Star flash phase ranging method based on orthogonal matching pursuit and interpolation refinement

Abstract

The invention provides a phase ranging method based on the combination of orthogonal matching pursuit (OMP) and three-point interpolation refinement. The phase ranging method is suitable for a BLE / SLE narrowband wireless communication system. The method comprises the following steps: firstly, constructing a delay-frequency dictionary, obtaining a multi-channel phase vector through bidirectional phase measurement, and carrying out expansion and normalization processing on the multi-channel phase vector; then, using an OMP algorithm to select an atom which is most matched with the measurement phase in a dynamically generated predefined dictionary, and obtaining rough time delay estimation; then, based on the selected atom and the matching amplitude of the two adjacent points, a three-point parabola interpolation formula is adopted to carry out sub-step size refinement on the time delay, and more accurate time delay estimation is obtained; and finally, converting the refined time delay into a distance value. According to the method, multipath interference is effectively suppressed through sparse modeling, a dictionary step size limit value is broken through through an interpolation step, the distance measurement precision is remarkably improved, and the method has good embedded deployment value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless communication and positioning technology, and particularly relates to a phase ranging method based on a multi-carrier phase difference (MCPD) ranging framework, combined with orthogonal matching pursuit (OMP) sparse reconstruction and three-point parabolic interpolation refinement. BACKGROUND

[0002] NearLink is a new type of short-range wireless communication technology, which has the characteristics of low power consumption, low delay, high reliability, etc., and is particularly suitable for high-precision ranging and positioning tasks in intelligent terminal, vehicle-mounted device, Internet of Things and other scenarios. The low-power version (SLE) supports multi-carrier modulation and two-way communication, providing a good physical layer basis for multi-carrier phase difference (MCPD, Multi-Carrier Phase Difference) ranging method.

[0003] The current mainstream MCPD ranging method mostly uses linear least squares fitting (LS) or FFT spectrum peak detection to estimate the propagation delay. These methods have certain accuracy in line-of-sight (LOS) environment, but in multipath or non-line-of-sight (NLOS) scenarios, they are easily disturbed by reflected paths, causing phase trajectory deviation and affecting ranging stability. In addition, traditional methods are usually limited by frequency resolution, making it difficult to achieve sub-step-level fine ranging.

[0004] Orthogonal matching pursuit (Orthogonal Matching Pursuit, OMP) is a classic sparse reconstruction algorithm, widely used in compressed sensing, signal recovery, image reconstruction and other fields. Its basic idea is: given a redundant dictionary, select the atoms (dictionary columns) most related to the observed signal step by step through iteration, and update the residual at each step by least squares to approximate the sparse representation of the original signal.

[0005] Applying the orthogonal matching pursuit (OMP) algorithm to the sparse reconstruction method in the MCPD ranging scenario can estimate the main path delay by constructing a delay-frequency dictionary and using OMP iteration to select the most matching atom. This kind of method has strong multipath resolution ability in theory, but has the following shortcomings in practical application: (1) Large dictionary storage overhead: the traditional OMP method needs to explicitly construct and store the complete delay-frequency dictionary matrix, with a space complexity of O(KN), which is difficult to deploy on embedded platforms.

[0006] (2) Ranging accuracy is limited by dictionary step size: the time delay estimated by OMP is a discrete grid point, and the accuracy is limited by the dictionary resolution. If higher accuracy is required, a denser dictionary is needed, further increasing the computational and storage burden.

[0007] (3) Lack of refinement mechanism: existing methods mostly take OMP output as the final result, without sub-grid interpolation refinement on the estimated result, making it difficult to achieve decimeter-level ranging accuracy.

[0008] (4) Poor embedded adaptability: traditional sparse reconstruction methods have high computational complexity and many iterations, making it difficult to achieve real-time operation on resource-constrained MCU platforms. SUMMARY

[0009] The present application proposes a ranging algorithm combining orthogonal matching pursuit (OMP) sparse solution and three-point interpolation refinement, aiming to improve the multipath resistance and ranging resolution of phase ranging methods under BLE, SLE and other narrowband platforms, especially suitable for wireless positioning scenarios that require low embedded resources but require sub-meter ranging accuracy.

[0010] TECHNICAL SCHEME A star flash phase ranging method based on orthogonal matching pursuit and interpolation refinement, comprising the following steps: S1. Constructing a delay-frequency implicit dictionary; The constructed delay-frequency implicit dictionary matrix has atoms as Fourier basis atoms; S2. Multi-channel phase measurement and preprocessing; Obtain and process phase information at multiple frequencies, eliminate clock error through round-trip communication between the transmitting end and the receiving end, and obtain the original phase data; expand the original phase into a continuous phase; remove the global offset to construct a complex phase vector with unit amplitude; use a filtering method to reduce errors caused by phase mutations; the complex phase vector is provided to step S3; S3. Coarse delay estimation using OMP method; Using the OMP method, the sparse representation of the observed signal is gradually approximated through iteration. In each iteration, the algorithm selects the atom that best matches the current residual from the dictionary, adds it to the support set, and performs least squares fitting on the support set to update the residual. Iteration continues until the residual is small enough or the number of iterations reaches a certain number, and the time delay index corresponding to the optimal atom is finally output as the main path estimate.

[0011] S4. Three-point parabolic interpolation refinement; Based on the delay estimation result of step S3, the three-point parabolic interpolation is used to refine the delay estimation result.

[0012] S5. Calculate the distance estimation value according to the refined delay estimation result.

[0013] ADVANTAGEOUS EFFECTS Compared with traditional methods, the method of the present application has the following advantages: 1. High resolution: sub-grid delay estimation is achieved between the dictionary grid through three-point parabolic interpolation, and the ranging accuracy can reach decimeter level (error ≤0.2 m under LOS condition) 2. Anti-multipath interference: OMP can effectively identify the main line-of-sight atom and suppress the interference of surrounding multipath components on the phase, and the error is kept in the range of 0.8 m~1.2 m in indoor multipath scenarios.

[0014] 3. Low computational complexity: a small number of OMP iterations are performed, and the computational complexity is O(KN), which is greatly reduced compared with traditional MUSIC / ESPRIT algorithms.

[0015] 4. Low storage overhead: no explicit storage of complete K x N dictionary is required, only the corresponding carrier phase vector is dynamically generated, and the memory occupation is less.

[0016] 5. Good scalability: suitable for BLE / SLE multi-carrier positioning system, and other multi-carrier phase ranging scenarios, K, N, parameters can be adjusted according to the hardware performance. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is the flow chart of the method of the present application; Figure 2 is the hardware experimental platform diagram of the star flash HI2821E of the embodiment of the present application; Figure 3 is the indoor parking lot ranging data comparison diagram of the embodiment of the present application; Figure 4 is the outdoor ranging data comparison diagram of the embodiment of the present application; Figure 5 is the algorithm memory occupation comparison diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions provided by the present application will be further described below in combination with specific embodiments and their drawings. The advantages and features of the present application will be clearer in combination with the following description.

[0019] A star flash phase ranging method based on orthogonal matching pursuit and interpolation refinement, comprising the following steps: Figure 1 S1. Delay-frequency implicit dictionary construction First, determine the maximum ranging distance required by the system , and thus calculate the maximum round-trip delay : , wherein (unit: m) is used to determine the maximum delay range covered by the dictionary, is the speed of light constant. ​

[0020] Then according to the dictionary size N (or the required ranging resolution), the dictionary delay step size is calculated : Set a set of candidate delays , construct a dictionary matrix containing N atoms , where the th atom (Fourier basis atom) is defined as: These atoms constitute the phase response templates of the signal at different delays. The dictionary should cover the entire delay range corresponding to the maximum ranging range.

[0021] Where, represents the th candidate delay value, K represents the number of subcarriers used in the multicarrier system, represents the frequency of the th subcarrier.

[0022] The delay-frequency implicit dictionary matrix finally constructed is , where the th atom is: The traditional OMP algorithm needs to store the dictionary matrix explicitly, and the space complexity is . The implicit dictionary matrix introduced in the present application utilizes the structural characteristics of the dictionary Fourier basis, dynamically generates atoms through functions, and does not need to store the complete matrix, thereby reducing the space memory occupation.

[0023] S2. Multichannel phase measurement and preprocessing This step aims to obtain and process the phase information at multiple frequency points, eliminate clock errors through round-trip communication between the transmitting end and the receiving end, and obtain the original phase data; expand the original phase into continuous phase; remove the global offset to construct a complex phase vector with unit amplitude; use filtering method to reduce the error caused by phase mutation; since the main path is significantly stronger than other paths, the phase vector presents a sparse structure in the delay domain, providing a basis for subsequent OMP processing.

[0024] The specific process is as follows: (1) Bidirectional half-duplex measurement: the transmitting end sends a carrier signal to the receiving end, the receiving end samples the carrier phase at K frequency points and returns, and the transmitting end measures the returned signal phase again to eliminate the transmit-receive clock error, obtaining the round-trip phase .

[0025] (2) Phase unwrapping: unwrap the original phase according to the modulus expansion, get the continuous phase .

[0026] (3) Offset correction and normalization: remove the global offset (such as carrier phase difference or temperature drift caused phase baseline) to construct the unit amplitude complex phase vector .

[0027] Suppose there are K carrier frequencies , each frequency corresponds to a measured phase difference , which forms a complex vector: Under the line-of-sight condition, the phase difference and the propagation distance r satisfy: where is the phase deviation introduced by noise or multipath.

[0028] Due to the small number of propagation paths (main path + several strong reflection paths), it is believed that there is a sparse structure in the time delay domain.

[0029] (4) Noise suppression: low-pass filter or median filter on to suppress noise mutations and deepening errors.

[0030] S3. Roughly estimate the time delay using the OMP method The OMP aims to linearly combine the observed phase signals with the least dictionary atoms , and gradually construct a sparse support set through iteration. The specific process is as follows: ① Initialize the residual vector , support set Λ and iteration number , as follows: Residual vector Support set Λ=∅ Iteration number =0 ② Iteration step (select an optimal atom each round): OMP algorithm gradually approximates the sparse representation of the observed signal through iteration. In each iteration, the algorithm selects the atom that best matches the current residual from the dictionary, adds it to the support set, and performs least squares fitting on the support set to update the residual. Iteration continues until the residual is small enough or the number of iterations reaches the set number, and finally outputs the time delay index corresponding to the optimal atom as the main path estimate.

[0031] ​​Compute the inner product of the current residual with all atoms in the dictionary: Select the index of the maximum inner product Update the support set: Solve the least squares problem in the current support set: where denotes the selected atom set extracted from the dictionary.

[0032] Update the residual: If the number of iterations reaches the upper limit, stop the iteration (also can judge the residual below the threshold to stop iteration).

[0033] ③ Result output: The path with the smallest residual is finally selected, and the path delay index corresponding to is the estimated main path delay.

[0034] Convert it to a distance estimate: S4. Three-point parabolic interpolation refinement The present application further refines the results based on the OMP method using three-point parabolic interpolation.

[0035] Specifically as follows: The delay index estimated by OMP , and the two points on the side are , The corresponding projection amplitude is: Estimate the true peak offset position δ using the interpolation formula, where δ ∈ [−0.5, 0.5], i.e. the relative refinement offset: Thus, a more accurate delay index estimate is obtained: .

[0036] This embodiment uses three-point parabolic interpolation, and for higher accuracy, multi-point interpolation refinement can also be used.

[0037] S5. The refined distance estimate is finally calculated as: The method constructs a fitted continuous model around the discrete peak value, thereby effectively breaking through the limitation of the dictionary step size, improving the ranging accuracy, and without the need of additional increase of the dictionary resolution, thereby avoiding the calculation complexity introduced by high-dimensional sparse solving.

[0038] To verify the effectiveness of the method, a comparison test is performed on the star flash SLE platform, and the standard OMP method is selected as the comparison baseline. The test environment includes an indoor parking lot (significant multipath) and an outdoor open area (LOS), the ranging range is 1-10 meters, and the same multi-carrier frequency configuration and hardware platform are used. The star flash hardware experimental platform is as shown in Figure 2 The HI2821E module is used for IQ data acquisition and ranging algorithm running result output.

[0039] Among them, the standard OMP method (method A) uses an explicit dictionary and OMP iteration solving, and outputs a coarse time delay estimation; the method of the application (method B) adopts OMP sparse reconstruction combined with an implicit dictionary and three-point interpolation refinement.

[0040] The indoor parking lot ranging data (Fig. 1) and the outdoor scene ranging data (Fig. 2) of the comparison between the standard OMP method and the method of the application are shown in Figure 3 and Figure 4 , and the memory occupation (Fig. 3) is shown in Figure 5 .

[0041] Among them, the ranging accuracy is measured by root mean square error (RMSE); the memory occupation is measured by the total memory (KB) of the dictionary storage and intermediate variables; The performance comparison results are listed in Table 1.

[0042] Table 1 Performance comparison The results show that: as can be seen, the method of the application breaks through the limitation of the dictionary step size by interpolation refinement, and the ranging accuracy is improved by 40%-60%; at the same time, the implicit dictionary generation and the light interpolation strategy are adopted, which significantly reduces the memory and calculation burden.

[0043] In addition, the method of the application also has the following advantages: Strong anti-multipath capability: OMP sparse modeling can effectively identify the main path and suppress multipath interference; Strong embedded deployability: ranging is realized on a 32-bit MCU platform, meeting the real-time positioning demand; Wide applicability: can be extended to BLE, SLE, UWB and other multi-carrier ranging systems, and has good universality.

[0044] The above merely describes the specific embodiments of the present application. It should be noted that for those skilled in the art, the specific embodiments of the present application cannot be limited to the above description, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. Those skilled in the art can make various changes in form and details, including making several simple deductions or replacements, without departing from the spirit and scope of the present application.

[0045] The following is a summary of the parameters involved in the present application: I. Dictionary construction and system parameters : Maximum ranging distance of the system (unit: meter), used to determine the maximum delay range covered by the dictionary.

[0046] : Light speed constant.

[0047] : Maximum round-trip propagation delay : Number of atoms in the dictionary, i.e. the number of candidate delay points.

[0048] Δτ: Delay step size corresponding to adjacent atoms in the dictionary.

[0049] : The th candidate delay value, which constitutes the horizontal axis of the dictionary.

[0050] K: Number of subcarriers used in a multicarrier system.

[0051] : The th subcarrier frequency.

[0052] : Delay-frequency dictionary matrix, containing atoms, each atom being a -dimensional complex vector.

[0053] : The th dictionary atom, II. Phase measurement and preprocessing : Phase difference at the kth frequency point.

[0054] : Phase noise or deviation caused by multipath at the kth frequency point.

[0055] : Normalized complex phase vector, r: Propagation distance III. OMP algorithm related parameters : The residual vector of the th iteration.

[0056] : The index set of the current selected atoms (support set). : The inner product value of the jth atom and the current residual, used to select the best matching atom.

[0057] : The least square solution under the current support set.

[0058] : The rough main path delay estimate output by OMP.

[0059] IV. Three-point interpolation refinement parameters : The projection amplitude (i.e. the matching strength) of the two points on both sides of the OMP output index and the center point.

[0060] δ: The interpolation offset, representing the sub-grid offset of the true peak value relative to the rough estimate index.

Claims

1. A star flash phase ranging method based on orthogonal matching pursuit and interpolation refinement, characterized in that: The following steps are involved: S1. Construct delay-frequency implicit dictionary; The constructed delay-frequency implicit dictionary matrix, whose atoms are Fourier basis atoms; S2. Multi-channel phase measurement and preprocessing; Acquire and process phase information at multiple frequency points, eliminate clock errors through round-trip communication between the transmitter and receiver, and obtain original phase data; Expand the original phase into a continuous phase; remove the global offset and construct a complex phase vector with unit amplitude; use a filtering method to reduce the error caused by phase mutation; and provide the complex phase vector to step S3; S3. Use the OMP method to roughly estimate the delay; Using the OMP method, the sparse representation of the observed signal is gradually approximated in an iterative manner; In each iteration, the algorithm selects the atom that best matches the current residual from the dictionary, adds it to the support set, and performs a least squares fit on the support set to update the residual. Iterations continue until the residual is small enough or the set number of iterations is reached. Finally, the delay index corresponding to the optimal atom is output as the main path estimate. S4. Refinement of three-point parabola interpolation; Based on the delay estimation result of step S3, the delay estimation result is refined using three-point parabola interpolation; S5. Calculate the distance estimation value based on the refined delay estimation result.

2. The method for star flash phase ranging based on orthogonal matching pursuit and interpolation refinement according to claim 1, characterized in that: Step S1 is specifically as follows: First determine the maximum ranging distance required by the system , and the maximum round-trip delay is calculated from this : in, Used to determine the maximum delay range covered by the dictionary, in meters. is the speed of light constant; Then calculate the dictionary delay step size based on the dictionary size N : Set a set of candidate delays { }, construct a dictionary matrix containing N atoms , among which atoms Defined as: These atoms constitute the phase response template of the signal under different time delays; the dictionary covers the entire time delay range corresponding to the maximum ranging range; in, Indicates the candidate delay values, K represents the number of subcarriers used in the multicarrier system, Indicates the The frequency of the subcarrier.

3. The method for star flash phase ranging based on orthogonal matching pursuit and interpolation refinement according to claim 1, characterized in that: Step S2 is specifically as follows: (1) Bidirectional half-duplex measurement: The transmitter sends a carrier signal to the receiver. The receiver samples the carrier phase at K frequency points and sends it back. The transmitter measures the phase of the returned signal again to eliminate the clock error between the transmitter and receiver and obtain the round-trip phase. ; (2) Phase unwrapping: the original phase according to Modulus expansion to obtain continuous phase ; (3) Offset correction and normalization: Remove the global offset and construct a unit amplitude complex phase vector ; Suppose there are K carrier frequencies , each frequency corresponds to a measured phase difference , forming a complex vector: Under line-of-sight conditions, the phase difference and propagation distance r satisfy: in Phase deviation introduced by noise or multipath; (4) Noise suppression: Perform low-pass filtering or median filtering to suppress noise mutations and deepen errors.

4. The method for star flash phase ranging based on orthogonal matching pursuit and interpolation refinement according to claim 1, characterized in that: Step S3 is as follows: (1) Initialize the residual vector , support set Λ and number of iterations ,as follows: Residual vector Support set Λ=∅ Number of iterations =0 (2) Iteration steps: In each iteration, the algorithm selects the atom that best matches the current residual from the dictionary, adds it to the support set, and performs a least squares fit on the support set to update the residual. Iterations continue until the residual is small enough or the set number of iterations is reached. Finally, the delay index corresponding to the optimal atom is output as the main path estimate. The details are as follows: Compute the inner product of the current residual with all atoms in the dictionary: Select the index with the largest inner product , update the support set: Solve a least-squares problem in the current support set: in represents the set of selected atoms extracted from the dictionary; Update residuals: If the number of iterations reaches the upper limit, stop the iteration; (3) Result output: Finally, the path with the smallest residual is selected, and the path delay index corresponds to This is the estimated main path delay; Convert this into a distance estimate: 。 5. The method for star flash phase ranging based on orthogonal matching pursuit and interpolation refinement according to claim 1, characterized in that: Step S4 is specifically as follows: Delay index based on the initial estimate of OMP in step S4 , and its two side points are , , the corresponding projection amplitude is: Use the interpolation formula to estimate the true peak offset position δ, where δ∈[−0.5,0.5], that is, relative Refinement offset: This results in a more accurate estimate of the delay index: 。 6. The method for star flash phase ranging based on orthogonal matching pursuit and interpolation refinement according to claim 1, characterized in that: In step S5, the distance estimation value for: in, is the refined delay estimation result obtained in step S4, is the speed of light constant.

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