High duty cycle signal discrimination and rejection method for wideband narrow beam receivers

Abstract

The application discloses a high-duty-cycle signal discrimination and suppression method for a wideband narrow-beam receiver, which comprises the following steps: background noise statistics, signal rough detection threshold generation, amplitude calculation and peak searching processing, single-beat statistics processing, multi-beat statistics processing, high-duty-cycle signal discrimination and suppression threshold output, and the like. The application can realize dynamic monitoring and identification of high-duty-cycle signals in communication, can adapt to dynamic changes of a signal environment more quickly, can reduce the detection probability of high-duty-cycle signals, and can improve the signal interception quality of the receiver.

Description

Technical Field

[0001] This invention relates to the field of passive radar receiver processing technology, and in particular to a method for identifying and suppressing high duty cycle signals in a broadband narrow beam receiver. Background Technology

[0002] Passive radar does not emit electromagnetic signals itself; instead, it detects and identifies targets by intercepting radar signals from target radiation sources. With the development and widespread use of various electronic devices, the electromagnetic environment has become increasingly complex, with various communication, radar, and jamming signals intertwined, severely impacting the normal operation of passive radar. In particular, the ubiquitous high duty cycle communication signals are easily detected by passive radar receivers, causing low duty cycle radar signals to be submerged among various communication signals. This severely affects the accuracy of subsequent sorting and data processing, and may even lead to data stream congestion and missed radar signals. Therefore, researching how to identify high duty cycle signals and suppress their impact on radar pulse signal interception is particularly important for passive radar receivers.

[0003] Wideband narrow-beam receivers typically use a single narrow-beam antenna stationed in a specific airspace to intercept radar signals. This results in a small airspace coverage area and low angle measurement accuracy. Radiation source signal detection thresholds generally include three types: background noise threshold, CFAR threshold, and manual threshold. The background noise threshold is usually determined by upsampling after statistical analysis of the average amplitude of the background noise; it is generally a pre-measured static threshold. The CFAR threshold of a wideband narrow-beam receiver typically eliminates frequency sidelobes by selecting a reference cell in the frequency domain and also helps eliminate dynamic background noise. While background noise thresholds and CFAR can reduce false alarms caused by background noise, they cannot distinguish or suppress real external radiation source signals such as high duty cycle communication signals. Currently, in engineering applications, wideband narrow-beam receivers rely on manual thresholds (where the operator observes the signal spectrum and dynamically adjusts the detection threshold at each frequency point) to suppress the detection of strong high duty cycle signals. However, manual thresholds greatly increase the workload of operators and cannot quickly adapt to the dynamically changing signal environment, often resulting in problems such as "the threshold is too low, causing communication signals to still be detected, and the threshold is too high, causing radar signals to be missed."

[0004] In summary, for broadband narrow-beam receivers, it is necessary to study automatic processing methods for high duty cycle signal identification and suppression to improve the radar signal acquisition quality of the receiver. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a method for identifying and suppressing high duty cycle signals in broadband narrow-beam receivers. This method enables dynamic monitoring and identification of high duty cycle signals, such as those used in communications, allowing for faster adaptation to dynamic changes in the signal environment, reducing the probability of high duty cycle signals being detected, and improving the signal acquisition quality of the receiver.

[0006] The technical solution adopted in this invention is as follows:

[0007] A method for identifying and suppressing high duty cycle signals in a broadband narrow beam receiver includes the following steps:

[0008] Step 1. Background noise statistics: In the absence of an input signal, the mean amplitude and standard deviation of the receiver's random noise are statistically analyzed to generate a static background noise statistical threshold;

[0009] Step 2. Signal coarse detection threshold generation: When there is an input signal, the amplitude mean and standard deviation of each frequency point are statistically analyzed to generate the dynamic noise statistical threshold of each frequency point, and compared with the static background noise statistical threshold. The maximum value is taken as the signal coarse detection threshold.

[0010] Step 3. Amplitude Calculation and Peak Search Processing: Collect receiver antenna data, perform transformation processing according to the beat to generate the spectrum amplitude data to be detected, and perform peak search processing in the frequency dimension to obtain the peak amplitude;

[0011] Step 4. Single-beat statistical processing: Compare the peak amplitude with the signal coarse detection threshold to determine whether the peak amplitude contains the target signal of the external radiation source, and generate single-beat statistical information;

[0012] Step 5. Multi-beat statistical processing: Based on the single-beat statistical information, after long-term accumulation and processing, multi-beat statistical information is generated, including frequency point number, number of peak beats, mean of all peak amplitudes, standard deviation of all peak amplitudes, number of target peak beats, mean of target peak amplitudes, and standard deviation of target peak amplitudes; and the mean of all peak amplitudes and the standard deviation of all peak amplitudes are output to Step 2 to update the signal coarse detection threshold;

[0013] Step 6. High Duty Cycle Signal Identification and Suppression Threshold Output: Based on the target peak beat count and the total number of processed beats, calculate the signal duty cycle of each frequency point. If it exceeds the set duty cycle threshold, it is determined that a high duty cycle signal exists at that frequency point. Then, based on the target peak amplitude mean and the target peak amplitude standard deviation, generate a high duty cycle signal suppression threshold, which serves as the receiver's final detection threshold at that frequency point to filter out high duty cycle signals.

[0014] Furthermore, step 1 includes the following sub-steps:

[0015] Step 101. With the array element RF link turned off or in an anechoic chamber, perform long-term statistical analysis on the FFT spectrum amplitude to obtain the statistical mean u of the amplitude at each frequency point. SN and standard deviation σ SN ;

[0016] Step 102. Calculate the static background noise statistical threshold:

[0017] TH bk =μ SN +c SN *σ SN

[0018] Among them, c SN This is the upward adjustment factor for the statistical threshold of static background noise.

[0019] Furthermore, step 2 includes the following sub-steps:

[0020] Step 201. With the array element RF link enabled, calculate the average amplitude u at each frequency point. i,a and standard deviation σ i,a Generate dynamic noise statistics thresholds for each frequency point;

[0021] Step 202. Based on the static background noise statistical threshold generated in Step 1, update the signal coarse detection threshold TH used to determine the presence of external radiation signals. CP :

[0022]

[0023] Among them, c DN This is the upward adjustment factor for the dynamic noise statistical threshold.

[0024] Furthermore, step 3 includes the following sub-steps:

[0025] Step 301. The receiver buffers and performs FFT transformation on the acquired broadband intermediate frequency data according to the clock cycle, generating one clock cycle of the spectrum amplitude data to be detected:

[0026] A = [y1, y2, ..., y N ]

[0027] Among them, y N This represents the amplitude value at the Nth frequency point;

[0028] Step 302. Perform peak search processing on frequency point by frequency point for the spectrum amplitude data to be detected. When the amplitude of a certain frequency point is greater than the amplitude of the neighboring frequency points, it is considered that the amplitude of that frequency point has a peak.

[0029] Furthermore, in step 302, in view of the frequency agility characteristics of high duty cycle signals, the amplitude data of several frequency points are merged and compared and the maximum value is taken, and then peak search processing is performed.

[0030] Furthermore, in step 4, the single-beat statistical information includes the frequency point number, amplitude value, whether it is a peak value, and whether it contains an external radiation source target signal.

[0031] Furthermore, step 5 includes the following sub-steps:

[0032] Step 501. After a long processing time, the statistics of each frequency point in all single-beat cycles are correlated and accumulated to generate multi-beat statistics information for each frequency point, including frequency point number and peak beat count N. i,a Mean of all peak amplitudes μ i,a Standard deviation of all peak amplitudes σ i,a Target peak beat count N i,p Target peak amplitude mean μ i,p Target peak amplitude standard deviation σ i,p ;

[0033] Step 502. Output the average value μ of all peak amplitudes. i,a and the standard deviation σ of all peak amplitudes i,a In step 2, update the coarse detection threshold of the signal.

[0034] Furthermore, in step 5, a recursive processing algorithm is used to calculate the mean and standard deviation of the amplitudes:

[0035] μ n =μ n-1 +(y n -μ n-1 ) / n

[0036]

[0037] Among them, y n μ represents the nth amplitude value. n σ represents the statistical mean of the first n amplitude values. n It represents the statistical standard deviation of the first n amplitude values.

[0038] Furthermore, step 6 includes the following sub-steps:

[0039] Step 601. Calculate the duty cycle for each frequency point:

[0040] Du i =N i,p / M

[0041] Among them, Du irepresents the duty cycle statistics for the i-th frequency point, and M represents the total number of clock cycles included in the long-time processing step 5.

[0042] Step 602. Compare the calculated duty cycle of each frequency point with a preset duty cycle threshold. If the duty cycle threshold is exceeded, it is considered that there is a high duty cycle signal at that frequency point. Based on the target peak amplitude mean μ output in step 5. i,p and the target peak amplitude standard deviation σ i,p A high duty cycle signal suppression threshold is generated, which serves as the receiver's final detection threshold TH at that frequency. det Filter out high duty cycle signals.

[0043] Further, in step 602, the detection threshold TH det The calculation methods include:

[0044] TH det =μ i,p +c p *σ i,p

[0045] Among them, c p The upward buoyancy coefficient for the detection threshold.

[0046] The beneficial effects of this invention are as follows:

[0047] a) The high duty cycle signal discrimination result and high duty cycle signal suppression threshold generated by this invention play a role before the generation of pulse-level detection results. They are a useful supplement to the traditional background noise statistical threshold and CFAR threshold. They help filter out high duty cycle signals in the pulse detection stage, which can greatly reduce the processing pressure of subsequent pulse preprocessing and signal sorting, and improve the receiver's interception quality of normal radar pulse signals.

[0048] b) The mean and standard deviation statistics involved in multi-beat (long-time) processing in this invention are implemented using a recursive algorithm, which reduces storage requirements and improves real-time processing capabilities, making it suitable for physical implementation in broadband digital receivers;

[0049] c) In dynamically changing signal environments, this invention can help broadband narrow-beam receivers quickly and automatically identify high duty cycle signals, replacing the manual thresholds commonly used in traditional engineering applications and reducing the workload of operators. Attached Figure Description

[0050] Figure 1 This is one of the flowcharts of the high duty cycle signal identification and suppression method of the present invention.

[0051] Figure 2 This is the second flowchart of the high duty cycle signal identification and suppression method of the present invention. Detailed Implementation

[0052] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0053] like Figure 1 As shown, this embodiment provides a method for identifying and suppressing high duty cycle signals in a broadband narrow-beam receiver. This method enables dynamic monitoring and identification of high duty cycle signals, such as those used in communication, quickly adapting to dynamic changes in the signal environment, reducing the probability of detecting high duty cycle signals, and improving the signal acquisition quality of the receiver. The method includes the following steps:

[0054] Step 1. Background noise statistics: In the absence of input signal, the amplitude mean and standard deviation of the receiver random noise are statistically analyzed to generate a static background noise statistical threshold.

[0055] Step 2. Signal Coarse Detection Threshold Generation: With an input signal, the mean and standard deviation of the amplitude at each frequency point are statistically analyzed to generate a dynamic noise statistical threshold for each frequency point. This threshold is then compared with the static background noise statistical threshold, and the maximum value is taken as the signal coarse detection threshold. Note that the signal coarse detection threshold is not the final signal detection threshold used by the receiver; it is primarily used to eliminate false alarms from background noise and identify potential external radiation source signals during single-cycle statistical processing, and is also used for duty cycle statistics.

[0056] Step 3. Amplitude Calculation and Peak Search: Acquire receiver antenna data, perform transformation processing according to the beat to generate the amplitude data of the spectrum to be detected, and perform peak search processing in the frequency dimension to obtain the peak amplitude. Preferably, considering the frequency agility characteristics that high duty cycle signals may have, the amplitude data of several frequency points are merged and compared, and the maximum value is taken before peak search processing is performed.

[0057] Step 4. Single-beat statistical processing: Compare the peak amplitude with the signal coarse detection threshold to determine whether the peak amplitude contains the target signal from an external radiation source, and generate single-beat statistical information. Preferably, the single-beat statistical information includes the frequency point number, amplitude value, whether it is a peak value, and whether it contains the target signal from an external radiation source.

[0058] Step 5. Multi-beat statistical processing: Based on the single-beat statistical information, multi-beat statistical information is generated after long-term accumulation and processing, including frequency point number, number of peak beats, mean amplitude of all peaks, standard deviation of all peak amplitudes, number of target peak beats, mean amplitude of target peaks, and standard deviation of target peak amplitude. The mean amplitude of all peaks and the standard deviation of all peak amplitudes are then output to Step 2 to update the signal coarse detection threshold. Preferably, to reduce storage requirements and improve real-time processing, a recursive processing algorithm can be used to calculate the mean amplitude and standard deviation.

[0059] Step 6. High Duty Cycle Signal Identification and Suppression Threshold Output: Based on the target peak clock count and the total number of processed clock counts, calculate the signal duty cycle at each frequency point. If the duty cycle exceeds the set threshold, it is determined that a high duty cycle signal exists at that frequency point. Then, based on the target peak amplitude mean and target peak amplitude standard deviation, generate a high duty cycle signal suppression threshold, which serves as the receiver's final detection threshold at that frequency point, filtering out high duty cycle signals.

[0060] like Figure 2 As shown, in a preferred embodiment of the present invention, the high duty cycle signal identification and suppression method specifically includes the following steps:

[0061] Step 1. The receiver buffers and performs FFT transformation on the acquired broadband intermediate frequency data according to the clock cycle, generating one clock cycle of the spectrum amplitude data to be detected:

[0062] A = [y1, y2, ..., y N ]

[0063] Among them, y N This represents the amplitude value at the Nth frequency point.

[0064] Step 2. Perform peak search processing on a frequency-by-frequency basis on the amplitude data of the spectrum to be detected. When the amplitude of a certain frequency point is greater than the amplitude of the neighboring frequency points, it is considered that the amplitude of that frequency point has a peak. Preferably, considering the frequency agility characteristics that high duty cycle signals may have, the amplitude data of several specified frequency points can be merged and compared, and the maximum value can be taken before performing peak search processing.

[0065] Step 3. With the array element RF links turned off or in an anechoic chamber, perform long-term statistical analysis of the FFT spectrum amplitude to obtain the statistical mean u of the amplitude at each frequency point. SN and standard deviation σ SN Calculate the statistical threshold for static background noise:

[0066] TH bk =μ SN +c SN *σ SN

[0067] Among them, c SN This is the upward adjustment factor for the statistical threshold of static background noise.

[0068] Step 4. Based on the previously obtained long-term statistical mean of amplitude at each frequency point, u i,a and standard deviation σ i,a Based on the static background noise statistical threshold generated in step 3, the coarse detection threshold TH used to determine the presence of external radiation signals is updated. CP :

[0069]

[0070] Among them, c DN This is the upward adjustment factor for the dynamic noise statistical threshold.

[0071] Step 5. Compare the amplitude of the detected peak frequency with the signal coarse detection threshold TH. CP By comparing the signals, we can determine whether there might be an external radiation source signal.

[0072] Step 6. Summarize all peak amplitude information detected in a single beat (corresponding to one frame of FFT spectrum data) to obtain statistical information of each frequency point in a single beat, including frequency point number, amplitude value, whether it is a peak, and whether there is a target signal.

[0073] Step 7. After a long processing time (e.g., about 1 second), the statistics of each frequency point in all single-beat cycles are correlated and accumulated to generate multi-beat statistics information for each frequency point, including the frequency point number and the number of peak beats N. i,a Mean of all peak amplitudes μ i,a Standard deviation of all peak amplitudes σ i,a Target peak beat count N i,p Target peak amplitude mean μ i,p Target peak amplitude standard deviation σ i,p Simultaneously, output the mean μ of all peak amplitudes. i,a and the standard deviation of all peak amplitudes σ i,a In step 4, update the signal coarse detection threshold.

[0074] Preferably, when calculating the mean and standard deviation of the amplitude, a recursive processing algorithm can be used to reduce data storage requirements and processing latency:

[0075] μ n =μ n-1 +(y n -μ n-1 ) / n

[0076]

[0077] Among them, y nμ represents the nth amplitude value. n σ represents the statistical mean of the first n amplitude values. n It represents the statistical standard deviation of the first n amplitude values.

[0078] Step 8. Calculate the duty cycle for each frequency point:

[0079] Du i =N i,p / M

[0080] Among them, Du i represents the duty cycle statistics for the i-th frequency point, and M represents the total number of beats included in the long-time processing step 5.

[0081] Step 9. Compare the duty cycle of each frequency point calculated in Step 8 with the preset duty cycle threshold. If the duty cycle threshold is exceeded, it is considered that there is a high duty cycle signal at that frequency point. Based on the target peak amplitude mean μ output in Step 7. i,p and the standard deviation of the target peak amplitude σ i,p A high duty cycle signal suppression threshold is generated, which serves as the receiver's final detection threshold TH at that frequency. det Filter out high duty cycle signals. Detection threshold TH det The calculation method is as follows:

[0082] TH det =μ i,p +c p *σ i,p

[0083] Among them, c p The upward buoyancy coefficient for the detection threshold.

[0084] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A method for identifying and suppressing high duty cycle signals in a broadband narrow-beam receiver, characterized in that, Includes the following steps: Step 1. Background noise statistics: In the absence of an input signal, the mean amplitude and standard deviation of the receiver's random noise are statistically analyzed to generate a static background noise statistical threshold; Step 2. Signal coarse detection threshold generation: When there is an input signal, the amplitude mean and standard deviation of each frequency point are statistically analyzed to generate the dynamic noise statistical threshold of each frequency point, and compared with the static background noise statistical threshold. The maximum value is taken as the signal coarse detection threshold. Step 3. Amplitude Calculation and Peak Search Processing: Collect receiver antenna data, perform transformation processing according to the beat to generate the spectrum amplitude data to be detected, and perform peak search processing in the frequency dimension to obtain the peak amplitude; Step 4. Single-beat statistical processing: Compare the peak amplitude with the signal coarse detection threshold to determine whether the peak amplitude contains the target signal of the external radiation source, and generate single-beat statistical information; Step 5. Multi-beat statistical processing: Based on the single-beat statistical information, after long-term accumulation and processing, multi-beat statistical information is generated, including frequency point number, number of peak beats, mean of all peak amplitudes, standard deviation of all peak amplitudes, number of target peak beats, mean of target peak amplitudes, and standard deviation of target peak amplitudes; and the mean of all peak amplitudes and the standard deviation of all peak amplitudes are output to Step 2 to update the signal coarse detection threshold; Step 6. High duty cycle signal discrimination and suppression threshold output: Based on the target peak number of beats and the total number of processed beats, calculate the signal duty cycle of each frequency point. If it exceeds the set duty cycle threshold, it is determined that there is a high duty cycle signal at that frequency point. Then, based on the mean of the target peak amplitude and the standard deviation of the target peak amplitude, a high duty cycle signal suppression threshold is generated, which serves as the final detection threshold for the receiver at that frequency point, filtering out high duty cycle signals.

2. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 1, characterized in that, Step 1 includes the following sub-steps: Step 101. With the array element RF link turned off or in an anechoic chamber, perform long-term statistical analysis on the FFT spectrum amplitude to obtain the statistical mean u of the amplitude at each frequency point. SN and standard deviation σ SN ; Step 102. Calculate the static background noise statistical threshold: TH bk =μ SN +c SN *s SN Among them, c SN This is the upward adjustment factor for the statistical threshold of static background noise.

3. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 2, characterized in that, Step 2 includes the following sub-steps: Step 201. With the array element RF link enabled, calculate the average amplitude u at each frequency point. i,a and standard deviation σ i,a Generate dynamic noise statistics thresholds for each frequency point; Step 202. Based on the static background noise statistical threshold generated in Step 1, update the signal coarse detection threshold TH used to determine the presence of external radiation signals. CP : Among them, c DN This is the upward adjustment factor for the dynamic noise statistical threshold.

4. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 1, characterized in that, Step 3 includes the following sub-steps: Step 301. The receiver buffers and performs FFT transformation on the acquired broadband intermediate frequency data according to the clock cycle, generating one clock cycle of the spectrum amplitude data to be detected: A=[y1,y2,…,y N ] Among them, y N This represents the amplitude value at the Nth frequency point; Step 302. Perform peak search processing on frequency point by frequency point for the spectrum amplitude data to be detected. When the amplitude of a certain frequency point is greater than the amplitude of the neighboring frequency points, it is considered that the amplitude of that frequency point has a peak.

5. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 4, characterized in that, In step 302, in response to the frequency agility characteristics of high duty cycle signals, the amplitude data of several frequency points are merged and compared, and the maximum value is taken before peak search processing is performed.

6. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 1, characterized in that, In step 4, the single-beat statistics information includes the frequency point number, amplitude value, whether it is a peak value, and whether it contains the target signal of an external radiation source.

7. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 1, characterized in that, Step 5 includes the following sub-steps: Step 501. After a long processing time, the statistics of each frequency point in all single-beat cycles are correlated and accumulated to generate multi-beat statistics information for each frequency point, including frequency point number and peak beat count N. i,a Mean of all peak amplitudes μ i,a Standard deviation of all peak amplitudes σ i,a Target peak beat count N i,p Target peak amplitude mean μ i,p Target peak amplitude standard deviation σ i,p ; Step 502. Output the average value μ of all peak amplitudes. i,a and the standard deviation σ of all peak amplitudes i,a In step 2, update the coarse detection threshold of the signal.

8. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 7, characterized in that, In step 5, a recursive processing algorithm is used to calculate the mean and standard deviation of the amplitudes: m n =μ n-1 +(y n -m n-1 ) / n Among them, y n μ represents the nth amplitude value. n σ represents the statistical mean of the first n amplitude values. n It represents the statistical standard deviation of the first n amplitude values.

9. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 7, characterized in that, Step 6 includes the following sub-steps: Step 601. Calculate the duty cycle for each frequency point: You i =N i,p / M Among them, Du i represents the duty cycle statistics for the i-th frequency point, and M represents the total number of clock cycles included in the long-time processing step 5. Step 602. Compare the calculated duty cycle of each frequency point with a preset duty cycle threshold. If the duty cycle threshold is exceeded, it is considered that there is a high duty cycle signal at that frequency point. Based on the target peak amplitude mean μ output in step 5. i,p and the target peak amplitude standard deviation σ i,p A high duty cycle signal suppression threshold is generated, which serves as the receiver's final detection threshold TH at that frequency. det Filter out high duty cycle signals.

10. The method for high duty cycle signal identification and suppression in a broadband narrow beam receiver according to claim 9, characterized in that, In step 602, the detection threshold TH det The calculation methods include: TH det =μ i,p +c p *s i,p Among them, c p The upward buoyancy coefficient for the detection threshold.

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