Radio frequency interference detection method and system based on third-order quantized signal tail probability

Abstract

The invention relates to the field of passive microwave remote sensing signal processing, in particular to a radio frequency interference detection method and system based on third-order quantization tail probability. The method comprises the following steps: acquiring a passive microwave remote sensing signal and acquiring analog signal power; performing third-order quantization of which the threshold is Th on the analog signal, and obtaining a second-order moment of a third-order quantization signal; according to the Th, constructing a tail consistency ratio statistical magnitude; and obtaining a radio frequency interference judgment result Flag according to the TCR. The method has the advantages that the method can be embedded into a real-time radio frequency interference detection module of a satellite-borne passive microwave remote sensing system, has the advantages of low calculation complexity, small resource occupation and wide application range, and can effectively detect various types of radio frequency interference.

Description

Technical Field

[0001] This application belongs to the field of microwave remote sensing signal processing and radio frequency interference detection, specifically relating to a radio frequency interference detection method and system based on the tail probability of a third-order quantized signal. Background Technology

[0002] Passive microwave remote sensing systems retrieve environmental parameters such as brightness temperature, sea surface salinity, and soil moisture by receiving thermal noise signals from the Earth or other targets' natural radiation. These systems are highly sensitive but susceptible to external man-made radio frequency interference. Radio frequency interference primarily originates from actively transmitted signals such as radar, communications, and satellite navigation. When these signals overlap with the passive receiver's protected frequency band in the spectrum, they severely disrupt the statistical characteristics of the received signal, leading to increased brightness temperature retrieval errors in the radiometer.

[0003] Existing traditional radio frequency interference (RF interference) detection algorithms can be mainly divided into five categories: time-domain algorithms, frequency-domain algorithms, statistical domain algorithms, polarization domain algorithms, and spatial domain algorithms. The basic principle of statistical domain algorithms is that natural radiated signals should follow a Gaussian distribution. Therefore, by constructing statistics, it can be determined whether the input signal follows a Gaussian distribution, and thus whether RF interference exists. A classic statistical domain algorithm is the kurtosis algorithm, which constructs the kurtosis statistic by the ratio of the fourth moment to the second moment of the input signal, and uses the deviation of the kurtosis value from the expected value (3) to determine RF interference.

[0004] In recent years, spaceborne microwave radiometers have needed to perform on-orbit RFI detection under computationally limited conditions to reduce the impact of radio frequency interference. For example, the CubeRRT (Cubesat Radiometer Radio Frequency Interference Technology validation mission) payload in 2018 used a kurtosis algorithm in orbit. The L-band synthetic aperture radiometer in the MICAP (Microwave Imager Combined Active and Passive) payload of the Chinese Ocean Salinity Satellite in 2024 also has on-orbit RFI detection capabilities. However, because synthetic aperture radiometers require significantly more computation than real aperture radiometers, the C and K-band synthetic aperture radiometers in the MICAP payload do not perform high-order quantization of the signal, only acquiring third-order quantized signals. Many traditional detection methods are no longer applicable in this context.

[0005] Future spaceborne integrated aperture microwave radiometers will face more serious radio frequency interference problems, and existing methods cannot solve the problem of on-orbit detection and elimination of radio frequency interference for spaceborne integrated aperture microwave radiometers. Summary of the Invention

[0006] The purpose of this application is to overcome the problems of large data volume and limited computing resources in on-orbit radio frequency interference processing of passive microwave remote sensing, and to propose a radio frequency interference detection method and system based on third-order quantization tail probability. This method can achieve real-time identification of radio frequency interference signals under third-order quantization conditions, and has the characteristics of high sensitivity, low complexity and low power consumption, and is more suitable for the structure of the spaceborne integrated aperture microwave radiometer.

[0007] To achieve the above objectives, this application proposes a radio frequency interference detection method based on third-order quantization tail probability, comprising: Step 1: Acquire passive microwave remote sensing signals with a total duration of T. and obtain signal power ; Step 2: Perform third-order quantization on the analog signal with a threshold of Th, and calculate the second moment of the third-order quantized signal. ; Step 3: Based on Th, , Construct the Tail-Consistency Ratio (TCR). Step 4: Make a radio frequency interference decision based on the TCR and obtain the radio frequency interference decision result Flag.

[0008] As an improvement to the above method, the specific process of step 1 includes: Passive microwave remote sensing signals with a total acquisition duration of T , for signal Integrating the square of the time from 0 to T, we obtain the signal power. ,in For signal At any moment The possible values ​​of , where For time indexing, .

[0009] As an improvement to the above method, the specific process of step 2 includes: For analog signals Perform third-order quantization with a threshold of Th to obtain a third-order quantized signal with N samples. : ; in, Represents a third-order quantized signal The i One sample; Represents analog signal In the i The value at each sampling point. For sample index, ; Calculate the second moment of a third-order quantized signal : .

[0010] As an improvement to the above method, the specific process of step 3 includes: Based on the threshold Th and signal power Calculate analog signals The tail probability whose absolute value is greater than the threshold Th : ; in, Let Gaussian probability distribution function be used. , Let Gaussian probability density function be used. ; Calculate the Tail Consistency Ratio (TCR): .

[0011] As an improvement to the above method, step 4 specifically includes the following process: Radio frequency interference (RF interference) is detected based on the deviation of the tail conformity ratio (TCR) from its expected value of 1, and the RF interference decision result is obtained. : ; in, For the set threshold, A value of 1 indicates the acquired signal. There is radio frequency interference. A value of 0 indicates that the acquired signal... There is no radio frequency interference.

[0012] This application also provides a radio frequency interference detection system based on third-order quantization tail probability, implemented using the above method, the system comprising: The signal power detection module is used to acquire passive microwave remote sensing signals. And acquire signal power ; The third-order quantization module is used to perform third-order quantization on analog signals with a threshold of Th and obtain the second-order moment of the third-order quantized signal. ; The tail consistency ratio calculation module is used to calculate the tail consistency ratio based on Th, , Construct the Tail Consistency Ratio (TCR) statistic; The radio frequency interference decision module is used to obtain the radio frequency interference decision result flag based on the TCR.

[0013] Compared with existing technologies, the advantages of this application are: 1. Radio frequency interference detection is achieved using only analog signal power and the second moment of the third-order quantized signal, eliminating the need for a high-order quantization ADC sampler and greatly saving resource consumption.

[0014] 2. It has the advantages of low computational complexity, low resource consumption, and wide applicability, and is especially suitable for spaceborne integrated aperture radiometers. Attached Figure Description

[0015] Figure 1 The flowchart shown is a radio frequency interference detection method based on third-order quantization tail probability. Figure 2(a) shows the time-frequency diagram of the continuous wave signal; Figure 2(b) shows the time-frequency diagram of the pulse signal; Figure 2(c) shows the time-frequency diagram of the linear frequency modulation signal; Figure 2(d) shows the time-frequency diagram of the glitch signal; Figure 2(e) shows the time-frequency diagram of the BPSK signal; Figure 2(f) shows the time-frequency diagram of the QPSK signal. Detailed Implementation

[0016] The technical solution of this application will be described in detail below with reference to the accompanying drawings.

[0017] This invention relates to the field of passive microwave remote sensing signal processing, and more particularly to a method and system for detecting radio frequency interference based on third-order quantization tail probability. The method includes: (1) Acquire passive microwave remote sensing signals with a total duration of T. and obtain signal power ; (2) Perform third-order quantization on the analog signal with a threshold of Th, and calculate the second moment of the third-order quantized signal. ; (3) According to Th, , Build the Tail Consistency Ratio (TCR); (4) Make a radio frequency interference decision based on the TCR and obtain the radio frequency interference decision result Flag.

[0018] This invention can be embedded in the real-time radio frequency interference detection module of a spaceborne passive microwave remote sensing system. It has the advantages of low computational complexity, small resource consumption, and wide applicability, and can effectively detect various types of radio frequency interference.

[0019] Example 1 The embodiments shown in this invention are illustrated with reference to... Figure 1 This paper presents a radio frequency interference detection method based on third-order quantization tail probability.

[0020] In this embodiment, the input signal consists of a superposition of Gaussian noise with a power of 1 and a radio frequency interference signal. The radio frequency interference signal is modeled into six types: single-frequency continuous wave, pulse signal, linear frequency modulated signal, transient pulse, binary phase shift keying (BPSK), and quaternary phase shift keying (QPSK), as shown in Figures 2(a)-2(f). Specifically, the radio frequency interference detection method based on third-order quantization tail probability comprises the following steps: Passive microwave remote sensing signals with a total acquisition duration of T , for signal Integrating the square of the time from 0 to T, we obtain the signal power. ,in For signal At any moment The possible values ​​of , where For time indexing, In this embodiment, the total acquisition time T for each type of radio frequency interference signal is 0.01s, and the total power of each type of input signal is 1.2512W.

[0021] For analog signals Perform third-order quantization with a threshold of Th to obtain a third-order quantized signal with N samples. , ,in Represents a third-order quantized signal The i One sample, Represents analog signal In the i The value at each sampling point. For sample index, ; Calculate the second moment of the third-order quantized signal , In this embodiment, , .

[0022] According to Th, Calculate analog signals The tail probability whose absolute value is greater than the quantization threshold Th , ,in Let Gaussian probability distribution function be used. ,in Let Gaussian probability density function be used. ; Calculate the tail consistency ratio (TCR). In this embodiment, the calculation results are as follows: continuous wave signal TCR=1.0061, pulse signal TCR=0.9915, linear frequency modulated signal TCR=1.0060, glitch signal TCR=0.9136, BPSK signal TCR=1.006, and QPSK signal TCR=1.006. Radio frequency interference is detected based on the deviation of TCR from its expected value of 1, and the radio frequency interference decision result is obtained. : ,in For configurable algorithm thresholds, A value of 1 indicates the acquired signal. There is radio frequency interference. A value of 0 indicates that the acquired signal... There is no radio frequency interference. In this embodiment, Therefore, all six types of radio frequency interference were successfully detected.

[0023] Example 2 This invention also provides a radio frequency interference detection system based on third-order quantization tail probability, implemented using the above method, the system comprising: (1) Signal power detection module, used to acquire passive microwave remote sensing signals. And acquire signal power ; (2) Third-order quantization module, used to perform third-order quantization on the analog signal with a threshold of Th and obtain the second-order moment of the third-order quantized signal. ; (3) Tail consistency ratio calculation module, used to calculate based on Th, , Construct the Tail Consistency Ratio (TCR) statistic; (4) Radio frequency interference decision module, used to obtain the radio frequency interference decision result Flag based on TCR.

[0024] The signal power detection module is used to acquire passive microwave remote sensing signals. And acquire signal power The specific process includes: acquiring passive microwave remote sensing signals with a total duration of T. The signal power is obtained by integrating the square of the signal x over time from 0 to T. ,in For signal The values ​​at time t, where For time indexing, .

[0025] The third-order quantization module is used to perform third-order quantization on analog signals with a threshold of Th and obtain the second-order moment of the third-order quantized signal. The specific process includes: processing analog signals. Perform third-order quantization with a threshold of Th to obtain a third-order quantized signal with N samples. , ,in Represents a third-order quantized signal No. i 1 sample, of which Represents analog signal In the i The values ​​at each sampling point are as follows: For sample index, ; Calculate the second moment of the third-order quantized signal , .

[0026] The tail consistency ratio calculation module is used to calculate the tail consistency ratio based on Th, , Constructing the Tail Consistency Ratio (TCR) statistic; the specific process includes: based on Th, Calculate analog signals The tail probability whose absolute value is greater than the quantization threshold Th , ,in Let Gaussian probability distribution function be used. ,in Let Gaussian probability density function be used. ; Calculate the tail consistency ratio (TCR). .

[0027] The radio frequency interference (RF) decision module is used to obtain the RF interference decision result Flag based on the TCR (Transmission Rate Response). The specific process includes: detecting RF interference based on the deviation between the TCR and its expected value of 1, and obtaining the RF interference decision result. : ,in For configurable algorithm thresholds, A value of 1 indicates the acquired signal. There is radio frequency interference. A value of 0 indicates that the acquired signal... There is no radio frequency interference.

[0028] This application may also provide a computer device, including: at least one processor, memory, at least one network interface, and a user interface. The various components in this device are coupled together via a bus system. It is understood that the bus system is used to implement communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0029] The user interface can include a display, keyboard, or clicking device. Examples include a mouse, trackball, touchpad, or touchscreen.

[0030] It is understood that the memory in the embodiments disclosed in this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0031] In some implementations, the memory stores elements such as executable modules or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0032] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. Programs implementing the methods of the embodiments of this disclosure can be included in the application programs.

[0033] In the above embodiments, the processor can also invoke programs or instructions stored in memory, specifically programs or instructions stored in an application program, for the following purposes: Follow the steps described above.

[0034] The above methods can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic diagrams disclosed above. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the disclosed methods can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0035] It is understood that the embodiments described in this application can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or combinations thereof.

[0036] For software implementation, the technology of this application can be implemented by executing the functional modules (e.g., procedures, functions, etc.) of this application. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0037] This application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, it can implement the steps in the above method embodiments.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application, and should all be covered within the scope of the claims of this application.

Claims

1. A radio frequency interference detection method based on third-order quantization tail probability, comprising: Step 1: Acquire passive microwave remote sensing signals with a total duration of T. and obtain signal power ; Step 2: Perform third-order quantization on the analog signal with a threshold of Th, and calculate the second moment of the third-order quantized signal. ; Step 3: Based on Th, , Build the Tail Consistency Ratio (TCR); Step 4: Make a radio frequency interference decision based on the TCR and obtain the radio frequency interference decision result Flag.

2. The radio frequency interference detection method based on third-order quantization tail probability according to claim 1, characterized in that, The specific process of step 1 includes: Passive microwave remote sensing signals with a total acquisition duration of T , for signal Integrating the square of the time from 0 to T, we obtain the signal power. ,in For signal At any moment The possible values ​​of , where For time indexing, .

3. The radio frequency interference detection method based on third-order quantization tail probability according to claim 1, characterized in that, The specific process of step 2 includes: For analog signals Perform third-order quantization with a threshold of Th to obtain a third-order quantized signal with N samples. : ； in, Represents a third-order quantized signal The i One sample; Represents analog signal In the i The value at each sampling point. For sample index, ; Calculate the second moment of a third-order quantized signal : .

4. The radio frequency interference detection method based on third-order quantization tail probability according to claim 1, characterized in that, The specific process of step 3 includes: Based on the threshold Th and signal power Calculate analog signals The tail probability whose absolute value is greater than the threshold Th : ； in, Let Gaussian probability distribution function be used. , Let Gaussian probability density function be used. ; Calculate the Tail Consistency Ratio (TCR): .

5. The radio frequency interference detection method based on third-order quantization tail probability according to claim 1, characterized in that, The specific process of step 4 includes: Radio frequency interference (RF interference) is detected based on the deviation of the tail conformity ratio (TCR) from its expected value of 1, and the RF interference decision result is obtained. : ； in, For the set threshold, A value of 1 indicates the acquired signal. There is radio frequency interference. A value of 0 indicates that the acquired signal... There is no radio frequency interference.

6. A radio frequency interference detection system based on third-order quantization tail probability, implemented according to the method of any one of claims 1-5, characterized in that, The system includes: The signal power detection module is used to acquire passive microwave remote sensing signals. And acquire signal power ; The third-order quantization module is used to perform third-order quantization on analog signals with a threshold of Th and obtain the second-order moment of the third-order quantized signal. ; The tail consistency ratio calculation module is used to calculate the tail consistency ratio based on Th, , Construct the Tail Consistency Ratio (TCR) statistic; and The radio frequency interference decision module is used to obtain the radio frequency interference decision result flag based on the TCR.

Images