Abstract:
A method for and system of intrusion detection by using ultrasonic signals are provided. The method includes a training phase and a detection phase. The training phase includes capturing multi-echo signals when no intrusion is present under surveillance. The captured ultrasonic multi-echo signals are analyzed and features are extracted, learned and modularized. The detection phase includes a continuous collection of ultrasonic multi-echo signals. The captured signals are analyzed and features are extracted to compare with the features learned during the training phase. When intrusion is present, the features will be substantially different from the features learned in the training phase. Thus, an intruder can be detected. Experimental results indicate that when MDR is less than 0.1%, the FAR would be less than 2.5% for the invention. Therefore, this invention is applicable to intrusion detection.

Description:
FIELD OF THE INVENTION 
   The present invention generally relates to intrusion detecting, and more specifically to a method for and system of intrusion detection by using ultrasonic signals. It is applicable to such a warehouse protection system, a factory monitoring system, and a vehicle intrusion alarm system. 
   BACKGROUND OF THE INVENTION 
   The conventional intrusion detection method uses cameras to capture images, and determines if an intruder is present by comparing the captured images. By using a camera to capture image, the surveillance area must be well-lit, especially at night in order to guarantee the quality of the captured image. The energy consumption is usually high using the conventional methods. In addition, because the lighting changes in the environment are usually frequent, complicated and unpredictable, false alarms are quite common when using image comparison. For example, a sunny day and an overcast day may generate quite different images; turning off the light in the corridor may affect the lighting condition in a room; the reflection of the surrounding objects in the monitor, and so on. All these problems pose difficult challenges for around-the-clock surveillance using image comparison. 
   Another technique commonly used in many intrusion detection methods is infrared. The use of passive infrared is to take advantage of the temperature difference among a number of regions, while the use of active infrared relies on the changes of the strength of the reflected signal. The former has the disadvantage of making mistake when surrounding temperature is high or unstable, for example: working heater and flame of the candle. Moreover, it is unable to detect a person covered with an insulating coating. The disadvantage of the active infrared method is that it can only detect a small area, usually along the line of sight. While radar is also used in Doppler methods to detect the speed of objects, it is not an appropriate solution for the indoor as it is too expensive and with a large volume. 
   The preprocess, feature extraction, and background module training have been used in many audio or image recognition applications and applicable to ultrasonic signals. As the ultrasonic detector can detect different signal features depending on the presence or the absence of an object within the detection range, it does not reply on the lighting and temperature changes in the surroundings. It is possible to exploit this trait in intrusion detection to replace the aforementioned methods troubled by some problems. 
   U.S. Pat. No. 4,319,349 disclosed an ultrasonic alarm system to detect the Doppler-shifted components which indicate the presence of moving objects in a protected area. It preprocess the transmitter signal and allows much simpler receiver circuitry to detect Doppler-shifted echoes indicating the presence of an intruder while rejecting similar echoes caused by non-intrusive objects which would otherwise produce a false alarm. However, the system has the disadvantage of being unable to detect a static or slowly moving object. 
   The prior arts mentioned above may be used as automatically controlling systems. However, they are not suitable to be used for intrusion detection, because of being understood their weakness and people can be trained to fool this system. 
   SUMMARY OF THE INVENTION 
   The present invention has been made to overcome the aforementioned drawbacks of conventional intrusion detection methods. The primary object of the present invention is to provide an intrusion detection method by using ultrasonic signals. By extracting the features of the ultrasonic signals and analyzing difference, the present invention can detect the presence or absence of an object regardless of condition effect of the surveillance environment. Thereby, it is free of environment interference, and very hard to be faked. 
   To achieve the aforementioned advantages, the intrusion detection method of the invention includes a training phase and a detection phase. The training phase is proceeded when there is no intrusion. In the training phase, a set of ultrasonic signals is captured from an ultrasonic sensor. Then, the preprocess and feature extraction of the ultrasonic signal set are performed. After that, feature training is conducted in order to establish at least one background module which is used for comparison in the detection phase. In the detection phase, ultrasonic signals are also collected, and same feature extraction for the ultrasonic signals is done. Then, a testing feature is formed by sampling a plurality of the extracted features. The testing feature is compared with the background module established in the training phase. Based on the results of the comparison, whether an intruder is detected or not is determined. 
   Another object of the present invention is to provide an intrusion detection system by using ultrasonic signals. The intrusion detection system comprises a signal capturing unit, a microprocessor, a random access memory (RAM), a programmable read only memory (PROM) for storing background modules, and a control unit. 
   The invention integrates the preprocess, feature extraction, and background module training techniques to the multi-echo ultrasonic signals. According to the invention, the ultrasonic signals may be multi-echo or single-echo ultrasonic signals. The single-echo ultrasonic signal is a sequence of single-echoes of the sensor, while multi-echo one is assembly of multi-echoes of the sensor. The preprocess can be noise filtering or signal amplifying and the feature extraction of the ultrasonic signals can be done by using wavelet transform (WT) technique or discrete cosine transform (DCT) technique. The background module can be established by using neural network (NN) or Gaussian mixture model (GMM). 
   The presence of an intruding person or object can affect a single-echo to multi-echo ultrasonic signals. Therefore, the detection strategy of the invention can perform the feature extraction on multi-echo ultrasonic signals to build a testing feature for comparison with the background module, or on individual single-echo signals then summarizes the individual comparison result, to reach the final conclusion. When the ultrasonic signals show different features from the background module, an intruder is detected. 
   The experimental results indicate that when the miss detection rate (MDR) is less than 0.1%, the false alarm rate (FAR) is less than 2.5% for the invention. Therefore, the present invention can be applicable to intrusion detection. 
   The foregoing and other objects, features, aspects and advantages of the present invention will become better understood from a careful reading of a detailed description provided herein below with appropriate reference to the accompanying drawings. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  shows a flowchart illustrating the operating flow of the intrusion detection method by using ultrasonic signals according to the invention. 
       FIG. 2  shows the detailed steps of the training phase in  FIG. 1 . 
       FIG. 3  shows the detailed steps of the detection phase in  FIG. 1 . 
       FIG. 4  shows an example on how to perform the feature extraction on a signal in  FIG. 1 . 
       FIG. 5  shows an example on how to perform the feature training to build the background module in  FIG. 1 . 
       FIG. 6  shows a block diagram of the intrusion detection system according to the invention. 
       FIG. 7  shows the relationship among window size, length of ultrasonic signal, and equal error rate (ERR) in an experimental environment according to the invention. 
       FIG. 8  shows a receiver operating characteristics (ROC) curve of false alarm rate versus miss detection rate according to the example of the invention. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     FIG. 1  shows the operating flow of the intrusion detection method by using ultrasonic signals according to the invention. Referring to  FIG. 1 , the intrusion detection method includes a training phase (shown in dotted arrows) and a detection phase (shown in solid arrows). The training phase proceeds when an intrusion does not occur. In the training phase, continuous ultrasonic echoes captured from an ultrasonic sensor are combined to signals and many signals in a moment are formed as a training signal set  101 . Then, the preprocess and feature extraction  103  for the training set are performed. After that, feature training  105  is conducted in order to establish at least one background module  107  which is used for comparison in the detection phase. In the detection phase, an ultrasonic signal (testing signal  111 ) is also collected, and same preprocess and feature extraction  103  for the detection signals are done. By sampling a plurality of the extracted features, they are compared with the background module  107  established in the training phase as shown in block  113 . Based on the results of the comparison, whether there is intruder or not is determined, as shown in block  115 . 
     FIG. 2  shows the detailed steps of the training phase in  FIG. 1 . Referring to  FIG. 2 , the training phase starts with power on. Once the power is on, it captures an ultrasonic echo and forms an ultrasonic signal (step  201 ), then checks if the ultrasonic signal is long enough to represent meaningful information (step  202 ). If yes, then preprocess and feature extraction are performed (step  203 ); otherwise, returns to step  201  to continue capturing an ultrasonic echo and combines it to the next signal. After step  203 , the features are learned in step  205  and update the background module  107  in a storage. In step  206 , the time out condition is checked. If the time is not out, then returns to step  201  to capture more ultrasonic signals for training; otherwise, the background module is ready for further use in the detection phase and then activates and starts the detection phase. 
     FIG. 3  shows the detailed steps of the detection phase in  FIG. 1 . The detection phase initializes at the end of the training phase. Referring to  FIG. 3 , steps  201 - 203  are also performed in the detection phase. After step  203 , the newly extracted feature of the detection signal is compared with the background module  107  stored during the training phase, as shown in step  304 . Based on the results of the comparison, it is to determine if an intrusion occurs, as shown in step  305 . If so, it activates an alarm; otherwise, returns to step  201  and continues the detection phase to analyze the next ultrasonic signal. 
   The echoes of the ultrasonic sensor show up layout of the surrounding objects. The ultrasonic signals are continuous connection of the echoes in time domain and may be multi-echo or single-echo ultrasonic signals in the invention. When there is no intrusion, the ultrasonic signals are at a stable status. The presence of an intruding person or object can disturb a single-echo to multi-echo ultrasonic signals to be different from original stable signals. Therefore, the detection strategy can perform the feature extraction on ultrasonic signals for comparison with the background module, or on individual single-echo signals then summarizes the individual comparison result to reach the final conclusion. When the ultrasonic signals show different features from the background module, it concludes that an intruder is detected. 
   An ultrasonic signal is a one-dimensional sequence, like a waveform which is the connection of ultrasonic echoes. Therefore, the feature extraction of the signals can be done by using wavelet transform technique or discrete cosine transform technique. In addition, some preprocess techniques, such as noise reduction, or signal amplification, can be applied before the transformation. The background module can be established by using neural network or Gaussian mixture model. 
     FIG. 4  shows an example on how to perform the feature extraction in  FIG. 1 . Referring to  FIG. 4 , the feature extraction is to apply a window {right arrow over (w)} of size W to N-echo signals {right arrow over (s)} i , i=1, 2, . . . , N of time length T, then move the window {right arrow over (w)} forwards a distance each time so that the new window {right arrow over (w)} k  and the previous window {right arrow over (w)} k-1  have an overlapped size D. By repeating the movement of window M times on the N-echo signals {right arrow over (s)} ij , i=1, 2, . . . , N, j=1, 2, . . . , M, the DCT coefficients of the ultrasonic signals are extracted as the features {right arrow over (ƒ)} ij , i=1, 2, . . . , N, j=1, 2, . . . , M, as shown in  FIG. 4 . The features of the separate signals are collected to a training set 
   
     
       
         
           
             { 
             
               
                 
                   f 
                   → 
                 
                 j 
               
               = 
               
                 
                   ⋃ 
                   
                     ∀ 
                     i 
                   
                 
                 ⁢ 
                 
                   
                     f 
                     → 
                   
                   ij 
                 
               
             
             } 
           
           , 
           
               
           
           ⁢ 
           
             j 
             = 
             1 
           
           , 
           2 
           , 
           … 
           ⁢ 
           
               
           
           , 
           
             M 
             . 
           
         
       
     
   
   Using GMM to modularize the training set, as shown in  FIG. 5 , the background module can be expressed as the following parameters:
 
λ={p i , {right arrow over (u)} i , Σ i }, i=1, 2, . . . , C
 
These parameters represent, respectively, the mixture weight, mean vector and covariance matrix of C groups (mixtures) in the GMM, and
 
               ∑     k   =   1     c     ⁢     p   k       =   1.         
The training phase includes the calculation of these parameters K times, with using the result from the previous iteration as the initial value of the next iteration. The estimations of the parameters at each iteration are as the following:
 
               p   ⇀     i     =       1   M     ⁢       ∑     j   =   1     M     ⁢     p   ⁡     (       i   |       f   →     j       ,   λ     )                             μ   _     →     i     =         ∑     j   =   1     M     ⁢       p   ⁡     (       i   |       f   →     j       ,   λ     )       ⁢       f   →     j             ∑     j   =   1     M     ⁢     p   ⁡     (       i   |       f   →     j       ,   λ     )                           σ   _     i   2     =           ∑     j   =   1     M     ⁢       p   ⁡     (       i   |       f   →     j       ,   λ     )       ⁢     f   j   2             ∑     j   =   1     M     ⁢     p   ⁡     (       i   |       f   →     j       ,   λ     )           -       μ   _     i   2             
Where σ i  is the i-th value in the diagonal of the covariance matrix, and
 
   
     
       
         
           
             p 
             ⁡ 
             
               ( 
               
                 
                   i 
                   | 
                   
                     x 
                     → 
                   
                 
                 , 
                 λ 
               
               ) 
             
           
           = 
           
             
               
                 p 
                 i 
               
               ⁢ 
               
                 
                   b 
                   i 
                 
                 ⁡ 
                 
                   ( 
                   
                     x 
                     → 
                   
                   ) 
                 
               
             
             
               
                 ∑ 
                 
                   j 
                   = 
                   1 
                 
                 C 
               
               ⁢ 
               
                 
                   p 
                   j 
                 
                 ⁢ 
                 
                   
                     b 
                     j 
                   
                   ⁡ 
                   
                     ( 
                     
                       x 
                       → 
                     
                     ) 
                   
                 
               
             
           
         
       
     
     
       
         
           
             
               b 
               i 
             
             ⁡ 
             
               ( 
               
                 x 
                 → 
               
               ) 
             
           
           = 
           
             
               1 
               
                 
                   
                     ( 
                     
                       2 
                       ⁢ 
                       π 
                     
                     ) 
                   
                   
                     NW 
                     / 
                     2 
                   
                 
                 ⁢ 
                 
                   
                      
                     
                       ∑ 
                       i 
                     
                      
                   
                   
                     1 
                     / 
                     2 
                   
                 
               
             
             ⁢ 
             exp 
             ⁢ 
             
               { 
               
                 
                   - 
                   
                     1 
                     2 
                   
                 
                 ⁢ 
                 
                   
                     ( 
                     
                       
                         x 
                         → 
                       
                       - 
                       
                         
                           μ 
                           → 
                         
                         i 
                       
                     
                     ) 
                   
                   ′ 
                 
                 ⁢ 
                 
                   
                     
                       ∑ 
                       i 
                     
                     
                       - 
                       1 
                     
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         x 
                         → 
                       
                       - 
                       
                         
                           μ 
                           → 
                         
                         i 
                       
                     
                     ) 
                   
                 
               
               } 
             
           
         
       
     
   
   The detection phase is to extract the testing feature {right arrow over (ƒ)} from the ultrasonic signals (as in training phase mentioned before), then compare this testing feature with the background module λ, and obtain a likelihood value LK. If the value of LK is larger than a pre-determined threshold, it concludes to be an intrusion; otherwise, no intrusion is present. The method for obtaining the LK value is as the following: 
   
     
       
         
           
             Lk 
             ⁡ 
             
               ( 
               
                 
                   f 
                   → 
                 
                 | 
                 λ 
               
               ) 
             
           
           = 
           
             log 
             ⁢ 
             
                 
             
             ⁢ 
             
               p 
               ⁡ 
               
                 ( 
                 
                   
                     
                       f 
                       → 
                     
                     i 
                   
                   | 
                   λ 
                 
                 ) 
               
             
           
         
       
     
     
       
         where 
       
     
     
       
         
           
             p 
             ⁡ 
             
               ( 
               
                 
                   x 
                   → 
                 
                 | 
                 λ 
               
               ) 
             
           
           = 
           
             
               ∑ 
               
                 i 
                 = 
                 1 
               
               C 
             
             ⁢ 
             
               
                 p 
                 i 
               
               ⁢ 
               
                 
                   b 
                   i 
                 
                 ⁡ 
                 
                   ( 
                   
                     x 
                     → 
                   
                   ) 
                 
               
             
           
         
       
     
   
   The intrusion detection method of the invention can be implemented with an ultrasonic intrusion detection system, as shown in  FIG. 6 . Referring to  FIG. 6 , the ultrasonic intrusion detection system comprises a signal capturing unit  601 , a microprocessor  602 , a RAM  603 , a PROM  604  for storing background modules, and a control unit  605 . The signal capturing unit  601  further includes an ultrasonic sensor  601   a  and a sequentializing buffer  601   b . The microprocessor  602  further includes a preprocess and feature extraction unit  602   a , a feature training unit  602   b  and a feature comparison unit  602   c.    
   The training phase includes signal capturing, preprocess and feature extraction to obtain training features, and the training features are learned and stored in a storage as the background module. Therefore, in the training phase, the signal capturing unit  601  gets ultrasonic echoes from an ultrasonic sensor  601   a , forms the ultrasonic signals in sequentializing buffer  601   b , and save the signals to RAM  603 . A training timer is used to time the duration required for collecting signals. After timer out, the microprocessor  602  gets before capturing signals (training set) in RAM and performs preprocess and feature extraction  602   a  to obtain training features. The training features are learned and stored in the PROM  604  as the background module. 
   The detection phase includes signal capturing, preprocess and feature extraction to obtain testing features, which are compared with the background module loaded from storage. Therefore, in the detection phase, the signal capturing unit  601  gets ultrasonic echoes from an ultrasonic sensor  601   a , forms the ultrasonic signals in sequentializing buffer  601   b , and passes the signals to the microprocessor  602 . The microprocessor performs preprocess and feature extraction to obtain testing feature. The testing feature is compared with the background module and the detection result is sent to control unit  605 . The control unit  605  determines whether an intruder occurs or not, according to the comparison result and activates messages if needed. The RAM  603  stores the temporary data proceeded by the microprocessor  602  in both training and detection phases. 
   Many experiments are performed on various factors for demonstrating the present invention.  FIG. 7  shows the relationship among the window size W, the length of ultrasonic signals T, and the equal error rate in an experimental environment according to the invention. The equal error rate is the value that the false alarm rate equals to the miss detection rate. In the experimental environment, the overlapped window size D=W−1, the mixtures number C=20, initial value of the mixture weight p=1/C, initial value of the mixture means μ are randomly chosen from the training set, the initial covariance matrices are unit matrices, and the modeling iterative number K=20. The database includes background echoes of the ultrasonic sensor taken for 4.8 hours of no people and intruded echoes of the sensor for 20 minutes. The capturing frequency for the ultrasonic signals is around 15 Hz. 
   From  FIG. 7 , it can be seen that if W is increased, then the ERR is decreased. However, this will delay the time that an intruder is detected. Moreover, if T is long enough, then increasing it&#39;s value will not decrease the ERR. When W=20 and T=40, then an intruder is detected within 0.7 second with ERR=1%. 
     FIG. 8  shows a receiver operating characteristics curve of FAR versus MDR according to the invention. From  FIG. 8 , it can be seen that when MDR&lt;0.1%, FAR&lt;2.5%. Currently, an intrusion detection system with MDR&lt;0.1% and FAR&lt;5% is widely acceptable. Therefore, the present invention is applicable to intrusion detection. 
   Although the present invention has been described with reference to the preferred embodiments, it will be understood that the invention is not limited to the details described thereof. Various substitutions and modifications have been suggested in the foregoing description, and others will occur to those of ordinary skill in the art. Therefore, all such substitutions and modifications are intended to be embraced within the scope of the invention as defined in the appended claims.