Low signal-to-noise ratio radio frequency ranging method and system based on PSO optimized stochastic resonance array

Abstract

The application discloses a low signal-to-noise ratio radio frequency ranging method and system based on PSO optimization random resonance array, which comprises the following steps: acquiring reference channel signals and target channel signals under multiple frequency points; optimizing the parameters of a random resonance model based on a particle swarm optimization algorithm; adapting the parameters of the random resonance model according to the frequency point scale for each frequency point; performing vector average and quadrature demodulation on the multi-path random resonance enhanced outputs of the same frequency point to form a target channel complex quantity; determining a synthetic wavelength pseudo-range according to the phase difference between the corresponding purified carrier phases of at least two frequency points and the corresponding synthetic wavelength; and taking the synthetic wavelength pseudo-range as a distance priori. The application introduces reference channel phase compensation, zero baseline bias correction, PSO adaptive parameter optimization, parallel random resonance array and MAP integer ambiguity resolution, so that the nonlinearly enhanced signals can still output accurate phase observations which can be used for precise ranging.

Description

Technical Field

[0001] This invention relates to the field of radio frequency carrier phase ranging, and more particularly to a low signal-to-noise ratio radio frequency ranging method and system based on a PSO optimized random resonant array. Background Technology

[0002] Radio frequency carrier phase ranging can achieve high-resolution distance estimation using carrier phase, and is suitable for scenarios such as length measurement, industrial sensing, positioning in enclosed spaces, and weak signal ranging. This type of method typically requires the stable extraction of carrier phase from the received signal, and then the absolute distance is obtained by resolving multi-frequency phase differences, synthetic wavelengths, or integer ambiguities.

[0003] In situations involving long-distance transmission, attenuation due to obstruction, strong noise in the receiving link, or non-ideal characteristics of the hardware channel, the weak periodic carrier component in the target channel is easily submerged by noise, leading to phase jitter, phase jumps, and unstable phase differences between frequency points. Since the carrier phase itself is periodic, if the coarse pseudorange is unreliable, subsequent integer ambiguity resolution is prone to selecting the wrong candidate distance, ultimately resulting in absolute distance deviations at the meter level or even larger.

[0004] Existing low signal-to-noise ratio (SNR) processing methods, such as wavelet thresholding (WTD) and variational mode decomposition (VMD), typically reduce noise from the perspective of signal filtering or mode decomposition. However, their parameter selection is highly sensitive to the received SNR, hardware noise color, and frequency location. Under severely low SNR conditions, these methods may not be able to fully recover the weak periodic components synchronized with the carrier, and they also struggle to balance multi-frequency phase consistency and the reliability of subsequent ambiguity resolution.

[0005] Stochastic resonance is a technique that uses nonlinear systems and appropriate noise to enhance weak periodic signals. If stochastic resonance is directly used for carrier phase ranging, the nonlinear potential well, noise disturbance, and numerical integration process may introduce additional phase response, which improves the amplitude of the enhanced signal but may not necessarily provide accurate phase information.

[0006] Therefore, a method is needed that allows the nonlinearly enhanced signal to still output accurate phase observations that can be used for precision ranging. Summary of the Invention

[0007] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is that when random resonance nonlinear enhancement is used directly under low signal-to-noise ratio, the enhancement process may change the signal phase response; although the weak carrier of the target channel is amplified, the phase is still affected by the nonlinear system, hardware link and fixed bias, making it difficult to use as a precise ranging phase.

[0008] To achieve the above objectives, this invention provides a low signal-to-noise ratio radio frequency ranging method based on a PSO-optimized stochastic resonant array, comprising: The reference channel signal and target channel signal at multiple frequency points are acquired, and the reference channel signal and target channel signal are down-converted and digitized to obtain the reference channel noisy baseband complex sequence and target channel noisy baseband complex sequence corresponding to each frequency point. The fixed bias corresponding to each frequency point is obtained through zero baseline calibration. The parameters of the stochastic resonance model are optimized based on the particle swarm optimization algorithm. The template signal and the noisy received signal are used as inputs, and the correlation or coherence between the template signal and the stochastic resonance output is used as the fitness index to obtain the optimized parameters of the stochastic resonance model. For each frequency point, the parameters of the stochastic resonance model are adapted according to the frequency point scale. The target channel noisy baseband complex sequence of the corresponding frequency point is input into multiple parallel stochastic resonance branches. Each parallel stochastic resonance branch is added with independent noise perturbation and subjected to stochastic resonance processing to obtain multi-channel stochastic resonance enhanced output. Vector averaging and orthogonal demodulation are performed on the multi-channel random resonance enhancement outputs at the same frequency point to form the target channel complex phasor. Phase compensation is then performed based on the phase of the target channel complex phasor, the phase of the reference channel complex phasor at the corresponding frequency point, and the fixed bias to obtain the clean carrier phase corresponding to multiple frequency points. The pseudorange of the synthesized wavelength is determined based on the phase difference between the clean carrier phases corresponding to at least two frequency points and the corresponding synthesized wavelength. Using the synthetic wavelength pseudorange as a distance prior, within the candidate distance range, a maximum a posteriori probability joint cost is constructed based on the phase residual between the multi-frequency theoretical phase corresponding to each candidate distance and the clean carrier phase, as well as the deviation of the candidate distance from the distance prior. Integer ambiguity and absolute distance are then determined based on the maximum a posteriori probability joint cost.

[0009] This invention proposes a nonlinear phase compensation mechanism that coordinates the reference channel and the target channel: a complex phasor is formed for the enhanced target channel, and the phase difference is compensated by using the complex phasor of the reference channel and the zero baseline calibration bias to obtain the purified carrier phase related to the distance to be measured.

[0010] The reference channel and the target channel share the same external clock, the same down-conversion frequency, and the same sampling link. They both include a common local oscillator, a sampling clock, a fixed hardware phase, and a portion of the nonlinearly processed phase response. By subtracting the reference phase from the target phase and subtracting the zero baseline offset, the distance-independent common phase term can be canceled, transforming the coherent components of the stochastic resonant nonlinear system output into accurate propagation phase observations.

[0011] While retaining the ability to enhance weak signals with random resonance, it avoids the problem of phase unavailability caused by nonlinear enhancement; it improves the stability of multi-frequency phase points, providing reliable input for synthetic wavelength pseudorange and MAP integer ambiguity resolution.

[0012] Furthermore, the stochastic resonance model is a bistable stochastic resonance model, and the parameters of the stochastic resonance model include potential well parameter a, potential well parameter b, and integration step size h; The particle swarm optimization algorithm uses the potential well parameter a, the potential well parameter b, and the integration step size h as the particle position, iteratively updates the particle velocity and particle position within a preset parameter range, and determines the optimal parameter vector based on the fitness index.

[0013] The parameters a, b, and integration step size h of a stochastic resonance system are sensitive to noise levels, signal frequencies, and hardware receiving conditions. Manually derived parameters are difficult to balance different signal-to-noise ratios and frequencies. This invention employs particle swarm optimization (PSO) to drive an adaptive search for stochastic resonance parameters. The potential well parameters a, b, and numerical integration step size h are used as particle positions, and the optimal parameter vector is determined based on the correlation between the template signal and the stochastic resonance output.

[0014] PSO iteratively updates particle velocity and position within a preset feasible region, using coherence or correlation indices as fitness functions to automatically find parameter combinations that maximize the coherent components of weakly periodic carriers, thereby enabling the stochastic resonance system to match the current hardware noise and signal-to-noise ratio.

[0015] This invention reduces reliance on manual parameter tuning and improves processing consistency under different low signal-to-noise ratio conditions; it also shifts stochastic resonance enhancement from empirical settings to data-driven optimization, thereby enhancing engineering repeatability.

[0016] Furthermore, the particle swarm optimization algorithm has 60 particles, a maximum number of iterations of 80, learning factors c1 and c2 of 1.7, and inertia weight decreasing from 0.85 to 0.35. The potential well parameter a and the potential well parameter b have values ​​ranging from [1×10^-4, 8], and the integration step size h has values ​​ranging from [0.01, 0.60]. If the fitness index fails to improve for a preset number of consecutive rounds, the particle swarm optimization process is terminated early and the optimal parameter vector is output.

[0017] Furthermore, frequency scale mapping is performed for different frequency points to adapt the signals corresponding to different frequency points to the stochastic resonance processing scale. Each frequency point corresponds to multiple parallel random resonance branches. Each parallel random resonance branch is added with independent noise perturbation, and the random resonance enhancement output is obtained by numerically solving the Langevin equation. Vector averaging is performed on the stochastic resonance enhancement output of each parallel stochastic resonance branch to suppress non-phase interference and retain coherent components synchronized with the carrier.

[0018] The output of a single-channel stochastic resonance is affected by random disturbances and initial values ​​obtained from numerical solutions, which may result in residual phase jitter. Furthermore, the different frequency scales of multi-frequency signals can easily lead to unbalanced enhancement when processed with uniform parameters. This invention constructs a parallel stochastic resonance array with frequency scale mapping, performing scale adaptation on signals at each frequency point. Multiple independent noise disturbance branches are set up for each frequency point, and vector averaging and orthogonal demodulation are performed on the branch outputs.

[0019] In this invention, frequency scale mapping adapts high-frequency carriers at different frequencies to a scale suitable for stochastic resonance processing; the independent perturbations of multiple parallel SR branches suppress incoherent noise in vector averaging, while the coherent components synchronized with the carrier are preserved, and orthogonal demodulation further forms a complex observation with computable phase.

[0020] This invention improves the quality of weak periodic carrier recovery and suppresses phase jumps caused by random branches; it also provides more stable eight-frequency phase observations and reduces subsequent pseudorange divergence.

[0021] Furthermore, each frequency point corresponds to 16 parallel random resonance branches, and the Langevin equation for each parallel random resonance branch is solved using the fourth-order Runge-Kutta method.

[0022] Furthermore, the reference channel and the target channel are synchronously acquired using the same external reference clock. The reference channel is used to provide a common phase reference, and the target channel is used to acquire the signal after passing through the path under test. The purified carrier phase is obtained by subtracting the phase of the reference channel complex phase at the corresponding frequency from the phase of the target channel complex phase, and subtracting the fixed offset.

[0023] Further, at least one frequency point pair is selected from the plurality of frequency points, the beat frequency phase is determined according to the difference in the clean carrier phases corresponding to the frequency point pair, and the synthetic wavelength pseudorange is determined in combination with the synthetic wavelength corresponding to the frequency point pair. A candidate distance range is established with the synthesized wavelength pseudorange as the center, and the theoretical phase corresponding to each frequency point is calculated for each candidate distance within the candidate distance range; The maximum a posteriori probability joint cost is determined based on the annular phase residual between the theoretical phase and the corresponding clean carrier phase, as well as the degree to which the candidate distance deviates from the synthetic wavelength pseudorange. The candidate distance with the minimum joint cost is determined by global grid search and used as the absolute distance. The corresponding integer ambiguity is then determined.

[0024] Carrier phase exhibits periodic ambiguity, and single-frequency or direct phase ranging at low signal-to-noise ratios is prone to integer ambiguity misjudgment; traditional coarse pseudorange may experience meter-level offsets under severe noise. This invention combines multi-frequency carrier phases to form a synthetic wavelength pseudorange, and introduces this pseudorange as a priori constraint into MAP joint cost minimization and global grid search to jointly solve for integer ambiguity and absolute distance.

[0025] The phase difference between adjacent or selected frequency pairs in this invention corresponds to a longer synthetic wavelength, which can form coarse distance anchors; the MAP search simultaneously compares the multi-frequency ring phase residuals and pseudorange prior biases of candidate distances, and selects the distance point with the minimum joint cost from the global candidate grid.

[0026] This invention narrows the candidate distance range and reduces the probability of erroneous integers being selected. Under hardware playback conditions of -20 dB to 0 dB, the average pseudorange error of this method is reduced to 0.38 m to 0.93 m, which is better than the 1.43 m to 3.86 m range of WTD and VMD, ultimately benefiting millimeter-level absolute distance output.

[0027] In a second aspect, the present invention proposes a low signal-to-noise ratio radio frequency ranging system based on a PSO-optimized stochastic resonant array using the method described in the first aspect, comprising: The radio frequency acquisition module is used to acquire reference channel signals and target channel signals at multiple frequency points, and to perform down-conversion and digitization processing on the reference channel signals and target channel signals to obtain a noisy baseband complex sequence of the reference channel and a noisy baseband complex sequence of the target channel. The PSO-driven parameter optimization module is used to optimize the parameters of the stochastic resonance model based on the particle swarm optimization algorithm, taking the template signal and the noisy received signal as input. The parallel random resonance array module is used to perform frequency scale mapping for each frequency point, and to perform random resonance processing on the noisy baseband complex sequence of the target channel using multiple random resonance branches with independent noise perturbations. The output of each random resonance branch is vector averaged and orthogonally demodulated to form the target channel complex phasor. Phase compensation is performed based on the reference channel complex phasor of the corresponding frequency point and the fixed bias obtained by zero baseline calibration to obtain the clean carrier phase corresponding to multiple frequency points. The pseudorange calculation module is used to determine the synthetic wavelength pseudorange based on the phase difference of the clean carrier phases corresponding to at least two frequency points and the corresponding synthetic wavelength. The MAP fuzzy solution module is used to determine the integer fuzziness and absolute distance by using the synthesized wavelength pseudorange as the distance prior, calculating the joint cost of maximum a posteriori probability and performing a global search based on the multi-frequency phase residual and the deviation of the candidate distance from the distance prior.

[0028] Thirdly, the present invention provides an electronic device, including a processor, a memory, and a radio frequency acquisition interface; The memory stores program instructions; When the processor executes the program instructions, it implements the low signal-to-noise ratio radio frequency ranging method described in the first aspect.

[0029] Fourthly, a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the low signal-to-noise ratio radio frequency ranging method described in the first aspect.

[0030] Technical effect

[0031] This invention introduces reference channel phase compensation, zero baseline offset correction, PSO adaptive parameter optimization, parallel random resonance array, and MAP integer ambiguity resolution, so that the nonlinearly enhanced signal can still output accurate phase observations that can be used for precision ranging.

[0032] This invention has clear engineering applicability. The hardware side only needs to have dual-channel RF acquisition capability and a unified external reference clock, which can be implemented by dual-channel software radio devices such as the USRP B210 or other multi-channel RF receiving platforms; the software side mainly includes PSO parameter search, numerical solution of random resonant array, phase compensation, multi-frequency pseudorange calculation and MAP grid search, all of which can be implemented on general-purpose processors, embedded processors, GPUs or FPGA / CPU collaborative platforms.

[0033] This invention is suitable for deployment in low signal-to-noise ratio RF ranging, long cable length calibration, weak echo link ranging, industrial field RF sensing, phase ranging in complex electromagnetic environments, and hardware-in-the-loop ranging verification systems. Compared with simple filtering and noise reduction methods, this scheme utilizes the nonlinear enhancement capability of stochastic resonance to recover the weak periodic carrier and ensures phase measurability through reference compensation, making it particularly suitable for scenarios where pseudorange divergence occurs in traditional WTD, VMD, and other methods.

[0034] In terms of performance indicators, under severely low signal-to-noise ratio conditions of -20 dB, the PSO-SR array reduced pseudorange errors to 1.50 m, 1.13 m, 2.38 m, and 1.94 m for four 2 MHz synthesized wavelength frequency pairs; this represents a reduction of 64.0% to 77.2% compared to WTD and a reduction of 66.0% to 81.6% compared to VMD. Within the range of -20 dB to 0 dB, the average pseudorange error of this scheme is 0.38 m to 0.93 m, providing a reliable prior for MAP integer ambiguity resolution.

[0035] This solution does not limit the specific RF equipment model, number of frequency points, frequency spacing, number of parallel SR branches, or number of PSO particles, and can be expanded according to processing resources and ranging accuracy requirements. Its industrial value lies in improving the reliability of low signal-to-noise ratio ranging, reducing hardware transmit power or link signal-to-noise ratio requirements, reducing manual parameter debugging costs, and can be integrated as a software enhancement module for existing phase ranging systems.

[0036] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0037] Figure 1 This is a flowchart of a low signal-to-noise ratio radio frequency ranging method according to a preferred embodiment of the present invention; Figure 2 This is a comparison chart of pseudorange error performance of a preferred embodiment of the present invention. Detailed Implementation

[0038] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0039] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0040] Example 1: Low Signal-to-Noise Ratio Radio Frequency Ranging Method

[0041] This embodiment uses an external 10 MHz reference clock to drive a dual-channel RF acquisition device. Eight frequency points are set near the 400 MHz RF center, with frequency offsets of -4 MHz, -3 MHz, -2 MHz, -1 MHz, +1 MHz, +2 MHz, +3 MHz, and +4 MHz. The sampling rate is 10 MS / s. The first receiving channel Rx1 acquires the reference baseline signal, and the second receiving channel Rx2 acquires the target signal after passing through the path under test or a long cable. The two signals are down-converted and digitized to form noisy baseband complex sequences r0(t) and r1(t), respectively.

[0042] Step S1: Perform multi-frequency radio frequency signal acquisition. Simultaneously acquire reference channel and target channel data for each frequency point. The reference channel provides a common phase reference, and the target channel carries the propagation phase caused by the distance to be measured. Zero-baseline calibration can be performed before acquisition to obtain a fixed offset beta_i for each frequency point, which is used for subsequent phase compensation.

[0043] Step S2: Perform PSO optimization of stochastic resonance parameters. The known template signal s and the noisy received signal r are used as inputs, and the potential well parameters a and b of the bistable stochastic resonance model and the integration step size h are used as particle positions. The number of particles can be set to 60, the maximum number of iterations to 80, the learning factors c1=c2=1.7, and the inertia weight decreasing from 0.85 to 0.35. The parameter range can be set as a and b belonging to [1e-4, 8], and h belonging to [0.01, 0.60]. The correlation or coherence between the template signal and the stochastic resonance output is used as the fitness. If there is no improvement after several consecutive rounds, the process stops early, and the optimal parameter vectors a, b, and h are output.

[0044] Step S3: Perform stochastic resonant array enhancement. Frequency scale mapping is performed for each of the eight frequency points. For example, a, b, and h are adapted according to the frequency scale to ensure that different frequency points fall within a scale suitable for stochastic resonant processing. The noisy signal of the target channel at each frequency point enters K parallel stochastic resonant branches, where K is preferably 16. Independent noise perturbations are added to each branch, and the Langevin equation is solved using the fourth-order Runge-Kutta method to obtain the K-channel SR enhancement output.

[0045] Step S4: Extract carrier phase points and perform compensation. Vector averaging is performed on the K-channel SR outputs at the same frequency to retain synchronous coherent components and suppress non-phase interference. Then, the target channel complex phasor is formed through quadrature demodulation. The phase of the target channel complex phasor is subtracted from the phase of the reference channel complex phasor, and the zero baseline calibration bias beta_i is subtracted to obtain the cleaned carrier phase phi_i at that frequency. The above process is repeated for eight frequency points to obtain eight phase observation points from phi_1 to phi_8. This step is the key difference between this invention and the direct nonlinear enhancement method: through compensation, the stochastic resonant nonlinear system not only enhances the amplitude but also outputs accurate phase information.

[0046] Step S5 involves performing pseudorange estimation for the synthesized wavelength. Adjacent or preset frequency pairs are selected, such as -4 / -2 MHz, -3 / -1 MHz, -1 / +1 MHz, and +1 / +3 MHz, with a frequency interval of 2 MHz. The beat frequency phase is calculated based on the phase difference between the frequency pairs, and combined with the synthesized wavelength to form pseudoranges rho_12, rho_23, rho_34, etc. The pseudorange is not directly used as the final distance, but rather as a coarse distance prior for MAP fuzzy solution.

[0047] Step S6: Perform MAP integer ambiguity resolution. Establish a global grid within the candidate distance range centered on the pseudorange, for example, searching within a range of ±5 m for d_prior, with a prior standard deviation of 0.5 m. Calculate the theoretical phase at each frequency point for each candidate distance. Use the ring residual between the theoretical and measured phases, along with the degree to which the candidate distance deviates from the pseudorange prior, as the joint cost of MAP. Select the candidate point with the minimum cost to obtain the integer ambiguity N and the absolute distance d.

[0048] Step S7 outputs the absolute distance ranging result. This result integrates hardware dual-channel phase compensation, PSO-optimized SR enhancement, multi-frequency pseudorange prior, and MAP global search results, and can maintain a relatively stable absolute distance estimation even under severely low signal-to-noise ratio conditions in the target channel.

[0049] Example 2: Implementation of Distance Measuring System and Equipment

[0050] The system in this embodiment may include a hardware RF acquisition module, a PSO driving parameter optimization module, a parallel stochastic resonant array module, and a MAP fuzzy solution module. The hardware RF acquisition module includes an external clock source, a dual-channel RF receiving unit, a down-conversion unit, and a digitization unit, used to output noisy baseband complex sequences for the reference and target channels. The PSO driving parameter optimization module outputs stochastic resonant parameter vectors a, b, and h based on the template signal and the noisy received signal. The parallel stochastic resonant array module performs frequency scale mapping, independent noise perturbation, fourth-order numerical solution, vector averaging, and orthogonal demodulation, outputting cleaned multi-frequency phase observations. The MAP fuzzy solution module performs multi-frequency wavelength synthesis, pseudorange prior generation, MAP joint cost minimization, and global grid search, ultimately outputting the absolute physical distance.

[0051] This system can be implemented using electronic devices, including a processor, memory, and an RF acquisition interface. The memory stores the program instructions for performing the above steps; when the processor executes the program instructions, it performs multi-frequency acquisition control, PSO parameter optimization, stochastic resonant array calculation, phase compensation, pseudorange estimation, and MAP search. For scenarios with high real-time requirements, the multi-branch solution and multi-frequency processing of the stochastic resonant array can be deployed in parallel on a GPU, FPGA, or multi-core CPU.

[0052] Example 3: Performance Verification Methods and Results

[0053] To verify the effectiveness of this solution, the PSO-SR array of this invention can be compared with wavelet threshold denoising (WTD) and variational mode decomposition (VMD) under the same hardware data acquisition, calibration conditions, and pseudorange calculation process. The test signal-to-noise ratio range can be -20 dB to 0 dB, using four sets of 2 MHz synthetic wavelength frequency pairs.

[0054] Under -20 dB conditions, the pseudorange errors obtained using WTD for the four frequency pairs were 4.16 m, 4.13 m, 9.48 m, and 8.50 m, respectively, while those obtained using VMD were 8.15 m, 3.44 m, 8.79 m, and 5.71 m, respectively. After using the PSO-SR array of this invention, the pseudorange errors were reduced to 1.50 m, 1.13 m, 2.38 m, and 1.94 m, respectively. Compared to WTD, the error reduction was 64.0% to 77.2%; compared to VMD, the error reduction was 66.0% to 81.6%.

[0055] Within a signal-to-noise ratio (SNR) range of -20 dB to 0 dB, the average pseudorange error for the four frequency pairs in this invention ranges from 0.38 m to 0.93 m, while the corresponding average errors for WTD and VMD range from 1.43 m to 3.86 m. These results demonstrate that the PSO-optimized stochastic resonant array can generate more reliable coarse pseudorange anchors under severely low SNR conditions and reduce the risk of erroneous integers being selected in MAP integer ambiguity resolution.

[0056] like Figure 1 The diagram shown is a flowchart of a low signal-to-noise ratio radio frequency ranging method provided in an embodiment of the present invention. Figure 1 In this diagram, module A represents hardware RF acquisition, module B represents PSO driver parameter optimization, module C represents parallel random resonant SR array, and module D represents MAP fuzzy solution.

[0057] like Figure 2 The figure shown is a comparison chart of pseudorange error performance provided in the embodiments of the present invention. Figure 2 The four sub-figures in the figure correspond to four wavelength frequency pairs consisting of -4 / -2 MHz, -3 / -1 MHz, -1 / +1 MHz, and +1 / +3 MHz, respectively, and are used to illustrate the pseudorange error reduction effect of the PSO-SR array of the present invention compared with WTD and VMD at low signal-to-noise ratio.

[0058] The main symbols in the attached figure have the following meanings: r0(t) represents the noisy baseband complex sequence of the reference channel, r1(t) represents the noisy baseband complex sequence of the target channel; a, b, and h represent random resonance parameters; phi_i represents the carrier phase observation at the i-th frequency point; rho represents the synthetic wavelength pseudorange; N represents the integer ambiguity; and d represents the output absolute distance.

[0059] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A low signal-to-noise ratio radio frequency ranging method based on PSO-optimized stochastic resonant array, characterized in that, include: The reference channel signal and target channel signal at multiple frequency points are acquired, and the reference channel signal and target channel signal are down-converted and digitized to obtain the reference channel noisy baseband complex sequence and target channel noisy baseband complex sequence corresponding to each frequency point. The fixed bias corresponding to each frequency point is obtained through zero baseline calibration. The parameters of the stochastic resonance model are optimized based on the particle swarm optimization algorithm. The template signal and the noisy received signal are used as inputs, and the correlation or coherence between the template signal and the stochastic resonance output is used as the fitness index to obtain the optimized parameters of the stochastic resonance model. For each frequency point, the parameters of the stochastic resonance model are adapted according to the frequency point scale. The target channel noisy baseband complex sequence of the corresponding frequency point is input into multiple parallel stochastic resonance branches. Each parallel stochastic resonance branch is added with independent noise perturbation and subjected to stochastic resonance processing to obtain multi-channel stochastic resonance enhanced output. Vector averaging and orthogonal demodulation are performed on the multi-channel random resonance enhancement outputs at the same frequency point to form the target channel complex phasor. Phase compensation is then performed based on the phase of the target channel complex phasor, the phase of the reference channel complex phasor at the corresponding frequency point, and the fixed bias to obtain the clean carrier phase corresponding to multiple frequency points. The pseudorange of the synthesized wavelength is determined based on the phase difference between the clean carrier phases corresponding to at least two frequency points and the corresponding synthesized wavelength. Using the synthetic wavelength pseudorange as a distance prior, within the candidate distance range, a maximum a posteriori probability joint cost is constructed based on the phase residual between the multi-frequency theoretical phase corresponding to each candidate distance and the clean carrier phase, as well as the deviation of the candidate distance from the distance prior. Integer ambiguity and absolute distance are then determined based on the maximum a posteriori probability joint cost.

2. The low signal-to-noise ratio radio frequency ranging method as described in claim 1, characterized in that, The stochastic resonance model is a bistable stochastic resonance model, and the parameters of the stochastic resonance model include potential well parameter a, potential well parameter b, and integration step size h. The particle swarm optimization algorithm uses the potential well parameter a, the potential well parameter b, and the integration step size h as the particle position, iteratively updates the particle velocity and particle position within a preset parameter range, and determines the optimal parameter vector based on the fitness index.

3. The low signal-to-noise ratio radio frequency ranging method as described in claim 1, characterized in that, The particle swarm optimization algorithm has 60 particles, a maximum number of iterations of 80, learning factors c1 and c2 of 1.7, and inertia weight decreasing from 0.85 to 0.

35. The potential well parameter a and the potential well parameter b have values ​​ranging from [1e-4,8], and the integration step size h has values ​​ranging from [0.01, 0.60]. If the fitness index fails to improve for a preset number of consecutive rounds, the particle swarm optimization process is terminated early and the optimal parameter vector is output.

4. The low signal-to-noise ratio radio frequency ranging method as described in claim 1, characterized in that, Frequency scale mapping is performed for different frequency points so that the signals corresponding to different frequency points are adapted to the stochastic resonance processing scale. Each frequency point corresponds to multiple parallel random resonance branches. Each parallel random resonance branch is added with independent noise perturbation, and the random resonance enhancement output is obtained by numerically solving the Langevin equation. Vector averaging is performed on the stochastic resonance enhancement output of each parallel stochastic resonance branch to suppress non-phase interference and retain coherent components synchronized with the carrier.

5. The low signal-to-noise ratio radio frequency ranging method as described in claim 4, characterized in that, Each frequency point corresponds to 16 parallel random resonance branches, and the Langevin equation for each parallel random resonance branch is solved using the fourth-order Runge-Kutta method.

6. The low signal-to-noise ratio radio frequency ranging method as described in claim 1, characterized in that, The reference channel and the target channel are synchronously acquired using the same external reference clock. The reference channel is used to provide a common phase reference, and the target channel is used to acquire the signal after passing through the path under test. The purified carrier phase is obtained by subtracting the phase of the reference channel complex phase at the corresponding frequency from the phase of the target channel complex phase, and subtracting the fixed offset.

7. The low signal-to-noise ratio radio frequency ranging method as described in claim 1, characterized in that, Select at least one frequency point pair from the plurality of frequency points, determine the beat frequency phase based on the difference in the phase of the clean carrier corresponding to the frequency point pair, and determine the synthetic wavelength pseudorange in combination with the synthetic wavelength corresponding to the frequency point pair. A candidate distance range is established with the synthesized wavelength pseudorange as the center, and the theoretical phase corresponding to each frequency point is calculated for each candidate distance within the candidate distance range; The maximum a posteriori probability joint cost is determined based on the annular phase residual between the theoretical phase and the corresponding clean carrier phase, as well as the degree to which the candidate distance deviates from the synthetic wavelength pseudorange. The candidate distance with the minimum joint cost is determined by global grid search and used as the absolute distance. The corresponding integer ambiguity is then determined.

8. A low signal-to-noise ratio radio frequency ranging system based on a PSO-optimized stochastic resonant array according to any one of claims 1-7, characterized in that, include: The radio frequency acquisition module is used to acquire reference channel signals and target channel signals at multiple frequency points, and to perform down-conversion and digitization processing on the reference channel signals and target channel signals to obtain a noisy baseband complex sequence of the reference channel and a noisy baseband complex sequence of the target channel. The PSO-driven parameter optimization module is used to optimize the parameters of the stochastic resonance model based on the particle swarm optimization algorithm, taking the template signal and the noisy received signal as input. The parallel random resonance array module is used to perform frequency scale mapping for each frequency point, and to perform random resonance processing on the noisy baseband complex sequence of the target channel using multiple random resonance branches with independent noise perturbations. The output of each random resonance branch is vector averaged and orthogonally demodulated to form the target channel complex phasor. Phase compensation is performed based on the reference channel complex phasor of the corresponding frequency point and the fixed bias obtained by zero baseline calibration to obtain the clean carrier phase corresponding to multiple frequency points. The pseudorange calculation module is used to determine the synthetic wavelength pseudorange based on the phase difference of the clean carrier phases corresponding to at least two frequency points and the corresponding synthetic wavelength. The MAP fuzzy solution module is used to determine the integer fuzziness and absolute distance by using the synthesized wavelength pseudorange as the distance prior, calculating the joint cost of maximum a posteriori probability and performing a global search based on the multi-frequency phase residual and the deviation of the candidate distance from the distance prior.

9. An electronic device, characterized in that, Includes processor, memory, and radio frequency acquisition interface; The memory stores program instructions; When the processor executes the program instructions, it implements the low signal-to-noise ratio radio frequency ranging method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the processor performs the low signal-to-noise ratio radio frequency ranging method according to any one of claims 1-7.

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