Abstract:
In one aspect, a method includes transmitting a tone waveform from a laser detection and ranging (LADAR) sensor, detecting a target using an echo of the tone waveform reflected from the target, determining a radial velocity of the target using the echo of the monotone waveform from the target, transmitting, from the LADAR sensor, linear frequency modulation (FM) chirp signals and determining a range to target using echoes from the linear FM chirp signals.

Description:
BACKGROUND 
     A laser detection and ranging (LADAR) sensor, sometimes referred to as laser radar, uses laser beams to measure distances (or ranges) and instantaneous velocities. The LADAR sensor can be used to form images of scenes with a high degree of definition (e.g., 15 cm or better resolution at ranges greater 1,000 meters). LADARs may be mounted on stationary objects and on vehicles such as helicopters, for example. 
     SUMMARY 
     In one aspect, a method includes transmitting a tone waveform from a laser detection and ranging (LADAR) sensor, detecting a target using an echo of the tone waveform reflected from the target, determining a radial velocity of the target using the echo of the monotone waveform from the target, transmitting, from the LADAR sensor, linear frequency modulation (FM) chirp signals and determining a range to target using echoes from the linear FM chirp signals. 
     In another aspect, an article includes a non-transitory machine-readable medium that stores executable instructions. The instructions cause a machine to transmit a tone waveform from a laser detection and ranging (LADAR) sensor, detect a target using an echo of the tone waveform reflected from the target, determine a radial velocity of the target using the echo of the monotone waveform from the target, transmit, from the LADAR sensor, linear frequency modulation (FM) chirp signals and determine a range to target using echoes from the linear FM chirp signals. 
     In a further aspect, an apparatus includes circuitry to transmit a tone waveform from a laser detection and ranging (LADAR) sensor, detect a target using an echo of the tone waveform reflected from the target, determine a radial velocity of the target using the echo of the monotone waveform from the target, transmit, from the LADAR sensor, linear frequency modulation (FM) chirp signals and determine a range to target using echoes from the linear FM chirp signals. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a laser detection and ranging (LADAR) environment. 
         FIG. 2  is a flowchart of an example of a process to perform LADAR processing. 
         FIG. 3  is a series of graphs that includes a tone and corresponding Fourier Transforms. 
         FIG. 4  is a graph of frequency versus time of an example of a chirp waveform. 
         FIG. 5A  is a flow diagram of an example of receiver hardware used to collect and digitize a return signal. 
         FIG. 5B  is a flow diagram of an example of a process performed by the receiver hardware of  FIG. 5A . 
         FIG. 5C  is a diagram of example waveforms used in the flow diagram of  FIG. 5B . 
         FIG. 6  is a flow chart of an example of a process to perform target acquisition. 
         FIG. 7A  is a graph of amplitude versus time of a pulse waveform. 
         FIG. 7B  is a graph of frequency versus time of a linear frequency modulation (LFM) chirp waveform. 
         FIG. 8A  is a diagram of example waveforms used in the flow diagram of  FIG. 5B . 
         FIG. 8B  is a diagram of an example of the range compressed data as a train of compressed pulses. 
         FIG. 9  is a diagram of the time samples organized into respective range bins. 
         FIGS. 10A and 10B  are a flowchart of an example of a process to determine range-resolved vibration. 
         FIG. 11  is a computer on which the process of  FIG. 6  and/or the process of  FIGS. 10A and 10B  may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     Described herein are techniques to detect a target. Other techniques described herein determine the target&#39;s center of mass (or average) radial velocity (Doppler) and range distance from a LADAR sensor. 
     Referring to  FIG. 1 , a LADAR environment  100  includes a LADAR sensor  102  at a location, L s , to detect a target  104  at a location, L T  with a range to target, R T . The range to target, R T , is a length of a vector  108  pointing from the LADAR sensor  102  to the target  104 . The LADAR sensor  102  is disposed on a sensor platform  106  (e.g., a vehicle such as a helicopter) traveling at a velocity, V P . 
     The LADAR sensor  102  transmits a signal (waveform) and the signal reflects off the target back to the LADAR sensor  102 . Typically the received signal is the same waveform as the transmitted signal but shifted in time and frequency (Doppler). 
     Referring to  FIG. 2 , coherent LADAR applications typically require some high resolution of mapping target Doppler information against range to create enhanced imaging functions. These functions usually require high time-bandwidth waveforms to perform synthetic aperture or inverse synthetic aperture imaging, range-resolved Doppler or vibration imaging, and so forth. In order for the LADAR function to be performed efficiently, the target center of mass Doppler and range must be determined with reasonably high accuracy. In order to accomplish this, a process  200 , an example of LADAR processing, performs target acquisition  202  first and then performs a high resolution imaging  204 . 
     Referring to  FIG. 3 , in the acquisition phase, the target at range is detected and the Doppler shift of the target is determined using a tone waveform. In one example, a single tone  302  is divided into a series of T CIT &#39;, coherent integration time signals of 50 to 250 microseconds in length with a carrier-to-noise ratio (CNR) of 20 dB for a coherent integration time, T CIT , of 250 microseconds. 
     The signals  312   a - 312   c  are Fourier Transforms for the signals  302   a - 302   c , respectively with a coherent integration time, T CIT , of 250 microseconds and a speckle bandwidth of 1.2 kHz. The Fourier Transforms  312   a - 312   c  are averaged over a period of about 10 milliseconds to form the average Fourier Transform  312   d.    
     The signals  322   a - 322   c  are Fourier Transforms for the signals  302   a - 302   c , respectively with a coherent integration time, T CIT , of 250 microseconds and a speckle bandwidth of 12 kHz. The Fourier Transforms  322   a - 322   c  are averaged over a period of about 10 milliseconds to form the average Fourier Transform  322   d.    
     The signals  332   a - 332   c  are Fourier Transforms for the signals  302   a - 302   c , respectively with a coherent integration time, T CIT , of 50 microseconds and a speckle bandwidth of 12 kHz. The Fourier Transforms  332   a - 332   c  are averaged over a period of about 10 milliseconds to form the average Fourier Transform  332   d.    
     Thus, using the longer coherent integration time T CIT , (e.g., about 250 microseconds) and then averaging for 10 milliseconds is the same or better than using a shorter coherent integration time (e.g., about 50 microseconds) and average for the same dwell time of 10 milliseconds. 
     Referring to  FIG. 4 , after the target at range is detected and the Doppler shift of the target is determined, a course range is determined by using an initial linear frequency modulation (FM) chirped waveform. For example, to have a range resolution of about 15 meters requires a chirp bandwidth of about 10 MHz since range resolution is equal to the speed of light divided by two times the chirp bandwidth. The slope of the linear chirp is the chirp bandwidth divided by the target coherent integration time or 10 MHz divided by 20 microseconds or 0.5 MHz/microsecond. An initial linear FM chirped waveform  400  has a chirp time, T C , of 300 microseconds yielding a range ambiguity of 45 km and requiring a chirp bandwidth of 150 MHz. 
     Subsequent linear FM chirped waveforms are transmitted to reduce the range ambiguity. For example, by varying the chirp repetition rate (chirp time or chirp period), residual range ambiguities are removed in the search space and ensures that the target will not be masked by an expected optical backscatter signal. For example, transmitting a second linear FM chirp with a chirp time reduced to 250 microseconds and keeping the chirp slope the same, the unambiguous distance increases to 225 km. Transmitting a third linear FM chirp with a chirp time reduced to 200 microseconds will increase the unambiguous distance even further. 
     However, even if a longer unambiguous distance is not required, a third waveform ensures that at least two of the three dim reflections of the waveforms from a target are detected, because there is typically signal masking due to relatively large backscatter signal from the exit optics. This assumes that the target would be dimmer than a possible backscatter signal from the exit optics or clutter near the exiting aperture (due to aerosols, bugs, dirt, and so forth). Due to the ambiguity, a return of a nearly zero range distance could overlap with the target return at some long distance, therefore making the target undetectable. 
     In another example, the slope is varied while the chirp time is kept constant. This example also helps in avoiding backscatter masking. 
       FIGS. 5A to 5C  show a LADAR receiver hardware  500   a  and processing  500   b  performed at the LADAR receiver hardware  500   a . At the input of the receiver hardware  500   a , the return signal from the target is heterodyned using a heterodyne detector  502  (i.e., converted by mixing with a reference optical signal, the Local Oscillator, (e.g., an optical signal in the 200 THz region to the radio frequency (RF) region around 100 MHz of the received signal)). The RF analog signal is digitized by an analog-to-digital converter, A/D,  506  creating a digital data stream,  508 , that is stored in a buffer memory  510 . The stored data is a digital representation of the analog LADAR return from a target, and is captured in the memory storage, where the digital processing begins for determining the ranged-resolved vibration image. 
     Since the signal transmitted by the LADAR sensor  102  is a coherent train of repeating subsignals, the digitized return signal  508  is a digital coherent train of repeating subsignals. An example of the digitized return signal is a digitized return signal  524  for a tone waveform, which includes coherent subsignals  526 . A process  500   b  uses a matched filter convolution  538  on the repeating pattern  526  of the waveform  524  stored as raw data  508  in the memory buffer  510 . 
     The mixed signal from a mixer  532  is processed by the matched filter convolution  538 . When a repetitive component is used to create a waveform, such as the train of coherent subsignals (e.g., a train of coherent pulses, a train of coherent chirps), a matched filter corresponds to the repeating component of the waveform. For example, the matched convolution filter  538  includes a matched signal of a single coherent subsignal, for example, a matched signal  540 . The output of the matched convolution filter  538  is a train of compressed pulses or range compressed data  542  such as a signal  546  with compressed pulses  550 . 
     Referring to  FIG. 6 , an example of a process to perform target acquisition is a process  600 . Process  600  transmits a tone waveform ( 602 ) and detects a target from an echo of the transmitted tone waveform reflected from the target ( 604 ). For example, the LADAR sensor  102  transmits a tone to the target  104 . 
     Process  600  transmits a first linear FM chirp waveform ( 606 ) and determines a range to target from an echo of the first linear FM chirp waveform from the target ( 608 ). For example, the LADAR sensor  102  transmits the first linear FM waveform to the target  104 . 
     Process  600  transmits a second linear FM chirp waveform ( 610 ) and determines a range to target from an echo of the second linear FM chirp waveform reflected from the target ( 614 ). For example, the LADAR sensor  102  transmits the second linear FM waveform to the target  104 . In one example, the second linear FM chirp waveform has the same chirp slope as the first linear FM waveform but has a different chirp time than the first linear waveform. 
     Process  600  transmits a third linear FM chirp waveform ( 618 ) and determines a range to target from an echo of the third linear FM chirp waveform reflected from the target ( 622 ). For example, the LADAR sensor  102  transmits the third linear FM waveform to the target  104 . In one example, the second linear FM chirp waveform has the same chirp slope as the first and second linear FM waveforms but has a different chirp time than the first or second linear FM waveforms. 
     After target acquisition has been performed, high resolution imaging may be performed for example to determine a vibration spectrum of the target. In order to measure the vibration spectrum from a target, a series of precise instantaneous velocity or Doppler measurements are made. Each of these Doppler measurements will required a relatively large coherent integration time to make the measurement as precise as possible. A coherent integration time, T cit , can be anywhere from 1 microsecond to 10 millisecond, depending on the speed of the target motion and the vibration high frequency end (e.g., the maximum coherent time must be smaller than 1/(2*f max ), where f max  is the maximum vibration frequency). On the other hand, in order to have reasonable range resolution (e.g., on the order of 15 cm), time precision in the neighborhood of 1 ns or less is required, which translates into a bandwidth, BW, of about 1 GHz. Using this bandwidth, and a typical coherent time of 20 μs, the time bandwidth product, BT, of such a waveform would be:
 
 BT=T   cit   ×BW= 20 μs×1 GHz=20,000,
 
which, if greater than 100, would be considered a large time-bandwidth product waveform.
 
     Referring to  FIGS. 7A and 7B , there are multiple ways of achieving a large BT product waveform. In particular, the large BT product waveform includes a train of coherent subsignals (patterns). In one example, as shown in  FIG. 2A , a train of coherent pulses may be used. The bandwidth is achieved in the coherent pulse case by having the individual pulsewidths be about 1/BW. In  FIG. 7A , the bandwidth, BW, for 2 ns is 500 MHz and the coherent processing time is set to 25 microseconds, T cit . In this case the pulse spacing was set to a 20 nanoseconds period yielding a 50 KHz pulse repetition rate (PRF). 
     In another equivalent example, as shown in  FIG. 7B , a train of a train of coherent linear frequency modulation (LFM) chirps may be used. The bandwidth is achieved in the chirp case, by sweeping the bandwidth at each individual chirp. In the example depicted in  FIG. 2B , the bandwidth is also set to 500 MHz and the coherent processing time to 25 microseconds, T cit . Each chirp has a period, T chirp , of 20 ns yielding also a repetition rate of 50 KHz. 
     The return from a target located at a single range resolution bin will generate a train of compressed pulses after the subapertured matched filter. The pulse spacing will be the same as the original transmitted pulse spacing. By sampling the received signal at the pulse spacing period, the signal from a given range bin is obtained. The number of different range bins that can be obtained is determined by dividing the pulse spacing (in range) by the resolution range, which is the pulsewidth time c/2, where c is the speed of light. The pulse spacing determines the maximum range that is unambiguous. Returns beyond the pulse spacing would be misinterpreted as belonging to the second pulse after yielding an ambiguity that corresponds to this spacing. For example, if the pulse spacing is 10 microseconds and the resolution bins are 1 nanosecond wide, then 10,000 range bins can be obtained. Assuming that the return signal is from a heterodyne receiver, the train of pulses sample the beat frequency between the signal and the LO. Another advantage of this technique is that coherence of the target does not need to be known a priori. The pulse train can be indefinitely long, and each range bin can be sampled for a relatively long time. Various record lengths can be tried to optimize to whatever target induced loss of coherence may be. If the train of pulses is longer than the target coherence time, then the signal can be broken into components approximately as long as the coherence time, and then those components may be averaged in an incoherent way (e.g., using the magnitude only). 
     When using the LFM chirp, the matched filter of the repeating pattern process is referred to as the fast transform (e.g., a fast Fourier Transform) that will separate the return signal into range bins. After that separation, each single range bin is selected, and the signal is integrated for the coherent integration time, T cit . 
     A matched filter in the LFM chirp can be implemented by multiplying the return by a chirp of the same slope (a process called de-chirping) followed by a Fourier Transform, which is applied to each chirp element. This generates a Fourier Transform spectrum where the frequency resolution corresponds to the range resolution given by (c/2)*(1/BW) (where c is the speed of light). A specific range bin is selected, and all the samples that each chirp pulse generates are collected. If the chirp pulses are coherent to each other, then so will be the samples across a given range bin. The samples are collected for a period corresponding to the coherent integration time, T cit , and a second Fourier Transform is performed on that data. This second transform is referred as the slow transform (e.g., slow Fourier transform) because it uses the data collected at a longer period of time. Typically, any motion compensation would be done on the data of the slow transform. The slow Fourier transform becomes one of the frequency slices used to create a spectrogram. Since this process is done for each range bin, a spectrogram and a resulting vibration spectrum is formed for each range bin, hence the name range-resolved vibration. 
     Referring to  FIG. 8A , using a chirp waveform instead of a tone in  FIG. 5C , an example of the digitized return signal is a digitized return signal  724 , which includes coherent subsignals  726 . Using a matched filter  740  on the digitized return signal  724  generates a signal  746  with compressed pulses  750 . 
     Referring to  FIG. 8B , the compressed range data  342 , for example, the train of compressed pulses  746 , has a period of Tprf. The A/D sampling rate has a period of τ/2. The number of samples within the repeating pattern is n=2*Tprf/τ. 
     The train of compressed pulses  746  is used to form range bins. In particular, since the digital compressed pulses are coherent, like portions of a digital pulse are the same ranges. For example, data  802   a  at the beginning of a pulse  750   a  is the same range as data  802   b  at the beginning of a pulse  750   b  and is also the same range as data  802   c  at the beginning of the pulse  750   c . In another example, data  804   a  in the middle of the pulse  750   a  is the same range as data  804   b  in the middle of the pulse  750   b  and is also the same range as data  804   c  at the beginning of the pulse  750   c . In a further example, data  806   a  at the end of the pulse  750   a  is the same range as data  806   b  at the end of the pulse  750   b  and is also the same range as data  806   c  at the end of the pulse  750   c.    
     The same range data is grouped together and a Fourier transform is formed on it. For example, data from  802   a - 802   c ,  804 - 804   c  and  806 - 806   c  are grouped together forming range groups  814   a - 814   c . Each range group  814   a - 814   c  corresponds to return signals from that specific range sampled at a period of T prf . A Fourier transform is then performed on each of the range group data. 
     Referring to  FIG. 9 , the data between time, t=0 to T prf , is organized into bins corresponding to the n samples of the train of compressed pulses  346 . The resulting row corresponds to the returns for a single range-bin sampled at a period of T prf .  FIG. 9  is based on an assumption that the observation time is selected such that there are k samples at each range bin. 
     Referring to  FIGS. 10A and 10B  (also referred to herein collectively as  FIG. 10 ), an example of a process to determine range-resolved vibration is a process  1000 . Process  1000  transmits a signal with a coherent train of subsignals having a large bandwidth product ( 1002 ). In one example, the LADAR sensor  102  transmits a coherent train of pulses. In another example, the LADAR sensor  102  transmits a coherent train of LFM chirps. The large bandwidth product, BT, is greater than 100. 
     Process  1000  receives a return signal from the target ( 1004 ). For example, the LADAR sensor  102  receives the return signal reflected off the target  104 . Process  1000  mixes the return signal with a Local Oscillator laser ( 1008 ), detects a heterodyne signal ( 1010 ) and digitizes the output signal ( 1012 ). For example, the heterodyne detector  502  senses the return signal that is mixed with a LO laser waveform and the output of the heterodyne detector  502  is digitized by the A/D digitizer  506 . 
     Process  1000  performs a matched convolution ( 1022 ). For example, the output of the A/D digitizer  506  is sent to the match filter convolution  538  to form range compressed data  542 , for example, a range compressed coherent pulses. 
     Process  1000  forms range bins ( 1028 ) and retrieves data from a first bin ( 1032 ). Process  1000  compensates for the motion of a platform ( 1038 ). For example, the phase and frequency of the heterodyne signal is adjusted to subtract the motion sensed (using other sensors) from the platform. In one example, the LADAR sensor  102  is disposed on the platform  106 , which is moving. 
     Process  1000  performs a Fourier transform of a coherent period of data, T CIT , to form a single line (e.g., vertical) of a spectrogram ( 1042 ). 
     Process  1000  continues to add vertical lines to the spectrogram for as long as the pre-determined observation time. The length of the observation time determines the frequency resolution of the resulting vibration spectrum 
     Once the spectrogram is complete, process  1000  takes a centroid of individual velocity measurements ( 1052 ) by determining the instantaneous Doppler frequency of the peak intensity of each vertical line that represents the instantaneous velocity at that point in time. Process  1000  performs a slow Fourier transform of the centroid to determine a vibration ( 1058 ). 
     Upon the completion of the process  1000 , a vibration and intensity is determined for each range. 
     Referring to  FIG. 11 , a computer  1100  includes a processor  1102 , a volatile memory  1104 , a non-volatile memory  1106  (e.g., hard disk), a user interface (GUI)  1108  (e.g., a mouse, a keyboard, a display, for example). The non-volatile memory  1106  stores computer instructions  1112 , an operating system  1116  and data  1118 . In one example, the computer instructions  1112  are executed by the processor  1102  out of volatile memory  1104  to perform all or part of the processes  600  and  1000 . 
     The processes described herein (e.g., the processes  600  and  1000 ) are not limited to use with the hardware and software of  FIG. 11 ; they may find applicability in any computing or processing environment and with any type of machine or set of machines that is capable of running a computer program. The processes described herein may be implemented in hardware, software, or a combination of the two. The processes described herein may be implemented in computer programs executed on programmable computers/machines that each includes a processor, a non-transitory machine-readable medium or other article of manufacture that is readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and one or more output devices. Program code may be applied to data entered using an input device to perform any of the processes described herein and to generate output information. 
     The system may be implemented, at least in part, via a computer program product, (e.g., in a non-transitory machine-readable storage medium), for execution by, or to control the operation of, data processing apparatus (e.g., a programmable processor, a computer, or multiple computers)). Each such program may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the programs may be implemented in assembly or machine language. The language may be a compiled or an interpreted language and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. A computer program may be stored on a non-transitory machine-readable medium that is readable by a general or special purpose programmable computer for configuring and operating the computer when the non-transitory machine-readable medium is read by the computer to perform the processes described herein. For example, the processes described herein may also be implemented as a non-transitory machine-readable storage medium, configured with a computer program, where upon execution, instructions in the computer program cause the computer to operate in accordance with the processes. A non-transitory machine-readable medium may include but is not limited to a hard drive, compact disc, flash memory, non-volatile memory, volatile memory, magnetic diskette and so forth but does not include a transitory signal per se. 
     The processes described herein are not limited to the specific examples described. For example, the processes  600  and  1000  are not limited to the specific processing order of  FIGS. 6 and 10 . Rather, any of the processing blocks of  FIGS. 6 and 10  may be re-ordered, combined or removed, performed in parallel or in serial, as necessary, to achieve the results set forth above. 
     The processing blocks in  FIGS. 6 and 10  associated with implementing the system may be performed by one or more programmable processors executing one or more computer programs to perform the functions of the system. All or part of the system may be implemented as special purpose logic circuitry (e.g., an FPGA (field programmable gate array) and/or an ASIC (application-specific integrated circuit)). 
     Elements of different embodiments described herein may be combined to form other embodiments not specifically set forth above. Other embodiments not specifically described herein are also within the scope of the following claims.