基于距離檢測(cè)的自動(dòng)識(shí)別系統(tǒng)和對(duì)海雷達(dá)航跡抗差關(guān)聯(lián)算法
doi: 10.11999/JEIT141472
基金項(xiàng)目:
山東省自然科學(xué)基金青年基金(ZR2012FQ004)和飛行器測(cè)控與通信教育部重點(diǎn)實(shí)驗(yàn)室開放基金(CTTC-FX201302)
Anti-bias Association Algorithm for Automatic Identification System and Radar Based on Bias Detection
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摘要: 現(xiàn)有的多傳感器航跡抗差關(guān)聯(lián)算法，如基于圖像配準(zhǔn)技術(shù)、基于參照拓?fù)潢P(guān)系的關(guān)聯(lián)算法，計(jì)算流程復(fù)雜，難以在工程實(shí)踐中實(shí)時(shí)應(yīng)用。該文結(jié)合海面艦船目標(biāo)航跡分布特點(diǎn)，基于單元平均恒虛警檢測(cè)原理，提出了適用于自動(dòng)識(shí)別系統(tǒng)(AIS)和雷達(dá)航跡數(shù)據(jù)的可信關(guān)聯(lián)算法，為對(duì)海雷達(dá)系統(tǒng)誤差配準(zhǔn)和海戰(zhàn)場(chǎng)多傳感器信息融合的快速有效實(shí)現(xiàn)提供保證。蒙特卡洛仿真結(jié)果表明，可信關(guān)聯(lián)算法在海面目標(biāo)環(huán)境下具有較高的關(guān)聯(lián)準(zhǔn)確性；算法的平均耗時(shí)與現(xiàn)有抗差關(guān)聯(lián)算法相比顯著降低；基于航跡可信關(guān)聯(lián)結(jié)果完成對(duì)雷達(dá)的自動(dòng)標(biāo)校，將雷達(dá)量測(cè)數(shù)據(jù)的平均誤差降低了近90%。
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關(guān)鍵詞:
- 對(duì)海雷達(dá) /
- 抗差關(guān)聯(lián) /
- 自動(dòng)識(shí)別系統(tǒng) /
- 單元平均恒虛警檢測(cè) /
- 實(shí)時(shí)性
Abstract: The classical multi-sensor anti-bias association algorithms require complex calculation procedure, can not be used in real-time condition, for instance the algorithms based on image matching and reference topology feature. On the basis of tracks distribution feature on sea surface and Cell-Average Constant False Alarm Rate (CA-CFAR) detection theory, a real-time anti-bias association algorithm for Automatic Identification System (AIS) and the data track of radar is proposed, named confidential-association algorithm, to make real-time radar systematic error registration and multi-sensor information fusion come true. Monte-Carlo simulation results show that the accuracy of confidential-association algorithm maintains on a high level on the sea-surface environment, it has the superiority of simply calculation procedure and substantially reduces run-time in comparison to current anti-bias association algorithms. Radar automatically registration technique based on confidential-association algorithm reduces average error of the measured data by nearly 90%. -
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