Abstract:
In an EAS system and method, a reference antenna is used to spatially separate the interference and tag signal allowing the interference signal to be removed, which improves performance of the EAS receiver. The reference antenna is coupled to the system by an adaptive filter, which can be a software filter that is continually adapting itself to optimum performance. The continuous adaptation obviates the need for manually tuning the coupling network and permits the system to perform at its optimum level over long periods of time.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     Not Applicable 
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     Not Applicable 
     BACKGROUND OF THE INVENTION 
     1. Technical Field 
     This invention relates to electronic article surveillance (EAS) systems, and more particularly to noise reduction in EAS receivers. 
     2. Description of the Related Art 
     EAS systems are typically used to prevent unauthorized removal of articles from a protected area. EAS tags are attached to articles designated for protection, and when active, the EAS tags will trigger an action, such as setting off an alarm, if carried through an EAS interrogation zone. EAS interrogation zones are typically positioned at the exits of the protected area. For authorized removal of an article, the attached EAS tag is removed or deactivated so the article can be carried through the interrogation zone and removed from the protected area without triggering the EAS system. EAS markers, labels, and tags are used interchangeably herein and refer to markers, labels, tags, and the like that trigger EAS systems. 
     There are several types of EAS systems presently in use including RF, microwave, harmonic, and acoustomagnetic or magnetomechanical. Magnetomechanical EAS systems are used herein to describe one embodiment of the invention, but can be applied to other types of EAS systems within the scope of this disclosure. A magnetomechanical EAS marker is typically made of a “resonator”, an elongated strip of magnetostrictive ferromagnetic material, disposed adjacent a “bias”, a ferromagnetic element that, when magnetized, magnetically biases the strip and arms it to resonate mechanically at a preselected resonant frequency. The marker resonates when subjected to an electromagnetic interrogation field at a frequency at or near the marker&#39;s resonant frequency. The response of the marker can be detected by an EAS receiver, which can trigger an alarm. A complete description of a magnetomechanical EAS system is given in U.S. Pat. No. 4,510,489. 
     Common electronic devices such as flourescent lights, computer monitors, power control circuitry, and other electronic noise sources may interfere with EAS receivers. These noise sources increase the noise level at the EAS system&#39;s receive antenna causing a reduction in sensitivity. The reduction in sensitivity makes it more difficult to detect EAS tags that are far from the receive antenna, and makes desirable wide antenna separation to accommodate wide exits difficult to achieve. 
     Retailers are demanding wide separations between receive antennas, which requires that the detection algorithm be very sensitive as it must detect a tag far away from the receive antenna. In electrically noisy environments this is not always possible because the noise source may overlap the tag signal in both time and frequency, making, separation of the tag and noise signals difficult or impossible using only the receive antenna input. 
     One solution to the noise problem has been to change the antenna pattern so that there is a null in the direction of the interference source. Summing multiple sub-antennas (coils) within the main antenna in such a way as to produce the desired antenna pattern typically does this. The procedure involves manual optimization, which will not automatically adjust to changing environments, and does not always produce a working solution. The altered antenna pattern will generally affect tag sensitivity making tag detection more difficult. 
     An alternate solution involves adding a reference antenna together with a manually adjusted hardware-coupling network. The reference antenna is spatially separated from the main receive antenna in such a way that the reference antenna senses the interference signal but does not sense the tag signal. The two antenna inputs can then be combined using a coupling network in such a way that the noise is effectively canceled. The coupling network typically is tuned to match the noise source and environment. This procedure also involves manual optimization and will not automatically adjust to changing environments. The required hardware-coupling network may match gain and phase at only one or more frequencies, and will not easily work as a general primary vs. reference channel equalizer, as is desired. 
     The use of a reference antenna may make it possible to regain some of the system sensitivity lost to noise sources, however, because of the nature of a typical retailer&#39;s environment the noise sources may be turned on and off and/or moved periodically. This requires a service call for the coupling network to be manually retuned to restore system sensitivity. Even worse, some noise sources change during the day, so that full system performance is never restored. A network that regains lost sensitivity due to noise, and automatically tunes itself is desirable. 
     BRIEF SUMMARY OF THE INVENTION 
     An EAS receiver, and corresponding method, is provided that includes a primary antenna for receiving a first signal, which includes an EAS tag signal and an interference noise signal. The primary antenna is coupled to a primary channel for amplifying and filtering the first signal. A reference antenna is used for receiving a second signal, which includes the interference noise signal. The reference antenna is coupled to a reference channel for amplifying and filtering the second signal. An adaptive filter is connected to the reference channel output. The adaptive filter is responsive to an update algorithm. The output of the adaptive filter is approximately equal to the interference noise signal. A summing network is connected to the adaptive filter output and to the primary channel output. The output of the adaptive filter is subtracted from the output of the primary channel. The resulting output of the summing network is approximately equal to the EAS tag signal. The adaptive filter can be updated according to the output of the reference channel and to an error signal from the summing network. 
     A detection filter can be connected to the summing network to detect a valid EAS tag signal. A sample and hold circuit is connected to the detection filter for sampling and holding the EAS tag signal. A threshold comparison is performed between the sampled and held EAS tag signal and a selected threshold value. An output signal is provided to indicate whether the sampled and held EAS tag signal is greater than the selected threshold value indicating an EAS tag has been detected. 
     In a second aspect of the invention, an EAS receiver is provided that includes a primary antenna for receiving a first signal (z), which includes an EAS tag signal (x) and an interference noise signal (y) wherein z=x+y. The primary antenna is coupled to a primary channel for amplifying and filtering the first signal wherein the amplified and filtered first signal equals z 1 =x 1 +y 1 . The primary channel has a transfer function H 1 . A reference antenna is used for receiving a second signal, which includes the interference noise signal (y). The reference antenna is coupled to a reference channel for amplifying and filtering the second signal wherein the amplified and filtered second signal equals (y 2 ). The reference channel has a transfer function H 2 . An adaptive filter having a transfer function W=(H 2 ) −1 ·H 1  is connected to the reference channel. The adaptive filter is responsive to an update algorithm. An output of the adaptive filter (y W ) is approximately equal to the interference noise signal (y 1 ), wherein y w =W·y 2 ≈y 1 . A summing network is connected to the adaptive filter output and to the primary channel output. The output of the adaptive filter (y w ) is subtracted from the output of the primary channel (z 1 ), wherein the output of the summing network is approximately equal to the EAS tag signal (x 1 ), wherein e=z 1 −y w =x 1 +y 1 −y w ≈x 1 +y 1 −y 1 ≈x 1 . 
     The update algorithm is updated according to a filter update algorithm W (n+1) =W (n) +μ·y 2 ·e, where (n) refers to the nth iteration of receiver processing and μ is a gain constant affecting the tracking bandwidth. 
     In a third aspect of the invention, an EAS system responsive to the presence of an EAS tag within an interrogation zone is provided that includes an interrogation zone formed by a transmitter and antenna that generates an electromagnetic field within the interrogation zone. A receiver and receive antenna for detecting an EAS tag disposed in said interrogation zone. The receiver made as described herein, including a primary antenna for receiving a first signal, which includes an EAS tag signal and an interference noise signal. The primary antenna is coupled to a primary channel for amplifying and filtering the first signal. A reference antenna is used for receiving a second signal, which includes the interference noise signal. The reference antenna is coupled to a reference channel for amplifying and filtering the second signal. An adaptive filter is connected to the reference channel output. The adaptive filter is responsive to an update algorithm. The output of the adaptive filter is approximately equal to the interference noise signal. A summing network is connected to the adaptive filter output and to the primary channel output. The output of the adaptive filter is subtracted from the output of the primary channel. The resulting output of the summing network is approximately equal to the EAS tag signal if one is present. 
     Objectives, advantages, and applications of the present invention will be made apparent by the following detailed description of embodiments of the invention. 
    
    
     BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
     FIG. 1 is a block diagram of a prior art EAS receiver. 
     FIG. 2 is a block diagram illustrating one embodiment of the present invention. 
     FIG. 3 is a block diagram of an EAS system incorporating a receiver made in accordance with the present invention. 
     FIG. 4 is a block diagram illustrating an adaptive filter update algorithm used with the present invention. 
     FIG. 5 is a plot of noise probability density functions for Gaussian and non-Gaussian noise distributions verses signal amplitude. 
     FIG. 6 is a plot of the windowing function used to eliminate transmitter noise. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring to FIG. 1, a typical magnetomechanical EAS receiver is illustrated. The signal from the primary receive antenna  2  is amplified and filtered as represented by the primary channel block  4 , then passed into the detection filter  6 , which is typically a type of matched filter. The output of the detection filter  6  is sampled  8  at the optimum point in time and compared to a threshold  10 . If the detection filter  6  output is above the threshold  12 , the decision  14  is made that a tag is present and an alarm is sounded, otherwise it is decided that no tag is present. The threshold  12  is chosen to give an acceptable tradeoff between false alarm rate and detection rate. When interference is present at the primary antenna  2  and the noise level is high, then the threshold  12  must be raised to keep the false alarm rate low. This is at the expense of sensitivity, or detection rate and range. 
     Referring to FIG. 2, one embodiment of the present invention is illustrated and described below generally and in detail. Components in the figures that are identical are labeled with the same reference numerals. The tag signal is referred to as x, while the interference signal is referred to as y. In general, a reference antenna  3  is placed such that it senses the interference signal y, but not the tag signal x. The reference signal is passed through its own filtering and gain reference channel  5 , followed by an adaptive filter  16 , which can be part of the system software. The output of the adaptive filter  16  is an estimate of the interference signal y present at the output of the primary channel  4 . The interference estimate is subtracted from the primary channel output  18 , thereby canceling the interference. 
     The cleaned up, or “conditioned” signal is then passed to the remainder of the detector. Since the interference signal y has been removed, the threshold  12  at the sampled output  8  of the detection filter  6  can be lowered for comparison  10 , without causing excessive false alarm probability. This gives the receiver the desired sensitivity to detect the tag signal x in the presence of noise from an interference signal y. The adaptive filter  16  is continually monitoring its input and modifying its parameters using an update algorithm  17  so that it remains optimally tuned. 
     In detail, the primary antenna  2  receives both the tag signal x and the interference signal y, and the output of the primary antenna is denoted z, where z=x+y. The reference antenna  3  is placed so that it senses the interference signal y, but not tag signal x. Additional noise may also be received by antenna  3 , which can essentially be ignored as long as antenna  3  receives interference signal y and not tag signal x. The primary  2  and reference  3  antennas have separate gain and filtering paths referred to as primary channel  4  and reference channel  5 , represented by functions H 1  and H 2 , respectively. These channels are both assumed to be linear, so that the output of the primary channel z 1 =x 1 +y 1 , while the output of the reference channel is y 2 . 
     The adaptive filter  16  is represented by a function W, which is configured so that its output is a close approximation of y 1 , i.e., 
     
       
           y   w   =w·Y   2   ≈y   1 . 
       
     
     Therefore, the output of the summing junction  18  is 
     
       
           e=z   1   −y   w   =x   1   +y   1   −y   w   ≈x   1   +y   1   −y   1   ≈x   1 . 
       
     
     In order for W to form an approximation of y 1 , it must provide the transfer function 
     
       
           W= ( H   2 ) −1   ·H   1 . 
       
     
     In effect, the adaptive filter  16  equalizes the channel differences between the primary channel  4  and the reference channel  5 . An algorithm commonly used for adaptive equalizers is known as the Least Mean Squares, or LMS algorithm. Using this algorithm, the adaptive filter update algorithm  17  is 
     
       
         
           W 
           (n+1) 
           =W 
           (n) 
           +μ·y 
           2 
           ·e, 
         
       
     
     where the index n refers to the nth iteration of the receiver processing and μ is a gain constant affecting the tracking bandwidth. In this manner, the adaptive filter  16  automatically adapts to the correct setting. 
     Referring to FIG. 3, an EAS system incorporating the invention is illustrated. Transmitter  20  and transmit antenna  22  transmits the interrogation electromagnetic field into the interrogation zone that is defined between transmit antenna  22  and receive antenna  2 . In an alternate embodiment, transmit and receive, or transceiver, antennas can be used in place of separate antennas. Transmitter  20  and receiver  24  are controlled by controller  25 , which includes synchronization of transmit and receive windows in a pulsed embodiment. Receiver  24  includes the invention as described and illustrated hereinabove. Active EAS tag  26  produces a valid EAS tag signal upon being moved into the interrogation zone. The EAS tag signal along with noise from interfering noise source  28  is received by receive antenna  2 . Noise source  28  represents all noise in the environment of the EAS system including that which, when received at receive antenna  2 , may interfere with detection of EAS tag  26 . Reference antenna  3  receives noise from noise source  28 . Once a valid EAS tag signal is detected according to the description hereinabove, an output signal can trigger indicator  29 , which can be an alarm. 
     Referring again to FIG. 2, using an LMS algorithm for the update algorithm  17  performs satisfactorily when the interference noise signally is primarily Gaussian, which may be the case in certain installations. However, in many operational environments the noise signals are not Gaussian, but include impulse components in the noise signal. Furthermore, due to coupling of the transmitter into the reference antenna there may be correlation between the noise reference signal and the desired tag signal, which we do not want to cancel. To mitigate these problems, two improvements to the LMS update algorithm have been made and are illustrated in FIG.  4 . 
     Referring to FIG. 4, both the error signal, e, and the reference channel, y 2 , undergo trimming algorithms. In the trimming algorithms, the root mean square (RMS) level of the error and reference channel signals are estimated at  30 ,  31 , respectively, and tracked over a much larger time frame than a single receive window. The estimated RMS levels are then multiplied at  32 ,  33  by a trimming factor selected to eliminate impulse noise components in the signals. The estimated RMS value times the trimming factor yields the trimming threshold. For example, if the noise were truly Gaussian and the RMS estimator was perfect, then a trimming factor of  3  would eliminate less than one percent of the data. A complete description of the trimming factor is given hereinbelow with reference to FIG.  5 . The absolute value of the error signal  34  and the absolute value of the reference channel  35  are compared to their respective trimming thresholds at  36  and  37 , respectively. Any level above the threshold is replaced with zeros at  38  and  39 , respectively. In this manner, impulse noise does not adversely affect the tap weights of the adaptive algorithm. 
     A second adaptation of the LMS update algorithm is a windowing function at  40  and  41 , respectively, that is selected to reduce or eliminate portions of the signal where correlation between the reference signal and the desired EAS tag signal are suspected to exist. This is commonly in the portions of the signal closest in time to the transmitter signal. A complete description of the windowing function is given hereinbelow with reference to FIG.  6 . When a valid EAS tag is detected, the windowing function goes to zero. The tap weight vectors are then basically clamped at their present value and further updates are prevented until the tag is removed from the interrogation zone. Once adjusted with the windowing signals at  40  and  41 , the preprocessed error and reference signals are sent into a standard or block LMS update algorithm  42 , which sends an updated tap weight vector to tap weight storage  44 , used to update adaptive filter  16 , shown in FIG.  2 . 
     Referring to FIG. 5, the plots illustrate probability density verses signal amplitude. For normally distributed, or Gaussian, noise, the signal amplitude is most probable within a couple standard deviations (σ) of the mean  50 . The standard deviation σ is a classical measurement of the distribution spread. The distribution tails  52  are the areas of the curves that decay toward zero. For the Gaussian curve  54 , the tails decay toward zero at a rate proportional to e −x     2   . This indicates that the signal is very unlikely to have extremely high amplitude values. However, because the actual environmental noise distribution may include impulse noise, the tail of the impulse noise curve  56  decays more slowly toward zero, which means it is more likely to produce very high amplitude outputs. The trimming algorithm described hereinabove estimates the RMS level of the input signal, at  30  and  31  in FIG.  4 . The estimated RMS level is used as a measure of the spread of the signal&#39;s amplitude probability distribution. If the signal is Gaussian, the RMS value is equal to the standard deviation σ. The estimated RMS level is then multiplied by the trimming factor, typically around  3 . This value is chosen as a starting point since, for Gaussian noise, more than 99% of the signals will have absolute values less than this number, i.e., little trimming will occur, and will result in little change to the LMS tap weights. However, signal values 10 or even 100 times the estimated RMS value are possible for impulse noise. These signals would have a significant impact on the LMS tap weights if they were not trimmed to zero, at  38  and  39  in FIG.  4 . Once they are set to zero, they have no effect on the tap weights. 
     Referring to FIG. 6, the windowing function used in the update algorithm for a pulsed system is illustrated. The transmit burst  60  ends at time t 0 . At this time, the receiver front end opens up and begins listening to the environment for EAS tags. However, it takes until time t 1  for all of the transmit energy to dissipate from the transmit antenna. This energy, which is present in the transmit ring down  62 , will appear as interference in the primary antenna  2 , and perhaps in the reference antenna  3 . In addition, adjacent EAS systems will begin transmitting adjacent transmit bursts  64  nominally at time t 3 . Due to jitter on the timing reference, which is typically the power line signal, the systems may in fact begin transmitting at time t 2 . At time t 2 , the receiver window may still be open causing these adjacent system signals  64  to appear as noise in both the primary antenna  2  and the reference antenna  3 . Rather than permit the LMS canceller to waste resources canceling transmitter interference, another filter in the signal processing system, which is more efficient at removing this disturbance, is used. LMS canceller resources are limited because for a given number of LMS filter taps, there is only so much equalization that can be accomplished. LMS resources would be wasted by trying to cancel transmitter noise, rather than canceling the intended environmental noise. In order to keep the transmitter interference from influencing the tap weights, the windowing function  66  is utilized to zero out the portions of the signal inside the update algorithm, which would contain the transmitter interference. The zeroing occurs only in the update algorithm, after the filtering and cancellation portion. 
     When an alarm is active, indicating that an EAS tag has been detected in the system, the update algorithm,  17  in FIG. 2, is halted completely. When the tag is removed, the update algorithm continues. This minimizes adverse effects to the tap weights in case some of the tag signal reaches the reference antenna  3  and well as the primary antenna  2 . 
     It is to be understood that variations and modifications of the present invention can be made without departing from the scope of the invention. It is also to be understood that the scope of the invention is not to be interpreted as limited to the specific embodiments disclosed herein, but only in accordance with the appended claims when read in light of the forgoing disclosure.