Space-based positioning method and system combining direction-finding positioning and coherent accumulation positioning, and storage medium

Abstract

The invention provides a space-based positioning method and system combining direction-finding positioning and coherent accumulation positioning, and a storage medium, relates to the technical field of space-based passive positioning, and aims to solve the problems of low positioning precision, high calculation cost and long consumed time of an existing space-based positioning method. Comprising the steps of 1, constructing a target signal model received by a satellite, and performing coarse positioning on a target by using a direction-finding positioning algorithm to obtain a coarse estimation result of a target position; and 2, setting a traversal range by taking the rough estimation result of the target position as a center, and performing high-precision positioning on the target by using a coherent accumulation positioning algorithm. According to the space-based positioning method, the calculation amount of the space-based positioning method is greatly reduced, the calculation efficiency is improved, and high-precision and efficient space-based positioning is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of space-based passive positioning, and in particular to a space-based positioning method, system and storage medium combining direction finding positioning with coherent accumulation positioning. Background Art

[0002] How to use satellites to accurately determine the location of ground targets is an important issue in the fields of aerospace electronic intelligence reconnaissance, spectrum monitoring, and remote sensing. Compared with ground reconnaissance methods, using satellite platforms to intercept the target's radio signals and locate and track them is not affected by the region, has a large detection range, a long reconnaissance time, and a high cost-effectiveness ratio. Common space-based positioning systems include single-star direction-finding positioning system, dual-star time-frequency difference positioning system, and multi-star positioning system. Each system has unique advantages and limitations, and together constitutes the current diversified space-based positioning technology pattern. Among them, the multi-star positioning system, especially the three-star time difference positioning system, can comprehensively utilize the signal information provided by multiple satellites and achieve high-precision three-dimensional positioning of ground or air targets through precise time difference measurement technology. The advantages of this system are high positioning accuracy, wide coverage, and to a certain extent, it can resist interference and improve positioning stability. However, high precision is accompanied by an increase in system complexity. It not only requires a high degree of coordination between multiple satellites to ensure the synchronization and accuracy of signal transmission, but also puts forward strict requirements on the orbit design and configuration layout of satellites. Any change in configuration may have a significant impact on positioning accuracy. In addition, the construction and maintenance costs of multi-satellite systems are high, including satellite launches, operation and maintenance, and the establishment of ground monitoring systems, which are all economic burdens that cannot be ignored.

[0003] In contrast, the single-satellite direction-finding positioning system achieves basic positioning functions in a simpler way. This system mainly relies on the signal transmitted by a single satellite, and determines the approximate position of the target by measuring the direction of the signal by ground or air receivers. Due to its simple structure and low implementation cost, it is particularly suitable for application scenarios with limited resources or low requirements for positioning accuracy. However, the inherent limitation of single-satellite direction-finding positioning is that its positioning accuracy is relatively limited and is easily affected by environmental factors such as terrain obstruction and multipath effect, resulting in a certain degree of uncertainty in the positioning results, which makes it difficult to meet the needs of high-precision navigation and positioning. Summary of the invention

[0004] The technical problems to be solved by the present invention are:

[0005] Existing methods for space-based positioning have the problems of low positioning accuracy, high computational cost and long time consumption.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] The present invention provides a space-based positioning method combining direction finding positioning with coherent accumulation positioning, comprising the following steps:

[0008] Step 1: construct a target signal model received by the satellite, use the direction finding positioning algorithm to roughly locate the target, and obtain a rough estimate of the target position;

[0009] Step 2: With the rough estimate of the target position as the center, set the traversal range and use the coherent accumulation positioning algorithm to locate the target with high precision.

[0010] Furthermore, step 1 includes the following steps:

[0011] Step 1.1, construct a signal model;

[0012] Assume that the target signal received by the satellite is r(t) with a dimension of Nx1; the noise is a stable, zero-mean Gaussian white noise, and the noises are independent of each other; its expression is:

[0013]

[0014] Where: M is the number of target signals, A m is the amplitude of the mth target signal, θ m , is the spatial parameter of the mth target signal, ω m is the frequency of the mth target signal, φ m is the phase of the mth target signal, n(t) is the noise;

[0015] Step 1.2, calculate the data covariance matrix;

[0016] The autocorrelation function of the received signal is:

[0017] R=E(r(t)r(t) H ) (2)

[0018] Where: E() represents mathematical expectation, H represents conjugate transpose;

[0019] Step 1.3, perform eigendecomposition on the covariance matrix R to obtain the noise subspace U n ;

[0020] Perform eigenvalue decomposition on the covariance matrix R and obtain:

[0021]

[0022] Among them: U s is the eigenvector of the signal subspace;

[0023] ∑ is the matrix composed of the M largest eigenvalues ​​of R, corresponding to the eigenvalues ​​of the signal subspace, which are:

[0024] ∑=diag{λ 1 λ 2 ... λ M} (4)

[0025] λ m is the eigenvalue corresponding to the mth signal, 1≤m≤M;

[0026] U n is the eigenvector of the noise subspace;

[0027] Λ is a matrix composed of NM small eigenvalues ​​of R, corresponding to the eigenvalues ​​of the noise subspace, which is:

[0028] Λ=diag{λ M+1 λ M+2 ... λ N} (5)

[0029] λ M+i is the M+i-th eigenvalue, 1≤i≤NM;

[0030] Step 1.4, perform spectrum peak search on the target signal;

[0031] The steering vector in the signal subspace is orthogonal to the noise subspace, that is:

[0032]

[0033] Where: e(ω)=[1 e jω ... e jω(N-1) ] T ;

[0034] By minimizing the search, we get:

[0035]

[0036] Step 1.5, find the signal frequency and spatial parameters corresponding to the maximum point;

[0037] The estimation formula of the frequency and spatial parameters of the target signal is:

[0038]

[0039] Find the signal frequency and spatial parameters corresponding to the M maximum points of formula (8);

[0040] Step 1.6, calculate the target signal position;

[0041] Take the satellite position as the origin and the spatial parameters obtained in step 1.5 as the direction to construct a ray, calculate the intersection of the ray and the earth's surface, and when there are two intersections, select the intersection closer to the satellite position as the rough estimate of the target position.

[0042] Furthermore, step 2 includes the following steps:

[0043] Step 2.1: Determine the positioning area based on the direction finding positioning result;

[0044] Analyze the theoretical positioning error of the space-based direction-finding positioning algorithm, take the rough estimation result of the target position as the center, take multiple times of the theoretical positioning error as the radius, determine the traversal range, and divide the grid points on the earth's surface;

[0045] Step 2.2, construct the observation signal model;

[0046] The model of the received signal based on time delay and frequency shift is:

[0047]

[0048] Among them, r k (t) represents the signal received by the satellite in the kth time slot, β k is the channel attenuation, s k (t-τ k ) represents the target signal after transmission delay in the kth time slot, n k (t) is the zero-mean Gaussian white noise of the kth time slot, T is the observation time in each time slot, and f k is the Doppler shift;

[0049] Step 2.3, construct the cost function;

[0050] By using the signal characteristics of the satellite receiving radiation source signal, the unknown parameters are estimated based on the maximum likelihood estimation, and the cost function C(p) is derived as follows:

[0051]

[0052] Among them, A k is the frequency shift matrix, F k is the shift matrix;

[0053] Assume||s k || 2 =1, minimizing formula (10), the maximum likelihood estimate of channel attenuation is obtained as:

[0054]

[0055] Substituting formula (11) into formula (10), we get:

[0056]

[0057] Step 2.4, estimate the target position;

[0058] Estimation of target position It is obtained by the following formula:

[0059]

[0060] The present invention provides a space-based positioning system that combines direction finding positioning with coherent accumulation positioning. The system has a program module corresponding to the steps of the method described in any one of the above technical solutions, and executes the steps in the above-mentioned space-based positioning method that combines direction finding positioning with coherent accumulation positioning during operation.

[0061] The present invention provides a computer-readable storage medium, which stores a computer program. The computer program is configured to implement the steps of the space-based positioning method combining direction finding positioning with coherent accumulation positioning described in any one of the above technical solutions when called by a processor.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention discloses a space-based positioning method, system and storage medium that combine direction finding positioning with coherent accumulation positioning. The direction finding positioning algorithm is combined with the coherent accumulation positioning algorithm, and a phased processing strategy is adopted to achieve the purpose of reducing the computational complexity and improving the positioning accuracy. First, a direction finding positioning algorithm with low computational complexity is used to perform preliminary coarse positioning to obtain a rough estimate of the target position, so as to quickly narrow the spatial range where the target may exist. Then, the coherent accumulation positioning algorithm is used to perform precise positioning of coherent accumulation with the rough estimate result as the center. Different from the traditional exhaustive search directly in the entire search space, the present invention limits the search range to a smaller area around the rough estimate result, thereby significantly reducing the amount of computation and ultimately achieving a high-precision estimate of the target position.

[0064] The present invention greatly reduces the computational complexity of the positioning algorithm, improves computational efficiency, and realizes high-precision and efficient space-based positioning.

[0065] The present invention provides a new technical path for building an efficient and accurate space-based positioning system, and has broad application prospects in space-based navigation, emergency rescue, environmental monitoring and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of space-based positioning combining direction finding positioning with coherent accumulation positioning in an embodiment of the present invention;

[0067] Figure 2Schematic diagram of space-based direction finding and positioning in an embodiment of the present invention;

[0068] Figure 3 Schematic diagram of searching for a spatial parameter spectrum peak in an embodiment of the present invention;

[0069] Figure 4 Schematic diagram of grid search in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the scheme of the present invention, exemplary implementations or embodiments of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described implementations or embodiments are only implementations or embodiments of a part of the present invention, not all of them. Based on the implementations or embodiments of the present invention, all other implementations or embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0072] The present invention provides a space-based positioning method combining direction finding positioning with coherent accumulation positioning, such as Figure 1 As shown, the following steps are included:

[0073] Step 1: construct a target signal model received by the satellite, use the direction finding positioning algorithm to roughly locate the target, and obtain a rough estimate of the target position; Figure 2 As shown, the specific steps include:

[0074] Assume that two targets are located at (120°E, 32°N) and (122°E, 28°N) respectively, and the satellite orbit parameters are as follows.

[0075] Satellite orbit parameters

[0076] Serial number parameter Numeric 1 Semi-major axis (km) 6872 2 Orbital inclination (°) 89 3 Eccentricity 0 4 Ascending node right ascension (°) 102 5 Argument of perigee (°) 20 6 True anomaly (°) 25

[0077] When the satellite passes over the target, it receives the target signal and locates the target.

[0078] Step 1.1, construct a signal model;

[0079] Assume that the target signal received by the satellite is r(t), with a dimension of N×1; the noise is a stable, zero-mean Gaussian white noise, and the noises are independent of each other; its expression is:

[0080]

[0081] Where: M is the number of target signals. In this implementation example, M = 2. m is the amplitude of the mth target signal, θ m , is the spatial parameter of the mth target signal, ω m is the frequency of the mth target signal, φ m is the phase of the mth target signal, n(t) is the noise;

[0082] Step 1.2, calculate the data covariance matrix;

[0083] The autocorrelation function of the received signal is:

[0084] R=E(r(t)r(t) H ) (2)

[0085] Where: E() represents mathematical expectation, H represents conjugate transpose;

[0086] Step 1.3, perform eigendecomposition on the covariance matrix R to obtain the noise subspace U n ;

[0087] Perform eigenvalue decomposition on the covariance matrix R and obtain:

[0088]

[0089] Among them: U s is the eigenvector of the signal subspace;

[0090] ∑ is the matrix composed of the M largest eigenvalues ​​of R, corresponding to the eigenvalues ​​of the signal subspace, which are:

[0091] ∑=diag{λ 1 λ 2 ... λ M} (4)

[0092] λ m is the eigenvalue corresponding to the mth signal, 1≤m≤M;

[0093] U n is the eigenvector of the noise subspace;

[0094] Λ is a matrix composed of NM small eigenvalues ​​of R, corresponding to the eigenvalues ​​of the noise subspace, which is:

[0095] A=diag(λ M+1 λ M+2 ... λ N} (5)

[0096] λ M+i is the M+i-th eigenvalue, 1≤i≤NM;

[0097] Step 1.4: Perform a spectrum peak search on the target signal, such as Figure 3 As shown;

[0098] Since the signal subspace and the noise subspace are orthogonal, the steering vector in the signal subspace is also orthogonal to the noise subspace, that is:

[0099]

[0100] Where: e(ω)=[1 e jω ... e jω(N-1) ] T ;

[0101] Due to the existence of noise, the steering vector in the signal subspace cannot be completely orthogonal to the noise subspace, so in practice it is obtained by minimizing the search, that is:

[0102]

[0103] Step 1.5, find the signal frequency and spatial parameters corresponding to the maximum point;

[0104] The estimation formula of the frequency and spatial parameters of the target signal is:

[0105]

[0106] Find the signal frequencies and spatial parameters corresponding to the M maximum points in formula 8; that is, the frequencies and directions of the M target signals.

[0107] Step 1.6, calculate the target signal position;

[0108] Take the satellite position as the origin and the spatial parameters obtained in step 1.5 as the direction to construct a ray, calculate the intersection of the ray and the earth's surface, and when there are two intersections, select the intersection closer to the satellite position as the rough estimate of the target position.

[0109] Step 2: Taking the rough positioning result as the center, set the traversal range and use the coherent accumulation positioning algorithm to locate the target with high precision; Figure 4 As shown, the following steps are included:

[0110] Step 2.1: Determine the positioning area based on the direction finding positioning result;

[0111] The theoretical positioning error of the space-based direction-finding positioning algorithm is analyzed, and the traversal range of the coherent accumulation positioning algorithm is determined with the rough estimation result of the target position as the center and twice the theoretical positioning error as the radius, and the grid points are divided on the earth's surface.

[0112] Step 2.2, construct the observation signal model;

[0113] The model of the received signal based on time delay and frequency shift is:

[0114]

[0115] Among them, r k (t) represents the signal received by the satellite in the kth time slot, β k is the channel attenuation, s k (t-τ k ) represents the target signal after transmission delay in the kth time slot, n k (t) is the zero-mean Gaussian white noise of the kth time slot, T is the observation time in each time slot, and f k is the Doppler shift;

[0116] Step 2.3, construct the cost function;

[0117] By using the signal characteristics of the satellite receiving radiation source signal (such as arrival time difference, arrival frequency difference and arrival amplitude difference), the unknown parameters are estimated based on maximum likelihood estimation, and the cost function C(p) is derived as follows:

[0118]

[0119] Among them, A k is the frequency shift matrix, F k is the shift matrix.

[0120] Without loss of generality, assume that ||s k || 2 =1, minimizing formula (10), the maximum likelihood estimate of channel attenuation is obtained as:

[0121]

[0122] Substituting formula (11) into formula (10), we get:

[0123]

[0124] Step 2.4, estimate the target position;

[0125] Estimation of target position It is obtained by the following formula:

[0126]

[0127] The space-based positioning method (algorithm) proposed in the present invention that combines direction finding positioning with coherent accumulation positioning is the underlying technical core of the present invention, and various products can be derived based on the algorithm.

[0128] Based on the method proposed in the present invention, a space-based positioning system combining direction finding positioning with coherent accumulation positioning is developed using a programming language. The system has program modules corresponding to the steps of the above-mentioned technical solution, and executes the steps in the above-mentioned space-based positioning method combining direction finding positioning with coherent accumulation positioning during operation.

[0129] The computer program of the developed system (software) is stored on a computer-readable storage medium, and the computer program is configured to implement the steps of the above-mentioned space-based positioning method combining direction finding positioning with coherent accumulation positioning when called by a processor. That is, the present invention is materialized on a carrier to become a computer program product.

[0130] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The computer programs (also referred to as programs, software, software applications, or codes) of the present invention include machine instructions for programmable processors, and these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or device (e.g., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0132] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A space-based positioning method combining direction finding positioning and coherent accumulation positioning, characterized in that: The steps include: Step 1: construct a target signal model received by the satellite, use the direction finding positioning algorithm to roughly locate the target, and obtain a rough estimate of the target position; Step 2: With the rough estimate of the target position as the center, set the traversal range and use the coherent accumulation positioning algorithm to locate the target with high precision.

2. The space-based positioning method combining direction finding positioning and coherent accumulation positioning according to claim 1, characterized in that: Step 1 includes the following steps: Step 1.1, construct a signal model; Assume that the target signal received by the satellite is r(t), with a dimension of N×1; the noise is a stable, zero-mean Gaussian white noise, and the noises are independent of each other; Its expression is: Where: M is the number of target signals, A m is the amplitude of the mth target signal, θ m , is the spatial parameter of the mth target signal, ω m is the frequency of the mth target signal, φ m is the phase of the mth target signal, n(t) is the noise; Step 1.2, calculate the data covariance matrix; The autocorrelation function of the received signal is: R=E(r(t)r(t) H ) (2) Where: E() represents mathematical expectation, H represents conjugate transpose; Step 1.3, perform eigendecomposition on the covariance matrix R to obtain the noise subspace U n ; Perform eigenvalue decomposition on the covariance matrix R and obtain: Among them: U s is the eigenvector of the signal subspace; ∑ is the matrix composed of the M largest eigenvalues ​​of R, corresponding to the eigenvalues ​​of the signal subspace, which are: ∑=diag{λ1 λ2 ... λ M } (4) λ m is the eigenvalue corresponding to the mth signal, 1≤m≤M; U n is the eigenvector of the noise subspace; Λ is a matrix composed of NM small eigenvalues ​​of R, corresponding to the eigenvalues ​​of the noise subspace, which is: A=diag(λ M+1 l M+2 ... l N } (5) λ M+i is the M+i-th eigenvalue, 1≤i≤NM; Step 1.4, perform spectrum peak search on the target signal; The steering vector in the signal subspace is orthogonal to the noise subspace, that is: Among them: e(ω)=[1 e jω ...e jω(N-1) ] T ; By minimizing the search, we get: Step 1.5, find the signal frequency and spatial parameters corresponding to the maximum point; The estimation formula of the frequency and spatial parameters of the target signal is: Find the signal frequency and spatial parameters corresponding to the M maximum points of formula (8); Step 1.6, calculate the target signal position; Take the satellite position as the origin and the spatial parameters obtained in step 1.5 as the direction to construct a ray, calculate the intersection of the ray and the earth's surface, and when there are two intersections, select the intersection closer to the satellite position as the rough estimate of the target position.

3. The space-based positioning method combining direction finding positioning and coherent accumulation positioning according to claim 2, characterized in that: Step 2 includes the following steps: Step 2.1: Determine the positioning area based on the direction finding positioning result; Analyze the theoretical positioning error of the space-based direction-finding positioning algorithm, take the rough estimation result of the target position as the center, take multiple times of the theoretical positioning error as the radius, determine the traversal range, and divide the grid points on the earth's surface; Step 2.2, construct the observation signal model; The model of the received signal based on time delay and frequency shift is: Among them, r k (t) represents the signal received by the satellite in the kth time slot, β k is the channel attenuation, s k (t-τ k ) represents the target signal after transmission delay in the kth time slot, n k (t) is the zero-mean Gaussian white noise of the kth time slot, T is the observation time in each time slot, and f k is the Doppler shift; Step 2.3, construct the cost function; By using the signal characteristics of the satellite receiving radiation source signal, the unknown parameters are estimated based on the maximum likelihood estimation, and the cost function C(p) is derived as follows: Among them, A k is the frequency shift matrix, F k is the shift matrix; Assume||s k || 2 =1, minimizing formula (10), the maximum likelihood estimate of channel attenuation is obtained as: Substituting formula (11) into formula (10), we get: Step 2.4, estimate the target position; Estimation of target position It is obtained by the following formula:

4. A space-based positioning system combining direction finding positioning and coherent accumulation positioning, characterized in that: The system has a program module corresponding to the steps of the method described in any one of claims 1 to 3 above, and executes the steps of the above-mentioned space-based positioning method combining direction finding positioning with coherent accumulation positioning when running.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps in the space-based positioning method combining direction finding positioning with coherent accumulation positioning described in any one of claims 1 to 3 when called by a processor.

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