本文通過(guò)引入新息相關(guān)法，提出了一種基于新息相關(guān)性自調(diào)整的交互多模型算法。該方法首先利用模型概率識(shí)別出最可能近似真實(shí)模式的模型，而后針對(duì)該模型應(yīng)用新息相關(guān)法濾波，在系統(tǒng)的數(shù)學(xué)模型和噪聲的統(tǒng)計(jì)模型不精確的情況下，通過(guò)監(jiān)測(cè)殘差是否為白
噪聲，來(lái)相應(yīng)地調(diào)整卡爾曼增益，從而使卡爾曼濾波達(dá)到最佳。
關(guān)鍵詞：卡爾曼濾波 交互多模型算法 新息自調(diào)整
Abstract: a new IMM algorithm based on the auto-adjustable correlation of the residual is
provided. The algorithm first recognize the most probable model that near real mode by using
model probability, then filter for the recognized model using the correlation of the residual. The lgorithm adjust Kalman gain ,by monitor whether the residual is white noise when the system’s athmetical model and the statistical model of the noise are not accurate, so that Kalman filter is ptimal.
Keywords: kalman filter ;interacting multiple algorithm; the auto-adjustable of the residual