Platform attitude tracking with reference wave structure parafac decomposition measurement method

Abstract

The application provides a platform attitude tracking PARAFAC decomposition measurement method of a reference wave structure, and comprises the following steps: establishing an array direction vector of an airborne electromagnetic vector sensor according to the change rule between the attitude position of the airborne electromagnetic vector sensor and a received signal; arranging all electromagnetic vector sensor received signal data to obtain a parallel factor three-linear data model according to the relationship between the signal time, polarization, three-dimensional information structure and attitude; the observation matrix is windowed, a tracking algorithm is used to replace the traditional algorithm, and the complexity increase with time is avoided; the traditional parallel factor alternating least square algorithm is extended, the platform attitude data of the reference wave structure is tracked under the orthogonal constraint condition, and the platform attitude is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of navigation, and particularly relates to a platform attitude tracking PARAFAC decomposition measurement method of reference wave structure. BACKGROUND

[0002] The surveying technology needs accurate platform attitude data. The manned space engineering needs to frequently transport personnel and goods and dock with a space station, and higher requirements are put forward for the attitude measurement accuracy and reliability of a spacecraft. Satellites, rockets, missiles and other spacecraft are widely used, and the demand for attitude measurement is improved. The uniqueness of Parallel Factor (PARAFAC) data model decomposition has important application value. The traditional Parallel Factor alternating least square algorithm is promoted to decompose the Parallel Factor (PARAFAC) three-linear data to obtain a bearing matrix. Good effects are obtained in the CDMA, OFDM and other neighborhoods.

[0003] The existing spacecraft attitude measurement reference is insufficient, and the accuracy, stability and economy need to be improved; the aircraft array estimation direction of arrival is disturbed by the unknown attitude. SUMMARY

[0004] The technical problems to be solved by the application are to overcome the insufficient reference of the existing spacecraft attitude measurement, improve the accuracy, stability, refresh rate and economy, overcome the existing aircraft array estimation attitude tracking performance disturbance problem, provide a platform attitude tracking PARAFAC decomposition measurement method of reference wave structure, and apply the method to aircraft array aerospace detection.

[0005] The method comprises the following steps:

[0006] Step 1, establishing a tracking attitude measurement system: installing an electromagnetic vector sensor array with the same receiving unit and the same attitude on a spacecraft platform, measuring the installation coordinates of each sensor in the body coordinate system, forming a three-linear structure of the sensor array output data, performing PARAFAC decomposition by using a Parallel Factor alternating least square algorithm to obtain the spatial and polarization domain steering factors of each signal, setting the direction of arrival according to a navigation system, calculating the polarization domain steering factor under zero attitude, and calculating the platform attitude according to the relationship between the polarization steering factor and the attitude to realize the attitude measurement based on the wave structure information. The application provides a fast attitude tracking algorithm, and initial attitude data is required for the tracking condition. The initial attitude data is close to the real-time attitude value. The initial attitude can be provided by other sensors or an attitude estimation algorithm of the reference wave structure. The attitude tracking algorithm provided by the application is faster than the general attitude estimation algorithm of the reference wave structure, and the refresh rate of the attitude measurement can be effectively improved. The existing public signals of Beidou and GPS satellites can be used to realize the spacecraft attitude measurement, and the direction of arrival and polarization parameters of the signals can be calculated in real time by the navigation system.

[0007] Step 2, according to the received information, the received signal can be estimated in the following ways: multi-signal complete sampling attitude tracking estimation; or multi-signal incomplete sampling attitude tracking estimation; or single-signal complete sampling attitude tracking estimation. Multi-signal complete sampling attitude tracking refers to the case where independent three orthogonal dipole signals or three orthogonal magnetic ring signals are realized when multiple satellite signals are received. Multi-signal incomplete sampling refers to the case where independent non-three orthogonal dipole signals are realized when multiple satellite signals are received. Single-signal complete sampling attitude tracking estimation refers to the case where independent three orthogonal dipole signals or three orthogonal magnetic ring signals are realized when a satellite signal is received.

[0008] In step 1, the same electromagnetic vector sensors are installed on the aircraft platform to establish a parallel factor three linear data model. An array of attitude identical electromagnetic vector sensors is installed to realize three linear data decomposition calculation. The installation coordinates of each sensor in the body coordinate system are measured to realize the utilization of spatial wave arrival information. Existing public signals of Beidou, GPS and their mixed signals can be used to realize aircraft attitude measurement. The signal arrival and polarization parameters can be calculated in real time by the navigation system.

[0009] In step 2, the multi-signal complete sampling attitude tracking estimation includes the following steps:

[0010] Step 2-1-1, input the initial sliding window time signal sequence estimation value, attitude initial estimation, and the spatial domain and polarization domain steering factor of each signal.

[0011] Step 2-1-2, calculate the spatial domain steering factor and the Khatri-Rao product of the polarization domain steering factor, and combine them into a full array steering vector;

[0012] Step 2-1-3, multiply the real-time received data of each sensor by the full array non-updated steering vector matrix Moor-Penrose generalized inverse to obtain the first real-time estimation of the signal sequence;

[0013] Step 2-1-4, combine the real-time received data of each sensor with the received data during the old window period; combine the real-time signal sequence estimation with the signal sequence during the old window period;

[0014] Step 2-1-5, estimate the real-time update matrix of the full array steering vector according to the combined received data of each sensor and the signal sequence;

[0015] Step 2-1-6, the real-time updating matrix of the steering vector is the Khatri-Rao product of the spatial domain steering factor and the polarization domain steering factor, the column data of each signal sequence is inverse quantized into a matrix, which is multiplied by the polarization domain steering factor after being conjugate transposed to obtain the real-time update corresponding to the spatial domain factor; the column data inverse quantization matrix is multiplied by the spatial domain factor, and the real-time update corresponding to the polarization domain steering factor is obtained after modulus normalization;

[0016] Step 2-1-7, the real-time signal re-update estimate is obtained by multiplying the Moor-Penrose generalized inverse of the real-time updating matrix of the steering vector and the time shift update of the received data of each sensor, and is combined with the historical data;

[0017] Step 2-1-8, the zero-attitude polarization domain steering factor is updated according to the change of the navigation system condition, and the non-orthogonalized attitude matrix is calculated according to the relationship between the zero-attitude polarization domain steering factor and the real-time attitude polarization domain steering factor, and the orthogonalized attitude matrix is outputted.

[0018] Step 2-1-9, the effective data is redefined by time sliding window: the first column of the received data matrix of each sensor and the first row of the signal sequence are deleted, the remaining parameter time scale is updated to become the initial condition of the next round of tracking, and the process returns to step 2-1.

[0019] In step 2, the attitude tracking estimation is performed for the multiple signals with incomplete sampling, and for the array being an orthogonal dipole array, the non-orthogonalized attitude matrix has only two rows because of the selection of the array, and according to the principle that the tracked attitude change is a slight rotation, the following steps are performed:

[0020] Step 2-2-1, the estimated values of each signal sequence, the initial attitude estimation, and the spatial domain and polarization domain steering factors of each signal in the initial sliding window time are inputted.

[0021] Step 2-2-2, the Khatri-Rao product of the spatial domain steering factor and the polarization domain steering factor is calculated to combine into a full-array steering vector;

[0022] Step 2-2-3, the real-time received data of each sensor is left multiplied by the Moor-Penrose generalized inverse of the full-array non-updated steering vector matrix to obtain the first real-time estimate of the signal sequence;

[0023] Step 2-2-4, the real-time received data of each sensor is combined with the received data during the old window; the real-time estimate of the signal sequence is combined with the signal sequence during the old window;

[0024] Step 2-2-5, the real-time updating matrix of the full-array steering vector is estimated according to the combined received data of each sensor and the signal sequence;

[0025] Step 2-2-6, the real-time updating matrix of the steering vector is the Khatri-Rao product of the spatial domain steering factor and the polarization domain steering factor, the column data of each signal sequence is inverse quantized into a matrix, the matrix is multiplied by the polarization domain steering factor after being conjugate transposed to obtain the real-time updating corresponding to the spatial domain factor; the column data inverse quantization matrix is multiplied by the spatial domain factor, and the real-time updating corresponding to the polarization domain steering factor is obtained after modulus normalization;

[0026] Step 2-2-7, the real-time signal re-updating estimation is obtained by multiplying the Moor-Penrose generalized inverse of the real-time updating matrix of the steering vector and the time shift updating of the received data of each sensor, and the historical data is combined;

[0027] Step 2-2-8, the zero-attitude polarization domain steering factor is updated according to the change of the navigation system condition: two row element values of the attitude change matrix are calculated according to the relationship between the zero-attitude polarization domain steering factor and the real-time attitude polarization domain steering factor and the attitude matrix relationship obtained by the last measurement, and the micro-rotation matrix represented by the Euler angle has only three unknowns. Therefore, the attitude change matrix is obtained according to the principle that the corresponding values of elements are equal, and the real-time attitude is calculated.

[0028] Step 2-2-9, the effective data is redefined by time sliding window: the first column of the received data matrix of each sensor and the first row of the signal sequence are deleted, the remaining parameter time scale is updated, becomes the initial condition of the next round of tracking, and returns to step 3-1.

[0029] In step 2, the attitude tracking estimation during the complete sampling of the single signal comprises the following steps:

[0030] Step 2-3-1, input the estimated value of each signal sequence in the initial sliding window time, the initial attitude estimation, and the spatial domain and polarization domain steering factor of each signal.

[0031] Step 2-3-2, calculate the Khatri-Rao product of the spatial domain steering factor and the polarization domain steering factor, and combine to form a full-array steering vector;

[0032] Step 2-3-3, multiply the real-time received data of each sensor by the Moor-Penrose generalized inverse of the full-array non-updating steering vector matrix to obtain the first real-time estimation of the signal sequence;

[0033] Step 2-3-4, combine the real-time received data of each sensor with the received data during the old window; combine the real-time estimated value of the signal sequence with the signal sequence during the old window;

[0034] Step 2-3-5, estimate the real-time updating matrix of the full-array steering vector according to the combined received data of each sensor and the signal sequence;

[0035] Step 2-3-6, the real-time updating matrix of the steering vector is the Khatri-Rao product of the spatial steering factor and the polarization domain steering factor, the column data of each signal is inversely quantized into a matrix, the matrix is multiplied by the polarization domain steering factor after being conjugate transposed to obtain the real-time updating corresponding to the spatial factor; the column data inverse quantization matrix is multiplied by the spatial factor, and the real-time updating corresponding to the polarization domain steering factor is obtained after modulus normalization;

[0036] Step 2-3-7, the real-time signal re-updating estimation is obtained by multiplying the Moor-Penrose generalized inverse of the real-time updating matrix of the steering vector and the time shift updating of each sensor received data, and the historical data is combined;

[0037] Step 2-3-8, the zero-attitude polarization domain steering factor is updated according to the change of the navigation system condition: the linear equation about three unknowns of the attitude change matrix is calculated according to the relationship between the zero-attitude polarization domain steering factor and the real-time attitude polarization domain steering factor, and the attitude change matrix is obtained by solving the equation, and the real-time attitude is calculated;

[0038] Step 2-3-9, the effective data is redefined by time sliding window: the first column of each sensor received data matrix and the first row of the signal sequence are deleted, the remaining parameter time scale is updated, becomes the initial condition of the next round of tracking, and returns to step 4-1.

[0039] The application further provides an electronic device comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the method.

[0040] The application further provides a storage medium storing a computer program or instructions, and when the computer program or instructions are run on a computer, the steps of the method are executed.

[0041] Compared with the prior art, the application has the following beneficial effects:

[0042] 1) Since the original data received by the electromagnetic vector sensor polarization array presents a three-dimensional data structure of time, polarization and spatial phase delay, the electromagnetic vector sensor polarization array can quickly track the aircraft attitude measurement of the reference wave structure vector by using the data structure. The three-linear data structure can be used for PARAFAC decomposition to track the aircraft attitude. The application integrates the orthogonal structure of the attitude matrix into the PARAFAC decomposition, and improves the fitting accuracy of the algorithm.

[0043] 2) The three-linear data is decomposed into PARAFAC, the bearing matrix is tracked in real time, the aircraft attitude data is refreshed with high efficiency, and the operation efficiency is better than that of the traditional algorithm;

[0044] 3) The lack of spatial attitude reference is made up for;

[0045] 4) Reduce the requirement of attitude measurement signal source;

[0046] 5) Improve the stability and reliability of the aircraft attitude tracking of the reference wave structure vector. BRIEF DESCRIPTION OF DRAWINGS

[0047] The above and / or other aspects of the present application will become apparent and more readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0048] Figure 1 is a schematic diagram of electromagnetic wave signal propagation.

[0049] Figure 2 is the elliptical rotating electric field of polarized electromagnetic wave.

[0050] Figure 3 is a schematic diagram of a zero-rotation attitude electromagnetic vector sensor in the earth coordinate system. DETAILED DESCRIPTION

[0051] In the embodiments of the present application, a platform attitude tracking PARAFAC decomposition measurement method of a reference wave structure is provided, including the following steps:

[0052] Step 1, establish a complete polarization signal model received by an electromagnetic vector sensor;

[0053] The direction of electromagnetic wave signal propagation is shown in Figure 1 In the earth coordinate system, the spatial direction of arrival of electromagnetic wave is represented by the parameter , and φ and θ represent the azimuth angle and the elevation angle, respectively, where -π / 2<θ<π / 2, The wave vector u is:

[0054]

[0055] The polarization properties of electromagnetic wave are represented by the polarization ellipse descriptors (γ,η), where γ represents the polarization angle, -π / 2<γ≤π / 2, and η represents the polarization ellipticity, -π / 4≤η≤π / 4. The elliptical rotating electric field of polarized electromagnetic wave is shown in Figure 2 , respectively, the long axis direction vector and the short axis direction vector of the electric field polarization ellipse, and the electromagnetic wave structure vector are orthogonal to each other. The wave structure vector is used as the reference for attitude measurement, and the right-angle coordinate system formed by the wave structure vector as the three coordinate axes is called the wave structure coordinate system. The reference attitude of the independent three orthogonal dipoles at the origin is shown in Figure 3 , Figure 3 The coordinate system is the earth coordinate system. The present application takes a dipole array as an example to illustrate the effectiveness of the algorithm, and the attitude measurement method of the electromagnetic vector sensor can be analogously derived. At this time, the ideal guide vector of the independent three orthogonal dipole sensor is wherein:

[0056]

[0057] The ideal orientation vector of the independent triorthogonal dipole at the origin is further expressed as:

[0058]

[0059] wherein d(η) = [0 cos η sin η 0 -sin η cos η] T

[0060] wherein T represents matrix transposition;

[0061] Referring to Figure 1 know, represents the rotation relationship between the geodetic coordinate system and the wave structure coordinate system, when the attitude of the electromagnetic vector sensor and the geodetic coordinate system have attitude rotation differences, it is represented as the difference of , the geodetic coordinate system rotation difference matrix is simplified as b R .

[0062] Step 2, establish a model of a fully polarized signal received by an aircraft-mounted electromagnetic vector sensor array;

[0063] The attitude of the electromagnetic vector sensor and the geodetic coordinate system rotation difference matrix b R is caused by installation and aircraft platform motion, the present application sets the installation attitude as the positive attitude, and the attitude difference caused by the aircraft platform motion is the time-varying difference. According to the three-dimensional space rotation theory, it is known that the three-dimensional rotation matrix is described by a single rotation vector , wherein [φ1 φ2 φ3] is the rotation vector In the coordinate system, the geodetic coordinate system rotation difference matrix b R is equal to:

[0064] wherein e represents a natural constant;

[0065] The number of electromagnetic vector sensors is N, and the serial number is: n = 1, …, N. The installation position coordinates (x no ,y no ,z no ) T can be accurately measured; without changing the original point of the fuselage, the position coordinates (x n ,y n ,z n ) T of the electromagnetic vector sensor in the geodetic coordinate system are: (xn , y n , z n ) T = b R (x no , y no , z no ) T

[0066] Guiding vector a of electromagnetic vector sensor of aircraft n (φ,θ,γ,η) expression is:

[0067]

[0068] Wherein C is the selection matrix, composed of partial row vectors of three-dimensional unit matrix, indicating that the electromagnetic vector sensor is composed of part of the unit selected from three orthogonal dipoles; When C is a unit matrix, the received data completely reflects the wave structure information, at this time, the single-point receiving information is in a complete state; When C is composed of P row vectors of three-dimensional unit matrix, the receiving sensor unit, the received data incompletely reflects the wave structure information, the present application uses iterative operation to repair the receiving sensor unit data according to the incomplete wave structure information, so that the attitude matrix structure constraint can be included in the linear iterative operation, and the attitude matrix is solved. The present application sets the same selection matrix for each electromagnetic vector sensor, and selects p=1,…,P units to form the electromagnetic vector sensor.q n (φ,θ) is the phase delay, which is expressed as:

[0069]

[0070] Wherein Θ represents the frequency drift phase delay formed by the aircraft sampling, and the phase delay of the N electromagnetic vector sensors is expressed by vector , wherein q N (φ k ,θ k ) represents the phase delay vector of the Nth electromagnetic vector sensor, and the subscript k=1,…,K represents the serial number of the Kth navigation signal. The kth navigation signal s k There are T times of sampling data at t+1-T,…,t, and the sampling data s k (t) is obtained.

[0071] Step 3, a three-linear data model for tracking the change of aircraft attitude with time is established with reference to the wave structure;

[0072] The same electromagnetic vector sensor array with the same attitude is installed on the aircraft platform, and the installation coordinates of each sensor in the body coordinate system are measured. When the aircraft is flying, the parameters of the navigation signal and the attitude of the platform change slowly with time, so the following model needs to be introduced into the above model based on the dynamic sampling time index. Within a short period of time, the parameters of the navigation signal and the attitude of the platform are approximately constant. According to the change rule between the attitude and position of the airborne electromagnetic vector sensor and the received signal, the guide vector of the airborne electromagnetic vector sensor is established. According to the relationship between the three-dimensional information structure of signal time, polarization and space-frequency phase delay, the Parallel Factor (PARAFAC) three-linear data model is established by arranging all the electromagnetic vector sensor received signal data.

[0073] Definition: G(t) is a 3xK polarization domain guide factor real matrix,

[0074] Q(t) is an NxK spatial domain guide factor matrix,

[0075] S(t) is a TxK time domain sampling data matrix, S(t) = [s1(t)…s k (t)…s K (t)], where the kth column vector s k (t) has T sampling data at t+1-T,…,t.

[0076] The electromagnetic vector sensor array of order n receives k = 1,…,K superimposed signals X n (t) is:

[0077] X n (t) = Cb R (t)G(t)D n (Q(t))S T (t) + e n (4)

[0078] X n (t) is a PxT matrix, where D n (·) is the nth row of the extraction matrix and is constructed as a diagonal matrix. e n represents the noise corresponding to the signal model. By arranging all the electromagnetic vector sensor received signal matrices, the received data is arranged as a three-linear model of an NxPxT tensor array, and the (n,p,t) element of the three-linear model is the (p,t) element of X n Under the condition of noise and error, the mod-n product of the PARAFAC model is expressed as:

[0079] Z(t) = IK x1Q(t)x2(Cb R (t))x3S(t)+E (5)

[0080] where I K is the identity tensor; E represents the noise tensor;

[0081] By solving the following cost function: the corresponding factor matrices are estimated, where ||*|| F denotes the Frobenius norm.

[0082] The frontal slice model of the tensor matrix Z(t) is arranged as follows to obtain an NPxT matrix:

[0083]

[0084] where the symbol represents the Khatri-Rao product. Following equation (6), the data received by each sensor at t+1 is a vector x(t+1), and the data of the previous T sampling is combined to obtain: X(t+1)=[X(t), x(t+1)].

[0085] Step 4, establishing a tracking attitude measurement system: installing an electromagnetic vector sensor array with the same attitude on the aircraft platform, and measuring the installation coordinates of each sensor in the body coordinate system. In this way, the sensor array output data can form a three-linear structure, and the PARAFAC decomposition is performed by using the parallel factor alternating least squares algorithm to obtain the spatial domain and polarization domain steering factors of each signal. According to the setting of the direction of arrival of the navigation system, the polarization domain steering factor under zero attitude is calculated, and according to the relationship between the polarization steering factor and the attitude, the platform attitude can be solved, and the attitude measurement based on the wave structure information is realized. The present application provides a fast attitude tracking algorithm, and the tracking condition requires obtaining initial attitude data, and the initial attitude data is close to the real-time attitude value. The initial attitude can be provided by other sensors, or can be provided by the attitude estimation algorithm referring to the wave structure. The attitude tracking algorithm provided by the present application is faster than the general attitude estimation algorithm referring to the wave structure, and the refresh rate of the attitude measurement can be effectively improved. The existing public signals of Beidou, GPS and other satellites can be used to realize the aircraft attitude measurement, and the direction of arrival and polarization parameters of the signals can be calculated in real time by the navigation system.

[0086] Definition: G b (t) = Cb R (t) G(t), H(t) = Q(t) o G b (t). Where G b (t) is the polarization domain direction of arrival factor, G(t) is the zero attitude polarization domain direction of arrival factor, and H(t) is the full array multi-signal steering vector array. G b (t) ：，k represents G b(t)th column of H(t+1) :,k denotes the kth column of H(t+1), Q(t+1) :,k denotes the kth column of Q(t+1).

[0087] Equation (6) is simplified as:

[0088] X(t) = (Q(t) 0 G b (t)) S T (t) + E x = H(t) S T (t) + E x

[0089] where E x denotes a noise matrix;

[0090] Step 5, multi-signal perfect sampling attitude tracking estimation, comprising the following steps:

[0091] Sampling at a point in space contains three orthogonal dipole sampling or three orthogonal magnetic ring sampling. The sampling information at the point is in a perfect state. Taking three orthogonal dipole array sampling as an example.

[0092] Step 5-1: input the estimated value S(t) and b R (t) at time t, and calculate Q(t) at this time according to equation (3).

[0093] Step 5-2: update the direction of arrival according to the navigation system setting, calculate the polarization domain steering vector, and combine the matrix G(t) according to the foregoing. Further, G b (t) and H(t) are obtained.

[0094] Step 5-3: input x(t+1) according to the data received by each sensor at time t+1. Set H(t) = H(t+1), and obtain the next update estimation s T (t+1) of the estimated value s(t). H + (t) denotes the Moore-Penrose generalized inverse;

[0095] Step 5-4: form the estimated value

[0096] Step 5-5: estimate H(t+1): H(t+1) = X(t+1) S T (t+1).

[0097] Step 5-6: estimate Q(t+1) and G b (t+1) from H(t+1).

[0098] k takes values from 1 to K, and the following calculation is performed:

[0099] H k (t+1) = unvec(H(t+1) :,k )

[0100] Q * (t+1) ：，k = H H k (t+1)G b (t) ：，k

[0101]

[0102] where unvec() denotes inverse vectorization to a matrix;

[0103] Step 5-7: Time-shift update: s T (t+1) = H + (t+1)x(t+1),

[0104] Step 5-8: Update G(t+1) according to the change of navigation system condition at t+1, obtain U = G b (t+1) = b R (t+1)G(t+1), calculate U = G b (t+1)G + (t+1), U is the un-orthogonalized attitude matrix. Output the orthogonalized attitude b R (t+1) = U(U T U) -0.5 .

[0105] Step 5-9: Forget the old data, i.e. delete the first column of X(t+1) and the first row of S(t+1), become X(t) and S(t) of the next round, the time identifier (t+1) of b R (t+1) is also changed to (t), the time identifiers of the rest of the parameters are also changed from (t+1) to (t), return to step 5-1.

[0106] The speciality of the attitude estimation algorithm based on complete wave structure information is that b R is a real attitude matrix, the constraint condition is b R T b R = I3, I3 is a three-order unit matrix. The application based on complete wave structure information integrates the orthogonal structure of the attitude matrix into the PARAFAC decomposition, improves the fitting accuracy of the algorithm.

[0107] Step 6, attitude tracking estimation when multi-signal is non-complete sampling:

[0108] Taking the orthogonal dipole array as an example, steps 6-1~6-7, step 6-9 are the same as steps 5-1~5-7, step 5-9, and step 6-8 needs to be changed to: solve the platform attitude, and the formula is wherein represents the small change of the attitude from t to t+1. If the attitude is expressed by Euler angles then The orthogonal dipole selection array makes There are only two rows.

[0109] The right side of the above equation is known, so according to the principle of corresponding values, the matrix elements Δm1(t+1), Δm2(t+1) and Δm3(t+1) on the left side are obtained, that is, the formula is obtained Then, the attitude is solved according to The remaining steps are the same as the attitude tracking estimation when the multi-signal is completely sampled.

[0110] Step 7 is performed, and the attitude tracking estimation when the single signal is completely sampled is as follows:

[0111] The attitude cannot be correctly estimated when the single signal is incompletely sampled, so the attitude tracking estimation when the single signal is completely sampled is only studied. Taking the three orthogonal dipole array sampling as an example, the selection array C is a unit array at this time, so Since the single signal has no aliasing, the G b (t+1) can be obtained from the three linear relationships of the sampling data, and the b R (t) is known. Therefore, the equation In the equation, in addition to the three unknowns Δm1(t+1), Δm2(t+1) and Δm3(t+1), the remaining are all constants. Therefore, steps 7-1~7-7, step 7-9 are the same as steps 5-1~5-7, step 5-9, and step 7-8 needs to be changed to: solve this three-element linear equation to obtain Δm1(t+1), Δm2(t+1) and Δm3(t+1), and then obtain

[0112] The present application provides a platform attitude tracking PARAFAC decomposition measurement method with reference wave structure, and there are many methods and approaches to realize the technical scheme, and the above description is only the preferred embodiment of the present application, and it should be pointed out that, for ordinary skilled persons in the technical field, some improvements and refinements can be made without departing from the principle of the present application, and these improvements and refinements should also be regarded as the protection range of the present application. The components not explicitly described in the embodiment can be realized by the existing technology.

Claims

1. A platform attitude tracking (PARAFAC decomposition) measurement method with reference wave structure, characterized in that, The method comprises the following steps: Step 1, establishing a tracking attitude measurement system: installing an electromagnetic vector sensor array with the same receiving unit and attitude on an aircraft platform, measuring the installation coordinates of each sensor in the body coordinate system, forming a three-linear structure with the sensor array output data, performing PARAFAC decomposition by using a parallel factor alternating least squares algorithm, obtaining the spatial and polarization domain steering factors of each signal, setting the direction of arrival according to the navigation system, calculating the polarization domain steering factor under zero attitude, and calculating the platform attitude according to the relationship between the polarization steering factor and the attitude, so as to realize attitude measurement based on wave structure information; Step 2, performing attitude tracking estimation during complete sampling of multiple signals based on the tracking attitude measurement system established in step 1 according to the signal receiving condition; The complete sampling of multiple signals in step 2 refers to sampling a point in space to obtain three orthogonal dipole sampling or three orthogonal magnetic ring sampling containing multiple signals, and the sampling information of the point is in a complete state; the attitude tracking estimation during complete sampling of multiple signals comprises the following steps: Step 2-1-1, inputting the estimated values of each signal sequence, the initial attitude estimation, and the spatial and polarization domain steering factors of each signal in the initial sliding window time; Step 2-1-2, calculating the Khatri-Rao product of the spatial steering factor and the polarization domain steering factor, and combining to form a full-array steering vector; Step 2-1-3, multiplying the real-time receiving data of each sensor by the full-array non-updated steering vector matrix Moor-Penrose generalized inverse to obtain the first real-time estimation of the signal sequence; Step 2-1-4, combining the real-time receiving data of each sensor with the receiving data during the old window period; and combining the real-time estimated value of the signal sequence with the signal sequence during the old window period; Step 2-1-5, estimating the real-time update matrix of the full-array steering vector according to the combined receiving data of each sensor and the signal sequence; Step 2-1-6, the real-time update matrix of the steering vector is the Khatri-Rao product of the spatial steering factor and the polarization domain steering factor, the column data of each signal is inverse quantized into a matrix, the matrix is multiplied by the polarization domain steering factor after being transposed and conjugated, and the real-time update corresponding to the spatial steering factor is obtained; the column data inverse quantization matrix is multiplied by the spatial steering factor, and the real-time update corresponding to the polarization domain steering factor is obtained after the matrix is normalized; Step 2-1-7, multiplying the Moor-Penrose generalized inverse of the real-time update matrix of the steering vector by the time-shifted update of the receiving data of each sensor to obtain the real-time signal re-update estimation, and combining with the historical data; Step 2-1-8, updating the zero-attitude polarization domain steering factor according to the change of the navigation system condition, calculating the un-normalized attitude matrix from the relationship between the zero-attitude polarization domain steering factor and the real-time attitude polarization domain steering factor, normalizing the attitude matrix, and outputting; Step 2-1-9, redefining the effective data in the time sliding window: deleting the first column of the receiving data array of each sensor and the first row of the signal sequence, updating the time tags of the remaining parameters, becoming the tracking initial condition of the next round, and returning to step 2-1-1.

2. A platform attitude tracking (PARAFAC decomposition) measurement method with reference wave structure, characterized in that The method comprises the following steps: Step 1, establishing a tracking attitude measurement system: installing an electromagnetic vector sensor array with the same receiving unit and attitude on the aircraft platform, measuring the installation coordinates of each sensor in the body coordinate system, forming a three-linear structure with the sensor array output data, using the parallel factor alternating least squares algorithm for PARAFAC decomposition to obtain the spatial and polarization domain steering factors of each signal, setting the direction of arrival according to the navigation system, calculating the polarization domain steering factor under zero attitude, and calculating the platform attitude according to the relationship between the polarization steering factor and the attitude to realize attitude measurement based on wave structure information; Step 2, attitude tracking estimation in the case of multiple signal incomplete sampling based on the tracking attitude measurement system established in step 1 according to the signal reception condition; The multiple signal incomplete sampling in step 2 refers to non-three orthogonal dipole sampling or non-three orthogonal magnetic ring sampling of multiple signals at a point in space. Since the selection array makes the calculated orthogonalized attitude array have only two rows, according to the principle that the tracking attitude change is a small amount of rotation, the attitude tracking estimation in the case of multiple signal incomplete sampling adopts the following steps: Step 2-2-1, inputting the estimated value of each signal sequence in the initial sliding window time, the initial attitude estimation, and the spatial and polarization domain steering factors of each signal; Step 2-2-2, calculating the Khatri-Rao product of the spatial steering factor and the polarization domain steering factor to combine into a full-array steering vector; Step 2-2-3, multiplying the real-time receiving data of each sensor by the full-array non-updated steering vector matrix Moor-Penrose generalized inverse to obtain the first real-time estimation of the signal sequence; Step 2-2-4, combining the real-time receiving data of each sensor with the receiving data during the old window period; combining the real-time estimated value of the signal sequence with the signal sequence during the old window period; Step 2-2-5, estimating the real-time update matrix of the full-array steering vector according to the combined receiving data of each sensor and the signal sequence; Step 2-2-6, the real-time update matrix of the steering vector is the Khatri-Rao product of the spatial steering factor and the polarization domain steering factor, the column data of each signal is inverse quantized into a matrix, the conjugate transpose of the matrix is multiplied by the polarization domain steering factor to obtain the real-time update corresponding to the spatial factor, and the column data inverse quantization matrix is multiplied by the spatial factor to obtain the real-time update corresponding to the polarization domain steering factor after modulus normalization; Step 2-2-7, multiplying the Moor-Penrose generalized inverse of the real-time update matrix of the steering vector by the time-shifted update of the receiving data of each sensor to obtain the real-time signal re-update estimation combined with the historical data; Step 2-2-8, updating the zero attitude polarization domain steering factor according to the change of the navigation system condition: calculating the values of the two rows of the attitude change matrix according to the relationship between the zero attitude polarization domain steering factor and the real-time attitude polarization domain steering factor, and the relationship of the attitude matrix obtained by the last measurement, obtaining the attitude change matrix according to the principle that the corresponding values of the elements are equal, and calculating the real-time attitude. Step 2-2-9, time window re-circulation effective data: delete the first column of each sensor received data array and the first row of signal sequence, the rest of the parameter time scale is updated, and becomes the initial condition of the next round of tracking, returning to step 2-2-1.

3. A platform attitude tracking (PARAFAC decomposition) measurement method with reference wave structure, characterized in that Comprise the following steps: Step 1, establish a tracking and attitude measurement system: install an electromagnetic vector sensor array with the same receiving unit and the same attitude on the aircraft platform, measure the installation coordinates of each sensor in the body coordinate system, form a three-linear structure with the sensor array output data, perform PARAFAC decomposition using the parallel factor alternating least squares algorithm to obtain the spatial and polarization domain steering factors of each signal, set the direction of arrival according to the navigation system, calculate the polarization domain steering factor under zero attitude, and solve the platform attitude according to the relationship between the polarization steering factor and the attitude to realize attitude measurement based on wave structure information; Step 2, based on the signal reception, on the basis of the tracking and attitude measurement system established in step 1, perform single signal complete sampling attitude tracking estimation; The single signal complete sampling in step 2 refers to sampling at a point in space to obtain three orthogonal dipole sampling or three orthogonal magnetic ring sampling containing a single signal. The sampling information at this point is a single signal complete state. The single signal complete sampling attitude tracking estimation comprises the following steps: Step 2-3-1, input the initial sliding window time signal sequence estimation value, attitude initial estimation, and the spatial and polarization domain steering factors of each signal; Step 2-3-2, calculate the Khatri-Rao product of the spatial steering factor and the polarization domain steering factor, and combine them into a full array steering vector; Step 2-3-3, multiply the real-time sensor received data by the full array non-updated steering vector matrix Moor-Penrose generalized inverse to obtain the first real-time estimation of the signal sequence; Step 2-3-4, combine the real-time sensor received data with the received data during the old window period; Combine the real-time signal sequence estimation value with the signal sequence during the old window period; Step 2-3-5, estimate the real-time update matrix of the full array steering vector according to the combined sensor received data and signal sequence; Step 2-3-6, the real-time update matrix of the steering vector is the Khatri-Rao product of the spatial steering factor and the polarization domain steering factor. The column data of each signal is inverse quantized into a matrix. After the conjugate transpose of the matrix is multiplied by the polarization domain steering factor, the real-time update corresponding to the spatial factor is obtained. The column data inverse quantization matrix is multiplied by the spatial factor, and the real-time update corresponding to the polarization domain steering factor is obtained after the modulus normalization; Step 2-3-7, multiply the Moor-Penrose generalized inverse of the real-time update matrix of the steering vector by the time-shifted update of the sensor received data to obtain the real-time signal re-update estimation, which is combined with the historical data; Step 2-3-8, update the zero attitude polarization domain steering factor according to the change of the navigation system condition: calculate the linear equation of the three unknowns about the attitude change matrix according to the relationship between the zero attitude polarization domain steering factor and the real-time attitude polarization domain steering factor, and the relationship between the attitude matrix obtained by the last measurement, solve the equation to obtain the attitude change matrix, and then calculate the real-time attitude; Step 2-3-9, time window re-define valid data: delete the first column of each sensor received data array and the first row of signal sequence, the rest of the parameter time scale is updated to become the initial condition of the next round of tracking, return to step 2-3-1.

4. An electronic device, comprising: A processor and a memory, the memory storing program code which, when executed by the processor, causes the processor to perform the steps of the method of any one of claims 1 to 3.

5. A storage medium, characterized by A computer program or instructions stored, when the computer program or instructions run on a computer, perform the steps of the method of any one of claims 1 to 3.

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