Adaptive reconstruction method of reference unit in wideband signal constant false alarm rate detection
Patent Information
- Application Number
- CN202611214010.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-11
- Publication Date
- 2026-11-03
AI Technical Summary
In existing broadband digital reconnaissance receivers, the fixed-length window structure cannot adapt to the dynamic range of the target signal pulse width, resulting in poor adaptive matching capability, signal energy leakage to the reference cell, affecting noise estimation, and the inability to adaptively reconstruct the reference cell according to the signal pulse width.
By instantaneously unwinding the received signal to determine the instantaneous frequency sequence, dividing it into multiple data windows, calculating the frequency variance after removing the maximum value, determining the signal window and counting the number of consecutive signal windows, and dynamically determining the length of the protection unit and reference unit, adaptive reconfiguration can be achieved.
The system achieves adaptive reconstruction of the reference cell based on the signal pulse width, which improves the system's adaptive matching capability for targets with arbitrary pulse widths, ensures that the reference cell acquires clean noise samples, and enhances detection performance.
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Figure CN123034900A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of signal detection technology in broadband digital reconnaissance receivers, and particularly to an adaptive reconstruction method for a reference cell in broadband signal constant false alarm rate (CFAR) detection. Background Technology
[0002] Constant false alarm rate (CFAR) detection is a widely used signal detection method in radar and communication reconnaissance systems. Its core idea is to adaptively estimate noise power and calculate a detection threshold under unknown noise conditions to maintain a constant false alarm probability. Traditional cell-averaged CFAR detectors use a fixed-length sliding window containing a guard cell and a reference cell. The guard cell prevents target signal energy from leaking to the reference cell, while the reference cell collects local noise samples to estimate noise power. However, in modern broadband digital reconnaissance receivers, target signal pulse widths have an extremely wide dynamic range; narrowband pulses can be as short as sub-microseconds, while broadband pulses can reach tens of microseconds or more, a difference exceeding two orders of magnitude. The fixed-length window structure faces fundamental adaptation difficulties.
[0003] Currently, to overcome the above difficulties, a constant false alarm rate (CFAR) detection method based on phase feature screening has been proposed. This method uses the variance of the phase difference to distinguish between signal units and noise units, and removes units identified as signals from the reference window to reduce the contamination of noise estimation by signal energy. However, the phase difference distribution range of broadband linear frequency modulated (LFM) signals is large, causing the phase variance to exceed the threshold, making it impossible to effectively identify signal units. More importantly, regardless of how the internal algorithm adapts, the physical lengths of the protection unit and the reference unit remain fixed and immutable. When the protection unit cannot cover the broadband long pulse, signal energy leaks into the reference unit, causing a signal self-masking effect. The reference window inevitably falls inside the signal, and the power-based adaptive algorithm will completely fail in the signal-dense region. This makes it impossible for the fixed protection unit to adaptively enclose the boundary of the broadband signal, resulting in poor adaptive matching capability for targets with arbitrary pulse widths. If the preset value of the protection unit length is blindly increased to adapt to the broadband signal, when facing narrow-band short pulses or dense multi-target signals, the excessively long protection unit will cause the reference unit sampling point to be too far away from the unit to be detected, resulting in distortion of local noise estimation and making it impossible to adaptively reconstruct the reference unit according to the signal pulse width. Summary of the Invention
[0004] The purpose of this invention is to provide an adaptive reconstruction method for reference cells in broadband signal constant false alarm rate (CFAR) detection, which solves the problem that existing technologies have poor adaptive matching capabilities for targets with arbitrary pulse widths and cannot adaptively reconstruct reference cells according to signal pulse widths.
[0005] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides an adaptive reconstruction method for a reference cell in constant false alarm rate (CFAR) detection of broadband signals, the method comprising: The instantaneous phase of the received signal is unwrapped, and the instantaneous frequency sequence is determined based on the unwrapped instantaneous phase. The instantaneous frequency sequence is divided into multiple data windows. The instantaneous frequency values in each data window are sorted and the maximum value is removed. The frequency variance of each data window is determined based on the instantaneous frequency values after removing the maximum value. Data windows whose frequency variance is less than a preset frequency variance threshold are identified as signal windows, and the number of consecutive signal windows in the signal windows is counted. The length of the protection unit is determined based on the number of continuous signal windows and the window length of each data window. The length of the reference unit is then determined based on the length of the protection unit to complete the adaptive reconstruction of the reference unit in the constant false alarm rate detection of broadband signals. The length of the reference unit is not lower than the preset minimum threshold.
[0006] A second aspect of the present invention provides an adaptive reconstruction device for a reference cell in broadband signal constant false alarm rate (CFAR) detection, the device comprising: The sequence determination module is used to unwrap the instantaneous phase of the received signal and determine the instantaneous frequency sequence based on the unwrapped instantaneous phase. The partitioning and elimination module is used to divide the instantaneous frequency sequence into multiple data windows, sort the instantaneous frequency values in each data window and eliminate the maximum value, and determine the frequency variance of each data window based on the instantaneous frequency values after eliminating the maximum value. The judgment and statistics module is used to determine the data windows whose frequency variance is less than the preset frequency variance threshold as signal windows, and to count the number of consecutive signal windows in the signal windows. The adaptive reconstruction module is used to determine the length of the protection unit based on the number of continuous signal windows and the window length of each data window, and to determine the length of the reference unit based on the length of the protection unit, so as to complete the adaptive reconstruction of the reference unit in the constant false alarm rate detection of broadband signals. The length of the reference unit is not lower than the preset minimum threshold.
[0007] Compared to existing technologies, the adaptive reconstruction method for the reference unit in broadband signal constant false alarm rate (CFAR) detection provided by this invention unwraps the instantaneous phase of the received signal and determines the instantaneous frequency sequence based on the unwrapped instantaneous phase. The instantaneous frequency sequence is divided into multiple data windows, and the instantaneous frequency values within each data window are sorted and the maximum value is removed. The frequency variance of each data window is determined based on the instantaneous frequency values after removing the maximum value. Data windows whose frequency variance is less than a preset frequency variance threshold are identified as signal windows, and the number of consecutive signal windows within the signal windows is counted. The length of the protection unit is determined based on the number of consecutive signal windows and the window length of each data window. The length of the reference unit is then determined based on the length of the protection unit to complete the adaptive reconstruction of the reference unit in broadband signal CFAR detection. The length of the reference unit is not lower than a preset minimum threshold. In this way, the number of continuous signal windows reflects the actual width of the signal on the time axis. The length of the protection unit determined accordingly can adaptively match the boundaries of signals with different widths. The length of the reference unit is dynamically determined according to the length of the protection unit and is not lower than the preset minimum threshold. This ensures that the protection unit can effectively isolate signal energy regardless of how the signal pulse width changes, and the reference unit can always collect clean and appropriate noise samples. This achieves adaptive reconstruction of the reference unit according to the signal pulse width, and the system's adaptive matching capability for targets with arbitrary pulse widths is significantly improved. Attached Figure Description
[0008] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein: Figure 1 The flowchart of the adaptive reconstruction method for the reference cell in constant false alarm rate (CFAR) detection of broadband signals is illustrated schematically. Figure 1 ; Figure 2 The flowchart of the adaptive reconstruction method for the reference cell in constant false alarm rate (CFAR) detection of broadband signals is illustrated schematically. Figure 2 ; Figure 3 A schematic diagram illustrating the window property determination when the pulse width is 0.5 μs is shown. Figure 4 The adaptive detection threshold output diagram with a pulse width of 0.5 μs is schematically shown. Figure 5 A schematic diagram illustrating the window property determination for a pulse width of 2.5 μs is shown. Figure 6 The adaptive detection threshold output diagram with a pulse width of 2.5 μs is schematically shown. Figure 7A schematic diagram illustrating the window property determination for a pulse width of 5.5 μs is shown. Figure 8 The adaptive detection threshold output diagram with a pulse width of 5.5 μs is schematically shown. Figure 9 The diagram schematically illustrates the structure of the adaptive reconstruction device of the reference unit in broadband signal constant false alarm rate detection. Detailed Implementation
[0009] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.
[0010] It should be noted that, unless otherwise stated, the technical or scientific terms used in this invention should have the ordinary meaning as understood by those skilled in the art.
[0011] The methods in the embodiments of the present invention will be described in detail below.
[0012] Figure 1 The flowchart of the adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection in an embodiment of the present invention is illustrated schematically. Figure 1 , Figure 2 The flowchart of the adaptive reconstruction method for the reference cell in constant false alarm rate (CFAR) detection of broadband signals is illustrated schematically. Figure 2 See Figure 1 and Figure 2 As shown, the adaptive reconstruction method of the reference cell in the constant false alarm rate (CFAR) detection of broadband signals can include: S101. Unwrap the instantaneous phase of the received signal and determine the instantaneous frequency sequence based on the unwrapped instantaneous phase.
[0013] The input is a channelized complex signal sequence (i.e., the complex baseband signal sequence output after channelization of the received signal). The instantaneous phase of this complex signal sequence is extracted, and a continuous phase is obtained through unwinding. The instantaneous frequency at each sampling point is calculated based on the continuous phase, and the instantaneous frequency sequence is obtained point by point. The instantaneous frequency is calculated using the following formula: ; in, The first in the instantaneous frequency sequence The instantaneous frequency value at each point, The instantaneous phase after untangling, The sampling rate is the sampling rate after channelization.
[0014] S102. Divide the instantaneous frequency sequence into multiple data windows, sort the instantaneous frequency values in each data window and remove the maximum value, and determine the frequency variance of each data window based on the instantaneous frequency values after removing the maximum value.
[0015] Specifically, the instantaneous frequency sequence is divided into multiple data windows. The instantaneous frequency values within each data window are sorted, and the maximum value is removed. The frequency variance of each data window is determined based on the instantaneous frequency values after removing the maximum value, including: Step A1: Divide the instantaneous frequency sequence into multiple data windows according to the preset window length.
[0016] Step A2: Sort the instantaneous frequency values in each data window to obtain the sorting results.
[0017] Step A3: Remove the maximum value from each data window based on the sorting results to obtain the data window after removing the maximum value.
[0018] Step A4: Determine the frequency variance of each data window based on the difference between each instantaneous frequency value in the data window after removing the maximum value and the mean instantaneous frequency in the corresponding data window.
[0019] Specifically, let the total number of analysis points be... (Typical value is 1024 points), preset window length is (Typical value is 32 points), the instantaneous frequency sequence is divided into a total of 32 points. The first data window. For the first... Data windows ( Take the instantaneous frequency values within each window, sort the instantaneous frequency values within each data window, and eliminate the maximum value to remove potential spike pulses caused by phase unwinding. Calculate the variance of the instantaneous frequency based on the instantaneous frequency values after removing the maximum value. ; in, For the first The frequency variance of each data window For the first The actual number of points in each data window For the first After removing the maximum value from the data window, the first... A single instantaneous frequency value, For the first The average instantaneous frequency after removing the maximum value within each data window.
[0020] Before calculating the frequency variance of each data window, this invention sorts the instantaneous frequency values within the window and removes the maximum value. This operation is specifically designed to eliminate spike pulses that may be generated during phase unwinding, avoiding the interference of spike pulses on the accuracy of variance calculation and ensuring the reliability of the identification of signal windows and noise windows.
[0021] The above implementation uses a fixed window method. An alternative implementation can use a sliding window mechanism. Specifically, a fixed-length sliding window is set on the instantaneous frequency sequence. As the sliding window moves point by point, the set of instantaneous frequency values covered by the current sliding window is updated in real time. After each sliding, the instantaneous frequency values within the window are sorted and the maximum value is removed. The instantaneous frequency variance at the current window position is then calculated. Through this sliding window mechanism, a corresponding frequency variance estimate can be obtained at each sampling point, thereby achieving point-by-point adaptive parameter updates. This further improves the real-time performance of the system and the accuracy of the frequency variance estimation, making it suitable for applications with higher real-time requirements.
[0022] S103. Determine the data windows whose frequency variance is less than the preset frequency variance threshold as signal windows, and count the number of consecutive signal windows in the signal windows.
[0023] The preset frequency variance threshold is related to the signal-to-noise ratio and can be adjusted between 0.05 and 0.15, with 0.08 being the preferred setting.
[0024] Specifically, data windows whose frequency variance is less than a preset frequency variance threshold are identified as signal windows, and the number of consecutive signal windows within the signal window is counted, including: Step B1: Determine whether the frequency variance of each data window is less than the preset frequency variance threshold.
[0025] Determine whether the frequency variance of each data window is less than the preset frequency variance threshold. If yes, proceed to step B2a; otherwise, proceed to step B2b.
[0026] Step B2a: Use the data window with a frequency variance less than the preset frequency variance threshold as the first data window, and determine the first data window as the signal window; Step B3: Count the number of continuous signal windows in the first data window.
[0027] The method also includes the following steps in determining whether the frequency variance of each data window is less than a preset frequency variance threshold: Step B2b: Select the data windows whose frequency variance is greater than or equal to the preset frequency variance threshold as the second data windows, and determine the second data windows as noise windows.
[0028] The preset frequency variance threshold is This preset frequency variance threshold can be pre-calibrated through Monte Carlo simulation (e.g., by measurement). (For best results) The frequency variance of each data window obtained in step S102 Compared with the preset frequency variance threshold Comparison: If the frequency variance of each data window is less than a preset frequency variance threshold, the data window is determined to be a signal window; if the frequency variance of each data window is greater than or equal to the preset frequency variance threshold, the data window is determined to be a noise window. (After completing all...) After determining each data window, count the number of consecutive signal windows among the data windows that are determined to be signal windows, and record it as . .
[0029] S104. Determine the length of the protection unit based on the number of continuous signal windows and the window length of each data window, and determine the length of the reference unit based on the length of the protection unit, so as to complete the adaptive reconstruction of the reference unit in the constant false alarm rate detection of broadband signals.
[0030] The length of the reference element shall not be less than the preset minimum threshold.
[0031] The expression for the length of the protection unit is: ; in, To protect the unit length, The number of continuous signal windows. This represents the window length for each data window.
[0032] Specifically, the reference cell length is determined based on the protection cell length to achieve adaptive reconstruction of the reference cell in broadband signal constant false alarm rate (CFAR) detection, including: Step C1: Determine the initial value of the reference unit length based on the product of the protection unit length and the preset proportional coefficient.
[0033] The preset proportional coefficient is 0.5.
[0034] Step C2: Compare the initial value with the preset minimum threshold, and take the maximum value of the two as the reference cell length to complete the adaptive reconstruction of the reference cell.
[0035] The expression for the reference element length is: ; in, For reference cell length, To preset the minimum threshold, i.e., to preset the minimum reference cell threshold, To protect the unit length, the preset scaling factor is 0.5.
[0036] Specifically, after determining the reference cell length based on the protection cell length to complete the adaptive reconstruction of the reference cell in broadband signal constant false alarm rate detection, the method also includes: Step D1: Construct a sliding window filter sequence based on the protection cell length and the reference cell length.
[0037] The value of the sliding window filter sequence at the reference cell position is the reciprocal of twice the length of the reference cell, and the value at the protection cell position and the center cell to be detected position is zero.
[0038] The total length of the sliding window filter sequence is twice the length of the reference unit, twice the length of the guard unit, and the sum of the length of the central unit to be detected.
[0039] Obtaining the adaptive protection cell length and reference cell length Subsequently, the Constant False Alarm Rate (CFAR) detection employs a fast convolution algorithm. This is based on the length of the protection cell. and reference cell length Construct a sliding window filter sequence for constant false alarm rate detection. The total length of the filter sequence is The value of the filter sequence at the reference cell position is The value at the protection unit location and the central unit to be detected location is 0.
[0040] Step D2: Padded the amplitude or power sequence of the input signal to the same length as the sliding window filter sequence. Then, the padded amplitude or power sequence of the input signal and the padded sliding window filter sequence are subjected to Fast Fourier Transform, frequency domain multiplication, and Inverse Fourier Transform in sequence to obtain the time domain convolution result. The mean background noise power is obtained based on the time domain convolution result.
[0041] The amplitude or power sequence of the input signal The mean background noise power is calculated using a fast convolution algorithm. The specific operation involves converting the amplitude or power sequence of the input signal. With sliding window filter sequence Fill to the same length (Usually greater than or equal to) The power of 2 is then multiplied in the frequency domain using a Fast Fourier Transform (FFT), and finally converted back to the time domain using an Inverse Fast Fourier Transform (IFFT) to obtain the mean background noise power. : .
[0042] Step D3: Calculate the normalization factor based on the preset false alarm probability, and multiply the mean background noise power by the normalization factor to obtain the decision threshold.
[0043] The preset false alarm probability is 10 (commonly used). 6 .
[0044] Step D4: Compare the amplitude or power sequence of the input signal with the decision threshold, and output the decision result of whether the target exists.
[0045] The amplitude or power sequence of the input signal With dynamic decision threshold Comparison: If If the distance unit contains a target, then it is determined that a target exists; if If the distance cell does not contain a target, the result of the target presence or absence will be output.
[0046] In this embodiment of the invention, the adaptively generated non-fixed-length protection unit length and reference unit length are converted into fixed filter coefficients, and the convolution calculation is completed through a fixed-point fast Fourier transform operation. This ensures that the computational load of the system remains constant regardless of how the protection unit length and reference unit length change dynamically, which facilitates engineering implementation on hardware platforms such as FPGA or DSP.
[0047] The core solution of this invention is to convert time-domain signal characteristics into frequency-domain statistical characteristics. It utilizes the physical characteristic that the instantaneous frequency of a broadband signal is relatively stable over a very short time, while the instantaneous frequency of pure noise fluctuates randomly and drastically, to perform window segmentation. The overall process is as follows: First, the instantaneous frequency of the received complex signal is calculated, and it is divided into multiple fixed-length data windows on the time axis. Second, by removing extreme peak values within each window and calculating the frequency variance, it is compared with a preset threshold to accurately determine whether the window belongs to a signal segment or a noise segment. Subsequently, based on the statistical number of continuous signal windows, the length of the protection unit for the entire signal pulse width is dynamically calculated, and a reference unit length is adaptively generated proportionally. Finally, to avoid drastic changes in computational load caused by dynamic windows, this invention constructs these adaptive parameters into a sliding window filter and uses a fast convolution algorithm to complete the final constant false alarm rate (CFAR) detection. This architecture completely solves the defect of traditional CFAR that must rely on prior pulse width parameters, and the computation time is stable and controllable. This invention is based on a window structure adaptive method for physical boundary detection. It utilizes the stability characteristics of the instantaneous phase of a broadband signal to roughly calculate the true pulse width of the current signal, and then reconstructs the length of the protection unit and the reference unit in real time, fundamentally preventing the leakage of signal energy to the reference unit.
[0048] Table 1 Performance comparison between the present invention and existing CFAR detection methods
[0049] To further verify the effectiveness of the method of this invention, Table 1 presents a performance comparison between the present invention and existing CFAR detection methods in multiple dimensions. As shown in Table 1, both traditional CFAR detection and fast convolutional CFAR detection use fixed lengths for both the guard cell and the reference cell, making them unsuitable for signals with different pulse widths. Their adaptability to both narrowband and wideband signals is mutually exclusive. In contrast, this invention adaptively reconstructs the lengths of the guard cell and the reference cell by statistically analyzing the number of continuous signal windows. The length of the guard cell can be adaptively adjusted based on a rough estimate of the signal pulse width, while the length of the reference cell is dynamically adjusted based on the length of the guard cell. This allows the system to maintain good detection performance in both narrowband and wideband signal scenarios. Regarding computational complexity, although the present invention slightly increases compared to traditional CFAR detection, this increase is acceptable considering the performance improvement it brings. In terms of hardware implementation feasibility, this invention, like existing methods, is suitable for implementation on FPGA or DSP.
[0050] Figure 3 The window property determination diagram for a pulse width of 0.5 μs is schematically shown. Figure 4 The adaptive detection threshold output diagram with a pulse width of 0.5 μs is schematically shown. Figure 5 The window property determination diagram for a pulse width of 2.5 μs is schematically shown. Figure 6 The adaptive detection threshold output diagram with a pulse width of 2.5 μs is schematically shown. Figure 7 The window property determination diagram for a pulse width of 5.5 μs is schematically shown. Figure 8 The adaptive detection threshold output diagram with a pulse width of 5.5 μs is schematically shown. See also Figures 3 to 8 The simulation test results are shown under different pulse width conditions. Figure 3 , Figure 5 , Figure 7 The horizontal axis represents the data window number, and the vertical axis represents the frequency variance value / preset frequency variance threshold. Figure 3 , Figure 5 , Figure 7 The window attribute determination results correspond to pulse widths of 0.5μs, 2.5μs, and 5.5μs, respectively. The frequency variance curves calculated in segments can be effectively compared with the preset variance threshold, accurately marking the signal window and noise window. Moreover, the total number of calibrated continuous signal windows maintains a linear correspondence with the actual input pulse width. Figure 4 , Figure 6 , Figure 8 The horizontal axis represents the distance unit (or sampling point number), and the vertical axis represents the signal amplitude / power value and the detection threshold value. Figure 4 , Figure 6 , Figure 8Corresponding to the adaptive detection threshold output results under the three pulse width conditions mentioned above, the protection unit dynamically generated by the number of signal windows can adaptively envelop target signals of different widths. The background noise power and detection threshold curve remain stable within the signal, and there is no abnormal threshold rise caused by the reference window sliding into the wide-bandwidth target in the traditional fixed parameter method. This verifies the adaptive matching capability and detection robustness of the present invention for targets of arbitrary pulse widths.
[0051] Based on the above Figure 1 As can be seen from the implementation method, the present invention unwraps the instantaneous phase of the received signal and determines the instantaneous frequency sequence based on the unwrapped instantaneous phase; divides the instantaneous frequency sequence into multiple data windows, sorts the instantaneous frequency values in each data window and removes the maximum value, and determines the frequency variance of each data window based on the instantaneous frequency values after removing the maximum value; determines the data windows whose frequency variance is less than a preset frequency variance threshold as signal windows, and counts the number of consecutive signal windows in the signal windows; determines the length of the protection unit based on the number of consecutive signal windows and the window length of each data window, and determines the length of the reference unit based on the length of the protection unit, so as to complete the adaptive reconstruction of the reference unit in the constant false alarm rate detection of broadband signals, and the length of the reference unit is not lower than a preset minimum threshold. In this way, the number of continuous signal windows reflects the actual width of the signal on the time axis. The length of the protection unit determined accordingly can adaptively match the boundaries of signals with different widths. The length of the reference unit is dynamically determined according to the length of the protection unit and is not lower than the preset minimum threshold. This ensures that the protection unit can effectively isolate signal energy regardless of how the signal pulse width changes, and the reference unit can always collect clean and appropriate noise samples. This achieves adaptive reconstruction of the reference unit according to the signal pulse width, and the system's adaptive matching capability for targets with arbitrary pulse widths is significantly improved.
[0052] Based on the same inventive concept, as an implementation of the above-mentioned adaptive reconstruction method for the reference unit in broadband signal constant false alarm rate detection, this embodiment of the invention also provides an adaptive reconstruction device for the reference unit in broadband signal constant false alarm rate detection. Figure 9 This is a structural diagram of the adaptive reconstruction device of the reference unit in the broadband signal constant false alarm rate detection embodiment of the present invention. See also: Figure 9 As shown, the device may include: The sequence determination module 901 is used to unwrap the instantaneous phase of the received signal and determine the instantaneous frequency sequence based on the unwrapped instantaneous phase. The partitioning and elimination module 902 is used to divide the instantaneous frequency sequence into multiple data windows, sort the instantaneous frequency values in each data window and eliminate the maximum value, and determine the frequency variance of each data window based on the instantaneous frequency values after eliminating the maximum value. The judgment and statistics module 903 is used to determine the data windows whose frequency variance is less than the preset frequency variance threshold as signal windows, and to count the number of consecutive signal windows in the signal windows. The adaptive reconstruction module 904 is used to determine the length of the protection unit based on the number of continuous signal windows and the window length of each data window, and to determine the length of the reference unit based on the length of the protection unit, so as to complete the adaptive reconstruction of the reference unit in the constant false alarm rate detection of broadband signals. The length of the reference unit is not lower than the preset minimum threshold.
[0053] The device may further include: a construction and transformation module, used to construct a sliding window filter sequence based on the length of the reference unit after determining the length of the reference unit according to the length of the protection unit to complete the adaptive reconstruction of the reference unit in broadband signal constant false alarm rate detection; the value of the sliding window filter sequence at the reference unit position is the reciprocal of twice the length of the reference unit, and the value at the protection unit position and the center detection unit position is zero; the amplitude or power sequence of the input signal is padded to the same length as the sliding window filter sequence; the padded amplitude or power sequence of the input signal and the padded sliding window filter sequence are sequentially subjected to fast Fourier transform, frequency domain multiplication, and inverse Fourier transform to obtain a time domain convolution result; the mean background noise power is obtained based on the time domain convolution result; a normalization factor is calculated according to a preset false alarm probability; the mean background noise power is multiplied by the normalization factor to obtain a decision threshold; the amplitude or power sequence of the input signal is compared with the decision threshold, and a decision result of the presence or absence of the target is output.
[0054] The partitioning and elimination module 902 is specifically used to divide the instantaneous frequency sequence into multiple data windows according to a preset window length; sort the instantaneous frequency values in each data window to obtain a sorting result; eliminate the maximum value in each data window according to the sorting result to obtain a data window after eliminating the maximum value; and determine the frequency variance of each data window based on the difference between each instantaneous frequency value in the data window after eliminating the maximum value and the mean instantaneous frequency in the corresponding data window.
[0055] The judgment and statistics module 903 is specifically used to determine whether the frequency variance of each data window is less than a preset frequency variance threshold; if so, the data window with a frequency variance less than the preset frequency variance threshold is taken as the first data window and the first data window is determined as a signal window; the number of consecutive signal windows in the first data window is counted.
[0056] In the determination and statistics module 903, in the process of determining whether the frequency variance of each data window is less than the preset frequency variance threshold, the method further includes: taking the data window whose frequency variance is greater than or equal to the preset frequency variance threshold as the second data window, and determining the second data window as a noise window.
[0057] In the adaptive reconfiguration module 904, the expression for the length of the protection unit is: ; in, To protect the unit length, The number of continuous signal windows. This represents the window length for each data window.
[0058] In the adaptive reconfiguration module 904, the reference unit length is determined based on the protection unit length to complete the adaptive reconfiguration of the reference unit in the constant false alarm rate detection of broadband signals. This includes: determining the initial value of the reference unit length based on the product of the protection unit length and a preset proportional coefficient; comparing the initial value with a preset minimum threshold, and taking the maximum value of the two as the reference unit length to complete the adaptive reconfiguration of the reference unit.
[0059] It should be noted that the above description of the adaptive reconstruction device embodiment for the reference unit in broadband signal constant false alarm rate (CFAR) detection is similar to the description of the adaptive reconstruction method embodiment for the reference unit in broadband signal CFAR detection, and has similar beneficial effects. For any technical details not disclosed in the embodiments of the adaptive reconstruction device for the reference unit in broadband signal CFAR detection, please refer to the description of the adaptive reconstruction method embodiment for the reference unit in broadband signal CFAR detection.
[0060] The above are merely specific embodiments 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. An adaptive reconstruction method for a reference cell in broadband signal constant false alarm rate (CFAR) detection, characterized in that, include: The instantaneous phase of the received signal is unwrapped, and the instantaneous frequency sequence is determined based on the unwrapped instantaneous phase. The instantaneous frequency sequence is divided into multiple data windows. The instantaneous frequency values in each data window are sorted and the maximum value is removed. The frequency variance of each data window is determined based on the instantaneous frequency values after removing the maximum value. Data windows whose frequency variance is less than a preset frequency variance threshold are identified as signal windows, and the number of consecutive signal windows in the signal windows is counted. The length of the protection unit is determined based on the number of continuous signal windows and the window length of each data window. The length of the reference unit is then determined based on the length of the protection unit to complete the adaptive reconstruction of the reference unit in the constant false alarm rate detection of broadband signals. The length of the reference unit is not lower than a preset minimum threshold.
2. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 1, characterized in that, After determining the reference cell length based on the protection cell length to complete the adaptive reconstruction of the reference cell in broadband signal constant false alarm rate detection, the method further includes: Based on the length of the protection unit and the length of the reference unit, a sliding window filter sequence is constructed. The value of the sliding window filter sequence at the reference unit position is the reciprocal of twice the length of the reference unit, and the value at the protection unit position and the center detection unit position is zero. The amplitude or power sequence of the input signal is padded to the same length as the sliding window filter sequence. The padded amplitude or power sequence of the input signal and the padded sliding window filter sequence are then subjected to fast Fourier transform, frequency domain multiplication, and inverse Fourier transform in sequence to obtain the time domain convolution result. The average background noise power is obtained based on the time domain convolution result. The normalization factor is calculated based on the preset false alarm probability, and the mean background noise power is multiplied by the normalization factor to obtain the decision threshold. The amplitude or power sequence of the input signal is compared with the decision threshold, and a decision result indicating whether the target exists is output.
3. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 1, characterized in that, The step of dividing the instantaneous frequency sequence into multiple data windows, sorting the instantaneous frequency values within each data window and removing the maximum value, and determining the frequency variance of each data window based on the instantaneous frequency values after removing the maximum value includes: The instantaneous frequency sequence is divided into multiple data windows according to a preset window length; The instantaneous frequency values within each data window are sorted to obtain the sorting results; Based on the sorting results, the maximum value in each data window is removed to obtain the data window after removing the maximum value; The frequency variance of each data window is determined based on the difference between each instantaneous frequency value within the data window after removing the maximum value and the corresponding mean instantaneous frequency within the data window.
4. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 1, characterized in that, The step of determining data windows whose frequency variance is less than a preset frequency variance threshold as signal windows and counting the number of consecutive signal windows among the signal windows includes: Determine whether the frequency variance of each data window is less than the preset frequency variance threshold. If so, the data window whose frequency variance is less than the preset frequency variance threshold is taken as the first data window, and the first data window is determined as the signal window; Count the number of continuous signal windows in the first data window.
5. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 4, characterized in that, In determining whether the frequency variance of each data window is less than the preset frequency variance threshold, the method further includes: The data windows whose frequency variance is greater than or equal to the preset frequency variance threshold are designated as second data windows, and the second data windows are identified as noise windows.
6. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 1, characterized in that, The expression for the length of the protection unit is: ； in, The length of the protection unit is... The number of continuous signal windows. The window length of each data window.
7. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 1, characterized in that, The step of determining the reference unit length based on the protection unit length to complete the adaptive reconstruction of the reference unit in broadband signal constant false alarm rate detection includes: The initial value of the reference unit length is determined by multiplying the length of the protection unit by a preset proportional coefficient. The initial value is compared with the preset minimum threshold, and the maximum value of the two is taken as the reference cell length to complete the adaptive reconstruction of the reference cell.
8. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 7, characterized in that, The preset proportional coefficient is 0.
5.
9. The adaptive reconstruction method for the reference cell in broadband signal constant false alarm rate detection according to claim 2, characterized in that, The total length of the sliding window filter sequence is the sum of twice the length of the reference unit, twice the length of the protection unit, and the length of the central unit to be detected.
10. An adaptive reconstruction device for a reference unit in broadband signal constant false alarm rate detection, characterized in that, include: The sequence determination module is used to unwrap the instantaneous phase of the received signal and determine the instantaneous frequency sequence based on the unwrapped instantaneous phase. The partitioning and elimination module is used to divide the instantaneous frequency sequence into multiple data windows, sort the instantaneous frequency values in each data window and eliminate the maximum value, and determine the frequency variance of each data window based on the instantaneous frequency values after eliminating the maximum value. The determination and statistics module is used to determine the data windows whose frequency variance is less than a preset frequency variance threshold as signal windows, and to count the number of consecutive signal windows in the signal windows. An adaptive reconstruction module is used to determine the length of the protection unit based on the number of continuous signal windows and the window length of each data window, and to determine the length of the reference unit based on the length of the protection unit, so as to complete the adaptive reconstruction of the reference unit in broadband signal constant false alarm rate detection. The length of the reference unit is not lower than a preset minimum threshold.