Intelligent navigation fusion method based on through fusion

Abstract

The application relates to the technical field of satellite navigation, and particularly discloses an intelligent navigation fusion method based on communication and navigation fusion, which comprises the following steps: S1, acquiring communication signals and navigation signals; S2, distributing a plurality of subcarriers to the communication signals and the navigation signals according to the load condition of the communication service to obtain an initial distribution scheme; and S3, jointly optimizing the initial distribution scheme by using a BSO algorithm to reduce the PAPR (peak-to-average power ratio) of the communication signals and maintain the PSLR (peak side lobe ratio) of the navigation signals, so as to obtain a target distribution scheme. The application realizes the frequency domain fusion of the communication and navigation signals by dynamically allocating subcarrier resources, and jointly optimizes the carrier phase disturbance by introducing a brainstorming optimization algorithm, so that the PAPR is suppressed and the ranging accuracy of the navigation signals is ensured.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and more specifically to an intelligent navigation fusion method based on communication and navigation fusion. Background Technology

[0002] Traditional communication and navigation fusion systems generally employ a single-carrier signal scheme (such as BPSK modulation), and their channel capacity is limited by Shannon's formula:

[0003]

[0004] Where C represents channel capacity, B represents channel bandwidth, and S / N represents signal-to-noise ratio. Although the implementation structure is simple, the channel width is fixed and the spectral efficiency is significantly low, making it difficult to meet the high throughput requirements of low-Earth orbit satellites. Orthogonal Frequency Division Multiplexing (OFDM) technology can significantly improve spectral efficiency through parallel transmission of orthogonal subcarriers and has been introduced into communication and navigation fusion designs. However, existing solutions have inherent compatibility issues in the co-frequency reuse of communication and navigation signals: time-division / code-division multiplexing leads to power allocation efficiency loss; while frequency-domain compositing solutions improve signal compatibility, they fail to solve the power amplifier nonlinear distortion problem caused by the inherent peak-to-average power ratio (PAPR) of OFDM.

[0005] The PAPR of an OFDM signal is defined as follows:

[0006]

[0007] s[n] is the sampled value of the OFDM signal in the time domain, and N is the length of the OFDM symbol, i.e., the number of subcarriers. OFDM signals are prone to high PAPR due to multi-carrier superposition, forcing the power amplifier to fall back into its operating range, resulting in a significant reduction in transmission efficiency. Traditional PAPR suppression schemes have inherent limitations in communication-navigation fusion scenarios: amplitude-limiting filtering introduces out-of-band radiation, destroying the autocorrelation characteristics of the navigation signal; probabilistic optimization requires high-complexity search or transmission of sideband information, making it difficult to adapt to the limited processing capabilities of spaceborne platforms; companding transform easily leads to signal waveform distortion, directly reducing navigation and ranging accuracy.

[0008] Existing subcarrier allocation strategies are mostly statically designed and do not consider the time-varying characteristics of low-Earth orbit satellite communication load. This results in idle spectrum resources during off-peak hours, while insufficient navigation subcarriers lead to deterioration in positioning accuracy during peak hours, making it impossible to achieve a dynamic balance between resource efficiency and navigation performance. Summary of the Invention

[0009] To address the aforementioned problems, the purpose of this invention is to provide an intelligent navigation fusion method based on communication and navigation fusion. This method achieves frequency domain fusion of communication and navigation signals through dynamic subcarrier resource allocation and introduces a brainstorming optimization (BSO) algorithm to jointly optimize carrier phase perturbation, thereby suppressing PAPR while ensuring the ranging accuracy of navigation signals.

[0010] This invention provides an intelligent navigation fusion method based on communication and navigation fusion, comprising:

[0011] Step S1: Acquire communication signals and navigation signals;

[0012] Step S2: Allocate multiple subcarriers to the communication signal and the navigation signal according to the load of the communication service to obtain an initial allocation scheme:

[0013] Step S3: With the goal of reducing the peak-to-average power ratio (PAPR) of the communication signal and maintaining the peak-to-sidelobe ratio (PSLR) of the navigation signal, the initial allocation scheme is optimized by using the BSO algorithm to jointly optimize the carrier phase perturbation, thereby obtaining the target allocation scheme.

[0014] In one possible implementation, step S2 includes:

[0015] When the load condition is not busy, multiple subcarriers are interleaved according to parity index to the communication signal and the navigation signal, wherein the number of subcarriers of the communication signal and the number of subcarriers of the navigation signal are equal.

[0016] In one possible implementation, multiple edge subcarriers and DC / center subcarriers are also used as guard bands when the load is not busy.

[0017] In one possible implementation, step S2 further includes:

[0018] When the load condition is busy, a majority of subcarriers are allocated to the communication signal and a minority of edge subcarriers are allocated to the navigation signal, wherein the number of subcarriers for the communication signal is greater than the number of subcarriers for the navigation signal.

[0019] In one possible implementation, step S3 includes:

[0020] Multiple candidate solutions are generated randomly;

[0021] The k-means algorithm is used to cluster the multiple candidate solutions into a predetermined number of subgroups;

[0022] The individuals in the subgroup are updated either in a single cluster or across clusters until the change in individual fitness is less than 0.1 within a preset number of consecutive iterations or the preset number of iterations has been reached, at which point the target allocation scheme is output.

[0023] In one possible implementation, step S3 further includes:

[0024] Perform single-cluster updates according to the following formula:

[0025]

[0026] Where ξ is a dynamically adjusted weighting coefficient. For the original individual x old The i-th dimension For the new individual x new The i-th dimension, N(μ,σ) 2 ) represents the Gaussian disturbance term, N represents the normal distribution, μ is the mean of the distribution, and σ is the standard deviation of the distribution.

[0027] In one possible implementation, step S3 further includes:

[0028] Perform cross-cluster updates according to the following formula:

[0029]

[0030] Where w1 and w2 are the pre-set weighting coefficients, and ξ is the dynamically adjusted weighting coefficient. For the original individual x old The i-th dimension For the new individual x new The i-th dimension, N(μ,σ) 2 ) represents the Gaussian disturbance term, N represents the normal distribution, μ is the mean of the distribution, and σ is the standard deviation of the distribution.

[0031] In one possible implementation, step S3 further includes:

[0032] The parameter ξ(t) is determined using the following formula:

[0033]

[0034] In the formula, T is the maximum number of iterations, t is the current number of iterations, logsig() represents the logarithmic sigmoid function, k is the slope of the logarithmic sigmoid function, and random(0,1) is a random number between (0,1).

[0035] In one possible implementation, step S3 further includes:

[0036] Calculate the individual fitness f(θ) using the following formula:

[0037] f(θ)=α·PAPR(s(t))+β·max(0,ΔPSLR-1dB)

[0038] In the formula, α and β are weighting coefficients, ΔPSLR is the difference in peak sidelobe ratio of the navigation signal before and after optimization, and ΔPSLR is the difference in peak-to-average power ratio of the communication signal.

[0039] The intelligent navigation fusion method based on communication and navigation fusion provided by this invention aims to solve the problems of low spectral efficiency, excessive PAPR, and limited navigation accuracy in low-orbit satellite communication and navigation fusion systems. It achieves frequency domain fusion of communication and navigation signals through dynamic subcarrier resource allocation and introduces the Brainstorming Optimization (BSO) algorithm to jointly optimize carrier phase perturbation, thereby suppressing PAPR while ensuring the ranging accuracy of navigation signals. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the intelligent navigation fusion method provided in an embodiment of the present invention. Detailed Implementation

[0041] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of the present invention by way of example, but should not be used to limit the scope of the present invention. That is, the present invention is not limited to the described preferred embodiments, and the scope of the present invention is defined by the claims.

[0042] In the description of this invention, it should be noted that, unless otherwise stated, "a plurality of" means two or more; the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance; those skilled in the art can understand the specific meaning of the above terms in this invention as appropriate.

[0043] Figure 1 A flowchart illustrating the intelligent navigation fusion method provided in an embodiment of the present invention is shown below. Figure 1 As shown, this invention provides an intelligent navigation fusion method based on communication and navigation fusion, comprising:

[0044] Step S1: Acquire communication signals and navigation signals;

[0045] In one possible implementation, an OFDM signal framework is constructed based on the IEEE 802.11a protocol, using 64-point IFFT / FFT processing, with subcarrier index k∈[0,63]:

[0046] The input bitstream is QPSK modulated and mapped to the frequency domain symbol X. k The symbolic power satisfies E[|X] k | 2 ]=1, a communication signal is generated.

[0047] Using Gold sequences (generating polynomials G1(x) = x) 10 +x 3 +1 and G2(x) = x 10 +x 9 +x 8 +x 6 +x3 +x+1, code length L=1023,), after being modulated by BPSK, is shaped by a raised cosine filter with a roll-off factor α=0.22 to generate a navigation signal.

[0048] The navigation signal power is 1.2 times that of the communication signal, i.e., P nav =1.2P data This is to compensate for multipath losses and is used for power configuration.

[0049] The last 16 sampling points in the time domain are copied as a prefix to form 80 emission symbols, which are then used to add cyclic prefixes.

[0050] Step S2: Allocate multiple subcarriers to communication signals and navigation signals according to the load of communication services to obtain the initial allocation scheme:

[0051] In one possible implementation, when the load is not busy, multiple subcarriers are interleaved and allocated to the communication and navigation signals according to their parity indices, with the number of subcarriers for the communication signals and the navigation signals being equal. When the load is not busy, multiple edge subcarriers and DC / center subcarriers are also used as guard bands. When the load is busy, a majority of subcarriers are allocated to the communication signals, and a minority of edge subcarriers are allocated to the navigation signals, with the number of subcarriers for the communication signals being greater than the number of subcarriers for the navigation signals.

[0052] In one example, when the load is not busy, the 48 active subcarriers (index γ = {7:30}∪{34:57}) are interleaved according to their parity indices:

[0053] Communication subcarrier γ com Odd-number indices {7,9,...,29}∪{35,37,...,57} (24 in total);

[0054] Navigation subcarrier γ nav Even indexes {8, 10, ..., 30} ∪ {34, 36, ..., 58} (24 in total);

[0055] Guard band: edge subcarriers {1:6}∪{58:63} and DC / center subcarriers {0,31,32,33} (16 in total).

[0056] When the load condition is busy, 52 communication subcarriers (index {0}∪{7:57}) are configured for full load, and navigation signals are allocated to 12 edge subcarriers {1:6}∪{58:63}.

[0057] Step S3: With the goal of reducing the peak-to-average power ratio (PAPR) of the communication signal and maintaining the peak-to-side-lobule ratio (PSLR) of the navigation signal, the initial allocation scheme is optimized by using the BSO algorithm to jointly optimize the carrier phase perturbation, and the target allocation scheme is obtained.

[0058] In one possible implementation, multiple candidate solutions are randomly generated; the k-means algorithm is used to cluster the multiple candidate solutions into a preset number of subgroups; the individuals in the subgroups are updated in a single cluster or across clusters until the change in the fitness of the individuals in a preset number of consecutive iterations is less than 0.1 or the preset number of iterations has been reached, and then the target allocation scheme is output.

[0059] Specifically, the goal is to reduce PAPR while maintaining the peak sidelobe ratio (PSLR) of the navigation signal. First, 50 candidate solutions are randomly generated, with phase vector θ... i =[θ i1 ,θ i2 ,…,θ iM ], dimension M = number of subcarriers, θ ij ∈[0,2π]; secondly, clustering and grouping: the population is divided into 5 subgroups using the k-means algorithm (number of clusters k=5, Euclidean distance metric); then, iterative update: during the iteration of the BSO algorithm, four core individual update strategies are included: directional optimization based on single cluster center, heuristic exploration of random individuals in a single cluster, fusion innovation across cluster centers, and combinatorial evolution of individuals across clusters.

[0060] In one possible implementation, single-cluster updates are performed according to the following formula:

[0061]

[0062] Where ξ is a dynamically adjusted weighting coefficient. For the original individual x old The i-th dimension For the new individual x new The i-th dimension, N(μ,σ) 2 Let N be the Gaussian perturbation term, and let N, μ, σ be the Gaussian perturbation term. 2 This gives the algorithm the ability to escape local optima.

[0063] In one possible implementation, cross-cluster updates are performed according to the following formula:

[0064]

[0065] Where w1 and w2 are the pre-set weighting coefficients, and ξ is the dynamically adjusted weighting coefficient. For the original individual x old The i-th dimension For the new individual xnew The i-th dimension, N(μ,σ) 2 ) represents the Gaussian disturbance term, N represents the normal distribution, μ is the mean of the distribution, and σ is the standard deviation of the distribution.

[0066] In one possible implementation, the dynamic adjustment mechanism of the parameter ξ is key to optimizing algorithm performance; this value is set using the logarithmic sigmoid transfer function.

[0067] The parameter ξ(t) is determined using the following formula:

[0068]

[0069] In the formula, T is the maximum number of iterations, t is the current number of iterations, logsig() represents the logarithmic sigmoid function, k is the slope of the logarithmic sigmoid function, and random(0,1) is a random number between (0,1). This formula assigns a large value to ξ in the early stages of iteration to enhance global search capabilities; as iterations progress, the value of ξ gradually decays to a smaller value, focusing on local fine-tuning. This adaptive mechanism enables the algorithm to quickly locate high-quality solution regions in the early stages and then finely optimize the quality of the solutions in the later stages.

[0070] In one possible implementation, the individual fitness f(θ) is calculated according to the following formula:

[0071] f(θ)=α·PAPR(s(t))+β·max(0,ΔPSLR-1dB)

[0072] In the formula, α and β are weighting coefficients, α = 0.7 and β = 10. 3 ΔPSLR represents the difference in peak sidelobe ratio of the navigation signal before and after optimization, and ΔPSLR represents the difference in peak-to-average power ratio of the communication signal.

[0073] A penalty function mechanism is used to ensure that PSLR degradation does not exceed 1dB. Optimization terminates when the optimal fitness change is less than 0.1 over 10 consecutive generations or after 100 iterations.

[0074] The intelligent navigation fusion method based on communication and navigation fusion provided by this invention aims to solve the problems of low spectral efficiency, excessive PAPR, and limited navigation accuracy in low-orbit satellite communication and navigation fusion systems. It achieves frequency domain fusion of communication and navigation signals through dynamic subcarrier resource allocation and introduces the Brainstorming Optimization (BSO) algorithm to jointly optimize carrier phase perturbation, thereby suppressing PAPR while ensuring the ranging accuracy of navigation signals.

[0075] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart navigation fusion method based on communication and navigation fusion, characterized in that, include: Step S1: Acquire communication signals and navigation signals; Step S2: Allocate multiple subcarriers to the communication signal and the navigation signal according to the load of the communication service to obtain an initial allocation scheme: Step S3: With the goal of reducing the peak-to-average power ratio (PAPR) of the communication signal and maintaining the peak-to-sidelobe ratio (PSLR) of the navigation signal, the initial allocation scheme is optimized by using the BSO algorithm to jointly optimize the carrier phase perturbation, thereby obtaining the target allocation scheme.

2. The intelligent navigation fusion method according to claim 1, characterized in that, Step S2 includes: When the load condition is not busy, multiple subcarriers are interleaved and allocated to the communication signal and the navigation signal according to their parity index, wherein the number of subcarriers for the communication signal and the number of subcarriers for the navigation signal are equal.

3. The intelligent navigation fusion method according to claim 2, characterized in that, When the load is not busy, multiple edge subcarriers and DC / center subcarriers will also be used as guard bands.

4. The intelligent navigation fusion method according to claim 1, characterized in that, Step S2 further includes: When the load condition is busy, a majority of subcarriers are allocated to the communication signal and a minority of edge subcarriers are allocated to the navigation signal, wherein the number of subcarriers for the communication signal is greater than the number of subcarriers for the navigation signal.

5. The intelligent navigation fusion method according to claim 1, characterized in that, Step S3 includes: Multiple candidate solutions are generated randomly; The k-means algorithm is used to cluster the multiple candidate solutions into a predetermined number of subgroups; The individuals in the subgroup are updated either in a single cluster or across clusters until the change in individual fitness is less than 0.1 within a preset number of consecutive iterations or the preset number of iterations has been reached, at which point the target allocation scheme is output.

6. The intelligent navigation fusion method according to claim 5, characterized in that, Step S3 further includes: Perform single-cluster updates according to the following formula: Where ξ is a dynamically adjusted weighting coefficient. For the original individual x old The i-th dimension For the new individual x new The i-th dimension, N(μ,σ) 2 ) represents the Gaussian disturbance term, N represents the normal distribution, μ is the mean of the distribution, and σ is the standard deviation of the distribution.

7. The intelligent navigation fusion method according to claim 5, characterized in that, Step S3 further includes: Perform cross-cluster updates according to the following formula: Where w1 and w2 are the pre-set weighting coefficients, and ξ is the dynamically adjusted weighting coefficient. For the original individual x old The i-th dimension For the new individual x new The i-th dimension, N(μ,σ) 2 ) represents the Gaussian disturbance term, N represents the normal distribution, μ is the mean of the distribution, and σ is the standard deviation of the distribution.

8. The intelligent navigation fusion method according to claim 5 or 6, characterized in that, Step S3 further includes: The parameter ξ(t) is determined using the following formula: In the formula, T is the maximum number of iterations, t is the current number of iterations, logsig() represents the logarithmic sigmoid function, k is the slope of the logarithmic sigmoid function, and random(0,1) is a random number between (0,1).

9. The intelligent navigation fusion method according to claim 5, characterized in that, Step S3 further includes: Calculate the individual fitness f(θ) using the following formula: f(θ)=α·PAPR(s(t))+β·max(0,ΔPSLR-1dB) In the formula, α and β are weighting coefficients, ΔPSLR is the difference in peak sidelobe ratio of the navigation signal before and after optimization, and ΔRSLR is the difference in peak-to-average power ratio of the communication signal.

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