal in digital form or on a quantity derived from that signal, e.g. the proportion of the measured signal on the trailer divided by the signal on the cabin :
  • a second branch in Fig.3 illustrates a signal processing based on wavelet analysis rather than frequency analysis.
  • a continuous wavelet transform is applied. Different wavelet types are used.
  • the Daubechies D2 to D6 wavelet transforms give results with a good discrimination possibility.
  • Daubechie wavelets are well known in the art, for example from the handbook "Ten Lectures on Wavelets” (I. Daubechie, CBMS-NSF Regional Conference Series in Applied Mathematics, no.61 , 1 992).
  • the invention concerns a security system, only relative short signals (e.g. 0.5, 1 or 2 seconds) can be used in order to be able to detect an intrusion in a time period as short as possible. Therefore relatively large time steps can be employed to speed up the calculations.
  • the intrusions are easily recognized in the higher wavelet scales. Therefore, as basic criterion for the detection the sum of the coefficients of higher scale or the RMS value of these coefficients is used.
  • An alternative is to work with a denoising algorithm based on wavelets.
  • the third branch of the flow chart shows a "more channel” frequency analysis, where a correlation between more signals is performed.
  • the basic problem with the detection is that the external disturbances in a more disturbed environment can be higher than the real intrusions in a less disturbed environment. This implies that some normalization needs to be performed. For this a chi-squared cumulative distribution function can be used to calculate the probability that a certain measurement is equal to a predetermined average. The smaller the probability, the higher the chance that this measurement is abnormal and is an intrusion. To amplify this effect and to get a workable parameter, the probability p of a measurement is transformed to a useable detection parameter D.
  • This chi-squared cumulative distribution analysis is based on a First In - First Out (FIFO) database (i.e. a database wherein stored measurement data are taken in the order they have entered the database) of the measurements of the last t minutes.
  • FIFO First In - First Out
  • the transformation can in a possible embodiment be a weighing of the digital samples of the measured mechanical vibrations.
  • the detection measurements are added together to a final decision parameter FD.
  • FD final decision parameter
  • a decision parameter FD is calculated. This FD has to be compared with a reference. Due to the manipulation of the distribution functions as illustrated in Fig.4, the detection parameter D - and thus the final decision parameter FD - are not fully independent of the external disturbing situation. Analyzed in another way, the method of calculating this decision parameter is not normalizing the measurements for 100%. To take this into account the criterion to compare FD is adapted by using a regression based on a non-filtered, non- manipulated database of the measurement levels. This gives a regression coefficient of more than 90%, so it is a good indication for the level of disturbances.
  • the measurement samples each have a duration of at least 100 msec.
  • a transformation is performed on the sample set so that portions of the processed signal corresponding to an intrusion become more visible.
  • a non-linear summation is used. Various possibilities exist. Two options are explained more in detail below.
  • a sample being processed is denoted s,.
  • a constant s t represents a predefined constant. If s, ⁇ s t then s, is set to zero, otherwise it keeps its value. Next a transformation is performed on the non-zeroed s,.
  • the impact signals on the trailer are added together.
  • the low frequency signals are not combined. They are only used as an indication of the wind speed, as they are defined by the whole body movement of the trailer on its suspension system.
  • the intrusion detection is preferably determined by the parallel operation of different algorithms, based on the intrusion modus operandi. As the skilled person readily understands, only one algorithm can be applied as well.
  • the intrusion detection is done using the impacts and is based on two principles. Firstly, some of the signals on the trailer are not relevant, because they are clearly caused by other causes. They are removed from the signal. Two examples are given that demonstrate this first principle :
  • the signal in the trailer is caused by a movement in the cabin, for instance due to a movement of the driver.
  • the residual signal level increases when the weather conditions are worse, i.e. when there is more wind.
  • the impacts are not directly caused by the wind, but indirectly for instance by some loose binding cable that is blown up by the wind.
  • Fig 8 an algorithm is illustrated wherein based on the movement of the truck, measured through a low frequency component (G1 and G2 are the measurements of two sensors in this case), the criterion for a certain algorithm is defined by a regression based on the graph of Fig. 7, for every individual algorithm.
  • the values A, B and C are defined on the relevant graph of the type of Fig 7.
  • Fig 9 illustrates an algorithm to analyze if a measured impulse is caused by something in the cabin or not. If the impulse is relevant, it will be used to analyse if it can be caused by an intrusion or part of an intrusion (i.e., a single pulse in a train of impulses in a certain time), if it is caused by something in the cabin, the cause is known and the impulse on the trailer is not used for further analysis.
  • the algorithm gives as result the relevant impulses.
  • there are two possible ways for an intrusion either one important impulse or a train of consecutive impulses in a certain time period.
  • Fig. 10 describes the analysis for the first type and Fig. 1 1 for the second type.
  • Fig 1 1 gives one detection method of an intrusion, i.e. the detection of many relatively small impulses in a certain time period through a sliding sum. In the first place every individual impulse is analyzed to check if it is caused by the cabin or not. If not, it is added to a "floating sum", a sum of the impulse levels over a predefined time. This value is than compared with a criterion based on the algorithm of Fig. 8.
  • a first application concerns intrusion from outside in the load compartment of a truck or trailer for protection against theft of load, protection against the entrance of stowaways, protection against people putting smuggling material in the load.
  • the way of entering the trailer is not relevant: through the door, the roof, the side walls if soft sided, ...
  • the system works independently of the type of truck, the load, the weather conditions, the traffic conditions around the truck, ...
  • the system only reacts to intrusions in the trailer and not in the cabin, so the driver can be in the cabin, enter or leave, sleep in it, ... without activating the alarm.
  • a second application may be intrusion from outside in containers.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Burglar Alarm Systems (AREA)

Abstract

La présente invention concerne un procédé de détermination d'intrusion dans une entité fermée comprenant les étapes consistant à : recevoir des mesures de vibrations mécaniques mesurées à l'intérieur ou à l'extérieur de l'entité fermée, détecter un ou plusieurs impacts dans les mesures reçues, et déterminer s'il existe une intrusion ou non sur la base des impacts détectés.
PCT/EP2010/069724 2009-12-15 2010-12-15 Procédé et système de détection d'intrusion WO2011073241A1 (fr)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US28665509P 2009-12-15 2009-12-15
US61/286,655 2009-12-15
US29591210P 2010-01-18 2010-01-18
US61/295,912 2010-01-18
US32650710P 2010-04-21 2010-04-21
US61/326,507 2010-04-21

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WO2011073241A1 true WO2011073241A1 (fr) 2011-06-23

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014058699A1 (fr) * 2012-10-12 2014-04-17 Schweitzer Engineering Laboratories, Inc. Détection d'un accès non autorisé à un dispositif de communication, et réponse à ladite détection d'un accès non autorisé

Citations (4)

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Publication number Priority date Publication date Assignee Title
WO1995006925A1 (fr) * 1993-09-01 1995-03-09 A3P S.A.R.L. Dispositif de detection d'intrusion dans un batiment ou un vehicule par detection d'infrasons et/ou d'ondes de pression et procede de detection d'intrusion
US20060244591A1 (en) * 2004-12-16 2006-11-02 Fujitsu Ten Limited Data processing apparatus, intrusion sensor and antitheft apparatus
EP1883910A1 (fr) 2005-05-18 2008-02-06 Idteq As Systeme et methode de detection d'une intrusion
WO2008121041A1 (fr) 2007-04-02 2008-10-09 Datachassi Dc Ab Système de surveillance et de communication de véhicule et procédé pour doter un véhicule d'un système de surveillance

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1995006925A1 (fr) * 1993-09-01 1995-03-09 A3P S.A.R.L. Dispositif de detection d'intrusion dans un batiment ou un vehicule par detection d'infrasons et/ou d'ondes de pression et procede de detection d'intrusion
US20060244591A1 (en) * 2004-12-16 2006-11-02 Fujitsu Ten Limited Data processing apparatus, intrusion sensor and antitheft apparatus
EP1883910A1 (fr) 2005-05-18 2008-02-06 Idteq As Systeme et methode de detection d'une intrusion
WO2008121041A1 (fr) 2007-04-02 2008-10-09 Datachassi Dc Ab Système de surveillance et de communication de véhicule et procédé pour doter un véhicule d'un système de surveillance

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
I. DAUBECHIE: "Ten Lectures on Wavelets", CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS, no. 61, 1992

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014058699A1 (fr) * 2012-10-12 2014-04-17 Schweitzer Engineering Laboratories, Inc. Détection d'un accès non autorisé à un dispositif de communication, et réponse à ladite détection d'un accès non autorisé
US9130945B2 (en) 2012-10-12 2015-09-08 Schweitzer Engineering Laboratories, Inc. Detection and response to unauthorized access to a communication device
ES2550501R1 (es) * 2012-10-12 2016-02-24 Schweitzer Engineering Laboratories, Inc. Detección y respuesta a acceso no autorizado a un dispositivo de comunicación

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