阅读说明：本技术 基于重力梯度-地形异源数据匹配的导航定位方法 (Navigation positioning method based on gravity gradient-terrain heterogeneous data matching ) 是由 李海凤 佟佳慧 马杰 张闻博 李霖 于 2020-12-23 设计创作，主要内容包括：本发明涉及一种基于重力梯度-地形异源数据匹配的导航定位方法,属于导航技术领域,解决了现有技术重力导航应用范围和定位精度受限的问题。该方法包括：获得航行器当前位置坐标和所在航线的重力梯度张量序列；提取上述重力梯度张量序列对应的DQL特征；提取航行器当前位置坐标相对地形图中所在航线各地形单元位置坐标的DQL特征；将上述航行器当前位置坐标相对地形图中所在航线各地形单元位置坐标的DQL特征、上述重力梯度张量序列对应的DQL特征进行特征匹配,获得匹配程度最高的重力梯度张量序列中元素；搜索上述元素对应的重力梯度所在位置,根据该位置对航行器当前位置坐标进行修正。实现了重力导航应用范围和定位精度的提高。(The invention relates to a navigation positioning method based on gravity gradient-terrain heterogeneous data matching, belongs to the technical field of navigation, and solves the problem that the application range and the positioning accuracy of gravity navigation in the prior art are limited. The method comprises the following steps: obtaining the current position coordinate of the aircraft and the gravity gradient tensor sequence of the located route; extracting DQL characteristics corresponding to the gravity gradient tensor sequence; extracting DQL characteristics of the current position coordinates of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map; performing feature matching on the DQL features of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of a route in a topographic map and the DQL features corresponding to the gravity gradient tensor sequence to obtain elements in the gravity gradient tensor sequence with the highest matching degree; and searching the position of the gravity gradient corresponding to the element, and correcting the current position coordinate of the aircraft according to the position. The gravity navigation application range and the positioning precision are improved.)

1. A navigation positioning method based on gravity gradient-terrain heterogeneous data matching is characterized by comprising the following steps:

obtaining the current position coordinate of the aircraft and the gravity gradient tensor sequence of the located route;

extracting DQL characteristics corresponding to the gravity gradient tensor sequence;

extracting DQL characteristics of the current position coordinates of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map;

performing feature matching on the DQL features of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of a route in a topographic map and the DQL features corresponding to the gravity gradient tensor sequence to obtain elements in the gravity gradient tensor sequence with the highest matching degree;

and searching the position of the gravity gradient corresponding to the element, and correcting the current position coordinate of the aircraft according to the position.

2. The method for navigational positioning based on gravity gradient-terrain allogenic data matching according to claim 1, wherein the current position coordinates (x, y, z) of the vehicle are obtained by means of an inertial navigation system.

3. The method for navigation and positioning based on gravity gradient-terrain heterogeneous data matching according to claim 1 or 2, wherein the step of obtaining the gravity gradient tensor sequence of the located route further comprises the following steps:

by measurement at equal time intervals by means of a gravity gradiometer, obtainingObtaining the gravity gradient tensor sequence (gamma) of n different positions before the current position on the flight path₁ Γ₂ … Γ_n) (ii) a Wherein, the ith element Γ in the sequence_iIs composed of

Γ_i＝(Γ_xx Γ_yy Γ_zz Γ_xy Γ_yz Γ_zx Γ_xz Γ_zy Γ_yx)_i，i＝1 2 … n。

4. The navigation and positioning method based on gravity gradient-terrain heterogeneous data matching according to claim 3, wherein the extracting of the DQL features corresponding to the gravity gradient tensor sequence further comprises:

filtering out the repeated component of each element in the gravity gradient tensor sequence to obtain new elements only containing independent components, and sequentially arranging the new elements to form a new gravity gradient tensor sequence;

according to the new gravity gradient tensor sequence constructed in the above way, the difference delta gamma corresponding to n-1 pairs of adjacent elements is obtained_i；

Δ Γ obtained as described above_iThe DQL characteristic DQL (i) of each pair of adjacent elements is extracted by the following formula

In the formula, gamma₀Each pair of adjacent elements contains a DQL feature that contains 5 components;

sequentially arranging the DQL characteristics of n-1 pairs of adjacent elements as the DQL characteristics corresponding to the gravity gradient tensor sequence and marking as A₁。

5. The navigation positioning method based on gravity gradient-terrain heterogeneous data matching as claimed in claim 4, wherein the repetitive component Γ of each element in the gravity gradient tensor sequence is filtered out according to the gradient tensor rule in the following formula_yy、Γ_xz、Γ_zy、Γ_yx

Obtaining a signal containing only 5 independent components Γ_i＝(Γ_xx Γ_zz Γ_xy Γ_yz Γ_zx)_iThe new element of (1).

6. The method for navigation and positioning based on gravity gradient-terrain heterogeneous data matching according to claim 4 or 5, wherein the step of extracting DQL characteristics of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of the route in the topographic map further comprises the steps of:

acquiring a centroid position coordinate (epsilon eta zeta) of each terrain unit distributed at equal intervals with the current position in the terrain map;

acquiring a full-tensor gravity gradient measured value caused by each terrain unit according to the central position coordinates (epsilon eta zeta) of each terrain unit and the current position coordinates (x, y, z) of the aircraft; wherein the ith terrain cell induced full-tension gravity gradient measurement Γ_iIs' of gamma_i′＝(Γ_xx′ Γ_zz′ Γ_xy′ Γ_yz′ Γ_zx′)_i；

According to the measured value gamma of the full-tension gravity gradient caused by each terrain unit_i', obtaining a difference DeltaGamma of n-1 pairs of adjacent elements_i' approximately as the difference of its gravitational gradient tensor

ΔΓ_i′＝Γ_i+1′-Γ_i′

Δ Γ obtained as described above_i'DQL characteristic DQL' of each pair of adjacent elements is extracted by the following formula (i)

In the formula, gamma₀Each pair of adjacent elements contains a DQL feature that contains 5 components;

sequentially arranging the DQL characteristics DQL '(i) of n-1 pairs of adjacent elements as DQL characteristics of the current position coordinate of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map, and marking the DQL characteristics DQL' (i) as A₂。

7. The method as claimed in claim 6, wherein the centroid position coordinates (ε η ζ) of each terrain unit in the terrain map, which is equally spaced from the current position, are obtained by the following formula

ε＝x+i×r

η＝y+i×r

ζ＝z+i×r

Where i is 1 … n, r is the terrain cell separation distance, and (x, y, z) is the vehicle current position coordinates.

8. The method for navigation and positioning based on gravity gradient-terrain heterogeneous data matching of claim 7, wherein the ith terrain unit-induced full tensor gravity gradient measurement value Γ_i' obtained by the following formula

Where ψ represents an integration region, φ (ε η ζ) is a morphological function of the terrain, and ρ (ε η ζ) is a terrain density distribution function.

9. The gravity gradient-terrain heterogeneous data matching-based navigation positioning method according to claim 8, wherein the ρ (ε η ζ) satisfies a pratt density model in the following formula

Wherein D represents the thickness of the crust of the integral area, h represents the altitude of the central point of the terrain unitDegree, rho₀Is a constant coefficient.

10. The navigation and positioning method based on gravity gradient-terrain heterogeneous data matching according to any one of claims 1-2, 4-5, and 7-9, wherein the feature matching is performed on the DQL feature of the current position coordinate of the aircraft relative to the position coordinate of each terrain unit of the route in the terrain map and the DQL feature corresponding to the gravity gradient tensor sequence to obtain the element in the gravity gradient tensor sequence with the highest matching degree, further comprises:

obtaining the feature matching similarity I (A) of the DQL features of the current position coordinates of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map and the DQL features corresponding to the gravity gradient tensor sequence by a mutual information similarity calculation method in the following formula₁ A₂)

I(A₁ A₂)＝H(A₁)+H(A₂)-H(A₁ A₂)

Wherein

p(A_1i A_2i)＝p(A_1i)p(A_2i)

In the formula, p (A)_1i) Is A₁DQL characteristic DQL of the ith pair of adjacent elements (i) at A₁Probability of occurrence in all elements; p (A)_2i) Is A₂DQL characteristic DQL' (i) of the ith pair of adjacent elements is at A₂Probability of occurrence in all elements; p (A)_1i A_2i) Is p (A)_1i)、p(A_2i) A joint probability distribution of (a);

identification of highest I (A)₁ A₂) The corresponding gravity gradient tensor sequence.

Technical Field

The invention relates to the technical field of navigation, in particular to a navigation positioning method based on gravity gradient-terrain heterogeneous data matching.

Background

For aircraft that are flying at great distances or are traveling underwater for long periods of time, Inertial Navigation Systems (INS) are the core devices for their navigation. However, the positioning error of the inertial navigation system is accumulated continuously with the increase of time, so that the positioning accuracy of the inertial navigation system can be ensured only by periodically calibrating through other auxiliary navigation means when the inertial navigation system flies at a long distance or sails underwater for a long time. Auxiliary navigation means such as astronomical navigation, terrain matching navigation, radio navigation and GPS satellite navigation are often adopted for position correction, but the radio and GPS satellite navigation need to radiate signals to the outside, so that the signals are easy to detect and capture, and the use conditions of the astronomical navigation and the terrain matching are limited.

In order to verify the feasibility of the gravity gradient matching positioning technology, a navigation algorithm module is required to be provided with a gravity gradient real-time graph and a reference graph. The real-time graph refers to data obtained by online measurement of a sensor in the carrier motion process, and for the gravity form matching, the sensor is a full-tensor gravity gradiometer, and the precision of the full-tensor gravity gradiometer meets the requirement of measuring a weak space-variant gravity gradient field. The reference map refers to matching area data which is bound in advance on an aircraft/aircraft matching computer, the spatial resolution of the reference map determines the positioning precision of matching navigation, and the coverage of the reference map determines the working range of a navigation system.

At present, the gravity gradient reference map is obtained mainly by the following two methods: the first method relies on field measurements in geology and the like. The actual measurement of gravity data requires considerable manpower, material resources and time, the actual measurement data of other countries cannot be obtained due to the master relationship, the measured data cannot be directly used for navigation, and complicated correction and processing are required to be carried out on the low-density and irregular measurement data, so that the coverage range and the data density of the actual measurement data far cannot meet the global positioning navigation requirement of the submarine. The second method is to calculate the global gravitational field through an earth gravitational field potential model (spherical harmonic model), and correct the spherical harmonic parameters by means of satellite measurement data and local measurement data, and since the earth gravitational field model is an overall optimal approximation for the earth basic gravitational field, it is difficult to provide high-resolution field source details, and it is impossible to provide sufficient underwater navigation positioning accuracy.

Due to the restriction of a gravity gradient reference image preparation means, at present, no global gravity field data which meets the technical requirements of gravity gradient matching navigation and is large in range, high in precision and regular is available in all countries, the application range and the positioning precision of gravity navigation are severely restricted, and the gravity gradient matching technology can only be demonstrated and verified in a small range and cannot be popularized and applied.

Disclosure of Invention

In view of the foregoing analysis, the embodiments of the present invention are directed to providing a navigation and positioning method based on gravity gradient-terrain heterogeneous data matching, so as to solve the problem that the application range and the positioning accuracy of gravity navigation are limited in the prior art.

In one aspect, an embodiment of the present invention provides a navigation positioning method based on gravity gradient-terrain heterogeneous data matching, including the following steps:

obtaining the current position coordinate of the aircraft and the gravity gradient tensor sequence of the located route;

extracting DQL characteristics corresponding to the gravity gradient tensor sequence;

extracting DQL characteristics of the current position coordinates of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map;

performing feature matching on the DQL features of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of a route in a topographic map and the DQL features corresponding to the gravity gradient tensor sequence to obtain elements in the gravity gradient tensor sequence with the highest matching degree;

and searching the position of the gravity gradient corresponding to the element, and correcting the current position coordinate of the aircraft according to the position.

The beneficial effects of the above technical scheme are as follows: according to the gravity gradient matching technology which does not depend on external information and does not radiate energy to the outside, the requirements of passivity, autonomy, concealment and the like of navigation can be met simultaneously, and a brand-new autonomous navigation mode is provided. DQL feature matching is carried out according to the gravity gradient sequence and the current position of the aircraft, and the problem that the existing gravity gradient matching navigation does not have large-range, high-precision and regularized global gravity field data is solved. The correctness and adaptability of the gravity gradient matching algorithm of the aircraft/underwater vehicle can be effectively verified.

In a further development of the above method, the current position coordinates (x, y, z) of the vehicle are obtained by means of an inertial navigation system.

The beneficial effects of the above technical scheme are as follows: a method of acquiring coordinates of a current position of the aircraft is defined. The current position coordinate provided by the inertial navigation module is a coordinate obtained by autonomous detection which does not depend on any external information and does not radiate energy to the outside, and can work in the air, the earth surface or even underwater all day long and all time.

Further, obtaining a gravity gradient tensor sequence of the located route, further comprising:

obtaining a gravity gradient tensor sequence (gamma) of n different positions before the current position on the flight path by measuring with a gravity gradiometer at equal time intervals₁ Γ₂ … Γ_n) (ii) a Wherein each element Γ in the sequence_iContaining 9 components, the ith element being

Γ_i＝(Γ_xx Γ_yy Γ_zz Γ_xy Γ_yz Γ_zx Γ_xz Γ_zy Γ_yx)_i

i＝1 2 … n。

The beneficial effects of the above further improved scheme are: the method for acquiring the gravity gradient tensor sequence of the located route is defined. The tensor sequence measured by the gravity gradiometer at equal time intervals can be used for eliminating navigation errors caused by gravity, and high-precision navigation and positioning under a complex environment can be realized by combining two modes of gravity gradient and inertial navigation and positioning.

Further, the extracting the DQL feature corresponding to the gravity gradient tensor sequence further includes:

filtering out the repeated component of each element in the gravity gradient tensor sequence to obtain new elements only containing independent components, and sequentially arranging the new elements to form a new gravity gradient tensor sequence;

according to the new gravity gradient tensor sequence constructed in the above way, the difference delta gamma corresponding to n-1 pairs of adjacent elements is obtained_i；

Δ Γ obtained as described above_iThe DQL characteristic DQL (i) of each pair of adjacent elements is extracted by the following formula

In the formula, gamma₀Each pair of adjacent elements contains a DQL feature that contains 5 components;

sequentially arranging the DQL characteristics of n-1 pairs of adjacent elements as the DQL characteristics corresponding to the gravity gradient tensor sequence and marking as A₁。

The beneficial effects of the above further improved scheme are: and defining a method for extracting the DQL characteristics corresponding to the gravity gradient tensor sequence. The above method is a feature extraction method that is most suitable for the present invention, which is concluded by the inventors through a large number of experiments with a large amount of time.

Further, according to the gradient tensor rule in the following formula, the repetitive component gamma of each element in the gravity gradient tensor sequence is filtered_yy、Γ_xz、Γ_zy、Γ_yx

Obtaining a signal containing only 5 independent components Γ_i＝(Γ_xx Γ_zz Γ_xy Γ_yz Γ_zx)_iAre sequentially arranged to form a new gravity gradient tensor sequence.

The beneficial effects of the above further improved scheme are: a method of filtering out a repetitive component of each element in the gravity gradient tensor sequence is specifically defined. A large number of tests summarize a method for eliminating the element suitable for the characteristic extraction of the invention, thereby ensuring that the DQL characteristic extraction is more accurate.

Further, the extracting the DQL feature of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of the route on which the terrain map is located further comprises:

acquiring a full-tensor gravity gradient measured value caused by each terrain unit according to the central position coordinates (epsilon eta zeta) of each terrain unit and the current position coordinates (x, y, z) of the aircraft; wherein the ith terrain cell induced full-tension gravity gradient measurement Γ_iIs' of gamma_i′＝(Γ_xx′Γ_zz′Γ_xy′Γ_yz′Γ_zx′)_i；

According to the measured value gamma of the full-tension gravity gradient caused by each terrain unit_i' the difference Δ Γ between n-1 pairs of adjacent elements is obtained by the following formula_i' approximately as the difference of its gravitational gradient tensor

ΔΓ_i′＝Γ_i+1′-Γ_i′

Δ Γ obtained as described above_i'DQL characteristic DQL' of each pair of adjacent elements is extracted by the following formula (i)

In the formula, gamma₀Each pair of adjacent elements contains a DQL feature that contains 5 components;

sequentially arranging the DQL characteristics DQL '(i) of n-1 pairs of adjacent elements as DQL characteristics of the current position coordinate of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map, and marking the DQL characteristics DQL' (i) as A₂。

The beneficial effects of the above further improved scheme are: DQL characteristics of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of the route in the terrain map are defined. The above method is a feature extraction method that is most suitable for the present invention, which is concluded by the inventors through a large number of experiments with a large amount of time.

Further, the centroid position coordinates (ε η ζ) of each topographic unit distributed at equal intervals from the current position in the topographic map are obtained by the following formula

ε＝x+i×r

η＝y+i×r

ζ＝z+i×r

Where i is 1 … n, r is the terrain cell separation distance, and (x, y, z) is the vehicle current position coordinates.

The beneficial effects of the above further improved scheme are: a fastest positioning search mode is provided. A large number of experiments prove that the positioning correction result obtained by the further improved scheme is fastest and accurate.

Further, the ith terrain cell induced full-tension gravity gradient measurement Γ_i' obtained by the following formula

Where ψ represents an integral region, i.e., a space occupied by all the terrain units, φ (ε η ζ) is a morphology function of the terrain, i.e., an altitude function, and ρ (ε η ζ) is a terrain density distribution function.

The beneficial effects of the above further improved scheme are: a general method of calculating the full tensor gravity gradient measurement induced by each terrain element is presented. A large number of tests prove that the scheme is effective, and the obtained positioning result is accurate.

Further, the ρ (ε η ζ) satisfies a plateau density model in the following formula

Wherein D represents the thickness of the crust where the integral area is located, h represents the altitude of the center point of the terrain unit, and rho₀Is a constant coefficient, ρ₀＝2.67g/cm³D represents the thickness of the crust where the integration zone is located, and h represents the altitude of the center point of the terrain unit.

The beneficial effects of the above further improved scheme are: a general method of calculating a terrain density distribution function is presented. A large number of tests prove that the scheme is effective, and the obtained positioning result is accurate.

Further, the performing feature matching on the DQL features of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of the route in the topographic map and the DQL features corresponding to the gravity gradient tensor sequence to obtain elements in the gravity gradient tensor sequence with the highest matching degree further includes:

obtaining the feature matching similarity I (A) of the DQL features of the current position coordinates of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map and the DQL features corresponding to the gravity gradient tensor sequence by a mutual information similarity calculation method in the following formula₁ A₂)

I(A₁ A₂)＝H(A₁)+H(A₂)-H(A₁ A₂)

Wherein

p(A_1i A_2i)＝p(A_1i)p(A_2i)

In the formula, p (A)_1i) Is A₁DQL characteristic DQL of the ith pair of adjacent elements (i) at A₁Probability of occurrence in all elements; p (A)_2i) Is A₂DQL characteristic DQL' (i) of the ith pair of adjacent elements is at A₂Probability of occurrence in all elements; p (A)_1i A_2i) Is p (A)_1i)、p(A_2i) A joint probability distribution of (a);

to obtainHighest I (A)₁ A₂) The corresponding gravity gradient tensor sequence.

The beneficial effects of the above further improved scheme are: a feature matching method is defined. The above method is a feature matching method which is most suitable for the present invention and is concluded by the inventors through a large number of tests with a large amount of time, and can obtain the most accurate corrected coordinates.

In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.

FIG. 1 is a schematic diagram of the steps of a navigation positioning method based on gravity gradient-terrain heterogeneous data matching in embodiment 1 of the present invention;

fig. 2 is a schematic diagram of the principle of the navigation positioning method based on gravity gradient-terrain heterogeneous data matching in embodiment 1 of the present invention.

Detailed Description

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.

Example 1

The embodiment of the invention discloses a navigation positioning method based on gravity gradient-terrain heterogeneous data matching, which comprises the following steps as shown in figure 1:

s1, obtaining the current position coordinates of a vehicle and a gravity gradient tensor sequence of a located route; in particular, the vehicle is an aircraft or an underwater vehicle;

s2, identifying and extracting DQL characteristics corresponding to the gravity gradient tensor sequence;

s3, extracting DQL characteristics of the current position coordinates of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map;

s4, performing feature matching on DQL features of the current position coordinates of the aircraft relative to position coordinates of all terrain units of a route in a topographic map and DQL features corresponding to the gravity gradient tensor sequence to obtain elements in the gravity gradient tensor sequence with the highest matching degree;

s5, searching the position of the gravity gradient corresponding to the element, and correcting the current position coordinate of the aircraft according to the position. Alternatively, a direct replacement or other coordinate correction may be used. The direct alternative is to obtain the positions of the gravity gradients corresponding to the above elements by looking up a gravity gradient coordinate diagram built in the aircraft. And other correction modes comprise that for example, the difference between the position of the gravity gradient corresponding to the elements and the current position coordinate of the aircraft is obtained, and then the trained deep neural network is input to obtain the corrected position of the current position coordinate of the aircraft.

In the prior art, the gravity gradient acquired by the aircraft is matched with the gravity gradient reference map loaded by the aircraft, but the gravity gradient measurement difficulty is high, so that the gravity gradient reference map supply requirement meeting the navigation requirement is difficult to meet. However, the topographic map is easy to obtain, in the method of this embodiment, the aircraft loads the topographic map of the matching area, and then matches the gravity gradient measured during the navigation process with the DQL feature converted from the corresponding position of the loaded topographic map, so as to perform positioning, that is, the matching between the gravity gradient measured value and the topographic map is implemented, instead of the matching between the gravity gradient measured value and the gravity gradient reference map, as shown in fig. 2.

Compared with the prior art, the navigation positioning method provided by the embodiment can simultaneously meet the requirements of passivity, autonomy, concealment and the like of navigation according to the gravity gradient matching technology which does not depend on external information and radiates energy to the outside, and provides a brand-new autonomous navigation mode. DQL feature matching is carried out according to the gravity gradient sequence and the current position of the aircraft, and the problem that the existing gravity gradient matching navigation does not have large-range, high-precision and regularized global gravity field data is solved. The correctness and adaptability of the gravity gradient matching algorithm of the aircraft/underwater vehicle can be effectively verified.

Example 2

The improvement is made on the basis of the method in the embodiment 1, and in step S1, the obtaining the current position coordinates of the aircraft and the gravity gradient tensor sequence of the located route further includes:

s11, obtaining current position coordinates (x, y, z) of the aircraft through measurement of an inertial navigation module;

s12, obtaining a gravity gradient tensor sequence (gamma) of n different positions (including the current position) before the current position on the flight line through equal time interval measurement of a gravity gradiometer₁ Γ₂ … Γ_n) (ii) a Wherein each element Γ in the sequence_iContains 9 components, satisfies

Γ_i＝(Γ_xx Γ_yy Γ_zz Γ_xy Γ_yz Γ_zx Γ_xz Γ_zy Γ_yx)_i

i＝1 2… n，n≥3

(1)

Preferably, in step S2, the extracting DQL (Differential logarithmic loader, logarithmic scale difference space) features corresponding to the gravity gradient tensor sequence further includes:

s21, for each element in the gravity gradient tensor sequence, filtering out the repeated component of the element through the following formula

Obtaining a signal containing only 5 independent components Γ_i＝(Γ_xx Γ_zz Γ_xy Γ_yz Γ_zx)_iThe new elements are sequentially arranged to form a new gravity gradient tensor sequence;

s22, obtaining the difference delta gamma corresponding to n-1 pairs of adjacent elements through the following formula according to the new gravity gradient tensor sequence constructed as above_i

ΔΓ_i＝Γ_i+1-Γ_i (3)

S23. obtaining the delta gamma according to the method_iThe DQL characteristic of each pair of adjacent elements is extracted by the following formula

In the formula, gamma₀Each pair of adjacent elements contains a DQL feature that contains 5 components;

s24, sequentially arranging the DQL characteristics of n-1 pairs of adjacent elements to serve as the DQL characteristics corresponding to the gravity gradient tensor sequence and marking as A₁。

Preferably, in step S3, the extracting DQL features of the current position coordinates of the aircraft relative to the position coordinates of each terrain unit of the route on the terrain map, in order to solve the gravity gradient anomaly caused by the terrain according to the data of the heterogeneous terrain, further includes:

s31, obtaining the position coordinates (epsilon eta zeta) of the mass centers of all terrain units distributed at equal intervals with the current position in the terrain map

Wherein i is 1 … n, and r is the spacing distance of the terrain units;

s32, extracting a full-tensor gravity gradient measured value gamma caused by each terrain unit according to the central position coordinate (epsilon eta zeta) of each terrain unit and the current position coordinate (x, y, z) of the aircraft by the following formula_i′＝(Γ_xx′Γ_zz′Γ_xy′Γ_yz′Γ_zx′)_i

Wherein ρ (ε η ζ) satisfies the plateau density model in the following formula

Where ψ denotes the integration area, i.e. the space occupied by all terrain cells, ρ₀＝2.67g/cm³D represents the thickness of the crust where the integral region is located, h represents the altitude of the center point of the terrain unit, phi (epsilon eta zeta) is a morphological function of the terrain, namely an altitude function, and is obtained through the existing documents, and rho (epsilon eta zeta) is a terrain density distribution function;

s33, according to the measured value gamma of the full-tension gravity gradient caused by each terrain unit_i' the difference Δ Γ between n-1 pairs of adjacent elements is obtained by the following formula_i' approximately as the difference of its gravitational gradient tensor

ΔΓ_i′＝Γ_i+1′-Γ_i′ (8)

Difference Δ Γ of neighboring element gravity gradient tensors_i' actually consists of the following parts:

ΔΓ_i′＝ΔΓ_0i′+ΔΓ_Ti′+ΔΓ_Pi′+ΔΓ_Mi′ (9)

in the formula, Delta gamma_0i' is the change of the normal gravity gradient value of the earth, delta gamma_Ti' abnormal change of gravity gradient due to topographic relief, Delta gamma_PiAbnormal change of gravity gradient caused by uneven density of crust_Mi' abnormal change of gravity gradient brought by residual mass of sea, celestial body and the like.

The above formula demonstrates that the gravity gradient measurement is approximately equal to the terrain-generated gravity gradient, illustrating the rationality of the method of the present embodiment.

Since the difference of the gravity gradient tensor caused by other factors except the topographic relief is very small and can be ignored, the gravity gradient abnormality of the measuring point is considered to be almost equal to the gravity gradient abnormality caused by the topographic relief in the present invention

ΔΓ_i′≈ΔΓ_Ti′＝Γ_i+1′-Γ_i′ (10)

S34. obtaining the delta gamma according to the above_i'DQL characteristic DQL' of each pair of adjacent elements is extracted by the following formula (i)

In the formula, gamma₀Each pair of adjacent elements contains a DQL feature that contains 5 components;

s35, sequentially arranging the DQL characteristics DQL '(i) of n-1 pairs of adjacent elements as DQL characteristics of the current position coordinate of the aircraft relative to the position coordinates of all terrain units of the route in the topographic map, and marking the DQL characteristics DQL' (i) as A₂。

Can be combined with A₂And (4) putting the DQL into a buffer according to a spatial sequence, and obtaining an output DQL spatial reference map, namely a distribution map M1 of gravity gradient abnormal values when the sequence length meets the requirement.

Preferably, in step S4, the performing feature matching on the DQL feature of the current position coordinate of the aircraft relative to the position coordinate of each terrain unit of the route in the topographic map and the DQL feature corresponding to the gravity gradient tensor sequence to obtain an element in the gravity gradient tensor sequence with the highest matching degree further includes:

s41, obtaining the feature matching similarity I (A) of the DQL features of the current position coordinate of the aircraft relative to the position coordinate of each topographic unit of the airline where the topographic map is located and the DQL features corresponding to the gravity gradient tensor sequence by a mutual information similarity calculation method in the following formula₁ A₂)

I(A₁ A₂)＝H(A₁)+H(A₂)-H(A₁ A₂) (12)

Wherein

p(A_1i A_2i)＝p(A_1i)p(A_2i)

Wherein, H (A)₁) DQL features A corresponding to the gravity gradient tensor sequence₁Entropy of H (A)₂) DQL characteristic A of the current position coordinate of the aircraft relative to the position coordinate of each terrain unit of the route in the topographic map₂Entropy of p (A)_1i) DQL features A corresponding to the gravity gradient tensor sequence₁DQL characteristic DQL of the ith pair of adjacent elements (i) at A₁Probability of occurrence in all elements; p (A)_2i) DQL characteristic A of the current position coordinate of the aircraft relative to the position coordinate of each terrain unit of the route in the topographic map₂DQL characteristic DQL' (i) of the ith pair of adjacent elements is at A₂Probability of occurrence in all elements; p (A)_1i A_2i) Is p (A)_1i)、p(A_2i) A joint probability distribution of (a);

s42, identifying the highest I (A)₁ A₂) The corresponding gravity gradient tensor sequence.

And matching and positioning the DQL feature space real-time graph and the reference graph by using a similarity measurement criterion based on mutual information. Maximum I (A)₁ A₂) The corresponding position is the best matching position.

Compared with the embodiment 1, the navigation method provided by the embodiment further refines the steps from S2 to S4, and further solves the problem that the existing gravity gradient matching navigation does not have large-range, high-precision and regularized global gravity field data.

Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.