LU100017B1

LU100017B1 - A method and system for determining event-parameters of an object

Info

Publication number: LU100017B1
Application number: LU100017A
Authority: LU
Inventors: Mugdim Bublin
Original assignee: Ws Tech Gmbh
Priority date: 2017-01-09
Filing date: 2017-01-09
Publication date: 2018-08-14
Also published as: WO2018127602A1

Abstract

A method and system for determining at least one event-parameter of at least one object comprising the steps of detecting the at least on object by at least one distributed acoustic sensor; receiving at least one signal from the least one distributed acoustic sensor; filtering the at least one signal; determining in at least one of a time domain and a frequency domain at least one of a peak height, a peak width and a peak frequency of the at least one signal; and determining the at least one event-parameter of the at least one object using at least one of the peak height, the peak width and the peak frequency of the at least one signal.

Description

L

DESCRIPTION

A METHOD AND SYSTEM FOR DETERMINING EVENT-PARAMETERS OF AN

OBJECT

FIELD OF THE INVENTION

[0001] The field of the invention relates to a method and system for determining at least one event parameter of at least one object. More specifically, the present invention relates to a method and system for determining at least one event parameter of at least one object by determining in at least one of a time domain and a frequency domain a peak height, a peak width and a peak frequency of at least one signal, and by determining the at least one event-parameter of the at least one object using at least one of the peak height, the peak width and the peak frequency of the at least one signal.

BACKGROUND OF THE INVENTION

[0002] Distributed fiber sensors (DFS) can use fiber optic cable as an array of virtual audio sensors for different recognition events on a road, like traffic jams, wrong way drivers and traffic accidents, flat wheels for trains, intrusion detection etc. Different traffic and road monitoring technologies are known in the art. These monitoring technologies are based on sensors, for example, microwave sensors (e.g. radar), visual sensors (e.g. cameras), inductive sensors, mechanical sensors, acoustical sensors (e.g. microphones).

[0003] These sensors require different maintenance efforts and are basically point sensors, which are fixed at a certain position or place, like inductive loops for traffic counting or cameras for video monitoring. The range over which the sensors yield results is limited. At one extreme, an inductive sensor can only detect the presence of an object (e.g. a vehicle) at one location. At the other extreme, the visual sensors may yield information about several objects at a distance of a few hundred meters. In principle such sensors can be deployed at regular intervals. However, this deployment would drastically increase the cost of installation and maintenance as the sensors require infrastructure installed on, adjacent, above or under roads or rail tracks.

[0004] Distributed acoustic-optical sensors are a comparatively new approach. Such distributed acoustic-optical sensors permit supervision over long distances using fiber optics. These distributed acoustic-optical sensors are already commercially available (e.g. from Silixa or Optasense) for use in the oil and gas industry, e.g. to monitor pipelines or for the monitoring of railways. Systems with the distributed acoustic-optical sensors requireL advanced algorithms capable of recognizing events of interest. Finding such algorithms is especially difficult for monitoring roads, as there is a huge variety of situations to tackle. The algorithms must further cope with a large variety of scenarios (e.g. various kinds of vehicles, road surfaces, tunnels, bridges, avalanche and rock fall protection galleries, etc.).

[0005] The distributed acoustic-optical sensors, for example, optical time-domain reflectometry (OTDR) are a well-established technique used to check long-haul fiber optical connections in telecommunications domain. This known technology is based on emitting short pulses of light from a sender into the optical fiber, and recording intensity of the light reflected back to the sender by Rayleigh reflection. The refractive index of the optical fiber is slightly affected by any pressure applied to the optical fiber - including sound pressure - and it is possible to exploit this fact to construct the distributed acoustic-optical sensor. With OTDR, the intensities (amplitudes) of the reflected pulses are used for evaluating the phase of the Rayleigh reflected optical signal. The phase of an optical signal, comprising the short pulses of light is measured by using an interferometer and a delay line which brings light reflected at different distances - say, 10m apart - to the interferometer at the same time, wherein the delay from the reflected light is proportional to the distance. (Alternatively, no delay line is needed if a pulse forming unit may create two short pulses emitted on the optical fiber at a distance of about 20m). By reading out the interferometer every 100 nanoseconds the OTDR captures changes in the sound pressure at every ~10m along the optical fibre. Here, the sound pressure can be, for example, sound, vibrations and/or strains generated by external sources. It is possible to construct the distributed acoustic-optical sensor to emitting light pulses at a rate of say 1000 Hz from the pulse forming unit. The distributed acoustic-optical sensor is capable of detecting the sound pressure at distances of up to 40km at a regular interval of 10m along the optical fibre and up to acoustic frequencies of 500 Hz.

[0006] The known distributed acoustic sensor is already used in monitoring applications for oil pipelines, oil well monitoring or monitoring perimeter fences. The technology has been improved, when laser technology was refined to such an extent that a highly consistent pulse could be coupled into a cable. Accurate lasers generating a stable coherent light are necessary to be able to measure changes in phases of the optical signal correctly. Information about the object size and object distance enables correct event detection and, if needed, opportunities to take appropriate counter measures and actions. For example, to detect wrong-way drivers on the highway the lane where the detected vehicle is moving has be known, i.e. to estimate the distance between the optical fiber and the detected object.

[0007] The optical fiber sensors are immune to electromagnetic interference, lightweight,L small in size, high in sensitivity, have a large bandwidth, and ease in implementation, as fiber optical cables are often already installed for communication purposes in critical infrastructures, for example, gas and oil pipelines, railway tracks, bridges, but these are not limiting of the invention. A distributed fiber sensors (DFS) can use the optical fiber cables as an array of virtual audio sensors for event recognition on the road, for example, for traffic jams, wrong-way driver and traffic accidents.

[0008] The usage of a delay method for distance estimation is known from the International Patent Application WO 2011/058313 A2. A difference in time of arrival of the acoustic wave at each longitudinal sensing portion (i.e., fiber section) is used to determine a “lateral offset”, i.e. the shortest distance between the object and the fiber section. The difference in time of arrival is estimated by correlating the time of arrival of the acoustic wave at the different ones of the fibre sections. The estimation of difference in time of arrival could be relatively difficult and erroneous because an acoustic, vibrational signal might arrive at the fiber sections with lower distance later than at the fiber sections at higher distances due to multipath effects. Noise and interference from other sources might cause additional errors in the estimated time of arrival.

[0009] The problem for the prior art system is to detect the recognition events, for example, different cars, estimate size of the vehicles and determine accurately position of the object from audio signal or vibrations. A position along the fiber optic cable (x - direction) can be estimated according to the optical signal from different ones of the fiber sections. The fiber section returning the strongest signal having expected characteristics (waveform) is an approximately x position of the object (e.g. vehicle), the problem is how to determine the y-position i.e. the distance between the fiber and the object.

SUMMARY OF THE INVENTION

[0010] In view of the state of the known technology and in accordance with one aspect of the present invention, a method for determining at least one event-parameter of at least one object is provided that comprises the steps of detecting the at least on object by at least one distributed acoustic sensor, receiving at least one signal from the least one distributed acoustic sensor, filtering the at least one signal, determining in at least one of a time domain and a frequency domain at least one of a peak height, a peak width and a peak frequency of the at least one signal, and determining the at least one event-parameter of the at least one object using at least one of the peak height, the peak width and the peak frequency of the at least one L signal.

[0011] In accordance with another aspect of the present invention a method for determining at least one event-parameter of at least one object is provided that comprises the steps of launching acoustic radiation from the at least one object into at least one fiber section of at least one distributed acoustic sensor, detecting at least one signal, receiving at least one signal from the at least one least one distributed acoustic sensor, filtering the at least one signal, determining in at least one of a time domain and a frequency domain at least one of a peak height, a peak width and a peak frequency of the at least one signal, Mid determining the at least one event-parameter of the at least one object using at least one of the peak height, peak width and the peak frequency of the at least one signal.

[0012] In another aspect, the method further comprises the method step of storing the measured at least one event-parameter of the at least one object.

[0013] In another aspect, the method further comprises the method step of comparing the measured at least one event-parameter with the previously stored at least one event-parameter of the at least one object.

[0014] In another aspect, the method further comprises the method step of generating statistics over the measured at least one event-parameter of the at least one object according to past stored event-parameters of the at least one object.

[0015] In another aspect, the method further comprises the method step of generating statistics over the measured at least one event-parameter of the at least one object according to compared event-parameters of the at least one object.

[0016] In another aspect, the method further comprises the method step of using signal correlations between at least one first fiber section and at least one second fiber section arranged adjacent to the first fiber section and separated from the at least one first fiber section in a first direction.

[0017] In another aspect, the method further comprises the method step of using signal correlations between at least one first fiber section and at least one second fiber section arranged adjacent the first fiber section and separated from the at least one first fiber section in a second direction, which is different to the first direction.

[0018] In accordance with yet another aspect of the present invention, a system for determining at least one event-parameter of at least one object is provided, wherein the system comprises at least one distributed acoustic sensor, at least one object, a detector apparatus, andL a processor. The at least one distributed acoustic sensor has at least one first fiber section and at least one second fiber section arranged adjacent to the first fiber section and separated from the at least one first fiber section in a first direction. The at least one object is configured to launch acoustic radiation into the at least one first fiber section and the at least one second fiber section. The detector apparatus detects the acoustic radiation from the at least one first fiber section and the at least one second fiber section. The processor is configured to receive at least one signal from the detector apparatus. Further, the processor is configured to filter the at least one signal and to determine in at least one of a time domain and a frequency domain at least one of a peak height, a peak width and a peak frequency of the at least one signal. The processor is further configured to measure the at least one event-parameter of the at least one object using at least one of the peak height, the peak width and the peak frequency of the at least one signal.

[0019] In another aspect, the system is configured such that the at least one event-parameter of the at least one object is at least one of position, length, size, and distance.

[0020] In another aspect, the system is configured such that the filter is a band-pass phase preserving filter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The invention will now be described on the basis of figures. It will be understood that the embodiments and aspects of the invention described in the figures are only examples and do not limit the protective scope of the claims in any way. The invention is defined by the claims and their equivalents. It will be understood that features of one aspect or embodiment of the invention can be combined with a feature of a different aspect or aspects of other embodiments of the invention. This invention becomes more obvious when reading the following detailed descriptions of some examples as part of the disclosure under consideration of the enclosed drawings. Referring now to the attached drawings which form a part of this disclosure: [0022] FIG. 1 is an exemplary overview of the system according to one embodiment of the present invention; [0023] FIG. 2 is an exemplary diagram of a signal phase according to one embodiment of the present invention; [0024] FIG. 3 is an exemplary flow diagram according to one embodiment of the presentL invention; [0025] FIG. 4 is an exemplary diagram of a peak height distribution; [0026] FIG. 5 is an exemplary diagram of a peak width distribution; [0027] FIG. 6 is an exemplary diagram of a peak width/peak height scatter plot; [0028] FIG. 7 is an exemplary diagram of correlations between signals at particular fiber sections; [0029] FIG. 8 is an exemplary diagram of magnified correlations between signals at particular fiber sections at particular distances; and [0030] FIG. 9 is an exemplary diagram of magnified correlations between signals at particular fiber sections at different distances.

DETAILED DESCRIPTION

[0031] The object of the present invention is fully described below using examples for the purpose of disclosure, without limiting the disclosure to the examples. The examples present different aspects of the present invention. To implement the present technical teaching, it is not required to implement all of these aspects combined. Rather, a person skilled in the art will select and combine those aspects that appear sensible and required for the corresponding application and implementation.

[0032] As can be seen in Fig. 1, to detect, for example, wrong-way drivers on a highway a lane on which a detected vehicle (i.e. an object 1) is moving has to be known. There is therefore a need to estimate a distance between at least one distributed acoustic sensor 2 (an optical fiber cable) and the object 1. The actual position of the object 1 consists of the two coordinates x and y. The coordinate x indicates a position along the at least one distributed acoustic sensor 2 and the coordinate y indicates a distance of the object 1 from the at least one distributed acoustic sensor 2. The at least one distributed acoustic sensor 2 is divided into a plurality of optical fiber cable sections, in particular at least one first fiber section 2a and at least one second fiber section 2b. In the present case, each one of the at least one first fiber section 2a and at least one second fiber section 2b of the optical fiber cable can be regarded as being one single sensor. The position along the least one distributed acoustic sensor 2 (indicated by the x - coordinated) can be estimated by analysing a signal returned from different ones of the fiber sections 2a, 2b. The fiber section 2a, 2b with the strongest signal having expected characteristics (waveform) indicates approximately the x coordinate of theL object 1, i.e. the distance along the least one distributed acoustic sensor 2.

[0033] The least one distributed acoustic sensor 2 of a system 100 comprises at least one first fiber section 2a and at least one second fiber section 2b arranged adjacent to the first fiber section 2a and separated from the at least one first fiber section 2a in a first direction (here: in X - direction along the length of the least one distributed acoustic sensor 2). As can be seen in Fig. 1, the object 1 is a source of acoustic radiation launching acoustic radiation or a source of vibrations launching source vibrations into the at least one first fiber section 2a and the at least one second fiber section 2b. At least one source signal, e.g. acoustic and/or vibrational signal, arrives at the at least one first fiber section 2a and/or at least one second fiber section 2b. In other words, the acoustic signal or the vibrational signal is the at least one source signal.

[0034] The system 100 further comprises a detector apparatus 3 for converting at least one acoustic signal detected from the acoustic radiation from the at least one first fiber section 2a and the at least one second fiber section 2b to an optical signal, i.e. a measured signal. In other words, the acoustic signal (source signal) is converted into the optical signal (measured signal) by sending a laser pulse and receiving the sent laser pulse. The acoustic signal is then derived by measuring the change in the phase of the reflected pulse in comparison to the transmitted pulse. The change of the phase of the light is proportional to the source signals, i.e. the acoustic (or vibration) signals. The system 100 comprises furthermore a processor 4 configured to receive at least one electrical signal representing the optical signal from the detector apparatus 3, to filter the at least one signal, to detect the peak height and the peak width of the at least one signal, and to measure at least one event-parameter of the at least one source (i.e. object 1) using the peak height and peak width of the at least one signal. The conversion from the optical signal to an electrical domain is performed by a photodiode. The event parameter is at least one of position (x-direction), length, size, and distance (y-direction) of the object 1.

[0035] Measurements have shown that the object 1 present in the vicinity of the least one distributed acoustic sensor 2 generates characteristics “peaks” in the optical signal waveform in the fiber sections 2a, 2b close to the object 1, as can be seen in Fig. 2. The peaks in the signal (phase) are generated by presence of the object 1 in the vicinity of the fiber section as determined by calculating a moving average filtering. The moving average filtering is calculated by taking a mean value over several signal samples instead of using a single sample value. As can be seen in Fig. 2, theL peak is lower and broader for the greater distance of the object 1 from the least one distributed acoustic sensor 2. The distance is defined as the shortest distance between the object 1 to be detected and the fibre cable. The height and the width of the peaks depends on the object’s size and weight.

[0036] As can be seen in Fig. 3, at least one object 1 by at least one distributed acoustic sensor 2 is detected (step 310). Further at least one signal from the least one distributed acoustic sensor 2 is received (step 320).

[0037] In a further step 330, the at least one signal is filtered to obtain “peaks” signal over time. For that purpose, a band-pass phase preserving filter is proposed that filters signal in an appropriate frequency band (estimated previously during the calibration phase). The resulting signal after the signal filtering at the fiber section in the vicinity of the vehicle has the characteristic peaks as depicted in Fig. 2. These peaks are particularly distinctive in correlation signals between adjacent ones of the fiber sections 2a, 2b at which the peak occurs, i.e. peak detection means also vehicle detection at appropriate “x-position” (see Fig. 1). In this instance, the term “correlation” means the multiplying of the signals from adjacent ones of the fiber sections 2a, 2b. The presence of the peak itself indicates that one or more of the objects 1 are near the fiber sections 2a and 2b in which the peaks are seen. The peak height and the peak width of the at least one signal is determined (step 340) and the at least one event-parameter (e.g., position, length, size, and distance) of the at least one object using the peak height and peak width of the optical signal(s) is determined (step 350). The measured event parameters of the at least one object 1 are stored in method step 360.

[0038] The peak characteristics, especially peak height and width, are used as characteristics measures that enable size and distance estimation. The detected peak height and width are compared with the previously stored peak heights and widths for which the size and distance of the object is known (step 370). Statistics illustrating various peak characteristics for various different types of objects can be evaluated according to past records and compared with the detected peak characteristics in step to estimate the distance and size.

[0039] The usage of signal correlations between the adjacent fiber sections 2a and 2b and using correlation features like width and height enables the determination of the size and distance of the object 1. In this way “spatial” diversity can be exploited. The term “spatial diversity” means using the acoustic signals from different ones of the fiber sections 2a, 2b to detect the object 1 as well as estimate the positions, distance and other parameters of the acoustic signals. It should be noted that not only peaks in optical signal over a time domain L but also in a frequency domain of the acoustic/optical signal can be used to characterize events, for example by, mean of the frequencies for which the maximum in the spectrum is achieved (i.e. the mean of the frequencies is estimated by averaging over several adjacent fiber sections), standard deviation of the frequencies at a spectrum maximum, peak to average ratio of the spectrum, or other spectrum statistics features. Thus, using delay estimation a more robust detecting regarding multipath can be provided. Here, multipath means that the acoustic signal from the same source arrives over several paths at the fibre. The interference is a distribution generated by other sources than desired, for example, the vehicle generates the desired acoustic or vibration signals and industrial sources like manufactories generate interference. Further, noise effects are thermal and optical noise, which is always present in electronic devices. Here, the delay estimation means the correlation of acoustic signals at different fibre sections. The delay estimation estimates the delay for which the correlation is maximal. For example, the acoustic signals without a time shift from two fibre sections are multiplied sample by sample and the sum of the sample products is estimated. Then one of the acoustic signals is delayed for dl samples and the sum of the sample products is again estimated. After that the acoustic signal is again shifted for d2 samples and the sum of the sample products estimated etc. Finally, the delay shift dx for which the sum of the sample products is maximal is the estimated delay between the acoustic signals at the two fiber sections. Further, by usage of statistics of peak heights and widths distribution, and several measurements at different fiber sections instead only one the possibly erroneous estimation by single measurement can be drastically reduced. If the measurements from only one fiber section is taken, the estimation of the signal parameters is unreliable. If the measurements over several fibre sections are taken and are averaged (i.e. take mean or median value) an estimation becomes more reliable. Further improvements in the detection of the objects can be achieved by using the measurement at different times for the same vehicle to improve the estimation.

[0040] The assumption that the peak width and height correlates with size and distance is estimated by generating appropriate statistics for the peak height and width for lower and higher distances. As can be seen in Figs. 3 and 4 showing peak height distribution for greater and smaller distances between the optical fiber and a street 5 on which the object 1 is located, the peak height and peak width probability distributions are plotted for two distances “lower” (approximately 6m) and “higher” (approximately 12m). The greater the distance between the optical fiber 2 and the street, the smaller and the broader the peaks are in a statistical sense, 1. e. for the greater distance the lower peak heights and the larger peak widths have higherL probabilities and vice versa for the smaller distance. The distance between the o least one distributed acoustic sensor 2 and the object 1 can be relatively well estimated in a statistical sense using the spectra shown in Figs 3 and 4, since probability distributions of the peak height and peak width corresponds well with the distance of the object 1 from the optical fiber 2. The greater the distance the higher probability for lower and the broader peaks.

[0041] This is true if both the peak height and the peak width is used as criteria for estimation of the distance. Fig. 6 shows a peak width/peak height scatter plot with a decision boundary for discriminating between greater and smaller distances. The decision boundary in a peak width/height plot indicates the points on the one side from the boundary are classified as measurements coming from the object 1 with smaller distance and on the another side of the boundary the measurements are classified as the measurements from the object with higher distance.

[0042] The above decision boundary shown in Fig. 6 can be set as follows: [0043] If peak height < 1

No vehicle else

If peak height < 2 AND peak height > 1 Ifpeak(peak width < 3000 AND Classify as SMALL VEHICLE at LOW DISTANCE else

Classify as SMALL VEHICLE at HIGH DISTANCE end else if (peak width < 6000 AND peak height > 2) OR (peak width > 6000 AND peakjieight > 3.5)

Classify as BIG VEHICLE at LOW DISTANCE else

Classify as BIG VEHICLE at HIGH DISTANCE End βί [0044] Decision trees can be used. The decision trees are a set of hierarchical rules which L successively determine the size and distance using the rules like at least one of the above. There will always some “outliers” i.e. points lying on the “false” side of the decision boundary due to noise and inaccuracy due to estimation of the peak height and width. The number of outliers shown in Fig. 5 is relatively low. For this example, using the decision boundary as defined above the following performance figures are obtained:

Correct lower distance classification = 89.6%

False negative lower distance = 10.4%

False positive lower distance = 10.37%

Correct higher distance classification = 92.35%

False negative higher distance - 7.65%

False positive higher distance = 7.67% [0045] It will be noted that the peak width and peak height coming from the object 1 on the road is estimated not only once, but several times. For example, it is possible to obtain signals for the same vehicle on the same lane more than once. So even if sometimes the peak width and height is wrongly estimated, this error decreases relatively quickly after several width and height estimations for the same vehicle on the same lane. The probability than more than the half ([N/2] - is the rounded N/2) decisions are correct (Pe) for N estimations with correct decision probability of p can be calculated by the following formula: [0046]

[0047] For example, if the vehicle drives in the same lane for at least 100m and each 2m (the fiber sections 2a and 2b are spaced at the distance of 2m in this typical setting of the system 100) will be measured and 50 measurements of the peak height and the peak width from the same vehicle for the same lane will obtained. Assuming rather pessimistically that only 30 of the 50 measurements are valid (having a sufficient signal-to-noise ratio SNR and the probability of the correct distance estimation for any one of the measurement is 80% (in the example illustrated above the probability was about 90%) we get 0.9999 probability that more than the half of the measurements N would provide correct the distance estimation. The term L SNR is the ratio between signal power and noise power, which is usually expressed in dB.

[0048] A further increase in accuracy of the distance estimation can be achieved using diversity i.e. estimating p in the formula above not only according to measurements from a single one of the fiber sections 2a, 2b at the same time but using measurements from several (usually five to seven) closely spaced (sayl0-15m) fiber sections 2a, 2b and calculating the average values from the several closely spaced fiber sections 2a, 2b.

[0049] Other features that can be used for distance estimations according to the peak height and width statistics are mean, standard deviation, skewness etc. For example, after removing the outliers from the measurements, the standard deviation of the remaining peak height values according to Figure 5 is much higher for the lower distances (1.8) than for the higher distances (0.74). The outliers are defined as the values greater than mean twice the standard deviation.

[0050] A similar approach could be applied for distance estimation of other types of objects 1, for example to estimate the distance of an excavator from the optical fiber 2 in case of digging. In this example, the signals from different ones of the neighboring fiber sections 2a, 2b were used as the measure of the signal width instead of the peak width. From Figs. 6, 7 and 8, it can be seen that the greater the distance is, the lower are the values of the signal peaks. A relative decrease in signal power is lower for the higher distances. In the figures 7, 8 and 9 the (maximum) correlations between the optical signal at the fiber section 269 (approximately excavator horizontal position) and signals on fiber sections 240-300 were depicted. The signals were previously filtered by the band -pass filter in the range 5 to 50.

[0051] In Fig. 7, the correlations between the signals on the fiber sections 240-300 with the signal at the fiber section 269 (approximately excavator horizontal position) for different excavator distances are depicted. As can be seen from Fig. 7, the peaks (signals) are higher for the closer distances and “broader” (relative to the maximum) for the higher distances.

[0052] For example, in Fig. 8, it can be seen that in the case of 2m distances approximately 5 fiber sections (fiber sections 265 to 269) have the correlation above the half of the maximal correlation (correlation of fiber section 269 with itself). In the case of 10m distance (see Fig. 9), approximately 8 fiber sections have the correlation higher than the half of the maximum (fiber sections 265 to 272) and for 15m distance about 15 fiber sections have the correlation higher than the half of the maximum (fiber sections 264 to 278). This confirms the previous observations in the case of the vehicles that in general the greater the distance the lower and broader the signals are. In similar manner the peaks in spectrum domain can be also used toL estimate distance and other features of interesting events. In Figs. 7 to 9, the correlations (sums of the sample products) of the fibre sections with the number depicted on the x-axis with the signal on the fibre section 269 are above the half of the maximal correlation. As can be seen in said Figs. 7-9, the greater the correlation between, the higher are the peaks. Further, the greater the distance, the more ones of the fibre sections have similar correlation values as the peak maximum for the distance.

[0053] From the above description of the present invention, those skilled in the art will perceive improvements, changes, and modifications on the present invention. Such improvements, changes, and modifications within the skill in the art are intended to be covered by the appended claims.

Claims

Claims l

A method for determining at least one event parameter of at least one object (1), comprising the steps of: detecting (310) the at least one object (1) by at least one distributed acoustic sensor (DAS) (2); Receiving (320) at least one signal from the at least one distributed acoustic sensor (DAS) (2); Filter (330) of the at least one signal; Determining (340) in at least one of a time domain and a frequency range of at least a peak height, a peak width, and a peak frequency of the at least one signal; and determining (350) the at least one event parameter from the at least one object (1) using at least one of the peak height, the peak width and the peak frequency of the at least one signal.

2. A method for determining at least one event parameter of at least one object (1), comprising the steps of: emitting acoustic radiation from the at least one object (1) into at least one fiber section (2a, 2b) of at least one distributed acoustic sensor (DAS) (2); Detecting (310) the at least one signal; Receiving (320) the at least one signal from the at least one distributed acoustic sensor (DAS) (2); Filter (330) of the at least one signal; Determining (340) in at least one of a time domain and a frequency range of at least a peak height, a peak width, and a peak frequency of the at least one signal; and determining (350) the at least one event parameter from the at least one object (1) using at least one of the peak height, the peak width and the peak frequency of the at least one signal.

3. The method of claim 1 or 2, further comprising the step 1 store (360) of the at least one measured event parameter of the at least one object (1).

4. The method of claim 3, further comprising the step of comparing (370) the at least one measured event parameter with at least one previous stored event parameter of the at least one object (1).

5. The method of claim 3, further comprising the step of generating statistics from the at least one measured event parameter of the at least one object (1) from past stored event parameters of the at least one object (1).

6. The method of claim 4, further comprising the step of generating statistics of the at least one measured event parameter of the at least one object (1) from compared event parameters of the at least one object (1).

7. The method of claim 1, further comprising the method step of using signal correlations between at least one first fiber section and at least one second fiber section which is arranged adjacent to the at least one first fiber section and is separated from the at least one first fiber portion (2a) in a first direction.

8. The method of claim 1, further comprising the method step of using signal correlations between at least a first fiber section and at least one second fiber section, which is arranged adjacent to the at least one first fiber section and is separated from the at least one first fiber portion (2a) in a second direction different from a first direction.

A system (100) for determining at least one event parameter of at least one object (1), comprising: at least one distributed acoustic sensor (DAS) (2) comprising at least a first fiber portion (2a) and a second fiber portion (2b ) disposed adjacent to the first fiber portion (2a) and separated from the at least one first fiber portion (2a) in a first direction; the at least one object (1) emits acoustic radiation into at least one of the first fiber section (2a) and the second fiber section (2b); a detection device (3) for detecting the acoustic radiation through the at least one first fiber section (2a) and the at least one second fiber section (2b); a processor (4) configured to: receive at least one signal from the detection device (3); to filter the at least one signal; determine, in at least one of a time domain and a frequency domain, a peak height, a peak width, and a peak frequency of the at least one signal; and determine the at least one event parameter from the at least one object (1) using at least one of the peak height, the peak width, and the peak frequency of the at least one signal.

The system (100) of claim 9, wherein the at least one event parameter of the at least one object (1) is at least one of a position, a length, a size, and a distance.

The system (100) of claim 9 or 10, wherein the filter is a bandpass phase filter.