Development of Anti Intruders Underwater Systems:
Time Domain Evaluation of the Self-informed Magnetic
Networks Performance
Osvaldo Faggioni1, Maurizio Soldani1, Amleto Gabellone2, Paolo Maggiani3,
and Davide Leoncini4
1
INGV Sez. ROMA2, Stazione di Geofisica Marina, Fezzano (SP), Italy
{faggioni,soldani}@ingv.it
2 CSSN ITE, Italian Navy, Viale Italia 72, Livorno, Italy
[email protected]
3 COMFORDRAG, Italian Navy, La Spezia, Italy
[email protected]
4 DIBE, University of Genoa, Genova, Italy
[email protected]
Abstract. This paper shows the result obtained during the operative test of an anti-intrusion
undersea magnetic system based on a magnetometers’ new self-informed network. The experiment takes place in a geomagnetic space characterized by medium-high environmental noise
with a relevant human origin magnetic noise component. The system has two different input
signals: the magnetic background field (natural + artificial) and a signal composed by the magnetic background field and the signal due to the target magnetic field. The system uses the first
signal as filter for the second one to detect the target magnetic signal. The effectiveness of the
procedure is related to the position of the magnetic field observation points (reference devices
and sentinel devices). The sentinel devices must obtain correlation in the noise observations and
de-correlations in the target signal observations. The system, during four tries of intrusion, has
correctly detected all magnetic signals generated by divers.
Keywords: Critical Systems, Port Protection, Magnetic Systems.
1 Introduction
The recent evolution of the world strategic scenarios is characterized by a change of
the first threat type: from the military high power attack to terrorist attack. In these new
conditions our submarine areas control systems must redesigned to obtain the capability of detecting of very small targets closer to the objective. The acoustic systems, base
for actual and, probably, future port protection option, has a very good results in the
control of big volumes of water but they failure in the high definition controls as, for
example, the control of port sea bottom and docks proximity water volumes. The magnetic detecting is a very interesting option to give more effectiveness to the Anti Intruders Port System. The magnetic method has very good performance in the proximity
detecting and loses in the big water volumes controls while the acoustic method
achieves good performances in the free water but fails in the sea bottom surface areas
E. Corchado et al. (Eds.): CISIS 2008, ASC 53, pp. 100–107, 2009.
© Springer-Verlag Berlin Heidelberg 2009
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Development of Anti Intruders Underwater Systems
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and, more relevant in the docks proximity. The integration of these two methods is the
MAC System (Magnetic Acoustic) class of underwater alert composed systems. Past
studies about the magnetic method detecting shown a phenomenological limitation of
its detecting effectiveness due to the very high interference between magnetic signal
target (diver = low power source) and the environmental magnetic field. The geomagnetic field is a convolved field of several elementary contributes originated by sources
external or internal to the planet, static and dynamic, and more, in the area with human
activity, there is the presence of transient magnetic signals very large band with high
amplitude variations. Classically the way of target signal detection is the classification
of elementary signals of the magnetic field, tentative of the association of their hypothetic sources, and separation of the target signal to the noise: more or less the use of
frequency filters numerical techniques (LP, HP, BP) based to empirical experiences in
the C.O. frequency definition. The result is a subjective method depending to the decision of the operators (or of the array/chain designers) able of great successes or strong
failures. The effectiveness of the method is low and so its develop was neglected for
the detecting of little magnetic signals [1-3]. Of course also the increase of devices
sensibility doesn’t solve the problem because our problem is related to the target signal
informative capability of the magnetograms and not to the sensibility on the measures.
In the present paper we show the results obtained by means of a new approach to this
problem: we don’t use statistic-conjectural frequency filters, we use a reference magnetometer to inform the sentinel magnetometer of the noised field without the target
signal, the system use the reference magnetogram RM as function TD (or FD) filter for
the sentinel magnetogram SM. The result of de-convolution RM-SM is the target signal. The critical point of system is related to the design of the network: to obtain an
effective magneto-detecting system the devices must be put at a distance to have amplitude correlation in the noise measures and de-correlation in the target measure.
2 On the System Design Options and Metrological Response
To build the self-information mag-system we propose two design solutions for the
geometry of devices network, the Referred Integrated MAgnetic Network (RIMAN)
and the Self-referred Integrated MAgnetic Network (SIMAN) [4].
2.1 RIMAN System
The RIMAN system consists of a magnetometers array system and an identical standalone magnetometer (referring node) deployed within the protected area.
The zero-level condition is obtained through the comparison of the signal measured by each of the array’s magnetometers with the signal measured by the referring
magnetometer. If the protected area is confined we can assume the total background
noise constant and therefore the difference between each array’s sensor and the referring one is around zero. The zero-level condition can be altered only in presence of a
target approaching one or two (in case of middle-crossing) sensors of the array.
Signal processing of the RIMAN system is accurate and the risk of numeric alteration of the registered rough signal is very low. A standard data-logger system has to
measure the signals coming from each of the array’s magnetometers and respectively
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compare to the reference signal. The comparison functions ΔF(1,0), ΔF(2,0), ….,
ΔF(N,0) are subsequently compared to the reference level 0 and then only the non-zero
differential signal is taken. It means that the target is crossing a specific nodal magnetometer. For example, if the target is forcing the barrier between nodes 6 and 7, the
only differential non-zero functions are ΔF(6,0) and ΔF(7,0) which will indicate
the target’s position. A quantitative analysis of the differential signals can also show
the target’s relative position to the two nodes. If the protected area is too wide to allow
a stability condition of the total background noise, the RIMAN system can be divided
in more subsystems, each of one using an intermediate reference node, which have to
respect the stability condition among them. The intermediate reference nodes have
finally to be related to a common single reference node.
MAG1
MAG2
MAG3
MAG4
MAG0
C
P
U
'F10
2
'F40
'F20
1
4
0
0
'F30
3
Fig. 1. Scheme of Referred Integrated Magnetometers Array Network
2.2 SIMAN System
In the SIMAN system all the array’s magnetometers are used to obtain the zero-level
condition. The control unit has to check in sequence the zero-level condition between
each pair of magnetometers and signal any non-zero differential function.
MAG1
MAG2
MAG3
MAG4
MAG5
C
P
U
1
2
2
'F12
'F45
4
3
2
'F23
'F34
3
5
4
Fig. 2. Scheme of Referred Integrated Magnetometers Array Network
Development of Anti Intruders Underwater Systems
103
2.3 Signal Processing
Signal processing in the SIMAN system gives very good accuracy, too. The only
issue is related on the ambiguity in case the target crosses a pair of magnetometers at
the same distance from both. Such ambiguity can be solved through the evaluation of
the differential functions between the adjacent nodes. The drawback is that a SIMAN
system requires a continuous second-order check at all the nodes. The advantage of
using a SIMAN system is the possibility to cover an unlimited area. The stability
condition is requested only for each pair of the array’s magnetometers.
3 Results
The experiment consists of recording the magnetic field variations during multiple
runs performed by a diver’s team. The test system of magnetic control consists in the
elementary cell of SIMAN class system [5, 6]. Devices are two tri-axial fluxgate
magnetometers were positioned at a water depth of 12 meters at a respective start
distance of 12 meters; the computational procedure is based on vector component Z
(not variance in the vector direction on the time) measure (Fig. 3).
MAG1
MAG2
C
P
U
'F12
Fig. 3. Experiment configuration
The diver’s runs were performed at zero CPA (Closest Point of Approach) at about
1 meter from the bottom along 50 meters tracks centered on each magnetometer. The
runs’ bearing was approximately E-W and W-E. The two magnetometers were cableconnected with their respective electronic control devices (deployed at 1 meter form
the sensor) to a data-logger station placed on the beach coast at about 150 meters. The
environmental noise of the geomagnetic space of the area of measure is classified as
medium-high and it is characterized by contributes of human source coming from city
noise, industrial noise (electrical power point of production), electrical railway noise,
maritime commercial traffic, port activity traffic etc. (all far < 8 km). The target
source for the experiment was a diver equipped with standard air bottles system. The
result of the experiment is shows in the diagrams of figure 4. The magnetogram of the
sentinel devices (Fig. 4A) is characterized by 5 points of magnetic impulsive anomaly. These signals are compatible with the magnetic signal of our target, the signal
marked in figure 4 as 1, 2, 4 and 5 has very clear impulsive origin (mono or dipolar
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geometry) while the signal 3 has geometrical characteristic not fully impulsive so the
operator (human or automatic) proposes the classification in Table 1.
The availability of the reference magnetogram and its preliminary and subjective
observation well define the fatal error in the signal number 2. This signal appears in
magnetogram A and B because it is not related to the diver cross in the sentinel devices proximity but it is has very large space coherence (noise).
Table 1. Alarm classification table
Signal
1
2
3
4
5
Geometry
MONO
MONO
COMPLEX
MONO
DIPOLE
Alarm
YES
YES
YES
YES
YES
Uncertain
NO
NO
YES
NO
NO
K
0
-K
nT
K
0
-K
Fig. 4. Magnetograms obtained by the SIMAN elementary cell devices
nT
K/2
0
-K/2
s
Fig. 5. SIMAN elementary cell self-informed magnetogram
Development of Anti Intruders Underwater Systems
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In the figure 5 we show the result obtained by the time de-convolution B-A. This is
the numerical procedure to have the quantitative evaluation of the presence of impulsive signals in proximity of the sentinel devices. The magnetogram of SIMAN well
define the real condition of diver crosses in proximity of the sentinel devices. The
signal 2 is declassified and 3 is classified as dipolar target signal without uncertain
because the procedure of de-convolution cleans the complex signal from the superimposed noise component. The table of classification became the Table 2. The SIMAN
classification of impulsive signals (Table 2) has a fully correspondence to the 4
crosses of the diver first course (E.W, W-E directions) 1 and 3 signals and second
course 4 and 5 signals (E-W, W-E directions).
Table 2. Alarm classification table after the time de-convolution A-B
Signal
1
3
4
5
Geometry
MONO
DIPOLE
MONO
DIPOLE
Alarm
YES
YES
YES
YES
Uncertain
NO
NO
NO
NO
nT
K/2
0
-K/2
Fig. 6. Comparison to self-referred system and classical techniques performances
The effectiveness of the system (options RIMAN or SIMAN) is strongly depending to the position of the reference devices. The reference magnetometer must far
from the sentinel to have a good de-correlation of the target signal but also closer to
the sentinel to have a good correlation of the noise. This condition generates a reference magnetograms filter function that cut off the noise and it is transparent to the
target signal. In effect the kernel of self-information procedure is the RD-SD distance.
Now we analyzed the numerical quality of the self-informed system performance with
reference to the performance of classical LP procedure of noise cleaning and to the
variations of the distance RD-SD, reference devices – sentinel devices (Fig. 6). The
diagrams in figure 6 show the elaboration of the same data subset (source magnetogram in figure 4): A diagram corresponds to the pure extraction of the subset from
magnetograms in figure 4 (best distance RD-SD), B it is the LP filter (typical
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informative approach), C self-informed response with too short RD-SD distance, D
self informed response with too great RD-SD distance.
3.1 Comparison A/B
The metrological and numerical procedure effectiveness of magnetic detection is
defined with the increase of informative capability of the magnetogram with reference
to the signal target. We define, in quantitative way, the Informative Capability IC of a
composed signal, related to each its “i” harmonic elementary component to be the
ratio i energy – composed signal total energy:
⎛ n
⎞
ICi = Ei ⎜ ∑ ( E1 + ... + En ) ⎟
⎝ i =0
⎠
−1
(1)
In the A condition SIMAN produces a very high IC for the impulse 1 and the strong
reduction of the IC of impulse 2; the SIMAN permits correct classification of impulse
1 as the target and impulse 2 as environmental noise. In the B graphics the classic
technique of LP filter produces, in the best cut frequency choice, a good cleaning of
high frequency component but the IC of impulse 1 is lose by the effect of impulse 2
energy (survived to the filter). The numerical value of the IC2 is, more or less, the
same of the IC1; so the second impulse is classified as target: egregious false alarm.
3.2 Comparison A/C/D
The comparison A/C/D (fig. 6) shows the distance RD-SD be the kernel for the selfinformed magnetic system detecting effectiveness. In the present case the A distance
is 11 m, C distance 9 m, D distance 13 m (+/- 0.5 m). The C RD-SD distance produces a very good contrast of the high frequency noise but it loses also the target
signal because this one is present in the sentinel magnetogram and also in the reference magnetograms (too closer). The Q of target is drastically reduced and SIMAN
loses its detection capability. In D condition we have a too long distance. The noise in
the reference devices geomagnetic position is not correlated from the noise in the
sentinel devices; so SIMAN procedure adds the noise of reference magnetogram to
the noise of sentinel magnetograms with an high increase of self-informed signal
ETOT: also in this condition Q of target is loses.
4 Conclusion
The field test of self-informed magnetic detecting system SIMAN shows a relevant
growth of effectiveness in the low magnetic undersea sources (divers) detection respect to the classical techniques used in the magnetic port protection networks. The
full success of defense magnetic network crossing SIMAN in the detecting of the
diver crossing tries of our experimental devices barrier is due to the use of the magnetogram not perturbed by target magnetic signal as time domain filter of the perturbed magnetogram. This approach is quantitative and objective and it increases the
target signal informative capability IC without empirical and subjective techniques of
Development of Anti Intruders Underwater Systems
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signal cleaning as the well known frequency LPF. This performance of SIMAN is
related to the accuracy in the choice of distance between the points of acquisition
of the magnetograms. The good definition of this distance produces the best condition
of noise space correlation and target magnetic signal space de-correlation. One m of
delocalization of the reference magnetometer respect to its best position can compromise, in the magnetic environment of our experiment, the results and it can cancel the
effectiveness of SIMAN sensibility. The system of our test developed on sea bottom
with a good geometry has produced very high detecting performance irrespective of
the magnetic noise time variations.
Acknowledgments. The study of the self-informed undersea magnetic networks was
launched and developed by Ufficio Studi e Sviluppo of COMFORDRAG – Italian
Navy. This experiment was supported by Nato Undersea Research Centre.
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