AI Article Synopsis
- The paper introduces a new imaging algorithm called knowledge-aided Doppler beam sharpening (KA-DBS) for enhancing radar imaging in airborne wide-area surveillance, especially under conditions of short dwell times.
- The KA-DBS algorithm predicts forward and backward radar pulse information leveraging the strong spatial coherence between adjacent pulses, effectively doubling the number of usable pulses for imaging within each coherent processing interval.
- Tests using real airborne data demonstrate that this improved technique doubles the cross-range resolution compared to traditional methods, validating its effectiveness in high-resolution radar imaging.
Article Abstract
This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6514580 | PMC |
| http://dx.doi.org/10.3390/s19081920 | DOI Listing |
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