Abstract:
A distributed sensor network has a base station and clusters of sensor nodes. In a method of locating and classifying signal sources, at each node divides a received signal into blocks, performs Fourier-based transform on the blocks, selects peaks from the transformed blocks, selects subbands with features of interest based on the frequency of occurrence of the peaks across the blocks, collaborates with other nodes in the cluster to make a final selection of the subbands, encodes the subband features of the signal, and transmits the subband features to the base station. The base station processes the received subband features to locate and classify the signal sources.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to sensor networks, and more particularly to a distributed sensor network and a method for feature extraction and data reduction at the sensor nodes with specific application to determining ground and airborne vehicle locations. 
       BACKGROUND ART 
       [0002]    Distributed wireless sensor networks consisting of several single sensors offer important benefits for a multitude of applications including battlefield surveillance, situation awareness and monitoring, urban warfare, homeland security and border control. Distributed wireless sensor networks can be used to capture acoustic signatures of a wide variety of sources including ground and airborne vehicles as well as transient events such as gunshots. Among the benefits of distributed wireless sensor networks are: simplicity and ease of deployment, stealthy operation in urban areas, large coverage area, good spatial resolution for separating multiple closely spaced sources, low hardware complexity and hence low costs, and flexibility in configuring different dynamic sensor array configurations. 
         [0003]    Reducing the rate of data transmission from each sensor node to the base station not only reduces the cost and power consumption of each sensor node but also the complexity and cost of the base station. More importantly, it allows deploying a large number of sensor nodes to cover a large area without exceeding the bandwidth limitation of the wireless communication system. For example, in a system that uses zigbee-based communication protocols with sensor nodes based on the IEEE 802.15.4 standard, the data rate or bandwidth is 250 kilo bits per second (kbps) per channel. If each sensor node transmits 25 kbps, only 10 sensor nodes can communicate simultaneously to a base station. If each sensor node transmits 2 kbps, 125 sensor nodes can communicate simultaneously to a base station. 
         [0004]    A system with sensor-level detection, feature extraction and data compression for low bit rate transmission of essential target attributes to the base station can significantly reduce the data rate relative to prior known systems. In moderately large sensor networks with sensor nodes that use communication protocols such as zigbee-based communication protocols that use the, data rates of less than 2 kbps per node are needed to meet the bandwidth limitations, while guaranteeing the usefulness of the data for accurately locating moving sources. Such a system can make practical the widespread use of low cost distributed wireless sensor nodes in many applications. 
         [0005]    U.S. Pat. No. 7,005,981 to Wade discloses a system and method with sensor systems or nodes with the steps of pre-processing collected data, and applying a matched extraction/compression scheme to the pre-processed data. U.S. Patent Application Publication No. 2008/0069334 to Denby et al. discloses a system and method with a central server and agents with the steps of applying a statistical test to measurement data, and based on the results of the statistical test, determining whether an update needs to be sent from the agent to the server. 
       DISCLOSURE OF THE INVENTION 
       [0006]    A distributed sensor network for locating and classifying signal sources includes a base station and clusters of sensor nodes. Each sensor node has one or more sensors, memory, a field programmable gate array (FPGA) or other processing device, and a communications link with the base station and other nodes in the same cluster. A method of feature extraction and data reduction of an analog signal received by a sensor node in a cluster in the sensor network includes the steps of converting the analog signal into a digital signal, storing a selected time increment, such as one second, of the signal, dividing the signal for the time increment into blocks, performing a transform on each block, selecting peaks from each transformed block, selecting subbands based on the frequency of occurrence of the peaks in the transformed blocks, collaborating with the other sensor nodes in the cluster to select the common subbands, performing a transform on the signal for the time increment, encoding the subband features of the signal for the time increment, and transmitting the subband features of the signal for the time increment to the base station. The method is implemented through software instructions in the processing device, and the elements of the sensor node are a means for performing each of the steps of the method. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]    Details of this invention are described in connection with the accompanying drawings that bear similar reference numerals in which: 
           [0008]      FIG. 1  is a pictorial top plan view of a distributed sensor network embodying features of the present invention. 
           [0009]      FIG. 2  is a block diagram of a sensor node of the network of  FIG. 1 . 
           [0010]      FIG. 3  is a block diagram of a sensor node method for the network of  FIG. 1 . 
           [0011]      FIG. 4  is a block diagram of a base station method of the network of  FIG. 1 . 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0012]    Referring now to  FIG. 1 , a distributed sensor network  11  embodying features of the present invention includes a base station  14 , and spaced clusters  15  each having a plurality of spaced sensor nodes  16 . The network  11  finds the direction of arrival (DOA), locates, and classifies the acoustic sources including ground vehicles  18  and airborne vehicle  19 . 
         [0013]    In the illustrated embodiment, the sensor nodes  16  detect acoustic signals. By way of example, and not as a limitation, sensors that detect magnetic, seismic, chemical, and/or photonic signals can also be used.  FIG. 2  shows a sensor node  16  having an acoustic sensor or microphone  21 , a wireless transceiver or mote  22 , a sensor board  23 , a battery pack  24 , an external antenna  25  and an enclosure box  26 . The Telos-B, MICAz and MICA2 motes by Crossbow Technology Inc. are examples of suitable motes  22  that are currently available. 
         [0014]    The mote  22  connects to the sensor board  23  and provides a wireless communication link with the base station  14  and with the other sensor nodes  16  in the cluster  15 . The mote  22  at the sensor node  16  receives time synchronization beacons from the base station  14 , wirelessly communicates with other sensor nodes  16  in cluster  15  for collaboration of subband information, and transmits compressed data to the base station  14 . In addition, the mote  22  also provides the ability to configure the sensor node  16  and to handle commands from the base station  14 . Although the base station  14  and sensor nodes  16  use wireless communication links in the illustrated embodiment, wired communication links can also be used. 
         [0015]    The sensor board  23  includes an FPGA  28 , memory  29 , a plurality of analog channels  30 , a header  31  and a single chip transceiver  32 . The memory  29  includes pseudo-SRAM  34  and flash memory  35 . The PSRAM  34  can be used as a buffer for sensor data or as temporary storage for intermediate variables. Five channels  30  are shown. Four of these channels  30  have 12 bit A/D converters  37 . The fifth channel  30  has a 16 bit A/D converter  38 , and is used primarily for vehicle tracking. The header  31  shown has one Joint Test Action Group (JTAG) connector which can be used to program the FPGA  28 , and three 8-bit expansion headers which can be used to connect to external components such as a digital compass, GPS etc. The Chipcon CC100 by Texas Instruments Inc. is an example of a suitable, currently available single chip transceiver  32 . The single chip transceiver  32  is a high frequency radio that can be used for node self-location. 
         [0016]    As shown in  FIG. 3 , a method embodying features of the present invention including the following steps. Synchronizing  40  the time at the sensor nodes  16  starts with a time synchronization beacon that is broadcasted by the base station  14  every selected time increment of one second. Other time increments may also be selected. The mote  22  of the sensor node  16  receives the time synchronization beacon and triggers the sampling of the acoustic signal received by the microphone  21 . Since all sensor nodes  16  will receive the time synchronization beacon at virtually the same time, sampling is triggered at approximately the same time at all sensor nodes  16 . Converting  41  the synchronously sampled analog signal into a digital signal is then performed by the A/D converter  37 . In the illustrated embodiment the A/D converter  37  samples at a rate 1024 Hz with a resolution of 12 bits which is found to be sufficient for the vehicle localization application. 
         [0017]    For each selected time increment of one second, storing  42  the digital signal is next. The first 876 samples of the 1024 samples are stored. The next step is dividing  43  the stored samples into blocks of 128 samples with an overlap of 64 samples, resulting in thirteen blocks. Each block is padded with a mean value such that each block includes 1024 samples. The 12 bit A/D converter  37  has a dynamic data range of 0 to 4095, and 2047 is chosen as the mean value for padding. 
         [0018]    Performing  44  a 1024-point Discrete Cosine Transform (DCT) on each block after padding is the next step. The 1024 output DCT coefficients, each 32 bit, are stored in a separate buffer. Other Fourier-based transforms can also be used instead of the DCT. The DCT, the Short Time Fourier Transform (STFT), and the Modified DCT (MDCT) were implemented and benchmarked. The DCT-based method provided the best overall performance amongst the methods tried. The transform converts the signal for each block from the time domain to the frequency domain. 
         [0019]    The range of the 5th to the 512th coefficients corresponds to the range of 2 Hz to 256 Hz in the frequency spectrum. The following step is selecting  45  five peaks from this range for each block. Other numbers of peaks could also be selected. The peak finding process uses a sliding window. In the illustrated embodiment, the window size is eleven. Other window sizes can be used. If the center coefficient in the sliding window is the maximum coefficient in the window and above a selected threshold, then the corresponding frequency index is recorded as a peak. The sliding window is moved by one coefficient and the comparison process is repeated. One method that can be used to select the threshold is finding the median value of the coefficients inside the sliding window and then using a percentage, for example 120%, of the median as the local threshold. After sliding through the specified range of frequencies, if more than five peaks are detected, then only the peaks corresponding to the five highest DCT coefficient values are retained. 
         [0020]    The next step is computing  46  a histogram of the peaks selected from all of the blocks. In the illustrated embodiment, the bin width corresponds to a frequency range of 17 Hz. Selecting  47  three subbands, corresponding to the bins with the highest number of occurrences of peaks, follows computing  46  a histogram. These subbands include the most persistent components, and computing the histogram identifies subbands that carry target information. 
         [0021]    After selecting  47  the subbands at the sensor node  16 , collaborating  48  with the other nodes  16  in the cluster  15  is performed to select the most commonly occurring subbands. Collaborating  48  involves each sensor node  16  broadcasting wirelessly in a round robin fashion that sensor node&#39;s  16  three subbands, and receiving the subbands from all the other sensor nodes  16  in the cluster  15 . After collaborating  48 , each sensor node  16  computes a histogram of the subbands, selecting  49  the three common subbands that occurred consistently across the sensor nodes  16  in cluster  15 . 
         [0022]    After selecting  49  the common subbands, the 876 samples stored in the buffer are padded with the mean value to provide 1024 samples. The next step after padding is performing  50  a 1024-point DCT the samples. Since each bin corresponds to a frequency range of 17 Hz, the coefficients corresponding to each subband include the coefficient for the center of the bin and the 16 coefficients on each side of the center, making a total of 33 coefficients for each subband. Of the 1024 coefficients, a total of 3×33=99 coefficients, or about 10% (10-to-1 reduction), are selected to represent the original signal. 
         [0023]    Encoding  51  the 99 selected coefficients is next. The DCT coefficients are encoded based on the radix 10 IEEE-754 standard. Each of the 32 bit DCT coefficients is represented using 16 bits in IEEE 754 format, where one bit is allotted to represent the sign of the coefficient, 4 bits to represent the exponent part and 11 bits to represent the significant part. Assuming that three DCT subbands are selected by the detection scheme, and there are 33 DCT coefficients in each subband, then the effective bit rate required for transmitting the DCT coefficients is 1.54 kbps. Including headers, such as Zigbee wireless data packet headers, the actual bit rate achieved can be approximately 2 kbps. After encoding, the next step is transmitting  52  the coefficients from each sensor node  16  in each cluster  15  to the base station  14  via the mote  22  and antenna  25 . 
         [0024]    The method exploits the peaky nature of the time-frequency of the acoustic signatures of different types of vehicles. That is, the spectra of the time-windowed signals exhibit disjoint identifiable peaks within some subbands, the features of which may then be encoded and transmitted to the base station  14 . 
         [0025]    Referring again to  FIG. 2 , several of the steps of the method are implemented through software instructions executed by the FPGA  28  such that the FPGA  28  is a means for dividing  55  the signal into blocks, a means for performing  56  a first Fourier-based transform on the blocks, a means for selecting  57  a plurality of peaks in each transformed block, a means for selecting  58  a plurality of subbands of the signal based on the frequency of occurrence of the peaks in the transformed blocks, a means for performing  59  a second Fourier-based transform on the entire time increment of the signal and a means for encoding  60  the subbands before transmitting the subbands. These steps can be performed by other processors, and portions of these steps could be implemented through hardware such that other means could be used. The mote  22  is a means for transmitting  63  the subbands of the signal to the base station  14 . Other wired or wireless devices could be used. The mote  22  and the FPGA  28  combined provide a means for collaborating  64  with the other sensor nodes  16  in the cluster  15  to select the common subbands, and other means can be used. 
         [0026]      FIG. 4  shows a base station  14  method embodying features of the present invention. The first step is decoding  67  the encoded coefficients received from each sensor node  16  using the IEEE 754 decoder. The next step is performing  68  a 1024-point inverse DCT on the decoded coefficients to recover the signals in the time domain. Applying  69 , at the base station  14 , a wideband geometric averaging Capon method to all the recovered time domain signals of each sensor node  16  within the cluster  15  to estimate the DoA of moving signal sources is the next step. (see M. R. Azimi-Sadjadi, N. Roseveare, and A. Pezeshki, “Wideband DoA Estimation Algorithms for Multiple Moving Sources using Unattended Acoustic Sensors”, IEEE Trans. on Aerospace and Electronic Systems, vol. 44, pp. 1585-1599, October 2008) 
         [0027]    The next step is applying  70  a Maximum Likelihood-based method to triangulate and locate the vehicle using the DoA estimates of the moving vehicles obtained from each of the clusters  15 . A Maximum Likelihood-based method that offers robustness to erroneous DoA estimates is developed to estimate the locations at every one second time segment. Successive location results are then used to form the path of the vehicle. 
         [0028]    Other steps include extracting  71  subband features and then classifying  72  the signal sources, such as vehicles, at the base station  14  through the use of the extracted subband features. The occurrences and significance of the subbands over an observation period are representative of the frequency harmonics of the sources as well as the sources&#39; transient behavior as the sources maneuver in the field. The occurrences of the selected DCT subband peaks in several one second snapshots can be used for vehicle classification. The center frequencies of the subbands are accumulated over a period of ten seconds in order to gather enough clues for accurate classification. For ground vehicles there are four possible classes of vehicles, namely: light-wheeled, heavy-wheeled, light-tracked and heavy-tracked. The window length of 10 seconds appears to be optimum as the decision about the class membership cannot be made in smaller size windows and enough clues need to be gathered before final decision making. Any classifier such as a back-propagation neural network (BPNN) can be used to classify the vehicles based on the extracted features. 
         [0029]    Although the present invention has been described with a certain degree of particularity, it is understood that the present disclosure has been made by way of example and that changes in details of structure may be made without departing from the spirit thereof.