An efficient frequency search method based on local maximum value distribution law

Abstract

The present invention discloses an efficient frequency search method based on the local maximum value distribution law, which belongs to the field of aerospace measurement and control communication technology. The present invention aims to reduce the probability of false capture and the capture time. It includes the following steps: executing the first search phase, detecting the direct-spread signal, and performing a frequency search for the direct-spread signal, with the search step set to Δα; setting the initial frequency point of the search to f ini , in the current first search stage, the frequency point corresponding to the j-th search is recorded as f j =f ini +(j‑1)Δα, the corresponding detection quantity is V j ; Assume that the decision threshold is γ, then when V j When ≥γ, the current search phase ends and step 2 is executed; the current search frequency point is recorded as f th , and record the current detection value as V th The frequency search times of the first search phase is N = (f th ‑f ini )Δα+1; Execute the second search phase to constrain the frequency search range, specifically: the subsequent search frequency is limited to satisfy f i =f th +mR c +nf s , the detection amount corresponding to each search frequency point is {V1,V2,V M}; Select the larger judgment as V max =max{V th ,V1,V2,V M}, then V max The corresponding frequency point is used as the final frequency estimation value.

Description

Technical Field

[0001] The present invention relates to the technical field of aerospace measurement, control and communication technology, and in particular to an efficient frequency search method based on a local maximum value distribution law. Background Art

[0002] Direct sequence spread spectrum (DSSS) signals are widely used in aerospace tracking and control systems due to their advantages in anti-interference and anti-interception capabilities. DSSS signal acquisition requires a rough estimate of the signal's frequency and pseudo-code phase before the receiver can begin tracking. This helps the receiver initialize the tracking loop and track the signal. Therefore, signal acquisition performance is crucial for the tracking loop to successfully engage, lock, and properly track the received signal.

[0003] Typically, signal acquisition is determined by correlating the input signal with the local pseudo-code and carrier wave, accumulating the energy, and comparing the measured value with a preset threshold. If the range of all possible frequencies and code phases is divided into a grid, signal acquisition is a process of searching this two-dimensional grid and locking onto the correct cell. Therefore, the selection and design of the search strategy directly impacts signal acquisition performance, and optimizing the search strategy can improve signal acquisition performance.

[0004] Conventional frequency search strategies can be categorized into serial and parallel search methods based on the search method. The serial search method first determines the frequency search range, then searches each frequency point within the range one by one in a specific order to obtain the detection variable. Serial search is simple to implement, but the search speed is relatively slow. Parallel search utilizes FFT transforms to complete the frequency search, which can reduce the computational effort and speed up the search to a certain extent. However, for applications with a large frequency search range, this significantly increases the hardware implementation complexity. To improve DSSS signal capture performance, a large number of improved search strategies have been proposed based on conventional search methods, such as global search strategies that reduce the probability of false capture, local search strategies that reduce the search scale, and intelligent search strategies that improve capture accuracy.

[0005] The complex electromagnetic environment in space poses additional challenges to space tracking and control systems. To mitigate sudden malicious interference in localized frequency bands and enhance system flexibility and security, the frequency of space tracking and control signals must be adaptable over a wide frequency range. This requires the receiver to implement blind reception across a wide frequency band, which inevitably results in a significant increase in the search scale. Conventional global search strategies significantly increase acquisition time and the probability of false capture using conventional local search strategies. Therefore, further research is needed to address the problem of capturing space tracking and control signals with wide-area frequency adaptability. Summary of the Invention

[0006] In view of this, the present invention provides an efficient frequency search method based on the local maximum value distribution law, aiming to reduce the probability of false capture and the capture time.

[0007] To achieve the above object, the technical solution of the present invention is an efficient frequency search method based on the local maximum value distribution law, comprising the following steps:

[0008] Step 1: Execute the first search phase. In the first search phase, the DS signal is detected and a frequency search is performed for the DS signal. The search step is set to Δα; the initial frequency point of the search is set to f ini , then in the current first search stage, the frequency point corresponding to the j-th search is recorded as f j =f ini +(j-1)Δα, j=1,2,...,N, N is the number of frequency points searched in the current stage, and the corresponding detection amount is V j ; Assume that the decision threshold is γ, then when V j When ≥γ, the current search phase ends and step 2 is executed; the current search frequency point is recorded as f th , and record the current detection value as V th ; The number of frequency searches in the current search phase is N = (f th -f ini ) / Δα+1;

[0009] Step 2: Execute the second search phase to constrain the frequency search range. The specific steps are as follows:

[0010] Subsequent search frequency points are limited to satisfy f i =f th +mR c +nf s , i=1,2,...,M, M is the number of search frequency points in the second search phase, where the detection amount corresponding to each search frequency point is {V1,V2,...,V M}; The frequency search times M in the current search phase is [f min ,f max ] all satisfy f i =f th +mR c +nf s The number of frequency points, R c is the pseudo code rate, f s is the sampling frequency, m and n are two positive integers;

[0011] Step 3: Select the largest decision as V max =max{V th ,V1,V2,...,V M}, then V max The corresponding frequency point is used as the final frequency estimation value.

[0012] Beneficial effects:

[0013] 1. The present invention provides an efficient frequency search method based on the distribution of local maximum values. This method constrains the search range of candidate frequencies based on the distribution characteristics of local maximum values ​​at frequencies with a specific combination of pseudo-code rate and sampling frequency during blind detection over a wide frequency band. This method effectively reduces the search scale and improves frequency search efficiency while avoiding false capture of frequencies with local maximum values.

[0014] 2. To address the issues of significantly increased search size due to an increase in the frequency adaptive range and the tendency for false captures to occur due to localized peaks in the frequency search area, this paper proposes an efficient frequency search method based on the distribution of local peaks. This method exploits the relationship between the frequency difference corresponding to the local peak and the pseudo-code rate and sampling rate to constrain the frequency search range, reduce the search frequency points, and reduce the frequency search scale, while also reducing the probability of false captures caused by local peaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 :Direct spread spectrum signal acquisition algorithm flow based on local search strategy

[0016] Figure 2 :Flowchart of efficient frequency search method DETAILED DESCRIPTION

[0017] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0018] The present invention provides an efficient frequency search method based on the distribution of local maximum values. This method constrains the search range of candidate frequencies based on the distribution characteristics of local maximum values ​​at frequencies with a specific combination of pseudo-code rate and sampling frequency during blind detection over a wide frequency band. This effectively reduces the search scale and improves frequency search efficiency while avoiding false capture of frequencies with local maximum values.

[0019] 1: Direct Sequence Signal Capture Algorithm Model

[0020] The typical capture algorithm process based on local search strategy is as follows: Figure 1 As shown. The input signal r(t) and the local pseudo code The detection value V is obtained by correlation processing and energy accumulation with the local carrier u(t). V is compared with the preset threshold γ. If it is greater than the threshold, the signal is declared to be captured successfully. Otherwise, the local pseudo code delay and carrier frequency are adjusted to continue searching until the capture is successful.

[0021] From the above capture principle, it can be seen that DSSS signal capture is a process of performing a two-dimensional search for frequency and pseudo code and locking the correct cell. If the detection volume of non-target signal cells in the search area exceeds the threshold, false capture may occur, which usually occurs at local maximum value points. In addition, when the frequency adaptability range is large, while ensuring a certain search step condition, the number of cells that need to be searched will increase significantly. Since the frequency difference corresponding to the local maximum value and the global maximum value (located in the correct cell) in the search area has a certain relationship with the pseudo code rate and sampling rate, it is considered to use this relationship to design a frequency search strategy, on the one hand to reduce the search range, and on the other hand to reduce the probability of false capture caused by local maximum values.

[0022] 2: Efficient frequency search method based on local maximum value distribution law

[0023] The frequency of local maximum value in the search area f i The frequency difference Δf from the global maximum frequency f0 is defined as

[0024]

[0025] If R c is the pseudo code rate, f s is the sampling frequency, then

[0026] Δf=mR c +nf s , and f min -f0≤Δf≤f max -f0 (2)

[0027] Where m and n are integers, f max and f min are the upper and lower bounds of the frequency adaptation range respectively. That is, when the detected quantity has a local maximum value at a certain frequency point, the frequency difference corresponding to the frequency point satisfies formula (2). Therefore, if a local maximum value that has been detected is found, the frequency difference corresponding to the frequency point f i It is possible to infer the possible global maximum value corresponding to the frequency point f0, and the inference method is

[0028]

[0029] Based on the above analysis, the steps of the efficient frequency search method proposed in the present invention are as follows: Direct-sequence signal capture is a process of performing a two-dimensional search on the frequency and pseudo code and locking the correct cell.

[0030] Step 1: Execute the first search phase. In the first search phase, the DS signal is detected and a frequency search is performed for the DS signal. The search step is set to Δα; the initial frequency point of the search is set to f ini , then in the current first search stage, the frequency point corresponding to the j-th search is recorded as f j =fini +(j-1)Δα, j=1,2,...,N, N is the number of frequency points searched in the current stage, and the corresponding detection amount is V j ; Assume that the decision threshold is γ, then when V j When ≥γ, the current search phase ends and step 2 is executed; the current search frequency point is recorded as f th , and record the current detection value as V th ; The number of frequency searches in the current search phase is N = (f th -f ini ) / Δα+1;

[0031] Step 2: Execute the second search phase to constrain the frequency search range. The specific steps are as follows:

[0032] Subsequent search frequency points are limited to satisfy f i =f th +mR c +nf s , i=1,2,...,M, M is the number of search frequency points in the second search phase, where the detection amount corresponding to each search frequency point is {V1,V2,...,V M}; The frequency search times M in the current search phase is [f min ,f max ] all satisfy f i =f th +mR c +nf s The number of frequency points, R c is the pseudo code rate, f s is the sampling frequency, m and n are two positive integers;

[0033] Step 3: Select the largest decision as V max =max{V th ,V1,V2,...,V M}, then V max The corresponding frequency point is used as the final frequency estimation value.

[0034] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An efficient frequency search method based on the local maximum value distribution law, characterized in that: The steps include: Step 1: Execute the first search phase. In the first search phase, the DS signal is detected and a frequency search is performed for the DS signal. The search step is set to Δα; the initial frequency point of the search is set to f ini , then in the current first search stage, the frequency point corresponding to the j-th search is recorded as f j =f ini +(j-1)Δα, j=1,2,...,N, N is the number of frequency points searched in the current stage, and the corresponding detection amount is V j ; Assume that the decision threshold is γ, then when V j When ≥γ, the current search phase ends and step 2 is executed; the current search frequency point is recorded as f th , and record the current detection value as V th ; The number of frequency searches in the current search phase is N = (f th -f ini ) / Δα+1; Step 2: Execute the second search phase to constrain the frequency search range. The specific steps are as follows: Subsequent search frequency points are limited to satisfy f i =f th +mR c +nf s , i=1,2,...,M, M is the number of search frequency points in the second search phase, where the detection amount corresponding to each search frequency point is {V1,V2,...,V M }; The frequency search times M in the current search phase is [f min ,f max ] all satisfy f i =f th +mR c +nf s The number of frequency points, R c is the pseudo code rate, f s is the sampling frequency, m and n are two positive integers; Step 3: Select the largest judgment as V max =max{V th ,V1,V2,...,V M }, then V max The corresponding frequency point is used as the final frequency estimation value.

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