基于t分布混合模型的抗差關(guān)聯(lián)算法

李保珠; 董云龍; 李秀友; 關(guān)鍵

doi:10.11999/JEIT161084

基于t分布混合模型的抗差關(guān)聯(lián)算法

doi: 10.11999/JEIT161084

基金項(xiàng)目:

國家自然科學(xué)基金(61471382, 61401495, 61501487, 61531020)，山東省自然科學(xué)基金(2015ZRA06052)

計(jì)量
- 文章訪問數(shù): 1443
- HTML全文瀏覽量: 206
- PDF下載量: 301
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-10-14
- 修回日期: 2017-02-17
- 刊出日期: 2017-07-19

Anti-bias Track Association Algorithm Based on t-distribution Mixture Model

Funds:

The National Natural Science Foundation of China (61471382, 61401495, 61501487, 61531020), The Natural Science Foundation of Shandong Province (2015ZRA06052)

摘要

摘要: 針對傳感器系統(tǒng)誤差和觀測目標(biāo)不完全一致的情況下目標(biāo)航跡關(guān)聯(lián)中魯棒性問題，該文提出一種基于t分布混合模型的抗差關(guān)聯(lián)算法。將航跡關(guān)聯(lián)問題轉(zhuǎn)化為圖像匹配中的非剛性點(diǎn)集匹配問題，針對非共同觀測目標(biāo)影響關(guān)聯(lián)性能的問題，將非共同觀測目標(biāo)的航跡視為圖像匹配中的異常點(diǎn)，建立了對異常點(diǎn)具有更好魯棒性的重拖尾t分布混合模型，利用期望最大化(EM)算法求解t分布混合模型的閉合解，在求解中為了確保航跡點(diǎn)間的運(yùn)動一致性(CPD)，加入Tikhonov正則項(xiàng)。最后通過實(shí)驗(yàn)仿真驗(yàn)證，所提算法在系統(tǒng)誤差和觀測目標(biāo)不完全一致情況下的魯棒性和有效性。
- 航跡關(guān)聯(lián) /
- 系統(tǒng)誤差 /
- t分布混合模型 /
- 期望最大化算法 /
- 運(yùn)動一致性
Abstract: In order to solve the problem of robust track-to-track association in the presence of sensor biases and non-identical observation, an anti-bias track association algorithm based on t-distribution mixture model is proposed. The robust track-to-track association problem is turned into the non-rigid point matching problem. The tracks of non-common are regarded as outliers in the point matching for the effects of the track-to-track association caused by non-identical observation. The heavy-tailed t-distribution mixture model is established with better robustness to outliers. The closed-form solution of t-distribution mixture model is solved by Expectation Maximization (EM) algorithm. The conditional expectation function is added a regular item of point set, so that the points have a feature of Coherent Point Drift (CPD). Finally, the effectiveness of the proposed algorithm is verified by simulation experiments at the presence of sensor biases and missed detections.
- Track association /
- Sensor biases /
- t-distribution mixture model /
- Expectation Maximization (EM) algorithm /
- Coherent Point Drift (CPD)

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參考文獻(xiàn)(19)

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