Digital detection filters for electronic article surveillance

Details Digital detection filters for electronic article surveillance Digital detection filters for electronic article surveillance

2002-03-22

2004-03-02

Lee, Benjamin C. (Department: 2632)

Communications: electrical

Condition responsive indicating system

Specific condition

C340S010100, C375S343000, C708S314000, C708S422000

Reexamination Certificate

active

06700490

ABSTRACT:

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
Not Applicable
BACKGROUND OF THE INVENTION
1. Field of the Invention
This application relates to digital implementation of electronic article surveillance (EAS) detection filtering, and more particularly to detection filtering in pulsed EAS systems.
2. Description of the Related Art
EAS systems, such as disclosed in U.S. Pat. Nos. 4,622,543, and 6,118,378 transmit an electromagnetic signal into an interrogation zone. EAS tags in the interrogation zone respond to the transmitted signal with a response signal that is detected by a corresponding EAS receiver. Previous pulsed EAS systems, such as ULTRA*MAX sold by Sensormatic Electronics Corporation, use analog electronics in the receiver to implement detection filters with either a quadrature demodulation to baseband or an envelope detection from an intermediate frequency conversion. The EAS tag response is a narrow band signal, in the region of 58000 hertz, for example.
An EAS tag behaves as a second order resonant filter with response
s
(
t
)=
A·e
−&agr;·t
·sin(2
·&pgr;·f
0
·t
+&thgr;),
where A is the amplitude of the tag response, f
0
is the natural frequency of the tag, and &agr; is the exponential damping coefficient of the tag. The natural frequency of the tag is determined by a number of factors, including the length of the resonator and orientation of the tag in the interrogation field, and the like. Given the population of tags and possible trajectories through the interrogation zone, the natural frequency is a random variable. The probability distribution of the natural frequency has a bell shaped curve somewhat similar to Gaussian. For simplifying the receiver design it may be assumed uniform without a great loss in performance. Its distribution is assumed to be bounded between some minimum and maximum frequencies, f
min
and f
max
, respectively.
The exponential damping coefficient &agr;, in effect, sets the bandwidth of the tag signal. Nominal values for &agr; are around 600 with magnetomechanical or acousto-magnetic type tags. On the other hand, for ferrite tags cc will be much larger, on the order of 1200 to 1500.
The phase of the tag response depends on the transmit signal and many of the same parameters as the natural frequency. The transmit signal determines the initial conditions on the tag when the transmitter turns off. This sets the phase of the response as it goes through its natural response. The amplitude of the tag's response is dependent on all of the same parameters: orientation and position in the field, physics of the tag, etc.
Pulse EAS systems, such as ULTRA*MAX systems, operating around 60000 Hz preside in a low frequency atmospheric noise environment. The statistical characteristic of atmospheric noise in this region is close to Gaussian, but somewhat more impulsive, e.g., a symmetric &agr;-stable distribution with characteristic exponent near, but less than, 2.0. In addition to atmospheric noise, the 60000 hertz spectrum is filled with man made noise sources in a typical office/retail environment. These man made sources are predominantly narrow band, and almost always very non-Gaussian. When many of these sources are combined with no single dominant source, the sum approaches a normal distribution due to the Central Limit Theorem. The classical assumption of detection in additive white Gaussian noise is used herein. The “white” portion of this assumption is reasonable since the receiver input bandwidth of 3000 to 5000 hertz is much larger than the signal bandwidth. The Gaussian assumption is justified as follows.
Where atmospheric noise dominates the distribution is known to be close to Gaussian. Likewise, where there are a large number of independent interference sources the distribution is close to Gaussian due to the Central Limit Theorem. If the impulsiveness of the low frequency atmospheric noise were taken into account, then the locally optimum detector could be shown to be a matched filter preceded by a memoryless nonlinearity (for the small signal case). The optimum nonlinearity can be derived using the concept of “influence functions”. Although this is generally very untractable, there are several simple nonlinearities that come close to it in performance. To design a robust detector some form of nonlinearity must be included.
When there is a small number of dominant noise sources we include other filtering, prior to the detection filters, to deal with these sources. For example, narrow band jamming is removed by notch filters or a reference based LMS canceller. After these noise sources have been filtered out, the remaining noise is close to Gaussian.
Referring to
FIG. 1
, when the signal of interest is completely known a matched filter is the optimum detector. In our case, say we knew the resonant frequency of the tag and its precise phase angle when ringing down. The signal we're trying to detect is
s
(
t
)=
A·e
−&agr;·t
·sin(2
·&pgr;·f
0
·t
+&thgr;).
Then the matched filter is simply the time reversed (and delayed for causality) signal, s(T
r
−t) at
2
. The matched filter output is sampled at
4
at the end of the receive window, T
r
, and compared to the threshold at
6
. A decision signal can be sent depending on the results of the comparison to the threshold. The decision can be a signal to sound an alarm or to take some other action. Note that we do not have to know the amplitude, A. This is because the matched filter is a “uniformly most powerful test” with regard to this parameter. This comment applies to all the variations of matched filters discussed below.
Referring to
FIG. 2
, when the signal of interest is completely known except for its phase &thgr;, then the optimum detector is the quadrature matched filter (QMF). QMF is also known as noncoherent detection, since the receiver is not phase coherent with the received signal. On the other hand, the matched filter is a coherent detector, since the phase of the receiver is coherent with the received signal. The receive signal r(t) which includes noise and the desired signal s(t) is filtered by s(T
r
−t) at
8
as in the matched filter, and again slightly shifted in phase by &pgr;/2 at
10
. The outputs of
8
and
10
are each squared at
12
, combined at
14
, sampled at
16
, and compared to the threshold at
18
.
Referring to
FIG. 3
, when the signal of interest is completely known except for its frequency f
n
and phase &thgr;, then the optimum detector is a bank of quadrature matched filters (QMFB). A quadrature matched filter bank can be implemented as a plurality of quadrature matched filters
20
,
22
,
24
, and
26
, which correlate to quadrature matched filters with center frequencies of f
1
, f
2
through f
n
, respectively. The outputs of the quadrature matched filters are summed at
28
, sampled at
29
and compared to a threshold at
30
.
Referring to
FIG. 4
a block diagram of a conventional analog EAS receiver is illustrated. The antenna signal
32
passes through a gain and filtering stage
34
with center frequency equal to the nominal tag frequency and bandwidth of about 3000 hertz, for example. Following this, the signal is demodulated to baseband with a quadrature local receive oscillator
36
. The oscillator frequency may or may not be matched precisely to the transmit frequency. Furthermore, the oscillator phase is not necessarily locked to the transmit oscillator's phase.
The in-phase (I) and quadrature-phase (Q) baseband components are subsequently lowpass filtered by the in-phase
38
and quadrature-phase
40
baseband filters, respectively. This serves to remove the double frequency components produced by the mixing process, as well as further reduces the detection bandwidth. These baseband filters are typically 4
th
order analog filters, e.g., Butterworth and Chebychev type.
The outputs of the baseband filters
38
, and
40
are passed through rectifiers
42
and
44
, respectively, which removes the sign information from the I and Q components. The outputs of the

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