1. A method of integrated navigation and target tracking for high-dimensional GNSS/INS deep coupling comprising a cubature Kalman filtering (CKF) method, wherein the cubature Kalman filtering method comprises:S1, constructing a high-dimensional GNSS/INS deep coupling filter model to obtain a constructed filter model;

S2, generating an initialization cubature point for the constructed filter model by using standard cubature rules;

S3, performing CKF filtering on the constructed filter model by using novel cubature point update rules;

wherein the step S3 comprises:

S31, calculating a state priori distribution at k moment by using the following formula:

wherein in the formula, xk?represents a state priori estimate at k moment, xk|k?1 is a mean of the xk?, Pk|k?1 is a variance of the xk?, xk|k?1 represents a state estimate at k moment speculated from a measurement and a state at k?1 moment, Pk|k?1 represents a covariance of xk|k?1, wi=1/2nx, and Qk?1 is a system noise variance matrix;

S32, calculating a cubature point error matrix xi,k|k?1?of the prediction process using the following formula, and defining ?k-=Pk|k?1?Qk?1 as an error variance of a Sigma point statistical linear regression (SLR) in a priori PDF approximation process;

xi,k|k?1?=xi,k|k?1?xk|k?1, 0?i?2nx,

wherein xi,k|k?1=f(xi,k?1|k?1+) is a cubature point after xi,k?1|k?1+propagates through a system equation;

S33, taking the cubature point after the propagation of the system equation as a cubature point of a measurement update process;

S34, using a CKF measurement update to calculate a likelihood distribution function of a measured value;

wherein,

wherein in the formula, zk?represents a measurement likelihood estimate at k moment, zk|k?1 is a mean of the zk?, Pzz,k|k?1 is a variance of the zk?, zk|k?1 is a measurement at k moment predicted from the state at k?1 moment, h(x) is a measurement equation, wi=1/2nx, and Rk is a measurement noise variance matrix;

S35, calculating a posterior distribution function of a state variable x;

wherein

wherein in the formula, xk+, is a posterior estimate of the state variable at k moment, a mean and a variance of the xk+ are xk|k and Pk|k, respectively, Kk=Pxz,k|k?1(Pzz,k|k?1)?1 is a Kalman gain matrix, Pxz,k|k?1 is a cross-covariance between a posterior estimate of the state variable and the measurement likelihood estimate;

S36, defining an error caused by a Sigma point approximation to a posterior distribution as ?k+=Pk|k, w=[w1 . . . w2nx]is a weight of a CKF cubature point SLR, a SLR of the priori distribution at k moment accurately captures a mean and a covariance of the state, and consider effects of system uncertainty and noise, then

xi,k|k?1?w=0,

xi,k|k?1?diag(w)xi,k|k?1?T=Pk|k?1?Qk,

wherein in the formula, xi,k|k?1?is the cubature point error matrix of the prediction process, ?=diag(w) represents that the matrix ? is constructed using w diagonal elements, in a SLR of a similar posterior distribution, the cubature point can at least accurately match the mean and the variance of the state, namely,

xi,k|kw=0,

xi,k|k+diag(w)(xi,k|k+)T=Pk|k,

wherein in the formula, Xi,k|k+is an updated cubature point;

S37, assuming both ?k?and ?k+are symmetric positive definite matrices, and xi,k|k =B·Xi,k|K?1?, then ?k?=Lk(Lk)T, ?k+=Lk+1(Lk+1)T wherein B is a transformation matrix to be solved, xi,k|k+is an updated cubature point error matrix; further ?k+=BLk (Lk)TBT , B=Lk+1?(Lk)?1, wherein ? is an arbitrary orthogonal matrix that satisfies ??T=Inx, when ? is taken as a unit matrix, B=Lk+1(Lk)?1 is obtained;

S38, obtaining an updated cubature point based on a posterior state estimate mean xk|k and an updated cubature point error matrix as x+i,k|k=xk|k=xi,k|k+, 0?i?2nx.