Method for rapidly solving interference subspace of high-precision anti-interference navigation

Abstract

The invention relates to the technical field of high-precision anti-interference satellite navigation, and particularly discloses a high-precision anti-interference navigation interference subspace quick solving method, which comprises the following steps of: solving a virtual interference subspace required by relatively strong short-time delay deception interference detection based on the position of a satellite to be detected, the phase center position of an array antenna and the attitude of an antenna array plane; and based on multi-stage Wiener filtering forward decomposition, respectively solving a dense suppression jamming subspace, an isolated suppression deception jamming subspace and a dense suppression deception jamming subspace. According to the method, virtual interference data and strong deception interference data do not need to be generated through simulation, so that a pseudo-random code sequence of deception interference does not need to be known, and strength, pseudo-code time delay, carrier phase and the like do not need to be reasonably adjusted; meanwhile, multi-level Wiener filtering forward decomposition based on the data level and the covariance matrix level is good in rank reduction performance, simple in algorithm and small in operand; the method has the advantages of less prior information constraint, easy engineering realization and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of high-precision anti-interference satellite navigation, and in particular to a method for quickly solving interference subspace of high-precision anti-interference navigation. Background Art

[0002] The outstanding features of high-precision anti-interference satellite navigation applications such as precision approach and landing of aircraft, landing of aircraft on aircraft carriers, formation flying and automatic aerial refueling, rapid direction finding and autonomous north seeking of missile launchers / aircraft are complex interference environments and high requirements for positioning and orientation.

[0003] The complexity of the interference environment is mainly manifested in the variety of interference types and changes. From the perspective of interference mechanism, the interference types are divided into suppression interference, deception interference, and suppression and deception combined interference. Deception interference is further divided into regeneration and forwarding types. Interference changes mainly include the number, direction, modulation, frequency, intensity, etc. of interference that changes rapidly or slowly over time. Positioning and orientation requirements include real-time or near real-time positioning services at the decimeter / centimeter level and orientation services better than 1 degree / 0.5 degree.

[0004] High-precision positioning and orientation in complex interference environments requires solving three strongly coupled problems: complex interference detection and real-time suppression, satellite signal distortion tracing and control, and high-precision positioning and orientation with low distortion.

[0005] Through theoretical deduction, simulation analysis and experimental verification, the inventors decoupled the above three problems into three categories, seven subcategories and 10 sequential topics as shown in the table below.

[0006] Based on this, 10 high-precision anti-interference navigation invention patents corresponding to the above topics are planned and laid out, as shown in Table 1.

[0007] Table 1 List of decoupling and disassembly of high-precision positioning and orientation problems in complex interference environments

[0008]

[0009] In the present invention, the first category, the first subcategory, and the third invention listed in Table 1 - the fast solution method of the interference subspace are analyzed and explained.

[0010] The demand for fast solution of interference subspace mainly comes from the detection of strong short-delay deceptive interference, detection and suppression of suppressed deceptive combined interference, etc. In the detection of strong short-delay deceptive interference, the deceptive interference detection idea of ​​suppressing the true and convexing the false (i.e. suppressing satellite signals and highlighting deceptive interference) is adopted. It is necessary to virtualize medium-intensity interference in the direction of the incoming wave of the satellite signal to be detected, and the corresponding virtual interference subspace needs to be solved quickly. In the detection and suppression of suppressed deceptive combined interference, it is necessary to quickly solve the dense suppressed interference subspace, isolated suppressed deceptive interference subspace, and dense suppressed deceptive interference subspace.

[0011] The existing interference subspace solution methods are mainly based on eigendecomposition such as orthogonal triangular decomposition and singular value decomposition of the data covariance matrix.

[0012] Based on the subspace solution method of data covariance matrix eigendecomposition, the interference noise covariance matrix is ​​estimated from multiple snapshot observation data, and then the interference noise covariance matrix is ​​subjected to eigendecomposition such as orthogonal triangular decomposition and singular value decomposition to obtain mutually orthogonal interference subspace and noise subspace. The algorithm of covariance matrix eigendecomposition is complex, computationally intensive, and difficult to implement in engineering.

[0013] In the detection of strong short-delay deception interference, it is necessary to simulate medium-intensity interference in the direction of the incoming wave of the satellite signal to be detected to drive the anti-interference array antenna to form a gain null in that direction. The observed data does not include this virtual interference. If the method of simulating virtual interference data first and then superimposing it on the observed data is adopted, the pseudo-random code sequence must be known and the strength of the virtual interference, pseudo-code delay, carrier phase, etc. must be reasonably adjusted, which is difficult to implement.

[0014] In the detection and suppression of isolated / intensive suppression and strong deception combined interference, although the observation data contains strong deception interference and the deception interference will affect the subsequent navigation signal information processing, the intensity of the strong deception interference is lower than the interference characteristic value threshold and is not included in the interference subspace. When the direction of the strong deception interference is known, if the method of simulating the strong deception interference data first and then superimposing it on the observation data is adopted, the pseudo-random code sequence of the deception interference must also be known and the intensity, pseudo-code delay, carrier phase, etc. must be reasonably adjusted, which is difficult to achieve.

[0015] The invention discloses a method for quickly solving interference subspaces for high-precision anti-interference navigation, which uses multi-stage Wiener filtering (MWF) forward decomposition to quickly solve virtual interference subspaces, densely suppressed interference subspaces, isolated suppressed deceptive interference subspaces, and densely suppressed deceptive interference subspaces. The method does not require simulation of virtual interference data or strong deceptive interference data, and the algorithm of multi-stage Wiener filtering forward decomposition is simple, the amount of calculation is small, and the engineering implementation is easy.

[0016] Based on this technical background, the present invention studies a method for quickly solving the interference subspace of high-precision anti-interference navigation. Summary of the invention

[0017] In view of the deficiencies in the prior art, the present invention provides a method for quickly solving the interference subspace of high-precision anti-interference navigation. There is no need to simulate and generate virtual interference data and strong deception interference data, and thus there is no need to know the pseudo-random code sequence of the deception interference and reasonably adjust the intensity, pseudo-code delay, carrier phase, etc.; at the same time, based on the multi-level Wiener filter forward decomposition at the data level and the covariance matrix level, the rank reduction performance is good, the algorithm is simple, and the amount of calculation is small; it has the advantages of less constraints on prior information and easy engineering implementation.

[0018] In order to achieve the above object, the present invention provides a method for quickly solving interference subspace of high-precision anti-interference navigation, comprising:

[0019] Based on the position of the satellite to be detected, the phase center position of the array antenna, and the antenna array attitude, the virtual interference subspace required for strong short-delay deception interference detection is solved;

[0020] Based on multi-stage Wiener filter forward decomposition, the dense suppression interference subspace required for dense suppression interference suppression, dense suppression and strong deception combination interference suppression, isolated suppression deception interference subspace required for isolated suppression and relatively strong deception combination interference suppression, and dense suppression deception interference subspace required for dense suppression and relatively strong deception combination interference suppression are solved respectively.

[0021] The beneficial effects of the present invention include:

[0022] (1) The invention proposes a method for quickly solving the interference subspace for high-precision anti-interference navigation. It does not need to simulate and generate virtual interference data and strong deception interference data, and thus does not need to know the pseudo-random code sequence of the deception interference and reasonably adjust the strength, pseudo-code delay, carrier phase, etc. At the same time, the multi-level Wiener filter forward decomposition based on the data level and covariance matrix level has good rank reduction performance, simple algorithm and small amount of calculation. It has the advantages of less constraints on prior information and easy engineering implementation.

[0023] (2) The present invention proposes a method for quickly solving interference subspace for high-precision anti-interference navigation, which adopts methods such as "multi-level Wiener filtering at the data level + orthogonalization of deception interference guidance vector" and "multi-level Wiener filtering at the covariance matrix level + orthogonalization of deception interference guidance vector" to meet the needs of quickly solving interference subspace for strong short-delay deception interference detection, suppression of deception combination interference detection and suppression, and comprehensively solves the problem of quickly solving interference subspace.

[0024] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.

[0026] Figure 1 Schematic diagram of the flow of the method for quickly solving the interference subspace of the high-precision anti-interference navigation proposed by the present invention. DETAILED DESCRIPTION

[0027] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0028] The present invention provides a method for quickly solving interference subspace for high-precision anti-interference navigation. Figure 1 As shown, including:

[0029] Based on the position of the satellite to be detected, the phase center position of the array antenna, and the antenna array attitude, the virtual interference subspace required for strong short-delay deception interference detection is solved;

[0030] Based on multi-stage Wiener filter forward decomposition, the dense suppression interference subspace required for dense suppression interference suppression, dense suppression and strong deception combination interference suppression, isolated suppression deception interference subspace required for isolated suppression and relatively strong deception combination interference suppression, and dense suppression deception interference subspace required for dense suppression and relatively strong deception combination interference suppression are solved respectively.

[0031] In the present invention, there is no need to simulate and generate virtual interference data and strong deception interference data, and thus there is no need to know the pseudo-random code sequence of the deception interference and reasonably adjust the strength, pseudo-code delay, carrier phase, etc.; at the same time, the multi-level Wiener filter forward decomposition based on the data level and the covariance matrix level has good rank reduction performance, simple algorithm and small amount of calculation; it has the advantages of less constraints on prior information and easy engineering implementation.

[0032] According to the present invention, based on the position of the satellite to be detected, the phase center position of the array antenna, and the antenna array attitude, solving the virtual interference subspace required for strong short-delay deception interference detection includes:

[0033] According to the position of the satellite to be detected, the position of the phase center of the array antenna, and the attitude of the antenna array, the observation vector of the satellite to be detected at the phase center of the array antenna is calculated to obtain the direction of the signal from the satellite to be detected, that is, the azimuth and pitch angle.

[0034] According to the direction of the incoming wave of the satellite to be detected and the layout of the array antenna, the steering vector in the direction of the incoming wave of the satellite signal to be detected is calculated;

[0035] The vector is guided upward based on the satellite to be detected, and the anti-interference freedom of the array antenna is combined with the number of interference subspaces and each interference subspace to obtain the virtual interference subspace required for strong short-delay deception interference detection.

[0036] According to the present invention, the expression of the observation vector of the satellite to be detected at the phase center of the array antenna is: ,in, ;

[0037] The calculation formula for the azimuth angle is:

[0038] ;

[0039] The calculation formula for the pitch angle is:

[0040] ;

[0041] in, is the azimuth, is the pitch angle, is the total number of satellites to be detected;

[0042] The calculation formula of the steering vector in the direction of the incoming wave of the satellite signal to be detected is:

[0043] ;

[0044] in, is the carrier frequency, is the speed of light, is the number of array elements of the array antenna, is the vector transpose symbol, is the position vector of each element of the array antenna, , is a constant vector when the array element layout is determined, is the unit vector of the incoming wave direction of the satellite signal to be detected, is the steering vector in the direction of the incoming wave of the satellite signal to be detected;

[0045] The calculation formula of the unit vector of the incoming wave direction of the satellite signal to be detected is:

[0046] .

[0047] According to the present invention, the expression of the virtual interference subspace is:

[0048] , , ;

[0049] in, , is the ceiling operator, ;

[0050] based on The virtual interference subspaces are respectively suppressed from 1 to , to indivual, , No. to The optimal weight vector of the satellite signals to be detected.

[0051] According to the present invention, based on multi-stage Wiener filter forward decomposition, solving the dense suppression interference subspace required for dense suppression interference suppression and dense suppression and strong deception combined interference suppression includes:

[0052] Expand the covariance matrix in the interference incident direction to obtain the expanded covariance matrix;

[0053] The multi-level Wiener filtering based on the covariance matrix hierarchy is used to forward decompose the expanded covariance matrix to obtain the dense suppression interference subspace required for dense suppression interference suppression and dense suppression and strong deception combined interference suppression.

[0054] Preferably, the calculation formula of the expanded covariance matrix is:

[0055] ;

[0056] in, is the covariance matrix, is the Hadamard product operator, is the extended matrix, whose matrix elements are , , They are array antenna , The azimuth of the array element, is the zero-sink width widening factor;

[0057] The algorithm for forward decomposition of the expanded covariance matrix using multi-level Wiener filtering based on the covariance matrix hierarchy includes:

[0058] initialization: , , ;

[0059] Iteration Second-rate: ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] in, is the symbol of vector conjugate transpose;

[0068] The densely suppressed interference subspace is given by middle The corresponding large value Composition, that is ,in, .

[0069] According to the present invention, based on multi-stage Wiener filter forward decomposition, solving the isolated suppression and strong deception combination interference suppression required for the isolated suppression and deception interference subspace includes:

[0070] The multi-level Wiener filter based on data hierarchy is used to decompose the data forward to obtain the isolated suppressed interference subspace;

[0071] According to the prior information of the deception interference direction, an upward guidance vector of the deception interference direction is obtained;

[0072] The Schmidt orthogonalization process is performed on the upward directional vector of the deception interference to obtain the isolated suppression deception interference subspace required for the suppression of isolated suppression and stronger deception combination interference.

[0073] According to the present invention, the algorithm for forward decomposing data using multi-level Wiener filtering based on data hierarchy includes:

[0074] initialization: , ;

[0075] Iteration Second-rate: ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] in, is the scalar conjugate symbol;

[0081] The isolated suppression interference subspace is given by middle The corresponding large value Composition, that is ,in, ;

[0082] No. The upward guidance vector of the deceptive interference is ,in, ;

[0083] The deceptive interference upward steering vector is Schmidt orthogonalized, that is, from Subtract the previous The projection component on it gets a new orthogonal vector ;

[0084] The expression of isolated suppression deception interference subspace is obtained as follows:

[0085] .

[0086] According to the present invention, based on multi-stage Wiener filter forward decomposition, solving the dense suppression deception interference subspace required for dense suppression and strong deception combination interference suppression includes:

[0087] Expand the covariance matrix in the interference incident direction to obtain the expanded covariance matrix;

[0088] The multi-level Wiener filter based on the covariance matrix hierarchy is used to forward decompose the expanded covariance matrix to obtain the densely suppressed interference subspace. ; The densely suppressed interference subspace obtained in this step process uses the same operators, matrices, algorithms, and results as those obtained by the aforementioned solution, and the solution process of the two is the same; according to the prior information of the deceptive interference direction, the upward guidance vector of the deceptive interference direction is obtained;

[0089] The upward steering vector of the deception interference is Schmidt orthogonalized to obtain the dense suppression deception interference subspace required for the combined interference suppression of dense suppression and stronger deception.

[0090] According to the present invention, the The upward guidance vector of the deceptive interference is ,in, ;

[0091] The Schmidt orthogonalization is performed on the upward steering vector of the deception interference, that is, from Subtract the previous The projection component on it gets a new orthogonal vector ;

[0092] The expression of dense suppression deception interference subspace is:

[0093] .

[0094] In the present invention, the satellite to be inspected is used to coordinate the steering vector, multi-level Wiener filtering at the covariance matrix level, "multi-level Wiener filtering at the data level + orthogonalization of the deception interference steering vector", "multi-level Wiener filtering at the covariance matrix level + orthogonalization of the deception interference steering vector" and other methods to meet the needs of fast solution of interference subspace for strong short-delay deception interference detection, suppression of deception combined interference detection and suppression, and comprehensively solve the problem of fast solution of interference subspace.

[0095] The present invention will be described in more detail below by way of examples.

[0096] Embodiment 1:

[0097] like Figure 1 As shown, this embodiment proposes a method for quickly solving the interference subspace of high-precision anti-interference navigation, which uses the excellent rank reduction performance and simple recursive operation of multi-stage Wiener filter forward decomposition to quickly solve the virtual interference subspace required for strong short-delay deception interference detection, dense suppression interference suppression, dense suppression and strong deception combination interference suppression required dense suppression interference subspace, isolated suppression deception interference subspace required for isolated suppression and strong deception combination interference suppression, and dense suppression deception interference subspace required for dense suppression and strong deception combination interference suppression;

[0098] Specifically include:

[0099] 1. Solve the virtual interference subspace:

[0100] The steps of the virtual interference subspace fast solution method are as follows:

[0101] 1) According to The position of the satellite to be detected, the phase center position of the array antenna, the antenna array attitude, etc., calculate the observation vector of the satellite to be detected at the phase center of the array antenna , obtain the signal direction of the satellite to be detected, that is, the azimuth and elevation angle ;

[0102] in, ;

[0103] ;

[0104] ;

[0105] in, is the total number of satellites to be detected.

[0106] 2) Get the A steering vector in the direction of the incoming wave of the satellite signal to be detected ;

[0107] in, is the carrier frequency, is the speed of light, is the number of array elements of the array antenna, is the vector transpose sign;

[0108] The position vector of each element of the array antenna ( ), which is a constant vector when the array element layout is determined;

[0109] The unit vector of the incoming wave direction of the satellite signal to be detected is:

[0110] ;

[0111] 3) Obtain Virtual interference subspace , , ;

[0112] in, , is the ceiling operator;

[0113] ;

[0114] In this way, based on The virtual interference subspaces are respectively suppressed from 1 to , to indivual, , No. to The optimal weight vector of the satellite signals to be detected;

[0115] 2. Solve the dense suppression interference subspace:

[0116] The steps of the fast solution method for dense suppression interference subspace are as follows:

[0117] 1) Covariance matrix Expand in the interference incident direction to obtain the expanded covariance matrix ;

[0118] in, is the Hadamard product operator;

[0119] is the extended matrix, the matrix elements are ;

[0120] , For the array antenna , The azimuth of the array element, is the zero-sink width widening factor;

[0121] 2) Using multi-level Wiener filtering based on covariance matrix hierarchy Forward decomposition, the algorithm is as follows:

[0122] initialization: , , ;

[0123] Iteration Second-rate: ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] in is the symbol of vector conjugate transpose;

[0132] 3) The densely suppressed interference subspace is composed of middle The corresponding large value Composition, that is ,in ;

[0133] 3. Solve the isolated suppression deception interference subspace:

[0134] The steps of the fast solution method for isolated suppression deception interference subspace are as follows:

[0135] 1) Use multi-level Wiener filtering based on data hierarchy to decompose the data forward. The algorithm is as follows:

[0136] initialization: , ;

[0137] Iteration Second-rate: ;

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142] in, is the scalar conjugate symbol;

[0143] 2) The isolated suppression interference subspace is composed of middle The corresponding large value constitute, ,in ;

[0144] 3) Based on the deception interference, we can obtain the prior information. A deceptive interference to the upward guidance vector ,in ;

[0145] 4) Yes Perform Schmidt orthogonalization, that is, from Subtract the previous The projection component on it gets a new orthogonal vector , get the isolated suppression deception interference subspace ;

[0146] 4. Solve the dense suppression deception interference subspace:

[0147] The steps of the fast solution method for dense suppression deception interference subspace are as follows:

[0148] 1) Covariance matrix Expand in the interference incident direction to obtain the expanded covariance matrix , the matrices and operators in the formula are the same as those in the “fast solution method for densely suppressed interference subspace”;

[0149] 2) Using multi-level Wiener filtering based on covariance matrix hierarchy Perform forward decomposition, the algorithm is the same as the "fast solution method of densely suppressed interference subspace";

[0150] 3) The densely suppressed interference subspace is composed of middle The corresponding large value Composition, that is ,in ;

[0151] 4) Based on the deception interference, we can obtain the prior information. A deceptive interference to the upward guidance vector ,in ;

[0152] 5) Yes Perform Schmidt orthogonalization, that is, from Subtract the previous The projection component on it gets a new orthogonal vector , get dense suppression deception interference subspace .

[0153] An embodiment of the present invention proposes a method for quickly solving the interference subspace of high-precision anti-interference navigation. There is no need to simulate and generate virtual interference data and strong deception interference data, and thus there is no need to know the pseudo-random code sequence of the deception interference and reasonably adjust the intensity, pseudo-code delay, carrier phase, etc.; at the same time, based on the multi-level Wiener filter forward decomposition at the data level and the covariance matrix level, the rank reduction performance is good, the algorithm is simple, and the amount of calculation is small; it has the advantages of less constraints on prior information and easy engineering implementation.

[0154] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for quickly solving interference subspace for high-precision anti-interference navigation, characterized in that: include: Based on the position of the satellite to be detected, the phase center position of the array antenna, and the antenna array attitude, the virtual interference subspace required for strong short-delay deception interference detection is solved; Based on multi-stage Wiener filter forward decomposition, the dense suppression interference subspace required for dense suppression interference suppression, dense suppression and strong deception combination interference suppression, isolated suppression deception interference subspace required for isolated suppression and relatively strong deception combination interference suppression, and dense suppression deception interference subspace required for dense suppression and relatively strong deception combination interference suppression are solved respectively.

2. The method according to claim 1, characterized in that: Based on the position of the satellite to be detected, the phase center position of the array antenna, and the antenna array attitude, the virtual interference subspace required for solving the strong short-delay deception interference detection includes: According to the position of the satellite to be detected, the position of the phase center of the array antenna, and the attitude of the antenna array, the observation vector of the satellite to be detected at the phase center of the array antenna is calculated to obtain the direction of the signal from the satellite to be detected, that is, the azimuth and pitch angle. Calculate the steering vector in the direction of the incoming wave of the satellite signal to be detected according to the incoming wave direction of the satellite to be detected and the layout of the array antenna; Based on the satellite to be detected, an upwardly oriented vector is used, and the anti-interference freedom of the array antenna is combined, and the number of interference subspaces and each interference subspace are coordinated to obtain a virtual interference subspace required for strong short-delay deception interference detection.

3. The method according to claim 2, characterized in that The expression of the observation vector of the satellite to be detected at the phase center of the array antenna is: ,in, ; The calculation formula of the azimuth angle is: ； The calculation formula of the pitch angle is: ； in, is the azimuth, is the pitch angle, is the total number of satellites to be detected; The calculation formula of the steering vector in the direction of the incoming wave of the satellite signal to be detected is: ； in, is the carrier frequency, is the speed of light, is the number of array elements of the array antenna, is the vector transpose symbol, is the position vector of each element of the array antenna, , is a constant vector when the array element layout is determined, is the unit vector of the incoming wave direction of the satellite signal to be detected, is the steering vector in the direction of the incoming wave of the satellite signal to be detected; The calculation formula of the unit vector of the incoming wave direction of the satellite signal to be detected is: 。 4. The method according to claim 3, characterized in that The expression of the virtual interference subspace is: 、 、 ； in, , is the ceiling operator, ; based on The virtual interference subspaces are respectively suppressed from 1 to 2 , to indivual, , No. to The optimal weight vector of the satellite signals to be detected.

5. The method according to claim 1, characterized in that: Based on multi-stage Wiener filter forward decomposition, the dense suppression interference subspace required for dense suppression interference suppression and dense suppression and strong deception combined interference suppression includes: Expand the covariance matrix in the interference incident direction to obtain the expanded covariance matrix; The multi-level Wiener filtering based on the covariance matrix hierarchy is adopted to forward decompose the expanded covariance matrix to obtain the dense suppression interference subspace required for dense suppression interference suppression and dense suppression and strong deception combined interference suppression.

6. The method according to claim 5, characterized in that The calculation formula of the expanded covariance matrix is: ； in, is the covariance matrix, is the Hadamard product operator, is the extended matrix, whose matrix elements are , , They are array antenna , The azimuth of the array element, is the zero-sink width widening factor; The algorithm for forward decomposing the expanded covariance matrix using multi-level Wiener filtering based on the covariance matrix hierarchy includes: initialization: , , ; Iteration Second-rate: ; ； ； ； ； ； ； ； in, is the symbol of vector conjugate transpose; The densely suppressed interference subspace is given by middle The corresponding large value Composition, that is ,in, .

7. The method according to claim 1, characterized in that Based on multi-stage Wiener filter forward decomposition, the isolated suppression deception interference subspace required to solve the isolated suppression and strong deception combination interference suppression includes: The multi-level Wiener filter based on data hierarchy is used to decompose the data forward to obtain the isolated suppressed interference subspace; According to the prior information of the deception interference direction, an upward guidance vector of the deception interference direction is obtained; The deception interference upward directional vector is subjected to Schmidt orthogonalization processing to obtain the isolated suppression deception interference subspace required for the isolated suppression and stronger deception combination interference suppression.

8. The method according to claim 7, characterized in that The algorithms for forward decomposition of data using multi-level Wiener filtering based on data hierarchy include: initialization: , ; Iteration Second-rate: ; ； ； ； ； in, is the scalar conjugate symbol; The isolated suppression interference subspace is given by middle The corresponding large value Composition, that is ,in, ; No. The upward guidance vector of the deceptive interference is ,in, ; The deceptive interference upward steering vector is Schmidt orthogonalized, that is, from Subtract the previous The projection component on it gets a new orthogonal vector ; The expression for obtaining the isolated suppression deception interference subspace is: 。 9. The method according to claim 6, characterized in that Based on multi-stage Wiener filter forward decomposition, the dense suppression deception interference subspace required for solving the dense suppression and strong deception combination interference suppression includes: Expand the covariance matrix in the interference incident direction to obtain the expanded covariance matrix; The expanded covariance matrix is ​​forward decomposed by using a multi-level Wiener filter based on the covariance matrix hierarchy to obtain a densely suppressed interference subspace; the densely suppressed interference subspace obtained in this step process uses the same operators, matrices, algorithms, and results as those obtained by the aforementioned solution, and the solution process of the two is the same; according to the prior information of the deceptive interference direction, the upward steering vector of the deceptive interference direction is obtained; The upward steering vector of the deception interference is subjected to Schmidt orthogonalization processing to obtain the dense suppression deception interference subspace required for the dense suppression and stronger deception combination interference suppression.

10. The method according to claim 9, characterized in that Get the The upward guidance vector of the deceptive interference is ,in, ; The upward steering vector of the deception interference is subjected to Schmidt orthogonalization, that is, from Subtract the previous The projection component on it gets a new orthogonal vector ; The expression of the dense suppression deception interference subspace is: 。

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