靜軌光學(xué)衛(wèi)星與自動(dòng)識(shí)別系統(tǒng)的目標(biāo)點(diǎn)跡關(guān)聯(lián)與誤差校正
doi: 10.11999/JEIT170896 cstr: 32379.14.JEIT170896
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①(國(guó)防科技大學(xué)電子科學(xué)學(xué)院 長(zhǎng)沙 410073) ②(海軍航空大學(xué)信息融合所 煙臺(tái) 264001)
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(91538201)
Target Point Tracks Association and Error Correction with Optical Satellite in Geostationary Orbit and Automatic Identification System
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LIU Yong① YAO Libo② WU Yuzhou② XIU Jianjuan② ZHOU Zhimin①
Funds:
The National Natural Science Foundation of China (91538201)
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摘要: 靜軌光學(xué)衛(wèi)星對(duì)艦船目標(biāo)監(jiān)視時(shí)，由于探測(cè)距離較遠(yuǎn)存在較大的目標(biāo)定位誤差，影響后續(xù)目標(biāo)跟蹤的準(zhǔn)確性。由于任務(wù)區(qū)域主要是海面，可能無(wú)法找到地面控制點(diǎn)(GCP)進(jìn)行坐標(biāo)校正。為了提高無(wú)控下靜軌光學(xué)衛(wèi)星對(duì)艦船目標(biāo)的定位精度，同時(shí)實(shí)現(xiàn)多源數(shù)據(jù)的融合，該文提出一種基于船舶自動(dòng)識(shí)別系統(tǒng)(AIS)數(shù)據(jù)的靜軌光學(xué)衛(wèi)星艦船目標(biāo)點(diǎn)跡關(guān)聯(lián)與誤差校正方法。利用有理多項(xiàng)式系數(shù)(RPC)模型實(shí)現(xiàn)物方坐標(biāo)到像方坐標(biāo)的轉(zhuǎn)換，通過迭代最近點(diǎn)(ICP)與全局最近鄰(GNN)算法進(jìn)行點(diǎn)跡關(guān)聯(lián)，由關(guān)聯(lián)點(diǎn)對(duì)實(shí)現(xiàn)誤差校正。利用高分4號(hào)衛(wèi)星圖像與AIS數(shù)據(jù)進(jìn)行了實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明該算法具有很高的關(guān)聯(lián)正確率，同時(shí)極大提高了定位精度，基本可以滿足實(shí)用性要求。
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關(guān)鍵詞:
- 靜軌光學(xué)衛(wèi)星 /
- 自動(dòng)識(shí)別系統(tǒng) /
- 數(shù)據(jù)關(guān)聯(lián) /
- 誤差校正
Abstract: When ship target is monitored by the geostationary optical satellite, the positioning error is large due to the long distance between the target and the satellite, which affects the accuracy of the follow-up target tracking. As the monitoring area is mainly the ocean, it may not be possible to find the Ground Control Point (GCP) for coordinate correction. In order to improve the positioning accuracy of the geostationary optical satellite for ship without GCP, and to realize the fusion of multi-source data, a novel target point association and error correction with optical satellite in geostationary orbit and ship Automatic Identification System (AIS) is proposed. By means of the Rational Polynomial Coefficient (RPC) model, AIS coordinates are transformed into image coordinates. The Iterative Closest Point (ICP) and Global Nearest Neighbor (GNN) algorithm are combined and used for data association. Then, the error is corrected using the point pair of association. Experimental results using GF-4 images and AIS data verify the feasibility of the proposed method and show that the association algorithm has a high correlation rate, and the average positioning accuracy after error correction is improved greatly compared with the positioning accuracy before correction. -
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