Trajectory Estimation of Aircraft in a Double-Satellite Passive Positioning System with the Adjoint Method

Abstract

A double-satellite passive positioning system is constructed based on the theory of space geometry, where two observation coordinate systems and a fundamental coordinate system exist. In each observation coordinate system, there exists a ray from the observation satellite to the aircraft. One difficulty lies in that these two rays may not intersect due to the existence of various errors. Under this situation, this work assumes that the middle point of common perpendicular between two rays is the actual position of aircraft. Based on the theory of space geometry, the coordinates of aircraft in the fundamental coordinate system can be determined. A dynamic model with the adjoint method is developed to estimate the trajectory of aircraft during the process of rocket propulsion. By assimilating observations, the trajectory of aircraft can be calculated. Numerical experiments are designed to validate the reasonability and feasibility of this model. Simulated results indicate that even by assimilating a small number of observations, the trajectory of aircraft can be estimated. In addition, the trajectory estimation can become more accurate when more observations are assimilated to the model.

1. Introduction

The determination of the position of an aircraft by a passive observer is a typical navigation problem [1]. In this paper, a double-satellite passive positioning system is constructed based on the theory of space geometry. In this system, two observation coordinate systems corresponding to two observation satellites and a fundamental coordinate system exist. In each observation coordinate system, there exists a ray from the observation satellite to the aircraft. In the ideal situation where no error exists in the double-satellite passive positioning system, these two rays intersect and the point of intersection is the aircraft. One difficulty lies in that these two rays may not intersect due to the existence of various errors which contain systemic error of observation satellite and random error of measurement. Under this situation, this work assumes that the middle point of common perpendicular between two rays is the actual position of aircraft. Based on the assumption and theory of space geometry, the coordinates of aircraft in the fundamental coordinate system can be determined.

Based on observations of aircraft in the fundamental coordinate system, the trajectory of aircraft could be estimated. Spingarn [1] used an extended Kalman filter to obtain the estimations of optimal filtered position combined with observations. Jamilnia and Naghash [2] used two different approaches, which were separated approach and integrated approach, to optimize trajectories of multistage launch vehicles simultaneously. In this paper, the adjoint method is used to estimate the trajectory of aircraft during the process of rocket propulsion. Based on the theory of inverse problem, the adjoint method is a useful tool for parameter estimation by assimilating observations into numerical models. Lardner [3], Zhang and Lu [4], Guo et al. [5], Cao et al. [6], and Chen et al. [7] studied the inversion of open boundary conditions for ocean dynamic models. As for the inverse problem of other parameters, Lu and Zhang [8] and Zhang et al. [9] inverted spatially varying bottom friction coefficient for the two-dimensional tidal model, and Yu and O’Brien [10] estimated wind drag coefficient and eddy viscosity coefficients in an Ekman layer model. Results of their work demonstrate that the adjoint method is a useful tool for parameter estimation and can improve the accuracy of simulated results.

This paper is organized as follows. The double-satellite passive positioning system is constructed in Section 2. The dynamic model with the adjoint method is developed to estimate the trajectory of aircraft in Section 3. In Section 4, numerical experiments are designed to validate the reasonability and feasibility of this model. Summaries and conclusions are presented in Section 5.

2. Double-Satellite Passive Positioning System

2.1. Transformation of Coordinates

Two coordinate systems, namely, the fundamental and observation coordinate system, are used in a passive positioning system. Figure 1 is a snapshot of these coordinate systems. In the fundamental coordinate system (𝑂_𝑐-𝑋_𝑐𝑌_𝑐𝑍_𝑐), the geocenter is taken as the origin. The earth and observation satellite are simplified as two particles and constitute a two-body problem under the effect of gravity. Considering that the time scale of studied problem is much shorter than that of earth rotation, the fundamental coordinate system is regarded as an inertial system. The aircraft is measured by observation satellite in the observation coordinate system (𝑂_𝑠-𝑋_𝑠𝑌_𝑠𝑍_𝑠). As shown in the observation coordinate system of Figure 1, the position of observation satellite (𝑂_𝑠) is taken as the origin, 𝑋_𝑠-axis is along the vector 𝑂_𝑐𝑂_𝑠, and 𝑍_𝑠-axis is perpendicular to 𝑋_𝑠-axis and pointing to the north exactly. 𝑌_𝑠-axis is then ascertained following right hand system. Generally speaking, observation satellite would not pass the south and north poles, so the observation coordinate is definite and reasonable.

Figure 1 

Outline of the fundamental (𝑂𝑐-𝑋𝑐𝑌𝑐𝑍𝑐) and observed (𝑂𝑠-𝑋𝑠𝑌𝑠𝑍𝑠) coordinate system.

Considering that observation satellite is passive detective satellite which only measures direction, the aircraft is observed and represented by two dimensionless ratios (𝛼 and 𝛽) in the observation coordinate: 𝑦𝛼=^𝑠𝑥_𝑠,𝑧𝛽=^𝑠𝑥_𝑠, (1) where 𝑥_𝑠, 𝑦_𝑠, and 𝑧_𝑠 are the coordinates of aircraft in the observation coordinate system. It should be noted that 𝑥_𝑠, 𝑦_𝑠, and 𝑧_𝑠 are just used to represent the position of aircraft and cannot be measured by one observation satellite.

The key to achieve double-satellite passive position is to transform coordinates from the observation coordinate system to the fundamental one. The basic idea of transformation is described as follows. Assume that 𝑄 is an arbitrary point in the observation coordinate system. Three components of vector 𝑂_𝑠𝑄 along 𝑂_𝑠𝑋_𝑠, 𝑂_𝑠𝑌_𝑠, and 𝑂_𝑠𝑍_𝑠 are set to 𝑄𝑥, 𝑄𝑦 and 𝑄𝑧, respectively. Considering that 𝑂_𝑠𝑋_𝑠, 𝑂_𝑠𝑌_𝑠, and 𝑂_𝑠𝑧_𝑠 are well-determined and can be described by the unit vectors 𝑖⃗, 𝑗 and 𝑘 in the fundamental coordinate system, 𝑄𝑥, 𝑄𝑦, and 𝑄𝑧 have the following relation with 𝑖⃗, 𝑗, and 𝑘: 𝑄𝑥=𝐴₁𝑂_𝑠𝑋_𝑠+𝐵₁𝑂_𝑠𝑌_𝑠+𝐶₁𝑂_𝑠𝑍_𝑠=𝑎₁𝑖⃗+𝑏₁𝑗+𝑐₁𝑘,𝑄𝑦=𝐴₂𝑂_𝑠𝑋_𝑠+𝐵₂𝑂_𝑠𝑌_𝑠+𝐶₂𝑂_𝑠𝑍_𝑠=𝑎₂𝑖⃗+𝑏₂𝑗+𝑐₂𝑘,𝑄𝑧=𝐴₃𝑂_𝑠𝑋_𝑠+𝐵₃𝑂_𝑠𝑌_𝑠+𝐶₃𝑂_𝑠𝑍_𝑠=𝑎₃𝑖⃗+𝑏₃𝑗+𝑐₃𝑘, (2) where 𝐴₁,𝐴₂, 𝐴₃, 𝐵₁, 𝐵₂, 𝐵₃, 𝐶₁, 𝐶₂, 𝐶₃, 𝑎₁, 𝑎₂, 𝑎₃, 𝑏₁, 𝑏₂, 𝑏₃, 𝑐₁, 𝑐₂, and 𝑐₃ are parameters and will be derived in the following part. Then 𝑂_𝑠𝑄 can be rewritten as 𝑂_𝑠𝑄=𝑎₁+𝑎₂+𝑎₃𝑖⃗+𝑏₁+𝑏₂+𝑏₃𝑗+𝑐₁+𝑐₂+𝑐₃𝑘, (3) which is the description of 𝑄 in the fundamental coordinate system. Detailed derivations are displayed as follows.

Assume that 𝑂_𝑠 is (𝑥₀,𝑦₀,𝑧₀) in the fundamental coordinate system, 𝑄 is (𝑥₁,𝑦₁,𝑧₁) in the observation coordinate system, and (𝑥₂,𝑦₂,𝑧₂) in the fundamental one. Because 𝑂_𝑠𝑋_𝑠 is along vector 𝑂_𝑐𝑂_𝑠, the unit vector of 𝑂_𝑠𝑋_𝑠 in the fundamental coordinate system is 𝑣₁=𝑥₀𝑥²₀+𝑦²₀+𝑧²₀,𝑦₀𝑥²₀+𝑦²₀+𝑧²₀,𝑧₀𝑥²₀+𝑦²₀+𝑧²₀. (4) Considering that 𝑂_𝑠𝑍_𝑠 points to the north exactly, 𝑂_𝑠𝑍_𝑠 lies in the plane 𝑂_𝑠𝑂_𝑐𝑍_𝑐. As a result, direction vector of 𝑂_𝑠𝑌_𝑠 is also the normal vector of 𝑂_𝑠𝑂_𝑐𝑍_𝑐. Then unit vector of 𝑂_𝑠𝑌_𝑠 in the fundamental coordinate system can be described as 𝑣₂=−𝑦₀𝑥²₀+𝑦²₀,𝑥₀𝑥²₀+𝑦²₀,0. (5) As unit vectors of 𝑂_𝑠𝑋_𝑠 and 𝑂_𝑠𝑌_𝑠 have been determined, that of 𝑂_𝑠𝑧_𝑠 can be calculated by vector multiplication cross 𝑣₃=−𝑥₀𝑧₀𝑥²₀𝑧²₀+𝑦²₀𝑧²₀+𝑥²₀+𝑦²₀²,−𝑦₀𝑧₀𝑥²₀𝑧²₀+𝑦²₀𝑧²₀+𝑥²₀+𝑦²₀²,𝑥²₀+𝑦²₀𝑥²₀𝑧²₀+𝑦²₀𝑧²₀+𝑥²₀+𝑦²₀². (6) Consequently, 𝑄 in the fundamental coordinate system is described as 𝑥₂,𝑦₂,𝑧₂=𝑥₁𝑣₁+𝑦₁𝑣₂+𝑧₁𝑣₃+𝑥₀,𝑦₀,𝑧₀. (7)

2.2. Double-Satellite Passive Positioning System

As described before, observation satellite is passive detective satellite and can measure direction only. It means that the position of aircraft can be determined only when two or more observation satellites are available. In this paper, a double-satellite passive positioning system is constructed to study the positioning problem. This means that there exist two observation coordinate systems, of which the origins are the two observation satellites, respectively. In each observation coordinate system, there exists a ray from the observation satellite to the aircraft. In the ideal situation where no error exists in the double-satellite passive positioning system, these two rays intersect and the intersection point is the aircraft. One difficulty lies in that these two rays may not intersect due to the existence of various errors. Under this situation, this work assumes that the middle point of common perpendicular between two rays is the actual position of aircraft.

As 𝛼, 𝛽, and the trajectory information of observation satellites are known, the equation of the ray from the observation satellite to the aircraft in the fundamental coordinate system can be obtained as long as (1,𝛼,𝛽) is transformed from the observation coordinate system to the fundamental one. According to the assumption, the position of aircraft can be determined based on the theory of space geometry. Detailed derivation is displayed as follows. (It should be noted that the unit vector rather than the equation of the ray is used in the derivation.)

Figure 2 is the schematic diagram of the double-satellite passive positioning system. 𝑀 and 𝑁 represent two satellites, and 𝐴 and 𝐵 represent the observations of the aircraft by satellites 𝑀 and 𝑁, respectively. Based on the assumption, the middle point of 𝐴𝐵 is the actual position of the aircraft, which is represented by 𝐶 in Figure 2. In the fundamental coordinate system, assume that the coordinates of 𝑀 and 𝑁 are (𝑥_𝑚,𝑦_𝑚,𝑧_𝑚) and (𝑥_𝑛,𝑦_𝑛,𝑧_𝑛), and unit vectors of 𝑀𝐴 and 𝑁𝐵 are (𝜑₁,𝜑₂,𝜑₃) and (𝜙₁,𝜙₂,𝜙₃). Consider 𝜃₁𝜑=arccos¹𝑥_𝑛−𝑥_𝑚+𝜑₂𝑦_𝑛−𝑦_𝑚+𝜑₃𝑧_𝑛−𝑧_𝑚𝑥_𝑛−𝑥_𝑚²+𝑦_𝑛−𝑦_𝑚²+𝑧_𝑛−𝑧_𝑚²,𝜃₂𝜙=arccos¹𝑥_𝑛−𝑥_𝑚+𝜙₂𝑦_𝑛−𝑦_𝑚+𝜙₃𝑧_𝑛−𝑧_𝑚𝑥_𝑛−𝑥_𝑚²+𝑦_𝑛−𝑦_𝑚²+𝑧_𝑛−𝑧_𝑚²,𝜃=arccos𝜑₁𝜙₁+𝜑₂𝜙₂+𝜑₃𝜙₃. (8) Then, 𝑀𝐴=cos𝜃₁+cos𝜃₂⋅cos𝜃sin²𝜃⋅𝑥_𝑛−𝑥_𝑚²+𝑦_𝑛−𝑦_𝑚²+𝑧_𝑛−𝑧_𝑚²,𝑁𝐵=cos𝜃₂+cos𝜃₁⋅cos𝜃sin²𝜃⋅𝑥_𝑛−𝑥_𝑚²+𝑦_𝑛−𝑦_𝑚²+𝑧_𝑛−𝑧_𝑚². (9) Finally, the coordinate of aircraft (𝐶) in the fundamental coordinate system can be calculated as 𝑥_𝑎,𝑦_𝑎,𝑧_𝑎=𝑥_𝑚,𝑦_𝑚,𝑧_𝑚+|𝑀𝐴|𝜑₁,𝜑₂,𝜑₃,𝑥_𝑏,𝑦_𝑏,𝑧_𝑏=𝑥_𝑛,𝑦_𝑛,𝑧_𝑛+|𝑁𝐵|𝜙₁,𝜙₂,𝜙₃,𝑥_𝑐,𝑦_𝑐,𝑧_𝑐=12𝑥_𝑚,𝑦_𝑚,𝑧_𝑚+𝑥_𝑛,𝑦_𝑛,𝑧_𝑛+𝑀𝐴𝜑₁,𝜑₂,𝜑₃+𝑁𝐵𝜙₁,𝜙₂,𝜙₃. (10)

Figure 2 

Outline of the double-satellite passive positioning system.

3. Dynamic model

As mentioned in Section 2, the coordinates of aircraft in the fundamental coordinate system are determined by the double-satellite passive positioning system, and they will be used as observations in the dynamic model. If adequate observations are obtained, the trajectory of the aircraft can be estimated by using the dynamic model with the adjoint method.

3.1. Forward Model (Governing Equation)

The governing equation of the aircraft during the process of rocket propulsion in the fundamental coordinate system can be described as follows: 𝑟̈⃗(𝑡)=𝐹_𝑒+𝐹_𝑇, (11) where 𝑟(⃗𝑡) is the vector of position, 𝑡 is time, 𝑟(̈⃗𝑡) is the double derivative in time of 𝑟(⃗𝑡), that is, acceleration, 𝐺_𝑚 is the constant of earth attraction, 𝐹_𝑒=−(𝐺_𝑚/|𝑟(⃗𝑡)|³)𝑟(⃗𝑡) is the acceleration caused by universal gravitation, and 𝐹_𝑇 is the acceleration caused by rocket propulsion, which is taken as the unknown parameter and will be optimized by the adjoint method. Assume that 𝐹_𝑥, 𝐹_𝑦 and 𝐹_𝑧, are the 𝑥-, 𝑦-, and 𝑧-components of 𝐹_𝑇, respectively, (11) can be changed into 𝑑²𝑥𝑑𝑡²𝐺=−^𝑚𝑟³𝑥+𝐹_𝑥,𝑑²𝑦𝑑𝑡²𝐺=−^𝑚𝑟³𝑦+𝐹_𝑦,𝑑²𝑧𝑑𝑡²𝐺=−^𝑚𝑟³𝑧+𝐹_𝑧,𝑟=𝑥²+𝑦²+𝑧², (12) where 𝑥, 𝑦, and 𝑧 are three components of 𝑟(⃗𝑡).

3.2. Adjoint Model and Corrections

The cost function is defined as 𝜅𝐽=2^𝑡2_𝑡1(𝑥−𝑥)²+𝑦−𝑦²+(𝑧−𝑧)²𝑑𝑡, (13) where 𝜅 is the unit matrix [8] 𝑥, 𝑦, 𝑧, 𝑥, 𝑥, and 𝑧 are observations and simulated coordinate of the aircraft, respectively. Then the Lagrangian function can be expressed as 𝐿=𝐽+^𝑡2_𝑡1𝜆𝑑²𝑥𝑑𝑡²+𝐺_𝑚𝑟³𝑥−𝐹_𝑥𝑑+𝜇²𝑦𝑑𝑡²+𝐺_𝑚𝑟³𝑦−𝐹_𝑦𝑑+𝜈²𝑧𝑑𝑡²+𝐺_𝑚𝑟³𝑧−𝐹_𝑧𝑑𝑡, (14) where 𝜆,  𝜇, and 𝜈 are the adjoint variables (namely, Lagrangian multipliers) of 𝑥, 𝑦, and 𝑧, respectively.

According to the theory of Lagrangian multiplier method, we obtain the first-order derivatives of Lagrangian function with respect to all the variables, 𝜕𝐿𝜕𝜆=0,𝜕𝐿𝜕𝜇=0,𝜕𝐿𝜕𝜈=0,𝜕𝐿𝜕𝑥=0,𝜕𝐿𝜕𝑦=0,𝜕𝐿𝜕𝑧=0,𝜕𝐿𝜕𝐹_𝑥=0,𝜕𝐿𝜕𝐹_𝑦=0,𝜕𝐿𝜕𝐹_𝑧=0. (15)(16)(17) Equation (15) give the forward model described by (12). From (16) the adjoint model can be deduced as 𝑑²𝜆𝑑𝑡²+𝐺_𝑚𝑦²+𝑧²−2𝑥²𝜆−3𝑥𝑦𝜇+𝑧𝜈𝑟⁵(𝑥)𝑑+𝜅𝑥−=0,₂𝜇𝑑𝑡²+𝐺_𝑚𝑥²+𝑧²−2𝑦²𝜇−3𝑦(𝑥𝜆+𝑧𝜈)𝑟⁵𝑦𝑑+𝜅𝑦−=0,₂𝜈𝑑𝑡²+𝐺_𝑚𝑥²+𝑦²−2𝑧²𝜈−3𝑧𝑥𝜆+𝑦𝜇𝑟⁵+𝜅(𝑧−𝑧)=0. (18) From (17), the corrections of 𝐹_𝑥, 𝐹_𝑦, and 𝐹_𝑧 are obtained 𝐹_𝑥−𝐹_𝑥𝐹=𝜆,_𝑦−𝐹_𝑦𝐹=𝜇,_𝑧−𝐹_𝑧=𝜈, (19) where 𝐹_𝑥,  𝐹_𝑦,  𝐹_𝑧 and 𝐹_𝑥, 𝐹_𝑦, 𝐹_𝑧 are prior and optimized values, receptively. Because the values of cost function decrease along the opposite direction of the gradient, by employing typical steepest descent method [11], 𝐹_𝑥, 𝐹_𝑦, and 𝐹_𝑧 can be optimized during iterations.

Equations (12), (18) and (19) are discretized using the finite difference method and are shown as follows: 𝑥^𝑛+1−2𝑥^𝑛+𝑥^𝑛−1Δ𝑡²𝐺=−^𝑚(𝑟^𝑛)³𝑥^𝑛+𝐹^𝑛_𝑥,𝑦^𝑛+1−2𝑦^𝑛+𝑦^𝑛−1Δ𝑡²𝐺=−^𝑚(𝑟^𝑛)³𝑦^𝑛+𝐹^𝑛_𝑦,𝑧^𝑛+1−2𝑧^𝑛+𝑧^𝑛−1Δ𝑡²𝐺=−^𝑚(𝑟^𝑛)³𝑧^𝑛+𝐹^𝑛_𝑧𝑟^𝑛=(𝑥^𝑛)²+𝑦^𝑛2+(𝑧^𝑛)²,𝜆^𝑛−1−2𝜆^𝑛+𝜆^𝑛+1Δ𝑡²+𝐺_𝑚𝑦^𝑛2+𝑧^𝑛2−2𝑥^𝑛2𝜆^𝑛−3𝑥^𝑛𝑦^𝑛𝜇^𝑛+𝑧^𝑛𝜈^𝑛(𝑟^𝑛)⁵+𝜅𝑥^𝑛−𝑥^𝑛𝜇=0,_𝑛−1−2𝜇^𝑛+𝜇^𝑛+1Δ𝑡²+𝐺_𝑚𝑥^𝑛2+𝑧^𝑛2−2𝑦^𝑛2𝜇^𝑛−3𝑦^𝑛𝑥^𝑛𝜆^𝑛+𝑧^𝑛𝜈^𝑛(𝑟^𝑛)⁵+𝜅𝑦^𝑛−𝑦^𝑛𝜈=0,_𝑛−1−2𝜈^𝑛+𝜈^𝑛+1Δ𝑡²+𝐺_𝑚𝑥^𝑛2+𝑦^𝑛2−2𝑧^𝑛2𝜈^𝑛−3𝑧^𝑛𝑥^𝑛𝜆^𝑛+𝑦^𝑛𝜇^𝑛(𝑟^𝑛)⁵+𝜅𝑧^𝑛−𝑧^𝑛𝐹=0,_𝑛𝑥−𝐹^𝑛_𝑥=𝜆^𝑛,𝐹^𝑛_𝑦−𝐹^𝑛_𝑦=𝜇^𝑛,𝐹^𝑛_𝑧−𝐹^𝑛_𝑧=𝜈^𝑛. (20)

The computed process is designed as follows. (a) Run the forward model with prescribed initial values of 𝐹_𝑥, 𝐹_𝑦, and 𝐹_𝑧. (b) Calculate the value of cost function 𝐽. (c) The difference between simulated results and observations plays as the external driving force of the adjoint model. The adjoint variables can then be obtained through the backward integration of the adjoint model. (d) Based on the corrections and typical steepest descent method, 𝐹_𝑥, 𝐹_𝑦, and 𝐹_𝑧 can be optimized. Repeating the process aforementioned, the difference between simulated results and observations is decreased. When the convergence criterion is met, the optimization stops.

The convergence criterion could be that the number of iteration steps is prescribed, the last two values of the cost function are sufficiently close, the magnitude of the gradient norm is sufficiently small, the discrepancy between the updated and old parameters is sufficiently small, or a combination of these. In this work, the convergence criterion is that the number of iteration steps is equal to 10000, which leads to satisfied values of cost function and gradient norm and is obtained through a trial process. In addition, this convergence criterion is reasonable and essential to investigate the influence of the number of observations on the accuracy of simulated results in the numerical experiments.

4. Numerical Experiments

The data used in the numerical experiments, containing 𝛼, 𝛽, and trajectory information of two observation satellites (satellites 06 and 09), are from problem B of the ninth National Graduate Mathematical Contest in Modeling (http://www.njnet.edu.cn/news/16/). These data are from 50 to 170 s with an interval of 0.2 s, corresponding to the process of rocket propulsion. The trajectories of observation satellites are plotted in Figure 3. 𝛼 and 𝛽 are plotted in Figure 4. Then the coordinates of aircraft in the fundamental coordinate system, which are used as observations, are determined according to formulas in Section 2 and are shown in Figure 5.

(a)

(b)

(c)

Figure 3 

Coordinates of observation satellites in the fundamental coordinate system.

(a)

(b)

Figure 4 

𝛼 and 𝛽 of observation satellites from 50 s to 170 s.

Figure 5 

Trajectory (left) and instantaneous coordinates (right) of the aircraft.

Four numerical experiments are designed to validate the sensitivity to the number of observations in the model. In experiment one (E1), the trajectory of aircraft is estimated by assimilating all observations. The number of observations is 601 in E1 with internal of 0.2 s. In E2–E4, the number of observations is decreased successively, which is 61, 21, and 13, respectively. In numerical experiments, the initial values of 𝐹_𝑥, 𝐹_𝑦, and 𝐹_𝑧 are all equal to 10 m²/s, and the time step is equal to 0.2 s. Simulated results are illustrated as follows.

Simulated results show good agreement with the observed ones in all four experiments. However, they are not plotted in a figure for very small differences with each other and the observations. Table 1 lists the mean absolute differences (MADs) and mean relative differences (MRDs) between simulated results and observations. In all four experiments, the MADs and MRDs are all very small, indicating the reasonability and feasibility of the model. Log of cost function and gradient norm are plotted in Figure 6. Cost function and gradient norm decrease fast during the iteration, implying the efficiency of the typical steepest method. Careful inspection shows that the MAD and MRD, as well as the value of cost function at the end of iterations, increase from E1 to E4. This is to say, the trajectory estimation of the aircraft becomes worse as the number of observations is decreased. Combining all these results, we can draw two conclusions. One is that even by assimilating a small number of observations, the trajectory of aircraft can be estimated using the dynamic model with the adjoint method. The other is that the trajectory estimation can become more accurate by assimilating more observations.

Table 1 

Difference between simulated coordinates and observed ones.

(a)

(b)

Figure 6 

Log of cost function and gradient norm during iteration.

5. Summary and Conclusions

In this paper, a double-satellite passive positioning system is constructed based on the theory of space geometry. In this system, two observation coordinate systems corresponding to two observation satellites and a fundamental coordinate system exist. In each observation coordinate system, there exists a ray from the observation satellite to the aircraft. In the ideal situation where no error exists in the double-satellite passive positioning system, these two rays intersect and the point of intersection is the aircraft. One difficulty lies in that these two rays may not intersect due to the existence of various errors. Under this situation, this work assumes that the middle point of common perpendicular between two rays is the actual position of aircraft. Based on the assumption and theory of space geometry, the coordinates of aircraft in the fundamental coordinate system can be determined.

In order to estimate the trajectory of aircraft during the process of rocket propulsion, a dynamic model with the adjoint method is constructed. The forward model is used to simulate the motion of aircraft, and the adjoint model is used to optimize parameters. By assimilating observations, which are the coordinates of aircraft in the fundamental coordinate system, the trajectory of aircraft can be estimated. Numerical experiments are designed to validate the reasonability and feasibility of this model. Results indicate that even by assimilating a small number of observations, the trajectory of aircraft can be estimated using the dynamic model with the adjoint method. In addition, the trajectory estimation can become more accurate by assimilating more observations.

Acknowledgments

Partial support for this research was provided by the National Natural Science Foundation of China through Grant 41206001 and 41076006, the Major State Basic Research Development Program of China through Grant 2013CB956500, the Natural Science Foundation of Jiangsu Province through Grant BK2012315, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.