CN101122637A - An Adaptive Dimensionality Reduction Filtering Method for SINS/GPS Integrated Navigation for SAR Motion Compensation - Google Patents
An Adaptive Dimensionality Reduction Filtering Method for SINS/GPS Integrated Navigation for SAR Motion Compensation Download PDFInfo
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Abstract
一种SAR(合成孔径雷达)运动补偿用SINS(捷联惯导)/GPS组合导航系统自适应降维滤波方法，该方法离线建立不同飞行机动情况的全维SINS/GPS组合导航滤波模型库；载机机动性发生变化时在线计算每个状态变量可观测度；依据可观测度从大到小排列状态变量，选择包含三维位置和速度在内的前d维状态变量建立最小维数降维滤波模型，采用随机生成法确定状态估计方差矩阵中新增状态变量对应的元素。在载机机动性下一次发生变化之前采用所建立的降维滤波器实现组合导航。本发明利用状态变量可观测度与估计精度之间的对应关系构建自适应降维滤波模型，在保证滤波精度的同时显著提高了系统实时性。
A kind of SAR (Synthetic Aperture Radar) motion compensation uses SINS (strapdown inertial navigation)/GPS integrated navigation system self-adaptive dimensionality reduction filter method, this method establishes the full-dimensional SINS/GPS integrated navigation filter model library of different flight maneuvers off-line; Calculate the observability of each state variable online when the maneuverability of the carrier aircraft changes; arrange the state variables from large to small according to the observability, and select the first d-dimensional state variables including the three-dimensional position and velocity to establish the minimum dimensionality reduction filter The model uses the random generation method to determine the elements corresponding to the newly added state variables in the state estimation variance matrix. Before the next change of the aircraft's maneuverability, the established dimensionality reduction filter is used to realize integrated navigation. The invention utilizes the corresponding relationship between the observability of the state variable and the estimation accuracy to construct an adaptive dimensionality reduction filtering model, which notably improves the real-time performance of the system while ensuring the filtering accuracy.
Description
Technical Field
The invention relates to a SINS (Strapdown Inertial Navigation) and GPS (global positioning System) combined Navigation self-adaptive dimension reduction filtering method for SAR (Synthetic Aperture Radar) motion compensation, in particular to a self-adaptive dimension reduction filtering model, which is particularly suitable for occasions such as real-time online measurement of SAR imaging System antenna phase center motion parameters.
Background
The SAR high-resolution remote sensing imaging has the advantages of all-weather imaging, penetrating imaging, stereo imaging and the like, has important application in the fields of battlefield reconnaissance, global real-time early warning, land resource mapping, environmental monitoring and protection, climate and natural disaster monitoring and early warning and the like, and is military and civil dual-purpose high-technology equipment for realizing earth observation. The SAR technology has been developed from a post-hoc imaging mode to a real-time imaging, real-time transmission mode. The precondition of SAR imaging is that three-dimensional position information of the antenna phase center must be accurately measured during the imaging process. Therefore, SAR real-time imaging needs to break through the technical bottleneck of real-time and high-precision measurement of the three-dimensional position of the phase center of the antenna urgently, and the SAR real-time imaging is called as an SAR real-time motion compensation technology.
The traditional SAR motion compensation scheme adopts an SINS/GPS integrated navigation system to measure the motion parameters of an antenna phase center, and the SINS/GPS integrated navigation system generally constructs a Kalman filtering model containing 15-dimensional state variables and 6-dimensional observation variables. Because the dimension of the filter model is higher, the multiplication and addition times of one-step filtering are 15 3 +6×15 2 The system is difficult to solve the position information of the SAR antenna phase center in real time, and generally, the system acquires original information in real time, processes the original information in real time to obtain the position information of the SAR antenna phase center and provides motion compensation parameters for the SAR system imaging in real time. The invention provides a self-adaptive dimension reduction filtering method of an SINS/GPS combined navigation system for SAR motion compensation, which aims to provide three-dimensional position information of an antenna phase center for an SAR real-time imaging system.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method overcomes the defects of the prior art, provides a SINS/GPS combined navigation self-adaptive dimension reduction filtering method with high precision and good real-time performance, and provides three-dimensional position information of an antenna phase center for an SAR real-time imaging system on line.
The technical solution of the invention is as follows: an adaptive dimensionality reduction filtering method of an SINS/GPS integrated navigation system for SAR motion compensation is characterized by comprising the following steps: (1) Establishing a full-dimensional (15-dimensional) filtering model base in an off-line manner, wherein the full-dimensional (15-dimensional) filtering model base comprises a state equation, an observation equation, a system noise variance array, a measurement noise variance array and a state estimation variance array of models under three different flight maneuver conditions; the three filtering models respectively correspond to three conditions of uniform linear motion, uniform accelerated linear motion and uniform circular motion of the SAR loader. (2) The system starts to work, judges that the maneuverability of the carrier changes, utilizes a state transition matrix, an observation matrix and an observability degree analyzing and calculating method based on a singular value decomposition principle on line in real time to calculate the observability degree of the 15-dimensional state variables, and rearranges the state variables from large to small according to the observability degree. (3) Selecting front d-dimensional state variables including three-dimensional positions and speeds to form a dimensionality reduction filtering model with the minimum dimensionality, wherein d is equal to the maximum arrangement serial number corresponding to the three-dimensional positions and the speed state variables; (4) Determining elements corresponding to newly added state variables in a state estimation variance matrix of the newly built dimension reduction filtering model by adopting a random generation method; (5) And starting and maintaining the dimension reduction filtering model until the mobility of the SAR aerial carrier changes again.
The principle of the invention is as follows: the filtering precision of each state variable in the SINS/GPS combined navigation system filtering model changes along with the change of the aircraft mobility of the SAR aircraft, and the observability degree quantitatively represents the filtering precision of each state variable under the current aircraft mobility condition. The SINS/GPS combined navigation self-adaptive dimension reduction filtering method for SAR motion compensation calculates the observability degree of each state variable in the current maneuvering state when the maneuvering characteristics of the SAR carrier change, and the state variables are sequenced from large to small according to the observability degree. And selecting the front d-dimensional state variables including the three-dimensional position and the speed from large to small to construct a dimensional minimum filtering model, wherein n is equal to the maximum arrangement serial number corresponding to the three-dimensional position and speed state variables. Determining a state transition matrix, a system noise variance matrix and a state estimation variance matrix corresponding to the dimensionality reduction filtering model, and finishing combined navigation filtering; and determining elements corresponding to the new supplementary state variables in the state estimation variance matrix by adopting a random generation method. And when the flight mobility of the SAR aerial carrier does not change, the current SINS/GPS combined navigation filtering model is kept. According to the invention, the calculated amount is greatly reduced by constructing the self-adaptive dimensionality reduction filtering model in real time and deleting the state variable with poor filtering precision on line; and meanwhile, high filtering precision state variables required by SAR imaging are saved. Although certain calculation cost is needed for calculating the observability of the state variables in real time on line and constructing the dimension reduction filtering model, considering that SAR imaging requires that the carrier keeps constant-speed linear motion under most conditions, as shown in FIG. 1, the time for the change of the mobility of the SAR carrier is not much, so that the method achieves the purpose of obviously reducing the overall calculation amount of the adaptive dimension reduction filtering model of the SINS/GPS combined navigation system for SAR motion compensation by using smaller calculation cost, namely calculating the observability of the state variables and constructing the dimension reduction filtering model, ensures the filtering precision same as that of the full-dimension filtering model, and meets the requirements of the SAR real-time imaging system.
Compared with the prior art, the invention has the advantages that: hair brushThe self-adaptive dimensionality reduction filtering method is adopted, so that the SINS/GPS combined navigation system for SAR motion compensation can ensure the filtering precision, obviously reduce the calculated amount and lay the technical foundation for the SAR real-time imaging system. This is due to: (1) Compared with the traditional SINS/GPS integrated navigation system filtering method, the filtering method disclosed by the invention has the advantages that the state variables are adaptively selected or rejected on line according to the observable degree of the system state variables to form a dimension reduction filtering model, and the state variables with poor filtering precision are abandoned, so that the precision of the filtering method is not influenced completely; (2)The calculation amount is reduced remarkably; because the filtering model dimension has great influence on the system calculation amount, n and m are assumed to be the dimensions of the state variable and the observation variable respectively, and the multiplication and addition times of one-step filtering are n 3 +m×n 2 . The self-adaptive dimension reduction filtering model provided by the invention mainly aims to reduce the dimension of the SINS/GPS combined navigation filtering model for SAR motion compensation, thereby achieving the purpose of reducing the calculated amount; (3) An SINS/GPS combined navigation filtering model library under different flight mobility conditions of an SAR carrier is established, the observable degree of state variables is calculated on line in real time, and a dimension reduction filtering model is constructed only by selecting from the model library, so that the system flow is greatly simplified; (4) And determining new supplementary elements in the state estimation variance matrix by adopting a random generation method when the SINS/GPS combined navigation filtering model is reconstructed on line every time. The random generation method has small calculation amount and high correlation degree with the variance of the last filtering value, and is favorable for quick convergence of the reconstructed dimension reduction filtering model.
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FIG. 1 is a typical rout-path diagram for a SAR aerial vehicle of the present invention;
FIG. 2 is a schematic diagram of a state variable sorting and selecting process according to the present invention;
FIG. 3 is a flowchart of the method operation of the present invention.
Detailed Description
As shown in fig. 3, the specific steps of the present invention are as follows:
(1) Firstly, constructing a full-dimensional SINS/GPS integrated navigation system filtering model library, comprising the following steps: three models of uniform linear motion, uniform accelerated linear motion and uniform circular motion.
a. The uniform circular motion full-dimensional SINS/GPS integrated navigation system filter model:
15 dimensional state variable
X(t)＝[φ x φ y φ z δv x δv y δv z δL δλ δh ε x ε y ε z ▽ x ▽ y ▽ z ] T The state equation of the SINS/GPS integrated navigation system is as follows:
the measurement equation is as follows:
Z(t)＝H(t)X(t)+V(t)
wherein F (t) is the state transition matrix of the system:
F N is determined by the coefficients of the following set of differential equations:
l and delta L are latitude and latitude estimation errors respectively; λ, δ λ are longitude and longitude estimation errors, respectively; h, δ h are height and height estimation errors, respectively; v x ，δv x ，v y ，δv y ，v z ，δv z Estimating errors for the three weft speeds and the speed respectively; phi is a x ，φ y ，φ z Respectively estimating errors of the three-dimensional postures; epsilon x ，ε y ，ε z Respectively, three-axis gyro errors; v x ，▽ y ，▽ z Respectively, errors of a triaxial accelerometer; f. of x ，f y ，f z Respectively measuring the three-axis specific force; x ， y ， z The pitch angle, the roll angle and the yaw angle of the SAR aerial carrier are respectively; r m ，R n ，ω ie The radius of the prime plane is the radius of the prime plane, and the rotation angular velocity of the earth.
G (t) is a system noise transfer matrix:
wherein:
w (t) is the random error of the gyroscope and accelerometer, W (t) = [ W = εx w εy w εz w ▽x w ▽y w ▽z ] T . Observation matrix
H v ＝[0 3×3 diag(1 1 1) 0 3×12 ]
H p ＝[0 3×6 diag(R m R n cosL 1) 0 3×9 ]
Observation vector Z (t) = [ δ v = x δv y δv z δL δλ δh] T 。
The system noise variance matrix Q is represented by W (t) = [ W εx w εy w εz w ▽x w ▽y w ▽z ] T The variances of the individual elements constitute a diagonal matrix of diagonal elements. The measurement noise variance matrix R is formed by Z (t) = [ delta v = x δv y δv z δL δλ δh] T The variance of each element constitutes a diagonal matrix of diagonal elements. The initial value of the state estimation variance matrix is determined as a 15-dimensional unit matrix, and the matrix is updated in real time according to the conventional Kalman filtering step in each subsequent combined filtering process.
All the parameters used in the formula are navigation parameters at the current moment, and are regarded as constant values in each filtering step.
b. Compared with uniform circular motion, the SINS/GPS integrated navigation system filtering model of uniform acceleration and uniform linear motion only forms a state transition matrix F N Are different.
The filtering model of the uniform acceleration linear motion full-dimensional SINS/GPS integrated navigation system is at a constant speedOrder v on the basis of circular motion filtering model z =0, and thus omitted
In
2ω ie sinLv z A term of delta L and a term of delta L,
in (1)
Item (1):
15 dimensional state variable
X(t)＝[φ x φ y φ z δv x δv y δv z δL δλ δh ε x ε y ε z ▽ x ▽y ▽ z ] T
The state equation of the SINS/GPS integrated navigation system is as follows:
the measurement equation is:
Z(t)＝H(t)X(t)+V(t)
wherein F (t) is the state transition matrix of the system:
F N is determined by the coefficients of the following set of differential equations:
l and delta L are latitude and latitude estimation errors respectively; λ, δ λ are longitude and longitude estimation errors, respectively; h, δ h are height and height estimation errors, respectively; v. of x ，δv x ，v y ，δv y ，δv z Estimating errors for the three weft speeds and the speed respectively; phi is a x ，φ y ，φ z Respectively estimating errors of the three-dimensional postures; epsilon x ，ε y ，ε z Respectively, three-axis gyro errors; v x ，▽ y ，▽ z Respectively, errors of a triaxial accelerometer; f. of x ，f y ，f z Are respectively provided withIs a triaxial specific force measurement; x ， y ， z The pitch angle, the roll angle and the yaw angle of the SAR aerial carrier are respectively; r M ，R n ，ω ie The radius of the prime plane is the radius of the prime plane, and the rotation angular velocity of the earth.
G (t) is a system noise transfer matrix:
wherein:
w (t) is the random error of the gyroscope and accelerometer,
W(t)＝[w εx w εy w εz w ▽x w ▽y w ▽z ] T . Observation matrix
H v ＝[0 3×3 diag(1 1 1) 0 3×12 ]， H p ＝[0 3×6 diag(R m R n cosL 1) 0 3×9 ]
Observation vector Z (t) = [ δ v = x δv y δv z δL δλ δh] T 。
The system noise variance matrix Q is represented by W (t) = [ W εx w εy w εz w ▽x w ▽y w ▽z ] T The variances of the individual elements constitute a diagonal matrix of diagonal elements. The measurement noise variance matrix R is formed by Z (t) = [ delta v = x δv y δv z δL δλ δh] T The variance of each element constitutes a diagonal matrix of diagonal elements. The initial value of the state estimation variance matrix is determined as a 15-dimensional unit matrix, and the matrix is updated in real time according to the conventional Kalman filtering step in each subsequent combined filtering process. All the parameters used in the formula are navigation parameters of the current moment, and filtering is carried out at each stepThe wave is considered to be a constant value.
c. The filtering model of the uniform linear motion full-dimensional SINS/GPS integrated navigation system is based on the uniform circular motion filtering model z ＝0，f x ＝f y ＝f z =0, and thus omitted
In (1)
2ω ie sin Lv z δL、 f y φ z -f z φ y ，
F in (1) z φ x -f x φ z 、
F in (1) x φ y -f y φ x -and the like: 15 dimensional state variable
X(t)＝[φ x φ y φ z δv x δv y δv z δL δλ δh ε x ε y ε z ▽ x ▽ y ▽ z ] T The state equation of the SINS/GPS integrated navigation system is as follows:
the measurement equation is:
Z(t)＝H(t)X(t)+V(t)
wherein F (t) is the state transition matrix of the system:
F N is determined by the coefficients of the following set of differential equations:
l and delta L are latitude and latitude estimation errors respectively; λ, δ λ are longitude and longitude estimation errors, respectively; h, δ h are height and height estimation errors, respectively; v. of x ，δv x ，v y ，δv y ，δ z ，δv z Estimating errors for the three weft speeds and the speed respectively; phi is a x ，φ y ，φ z Respectively estimating errors of the three-dimensional postures; epsilon x ，ε y ，ε z Respectively, three-axis gyro errors; v x ，▽ y ，▽ z Respectively, the errors of the three-axis accelerometer; f. of x ，f y ，f z Respectively measuring the three-axis specific force; x ， y ， z The pitch angle, the roll angle and the yaw angle of the SAR aerial carrier are respectively; r m ，R n ，ω ie The radius of the prime plane is the radius of the prime plane, and the rotation angular velocity of the earth.
G (t) is a system noise transfer matrix:
wherein:
w (t) is the random error of the gyroscope and accelerometer, W (t) = [ W = εx w εy w εz w ▽x w ▽y w ▽z ] T . Observation matrixH v ＝[0 3×3 diag(1 1 1) 0 3×12 ]，
H p ＝[0 3×6 diag(R m R n cosL 1) 0 3×9 ]
Observation vector Z (t) = [ δ v = x δv y δv z δL δλ δ h ] T 。
The system noise variance matrix Q is represented by W (t) = [ W εx w εy w εz w ▽x w ▽y w ▽z ] T The variance of each element constitutes a pairA diagonal matrix of diagonal elements. The measurement noise variance matrix R is formed by Z (t) = [ delta v = x δv y δv z δL δλ δh] T The variance of each element constitutes a diagonal matrix of diagonal elements. The initial value of the state estimation variance matrix is determined as a 15-dimensional unit matrix, and the matrix is updated in real time according to the conventional Kalman filtering step in each subsequent combined filtering process. All the parameters used in the formula are navigation parameters at the current moment, and are regarded as constant values in each filtering step.
(2) Secondly, the system starts to work, judges the mobility change of the carrier, identifies the current motion of the carrier as one of three conditions of uniform linear motion, uniform accelerated linear motion or uniform circular motion, calculates the observability degree on line in real time, and rearranges the state variables according to the observability degree from large to small.
When the output of the three-dimensional accelerometer is smaller than a specified threshold, the three-dimensional accelerometer is judged to be uniform-speed linear motion, when the vector sum of the three-dimensional accelerometer keeps a constant value, the three-dimensional accelerometer is judged to be uniform-speed linear motion, and when the vector sum of the three-dimensional accelerometer keeps a constant value, the three-dimensional accelerometer is judged to be uniform-speed circular motion. The period of judgment is the same as the filtering period, and is 1 second. The output judgment threshold of the accelerometer is determined by the sensitivity of the accelerometer selected by the system.
Calculating the observability degree of all 15-dimensional state variables under the condition of uniform linear motion, uniform accelerated linear motion or uniform circular motion so as to ensure that
And carrying out singular value decomposition on the T array to obtain:
T＝U*S*V T
wherein: u = [ U = 1 u 2 ...u m ]，V＝[v 1 v 2 ...v n ]Are all orthogonal matrices and are provided with a plurality of parallel,
is a matrix of order m × r, where Λ = diag (σ) 1 σ 2 ... σ r ) Is a diagonal matrix, σ 1 ≥σ 2 ≥...≥σ r ≧ 0 is referred to as the singularity of the matrix T. According to the size of the singular value, whether the corresponding state variable can be observed or not can be judged, the observability of which state variables is high, and the observability of which variables is low.
(3) And sequencing the state variables from large to small according to the observability degree, and taking the front d-dimensional state variables including the three-dimensional position and the three-dimensional speed to form a state vector of the minimum-dimension filtering model. As shown in fig. 2.
(4) And determining elements corresponding to the newly-added state variables in the state estimation variance matrix of the newly-built dimension reduction filtering model by adopting a random generation method.
Constructing a dimension-reduction filtering model according to the determined state variables, wherein a full dimension corresponding to the mobility of the current SAR aerial carrier is adoptedBased on the SINS/GPS combined navigation filtering model, only elements corresponding to state variables contained in the dimensionality reduction filtering model in a state transition matrix F (t), a system noise transition matrix G (t) and an observation matrix H (t) are reserved; taking the corresponding element of the state estimation variance matrix of the state variable contained in the last estimation dimension reduction filtering model as the mean value, and taking the observability sigma of the variable at the last estimation time prev Observable degree sigma of current time now Is the variance of
And determining an element corresponding to the state variable in the filtering variance matrix at the current moment, namely the estimation variance of the newly added state variable.
(5) And under the condition that the maneuverability of the current SAR aerial carrier is kept unchanged, the dimension reduction filtering model is kept to realize SAR motion compensation. And restarting to calculate the observability measure when the mobility of the SAR aerial carrier is changed.
The steps are circularly carried out, as shown in fig. 3, the SINS/GPS combined navigation self-adaptive dimensionality reduction filtering method for SAR motion compensation can be realized, and the calculated amount is greatly reduced while the filtering precision is ensured.
Claims (5)
1. An adaptive dimension reduction filtering method of an SINS/GPS integrated navigation system for SAR motion compensation is characterized by comprising the following steps:
(1) Establishing a full-dimensional, namely 15-dimensional filtering model base in an off-line manner, wherein the full-dimensional, namely 15-dimensional filtering model base comprises a state equation, an observation equation, a system noise variance matrix, a measurement noise variance matrix and a state estimation variance matrix of models under three different flight maneuvering conditions of SAR carrier uniform linear motion, uniform acceleration linear motion and uniform circular motion;
(2) The system starts to work, judges the mobility change of the carrier, identifies the current motion of the carrier as one of three conditions of uniform linear motion, uniform accelerated linear motion or uniform circular motion, calculates the observability degree on line in real time, and rearranges the state variables according to the observability degree from large to small;
(3) Selecting front d-dimensional state variables including three-dimensional positions and three-dimensional speeds to form a dimensionality reduction filtering model with the minimum dimensionality, wherein d is equal to the maximum arrangement serial number corresponding to the three-dimensional positions and the speed state variables;
(4) Determining elements corresponding to newly-added state variables in a state estimation variance matrix of the newly-built dimension reduction filtering model by adopting a random generation method;
(5) And starting and maintaining the dimension reduction filtering model until the mobility of the SAR aerial carrier changes again.
2. The adaptive dimension reduction filtering method for the SINS/GPS integrated navigation system for SAR motion compensation according to claim 1, wherein: the method for identifying whether the current motion of the carrier is uniform linear motion, uniform acceleration linear motion or uniform circular motion in the step (2) comprises the following steps: when the output of the three-dimensional accelerometer is smaller than a specified threshold, the three-dimensional accelerometer is judged to be in uniform-speed linear motion, when the vector sum of the three-dimensional accelerometer keeps a constant value, the three-dimensional accelerometer is judged to be in uniform-speed linear motion, and when the vector sum of the three-dimensional accelerometer keeps the constant value, the three-dimensional accelerometer is judged to be in uniform-speed circular motion.
3. The adaptive dimension reduction filtering method for the SINS/GPS integrated navigation system for SAR motion compensation according to claim 1, wherein: the method for calculating the observability measure in the step (2) comprises the following steps: and calculating the observability of the 15-dimensional state variable by using a state transition matrix, an observation matrix and an observability degree analysis calculation method based on a singular value decomposition principle.
4. The adaptive dimension reduction filtering method for the SINS/GPS integrated navigation system for SAR motion compensation according to claim 1, wherein: and (4) the dimension reduction filtering model in the step (3) is a minimum dimension filtering model formed by taking the front d-dimension state variables including three-dimensional position information and three-dimensional speed information after the state variables are sequenced from large to small according to the observability degree.
5. The adaptive dimension reduction filtering method for the SINS/GPS integrated navigation system for SAR motion compensation according to claim 1, wherein: the random generation method in step (4) is to estimate the time of the variable for the last timeTaking corresponding elements of the state estimation variance matrix as mean values, and according to the observability degree sigma of the variable at the last estimation moment prev Observable with the current time instant by a now Is the variance of
And determining the element corresponding to the state variable in the filtering variance matrix at the current moment.
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| CN102075295B (en) * | 2011-01-20 | 2013-02-27 | 浙江大学 | A Signal Encoding and Decoding Method Aiming at State Estimation Based on Communication Power Constraints |
| CN102075295A (en) * | 2011-01-20 | 2011-05-25 | 浙江大学 | Communication power constraint-based state estimation-targeted encoding and decoding method |
| CN103649683A (en) * | 2011-06-03 | 2014-03-19 | 罗伯特·博世有限公司 | Combined radar and gps localization system |
| CN103649683B (en) * | 2011-06-03 | 2016-08-17 | 罗伯特·博世有限公司 | The radar of combination and GPS alignment system |
| US9562778B2 (en) | 2011-06-03 | 2017-02-07 | Robert Bosch Gmbh | Combined radar and GPS localization system |
| CN102299766A (en) * | 2011-08-18 | 2011-12-28 | 浙江大学 | Joint optimization method for dimensionality reduction and quantification of communication signal for object state estimation |
| CN102299766B (en) * | 2011-08-18 | 2013-10-23 | 浙江大学 | A Joint Optimization Method for Dimensionality Reduction and Quantization of Communication Signals for Target State Estimation |
| CN102980583A (en) * | 2012-12-25 | 2013-03-20 | 哈尔滨工业大学 | Ballistic missile boosting section tracking method based on dimension-augmenting shifted Rayleigh filtering |
| CN102980583B (en) * | 2012-12-25 | 2015-03-25 | 哈尔滨工业大学 | Ballistic missile boosting section tracking method based on dimension-augmenting shifted Rayleigh filtering |
| CN103870714B (en) * | 2014-04-03 | 2017-04-12 | 武汉科技大学 | Navigation system observable analysis method based on high-order observable matrix |
| CN103870714A (en) * | 2014-04-03 | 2014-06-18 | 武汉科技大学 | Navigation system observable analysis method based on high-order observable matrix |
| CN105158784A (en) * | 2015-07-07 | 2015-12-16 | 中国人民解放军第二炮兵工程大学 | COTM satellite communication system cascade Kalman filtering carrier attitude estimation method |
| CN107036598A (en) * | 2017-03-30 | 2017-08-11 | 南京航空航天大学 | Dual quaterion inertia/celestial combined navigation method based on gyro error amendment |
| CN111149018A (en) * | 2017-09-26 | 2020-05-12 | 焦点定位有限公司 | Method and system for calibrating system parameters |
| CN111149018B (en) * | 2017-09-26 | 2023-09-15 | 焦点定位有限公司 | Method and system for calibrating system parameters |
| CN109270507A (en) * | 2018-11-05 | 2019-01-25 | 金华航大北斗应用技术有限公司 | Error compensation model is moved in GNSS-R Bistatic SAR imager carrying |
| CN109270507B (en) * | 2018-11-05 | 2023-08-15 | 金华航大北斗应用技术有限公司 | GNSS-R bistatic SAR imaging airborne motion error compensation model |
| CN113607176A (en) * | 2021-10-11 | 2021-11-05 | 智道网联科技(北京)有限公司 | Combined navigation system track output method and device |
| CN113607176B (en) * | 2021-10-11 | 2021-12-10 | 智道网联科技(北京)有限公司 | Combined navigation system track output method and device |
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