Abstract:
Techniques to image life forms through obstructions and at long standoff ranges employ a radar system that simultaneously transmits a plurality of RF pulse trains having different transmission frequencies and receives returns of the RF pulse trains reflected from a life form target. The returns are processed to generate digital radar data associated with the transmission frequency of each RF pulse train. The digital radar data is segmented and averaged to generate a Doppler spectrum response associated with the transmission frequency of each RF pulse train. Target classification is performed using the Doppler spectrum responses to extract biometric data describing the life form target.

Description:
BACKGROUND 
     This disclosure relates to pulsed radar systems, and more specifically to a radar system and method of target classification capable of determining life form target type and movements. 
     See or sense through obstruction sensors are needed to satisfy current and future operations for enhanced capability to detect, locate, identify, and classify moving and stationary humans for rescue and clearing operations. The sensors could be used by the military, police, security, and firemen. Additionally the sensors could provide standoff human biometric monitoring for medical personnel to help save lives. 
     Radar technology sensors can be used for standoff range sensing. Radar can measure both the range to target and the “Doppler” or velocity of the target. 
     Prior approaches have involved impulse radars and pulse compression radars. Impulse radar transmits an ultra short pulse for high range resolution. Less than 1 nsec pulses are required to image a human target. The short pulses result in very little energy on target. In each of these cases, the goal it to achieve a range resolution for target imaging while applying as much energy on the target as possible. 
     Faced with the constraints of range resolution verses energy on target, Radar Systems use a concept called pulse compression. Pulse compression refers to a family of techniques that increase the bandwidth of radar pulses without shortening the pulse width. The result is a range resolution which is higher than that associated with an uncoded pulse. Many methods exist to achieve this, including binary phase coding, polyphase coding, frequency modulation, and frequency stepping. A side-effect of these techniques is the appearance of range sidelobes of significant amplitude in the range profile. These range sidelobes can result in a small target of interest being masked by a large target that is nearby. 
     Radar systems presently do not have adequate capability to image life forms for classification. For example classifying humans vs. dogs or classifying human movements. The reasons for this are fivefold. First, legacy radar systems are designed with imaging techniques that partition the illuminated area into high-resolution segments or pixels. These pixels are viewed like photographs. Humans use these radar photographs to design another layer of signal processing for target classification. This process is inefficient for extracting life form biometric information out of the radar data. Second, the instantaneous bandwidth to image a human would result in very short pico-second pulse widths which results in very little energy on target. Third, classical pulse compression techniques suffer from range sidelobes that distort target information and mask small target features. Fourth, until recently most radar applications and associated signal processing techniques were developed to detect fast moving targets with a large radar cross-section. For example, airplanes, missiles, and fast moving vehicles produce a large return with a large Doppler shift from DC, not small radar cross-section targets with very small Doppler shifts like a human target. Finally, there is no known technique for effectively imaging and classifying life-form targets. 
     More recently efforts have been made to apply pulsed radar to urban environments or an urban battlefield. In these environments the target signatures are much weaker. Instead of fast moving aircraft or missiles the targets are humans or slow moving vehicles, which present a much smaller radar cross-section and Doppler shift. Additionally the presence of buildings and other large structures exacerbates the range side lobe problem. 
     There is a demonstrated and ongoing need for a radar system that can accurately detect and classify life form movements in a heavily cluttered urban or foliage environments. 
     SUMMARY 
     Techniques are described to image life forms through obstructions and at long standoff ranges. In an implementation, a radar system simultaneously transmits a plurality of RF pulse trains having different transmission frequencies and receives returns of the RF pulse trains reflected from a life form target. The returns are processed to generate digital radar data associated with the transmission frequency of each RF pulse train. The digital radar data is segmented and processed to generate a Doppler spectrum response associated with the transmission frequency of each RF pulse train. Target classification is performed using the Doppler spectrum responses to extract biometric data describing the life form target. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a block diagram of a radar system in accordance with one or more embodiments of the present disclosure; 
         FIG. 2  illustrates a block diagram of a radar transmit receive module in accordance with one or more embodiments of the present disclosure; 
         FIG. 3  illustrates a block diagram of multiple transmit frequency generation in accordance with one or more embodiments of the present disclosure; 
         FIG. 4  illustrates a block diagram of a multiple channel Intermediate Frequency receiver in accordance with one or more embodiments of the present disclosure; 
         FIG. 5  illustrates a block diagram of multiple frequencies in one Intermediate Frequency receiver channel in accordance with one or more embodiments of the present disclosure; 
         FIG. 6  is a flow chart of the radar digital signal segmentation and processing in accordance with one or more embodiments of the present disclosure; 
         FIG. 7  is a flow chart of the radar digital signal processing and classification in accordance with one or more embodiments of the present disclosure; and 
         FIGS. 8A and 8B  illustrate a comparison between a conventional SAR system and a radar system in accordance with one or more embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The following description and the drawings illustrate specific embodiments of the invention sufficiently to enable those skilled in the art to practice them. Other embodiments may incorporate electrical, process, structural and other changes. Examples merely typify possible variations. Individual components are optional unless explicitly required, and the sequence of operations may change. Portions and features of some embodiments may be included in or substituted. Embodiments of the invention set forth in the claims encompass all equivalents of those claims. 
     The present disclosure describes a pulsed radar system and method of detecting and classifying movements and particular human or animal motion such as walking and breathing. The radar system simultaneously transmits a plurality of RF pulse trains each at a different frequency. The pulse widths are in the range of 2 to 200 nanoseconds. The frequencies are spaced greater than 400 MHz apart. In some embodiments, the frequencies may be harmonically related. For example, 3 three transmit frequencies of 1 GHZ, 2 GH and 4 GHZ. Each frequency pulse train is independently received, down converted and analog to digital converted. In some embodiments, the independent frequencies can be down converted into a single intermediate receiver channel with closely spaced orthogonal frequencies for independent sampling with a single analog to digital converter. 
     The digital radar data associated with each transmit frequency is segmented and averaged until the frequency and sample time product is nearly equal. In an example embodiment of 3 transmit frequencies of 1 GHz, 2 GHz and 4 GHz, the segmented data is 1 second of data, averaged two 1/2 second segments of data, average four 1/4 second segments of data. A Fast Fourier Transform (FFT) is performed on each of the data segments. Target classification is performed on the spectral ratio between each FFT to extract life form biometric information. 
       FIG. 1  is a simplified block diagram of the illustrative implementation of the radar system in accordance with the teachings of the present invention. The system  10  has multiple transmit/receive (Tx/Rx) channels  12 . Each of the Tx/Rx channels  12  have a different Transmit Frequency  13  (Tx Frequency n) and Local Oscillator Frequency  15  (LO Frequency n). The Tx/Rx channels simultaneously transmit Radio Frequency (RF) pulses into independent antenna ports  17 . Alternately the multiple antenna ports can be substituted with a power combiner into a single wideband antenna feed in accordance with conventional teachings. 
     The Tx/Rx channels simultaneously receive the RF reflected signal and output independent Intermediate Frequency (IF) to the IF receiver  14 . The IF receiver  14  digitizes analog information into digital data for signal processing in accordance with conventional teachings. 
     The digital data from the IF receiver  14  is segmented and pre-processed with FFT&#39;s  16 . Each frequency channel is segmented and processed differently depending on the relative frequencies of each channel. 
     The segmented and pre-processed data out of  16  contains individual Doppler spectrum associated with the target response to each transmitted frequency channel. Each frequency channels Doppler spectrum is sent to a spectrum classifier  18  for classification of life form type and movements such as humans walking, sitting or standing. 
       FIG. 2  is a simplified block diagram showing an illustrative implementation of a Tx/Rx channel in accordance with present teachings. The Tx/Rx channel  20  is adapted to transmit a unique frequency and receive its reflection off of targets. The unique frequency is set by the transmit frequency source  22 . The source frequency is modulated with an RF switch  23 . The pulse width is 2 nanoseconds (nsec) to 30 nsec wide. The on off isolation of RF switch  23  is greater than 60 dB. The pulse modulated RF pulse is amplified with a power amplifier  24 . The output of the power amplifier  24  is fed to a circulator  25 . The output of the circulator is fed to the antenna port while the circulator  25  return path is fed to receiver blanking switch  26 . In some embodiments the circulator can be replaced with two antenna ports one port fed directly from the power amplifier  24  and the other fed directly to receiver blanking switch  26 . 
     Receiver blanking switch  26  is set to it&#39;s on (low loss) state for the time interval associated with the target range of interest. The switch  26  is in it&#39;s off (high isolation) state for the remainder of the time to prevent unwanted RF signals entering into the receiver in accordance with present teachings. 
     Reflected RF energy from the target is amplified with a low noise amplifier  27  and down-converted through mixer  28  with an LO Frequency  21 . LO Frequency  21  is designed to be offset from the Tx Frequency  22  by an amount that simplifies the IF receiver design in accordance with present teachings. The mixer output is filtered  29  for a single sideband and amplified with a low noise amplifier  30  before sending it to the IF receiver. 
       FIG. 3  is a simplified block diagram showing an illustrative implementation of a preferred method to efficiently generate harmonically related frequencies. Harmonically related frequencies simplify the hardware and signal processing but the invention is not limited to this approach.  FIG. 3  is a simplified block diagram illustrating harmonic frequency generation. Frequency source  41  is set for the lowest frequency, for example 1 GHz. Three way power splitter  42  feeds amplifier  43  as the transmit frequency. The power splitter  42  also feeds a mixer that produces an offset frequency from the transmit frequency. Offset frequency  44  is typically set with a Direct Digital Synthesizer (DDS). The offset is typically 250 MHz to 500 MHz but not limited to these frequencies. Mixer  45  is typically a single sideband mixer or a double balanced mixer consistent with present teachings. Filter  46  filters out one of the sidebands and is amplified with amplifier  47  to generate the LO frequency. The third output of the power splitter is fed to a frequency doubler  48 . 
     The doubled frequency is fed to the next frequency generation circuit  49 . Frequency generation circuit  49  is an identical topology as  42 ,  44 ,  45 ,  46 ,  47 ,  43 , and  48  where the components are optimized for the doubled frequency. 
     The doubled frequency of  49  is fed to the last stage  50 . The last frequency generation topology is identical to  49  except the frequency doubler is replaced with a 50 ohm termination  51  and the components are optimized for a higher frequency. 
     Frequency generation topology  40  could be reversed with the frequency source  41  set to the highest frequency and with the frequency doubler circuits being replace by divide by two circuits. 
       FIG. 4  is a simplified block diagram showing an illustrative implementation of a standard three channel IF receiver  60 . The example IF receiver  60  has three identical channels. The number channels are not limited to three and will equal the number of simultaneous frequency channels required by the application, typically 2 to 7 channels, but could be more if required. IF receiver  60  converts the analog signals into digital data bits for signal processing. One receiver channel consistent with present teachings consists of an amplifier  61  an optional second down conversion through mixer  62  a matched filter  63  an analog to digital buffer amplifier  64  and the analog to digital converter  65 . 
       FIG. 5  is a simplified block diagram showing an illustrative implementation of an alternative IF receiver for better dynamic range performance. Analog range gated centerline IF receiver with multiple closely spaced IF frequencies  70  is a technique that takes advantage of setting the multiple LO frequencies so that the IF frequencies are KHz apart out of the Tx/Rx modules  12 . The multiple IF frequencies are fed into power combiner  71  for a single IF channel with multiple orthogonal frequencies. The multiple frequency channel is fed through IF blanking switch  73  and amplified by amplifier  75 . The IF is then split  76  into multiple range gates (RG)  77 . Range gates are switched to collect target energy at programmed times or ranges with timing generator  74 . This collected energy is summed in a narrow Surface Acoustic Wave (SAW) or crystal filter  78  to extract the center spectral line. The narrow filters  78  store the energy and can be multiplexed  79 ,  80  and sampled with an analog to digital (A to D) converter  81  at a lower rate for higher dynamic performance. The range gated energy is multiplexed while the frequencies are still orthogonal and are automatically separated in the FFT processing. Optional mixer  72  could also be used to lower the frequency for improved A to D performance. 
       FIG. 6  is a simplified block diagram showing an illustrative implementation of a preferred method to efficiently perform data segmentation and FFT&#39;s for generating target spectrum responses to multiple harmonic transmit frequencies  90 . The goal is to closely match the Doppler frequencies due to target movement for each of the transmit frequencies. Doppler frequency shift due to target movement equals two times the relative target velocity times the transmit frequency divided by the speed of light. With the same target for all frequencies the only variable is the transmit frequency. Therefore we will set the product of transmit frequency times the data collection duration nearly equal.  FIG. 6  shows an example of segmentation and FFT&#39;s for 3 frequency channels harmonically related. The following example is one possible embodiment of the invention used to exemplify the radar digital signal segmentation and processing and is not meant to represent all possible embodiments. Digital data from the lowest frequency, Tx freq  1  Digital Data  91 , is segmented by starting with a block of 64 digital I/Q data points. The data points are decimated by 4 (every 4 points averaged into one point). This creates an array of 16 I/Q data points. The data points are windowed and FFT&#39;d to generate a Doppler spectrum of transmit frequency  1 . 
     Digital data from the doubled frequency, Tx Freq  2  Digital Data  92 , is segmented into two blocks of 32 points each. Each block of 32 points is decimated by 2 (every two points averaged into one point). This generates two blocks of 16 points of I/Q data. The two blocks are independently windowed and FFT&#39;d and then averaged together (post detection integration) into a single spectrum of 16 points. 
     Digital data from the quadrupled frequency, Tx Freq  3  digital data  93 , is segmented into four blocks of 16 points each. The 4 blocks are independently windowed and FFT and then averaged together (post detection integration into a single spectrum of 16 points. 
     The output of the signal processing illustrated in  FIG. 6  is the Doppler spectrum for each transmit frequency sorted into like bins and data lengths for correlations.  FIG. 7  is a simplified block diagram showing an illustrative implementation of a classifier to transform the Doppler spectrum data into life form targets and actions. Classifier  100  is an example of the use of three transmit frequency Doppler spectrums and is not meant to describe all embodiments. The classifier  100  uses the Doppler spectrum data S 1 , S 2  and S 3   101  and calculates the sum and difference ratios  102 . The sum and difference ratios along with the direct spectrums  101  are fed into a classifier  103 . The classifier  103  shown is a simplified one layer convolution network classifier but the implementation could use any of the presently known classifier techniques. The result will be life form target classification  104  of human or animal actions. 
       FIGS. 8   a  and  8   b  show the difference between conventional imaging techniques using SAR data and this invention.  FIG. 8   a  is a diagram showing an illustrative example  110  of how current imaging SAR systems chop up the radar data into pixels  111 , which is then processed using image based classifiers  112 . This puts a burden on the hardware, operation and signal processing.  FIG. 8   b  shows how this invention uses the target response to multiple frequencies  113  to extract life form target types and movements  114 .