测绘学报
測繪學報
측회학보
ACTA GEODAETICA ET CARTOGRAPHICA SINICA
2015年
4期
384-391
,共8页
GPS/INS组合导航%不敏卡尔曼滤波%径向基神经网络%多重渐消因子
GPS/INS組閤導航%不敏卡爾曼濾波%徑嚮基神經網絡%多重漸消因子
GPS/INS조합도항%불민잡이만려파%경향기신경망락%다중점소인자
GPS/INS integrated navigation%unscented Kalman filter%radial basis function neural network%multiple fading factors
GPS/I NS组合导航非线性系统最优估计算法中，基于统计信息和假设检验理论的多渐消因子自适应滤波算法的应用前提条件是残差向量为高斯白噪声。本文针对观测异常会影响残差向量的数字特性分布，提出了一种神经网络辅助的多重渐消因子自适应SVD‐UKF算法。该算法采用神经网络算法削弱观测异常对残差序列高斯白噪声分布特性的影响，利用奇异值分解抑制U KF中先验协方差矩阵负定性变化，同时构造多重渐消因子对预测状态协方差阵进行调整，使得不同的滤波通道具有不同的调节能力，高效地应用于多变量复杂系统。最后利用车载实测数据进行了验证。结果表明，神经网络算法极大削弱了观测粗差对残差序列高斯白噪声分布特性的影响，拓展了多重渐消因子的应用范围，使其能在观测值含有粗差的条件下自适应调节不同滤波通道，消除滤波状态中的异常，提高组合导航解的精度和可靠性。
GPS/I NS組閤導航非線性繫統最優估計算法中，基于統計信息和假設檢驗理論的多漸消因子自適應濾波算法的應用前提條件是殘差嚮量為高斯白譟聲。本文針對觀測異常會影響殘差嚮量的數字特性分佈，提齣瞭一種神經網絡輔助的多重漸消因子自適應SVD‐UKF算法。該算法採用神經網絡算法削弱觀測異常對殘差序列高斯白譟聲分佈特性的影響，利用奇異值分解抑製U KF中先驗協方差矩陣負定性變化，同時構造多重漸消因子對預測狀態協方差陣進行調整，使得不同的濾波通道具有不同的調節能力，高效地應用于多變量複雜繫統。最後利用車載實測數據進行瞭驗證。結果錶明，神經網絡算法極大削弱瞭觀測粗差對殘差序列高斯白譟聲分佈特性的影響，拓展瞭多重漸消因子的應用範圍，使其能在觀測值含有粗差的條件下自適應調節不同濾波通道，消除濾波狀態中的異常，提高組閤導航解的精度和可靠性。
GPS/I NS조합도항비선성계통최우고계산법중，기우통계신식화가설검험이론적다점소인자자괄응려파산법적응용전제조건시잔차향량위고사백조성。본문침대관측이상회영향잔차향량적수자특성분포，제출료일충신경망락보조적다중점소인자자괄응SVD‐UKF산법。해산법채용신경망락산법삭약관측이상대잔차서렬고사백조성분포특성적영향，이용기이치분해억제U KF중선험협방차구진부정성변화，동시구조다중점소인자대예측상태협방차진진행조정，사득불동적려파통도구유불동적조절능력，고효지응용우다변량복잡계통。최후이용차재실측수거진행료험증。결과표명，신경망락산법겁대삭약료관측조차대잔차서렬고사백조성분포특성적영향，탁전료다중점소인자적응용범위，사기능재관측치함유조차적조건하자괄응조절불동려파통도，소제려파상태중적이상，제고조합도항해적정도화가고성。
The predicted residual vectors should be zero‐mean Gaussian white noise,which is the precondition for multiple fading factors adaptive filtering algorithm based on statistical information in GPS/INS integration system.However the abnormalities in observations will affect the distribution of the residual vectors.In this paper,a neural network aided adaptive unscented Kalman filter (UKF)algorithm with multiple fading factors based on singular value decomposition(SVD)is proposed.The algorithm uses the neural network algorithm to weaken the influence of the observed abnormalities on the residual vectors. Singular value decomposition instead of unscented transformation is adopted to suppress negative definite variation in priori covariance matrix of UKF.Since single fading factor in poor tracking of multiple variables has the limitation,multiple fading factors to adjust the predicted‐state covariance matrix are constructed with better robustness so that each filter channel has different adjustability.Finally,vehicle measurement data are collected to validate the proposed algorithm.It shows that the neural network algorithm can prevent the observed abnormalities from affecting the distribution of the residual vectors,expanding the applied range of the adaptive algorithm.The neural network algorithm aided SVD‐UKF algorithm with multiple fading factors is able to remove influences of state anomalies on condition of the observed abnormalities.The accuracy and reliability of the navigation solution can be improved by this algorithm.
