Abstract:
Radar data associated with radar illumination of a movable target is processed to monitor motion of the target. A plurality of filter operations are performed in parallel on the radar data so that each filter operation produces target image information. The filter operations are defined to have respectively corresponding velocity ranges that differ from one another. The target image information produced by one of the filter operations represents the target more accurately than the target image information produced by the remainder of the filter operations when a current velocity of the target is within the velocity range associated with the one filter operation. In response to the current velocity of the target being within the velocity range associated with the one filter operation, motion of the target is tracked based on the target image information produced by the one filter operation.

Description:
This invention was developed under Contract DE-AC04-94AL85000 between Sandia Corporation and the U.S. Department of Energy. The U.S. Government has certain rights in this invention. 
    
    
     FIELD OF THE INVENTION 
     The present work relates generally to airborne radar systems and, more particularly, to radar tracking of moving targets. 
     BACKGROUND OF THE INVENTION 
     Detection and tracking of moving vehicles is an increasingly important remote surveillance objective. Conventional radar systems (e.g., JSTARS) typically excel in ground moving target indication (GMTI) because of their significant wide-area search capabilities. However, existing radars are severely limited in their ability to follow individual moving objects, for example, objects such as vehicles experiencing the type of velocity changes associated with maneuvering in traffic. GMTI radars rely on the vehicle&#39;s motion for detection against ground clutter, sweeping out large areas and monitoring many moving objects simultaneously. Tracking with these systems becomes difficult when a vehicle slows down, and is virtually impossible when the vehicle stops. 
     Relatively little work has been done to address these GMTI problems, particularly in the context of smaller radar systems capable of airborne operation. In particular, conventional radar technology does not provide for airborne radar systems capable of continuously monitoring and tracking an object whether it is moving or stationary. 
     It is therefore desirable to provide for a relatively small radar system that is suitable for airborne operation and is capable of continuously monitoring and tracking a single moving object. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  diagrammatically illustrates an airborne radar system according to exemplary embodiments of the present work. 
         FIG. 2  diagrammatically illustrates an airborne radar system according to further exemplary embodiments of the present work. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary embodiments of the present work provide for processing radar information to permit tracking mobile high-value targets over realistic velocity changes such as experienced by a vehicle while maneuvering in traffic. Appropriately collected coherent radar data is continuously and simultaneously processed in several ways, which permits the processing to be “tuned” to different velocities. Some embodiments simultaneously process the same set of radar data with a plurality of conventional radar processing filters (also referred to herein as processing modes, or simply modes) arranged in parallel. For example, various embodiments employ various parallel combinations of conventional processing modes such as VideoSAR processing (as described in U.S. Pat. No. 7,498,968, incorporated herein by reference), exoclutter processing, and clutter attenuated Doppler processing. In contrast, conventional airborne radar systems typically use a single radar processing filter combined with detection and tracking stages. 
     An enhanced set of detection and location information is produced in response to the parallel processing operations, and is provided to a tracking filter (tracker) that maintains information about the position of the vehicle. The tracking filter implements a tracking algorithm that uses the received information to provide enhanced tracking performance. A conventional antenna pointing algorithm is updated based on the tracking information to keep the object of interest within the radar beam. 
     As an example of “tuning” the processing, the exoclutter mode may provide the best information for detection and tracking at relatively high vehicle velocities, while clutter attenuated Doppler and VideoSAR modes provide better information for detection and tracking as the vehicle slows to a stop. The VideoSAR mode provides information that permits detection and tracking contact to be maintained even when the vehicle is stopped. Even though a given mode may not be optimal for the current vehicle velocity, it may still provide information that can improve performance. For example, shadow information provided by the VideoSAR mode may be used to enhance detection and location of relatively fast moving vehicles that are optimally processed by the exoclutter mode. 
     Some embodiments use feedback information from the tracking filter to support focusing the moving object. The focused object can be used to improve detection or to provide situational awareness information to an operator, for example, by displaying the focused object at its true location within a VideoSAR image. 
     As mentioned above, exemplary embodiments of the present work provide for simultaneous processing of radar data, where the various parallel processing modes are “tuned” to respectively corresponding ranges of object speed, including the stopped “range”.  FIG. 1  diagrammatically illustrates an airborne radar system according to exemplary embodiments of the present work. The system includes a radar front end  20  having conventional coherent radar properties and multiple channels to support conventional direction of arrival processing. In some embodiments, the radar front end  20  receives radar echo signals and processes them in conventional fashion to capture associated radar data. This “raw” radar data, together with conventional motion measurement information, is applied at to various parallel processing filters, which are respectively “tuned” to support tracking objects that are moving at velocities in respective velocity ranges. Although  FIG. 1  shows three processing filters, various embodiments use various numbers of processing filters. 
     In some embodiments, filter  1  implements conventional exoclutter GMTI processing for use with fast moving objects; filter  2  implements conventional endoclutter GMTI for use with medium to slow moving objects, and filter  3  implements VideoSAR for use with slow to stopped objects. Examples of conventional processing filters used in various other embodiments include: a range-Doppler processing filter; a focused range-Doppler processing filter; a SAR processing filter; and a space-time adaptive processing (STAP) filter. Various embodiments use various numbers of processing filters in various parallel combinations. In various embodiments, the various parallel processing filters respectively implement various CPIs (coherent processing intervals). Some embodiments use multiple VideoSAR processing filters within the parallel filter combination, where the various VideoSAR filters respectively use various different CPI (coherent processing interval) lengths to enhance detection resolution between targets moving at various different, relatively slower velocities. In some embodiments, the various parallel processing filters respectively implement various different focusings, based upon the respective velocity ranges to which they are “tuned”. For example, in a processing filter “tuned” to a range of relatively high velocities, conventional corrections for aliased movers can be applied. 
     The respective outputs  4 - 6  of the filters  1 - 3  represent radar images that are sent to respectively corresponding detection stages (or detectors)  7 - 9 . The detection stages  7 - 9  operate in conventional fashion in some embodiments, comparing the processed radar data at  4 - 6  against a threshold. The threshold is adjusted by conventional continuous false alarm rate (CFAR) processing in some embodiments. Some embodiments use a simple conventional template matching technique (e.g., size, etc.) to reduce false alarms. 
     Each of filters  1 - 3  also performs location processing of the multiple channel data. The results  10 - 12  of the location processing respectively performed by filters  1 - 3  are forwarded to respectively corresponding location processors  13 - 15 . The location processors  13 - 15  operate in conventional fashion in some embodiments, estimating range-angle-Doppler information for each detection (i.e., for each CPI). 
     The detection information output at  16 - 18  by the respective detection stages  7 - 9 , and the location information output at  19 - 21  by the respective location processors  13 - 15  is input to a detection collator  22 . The detection collator  22  selects the best of the current detections  16 - 18 , according to a criterion such as, e.g., signal-to-clutter-plus-noise. The selected detection  24  is then passed to a tracking stage (tracker)  23 . One of the current images at  4 - 6  will typically be clearly best in the signal-to-clutter-plus noise sense. The detection collator  22  selects the detection information (i.e., either  16 ,  17  or  18 ) for that image and passes it (at  24 ) to the tracker  23 . This cooperation of the detection collator  22  with the detection stages  7 - 9  effectively identifies the processing filter whose velocity range includes the current velocity of the target. Together with the selected detection  24 , the detection collator  22  also passes to the tracker  23  the corresponding set  25  of location information (i.e., either  19 ,  20  or  21 ) and the corresponding signal-to-clutter-plus-noise ratio  26 . 
     The tracker  23  generates state vector information for the object corresponding to the selected detection  24 . State vector information includes position, velocity, and acceleration information. In some embodiments, the tracker  23  uses all of the detections that it receives for a given target. In some embodiments, the tracker  23  uses a suitable filter criterion to discard some of the received detections. In general, the tracker  23  may use conventional tracking techniques. However, the tracker  23  also has added capabilities to handle stopped targets and target accelerations (i.e., going from the stopped state into a motion state or vice-versa). These capabilities are now described. 
     As a moving target object slows, the detections will transition through the various processing filters tuned to the various velocity ranges. Because the filters are tuned to different velocity ranges, they also have different associated time scales. The time scales respectively associated with filters tuned to various velocity ranges may vary from, for example, fractions of a second to several seconds. Consequently, there will be time gaps between selected detections  24  when a slowing vehicle disappears in one filter output and appears in the next filter output. For example, during the time gap, the detection collator  22  will not identify any image that satisfies a minimum signal-to-clutter-plus-noise threshold. The tracker  23  must be able to link detections that disappear from one filter to new detections that appear from another filter. For a slowing vehicle, some embodiments of tracker  23  project the target track forward in time until it stops, and then feed back information to dynamically prime the slower filters to facilitate comparing detections to the predicted track. The filter priming is achieved in some embodiments, by adaptively adjusting brightness thresholds and contrast ratios in localized regions of the filter&#39;s range-Doppler image based on the predicted track. Feedback information indicative of these filter-priming adjustments is shown diagrammatically at  27 . 
     In the case of a vehicle accelerating from a stop, the tracker  23  must also deal with filters with different time scales. Initial movement of a vehicle will first be detected in the shorter-time, higher velocity range filters before any motion is apparent in the longer-time, slower velocity range filters. New detections in the short-time filters should then be tied to detections in the long-time filters that will eventually disappear at some point in the future as the target keeps moving. In some embodiments, the tracker  23  projects the target track backward in time, rather than forward in time as described above for slowing vehicles. The backward time projection primes the long-time filter for eventual loss of detection as the target spends a larger fraction of the time window in moving rather than stationary states. 
     In some embodiments, the tracker  23  predicts motion using a conventional prediction filter. The predicted motion information (state vector) produced by the tracker  23  is also provided at  28  to update the pointing system of the radar at  29 , as is conventional, to permit the pointing system to maintain adequate illumination of the target of interest and provide optimal detection conditions. In some embodiments, the antenna pointing system is conventional, with sufficient degrees of freedom to follow moving objects at all times. 
     Some embodiments provide improvements in focusing the moving object. Focusing is conventionally performed in processing filters such as those described above with respect to  FIG. 1 . Focusing depends upon knowing the relative motion of the object and the radar. It improves detection because, for example, the energy from the object is not smeared out as much after focusing, meaning that it is concentrated in a smaller area of the image and is therefore more likely to trigger the threshold of the detector. Conventionally, the motion of the radar (as carried on the aircraft) is known, but the motion of the object is not, so the object is assumed to be stationary for focusing purposes. There are various known techniques to estimate the motion of the object from the raw radar data, but they often perform poorly. 
     According to various embodiments, the processing filters such as shown at  1 - 3  in  FIG. 1  may include additional functionality to receive target state vector information from the tracking stage  23 , and to use that information to improve focusing. This feedback of the target state vector information is shown diagrammatically at  27 . As described above, a moving object of interest lies typically in only one of the processing filters  1 - 3  at a given time. That particular filter applies focusing for the moving object. The processing filter receives the target state vector information (at  27 ) from the tracker  23 , together with a target range estimate (not explicitly shown), which is conventionally available from the aircraft motion measurement system. The processing filter calculates an estimated range to the target at each pulse. This calculation includes interpolating the feedback position, velocity and acceleration information to the time of the pulse, using the interpolated information to calculate a relative range of the object, and adding the relative range to the range estimated by the aircraft motion measurement system. The resulting estimated range calculation is used to correct the phase of the moving object for each pulse. The focusing operation also results in the object being moved back to its proper location in the (video)SAR image. 
     In some embodiments, the parallel processing filters are tuned such that at least some adjacent pairs of the velocity ranges overlap one another. In some embodiments, all adjacent pairs overlap one another. In some embodiments, some adjacent pairs overlap one another, and other adjacent pairs substantially adjoin one another without overlap. In some embodiments, all adjacent pairs substantially adjoin one another without overlap. 
     Some embodiments exploit the fact that a VideoSAR filter will always produce a focused image of the stationary background, thus providing good visual context to the area being observed. Although a moving object is typically not visible in the VideoSAR filter, it will, as mentioned above, be focused in an image produced by another of the filters. Accordingly, a suitable combiner may be used to combine the stationary background provided by the VideoSAR image with the image of the moving object. The focused moving object is cut out of the latter image, and pasted into the VideoSAR image of the stationary background. The resultant image is easier for an operator to interpret as it is visually displayed to the operator. This is illustrated diagrammatically in  FIG. 2 . At  200 , information indicative of the currently selected filter is fed from the detection collator  24  (see also  FIG. 1 ) to a selector  201 , which selects the image from the selected filter. The selected image  202  is routed to an image combiner  203  that also receives the image produced by the VideoSAR filter. The image combiner  203  cuts the target out of the selected image  202 , and pastes it into the VideoSAR image. The resulting image is then provided for display at  204 . 
     Although exemplary embodiments are described above in detail, this does not limit the scope of the present work, which can be practiced in a variety of embodiments.