用均場逼近網(wǎng)絡(luò)計算關(guān)聯(lián)概率
COMPUTING ASSOCIATION PROBABILITIES USING MEAN-FIELD APPROXIMATION NETWORKS
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摘要: 數(shù)據(jù)關(guān)聯(lián)問題是密集多回波環(huán)境下多目標跟蹤中的一個關(guān)鍵問題。在固定溫度參數(shù)T=1下,通過構(gòu)造適當(dāng)?shù)哪芰亢瘮?shù),使Boltzmann機演化達平衡狀態(tài)后,各神經(jīng)元狀態(tài)的平均值即為所要求的關(guān)聯(lián)概率的近似值。在此基礎(chǔ)上,提出了用均場逼近網(wǎng)絡(luò)快速計算關(guān)聯(lián)概率的新方法。仿真結(jié)果驗證了本文方法的有效性。Abstract: The data assciation problem is one of the key problems of multitarget tracking in dense multiple return environments. By constructing a suitable energy function, the average values of a Boltzmann machine (T = 1) are approximately equal to the association probabilities. Then, a new method for computing association probabilities using mean-field approximation network is presented. The simulations show that this method is effective.
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