Unmanned aerial vehicle passive detection signal discrimination method and device based on electromagnetic behavior characteristics

Abstract

The application discloses a kind of unmanned aerial vehicle passive detection signal discrimination method and device based on electromagnetic behavior characteristics, method includes: direction finding data acquisition is carried out, and the observation value of signal intensity and azimuth is obtained by sampling;Linear tracking model for tracking filtering is constructed;Initialization setting is carried out;Using linear tracking model, the tracking chain state of current time is updated;Using the tracking chain state after updating, the likelihood probability of electromagnetic behavior characteristics and electromagnetic behavior characteristics posterior probability of current time are calculated, and according to maximum probability principle, the electromagnetic behavior characteristics of unmanned aerial vehicle current time is discriminated, and the passive detection signal discrimination of current time is completed.The present application discriminates electromagnetic behavior characteristics based on the probability characteristics of electromagnetic behavior, not only solves the signal discrimination problem of passive detection for unknown model unmanned aerial vehicle, but also improves the observation accuracy of observation value and signal discrimination accuracy by designing reasonable linear tracking filtering model.

Description

Technical Field

[0001] The present invention belongs to the field of wireless detection technology, and in particular relates to a method and device for distinguishing passive detection signals of unmanned aerial vehicles based on electromagnetic behavior characteristics. Background Art

[0002] With the rapid development of drone technology, it has been widely used in fields such as unmanned reconnaissance and logistics transportation, but it has also brought various safety hazards. Detection and identification technology for drones has become a research hotspot. In existing technologies, traditional direction-finding and detection technologies have many limitations. For example, relying solely on interferometer direction-finding is susceptible to complex environmental noise, and it is difficult to effectively distinguish when faced with multi-directional, same-frequency signals, resulting in limited direction-finding accuracy. For example, the spin direction-finding method based on a rotating interferometer offsets array errors through the spin characteristics of the drone, improving direction-finding accuracy. Although it can reduce the influence of some amplitude and phase difference inconsistencies and mutual coupling effects, it can only provide a relatively basic azimuth angle estimate, making it difficult to further explore the behavioral characteristics of the radiation source signal.

[0003] Passive detection, identification, and location of drones through passive reception and processing of electromagnetic signals emitted by drones is one of the primary technical approaches for drone detection. Further analysis of drone signals requires further development beyond preliminary direction finding. Existing technologies attempt to utilize coprime linear arrays combined with compressed sensing for high-precision positioning of multiple radiating sources through the high degrees of freedom of sparse arrays. Alternatively, they employ direction-finding schemes that fuse digital beamforming with phase interferometry, using spatial filtering to separate co-frequency signals and enhance interference resistance. However, these methods primarily focus on estimating spatial parameters (such as azimuth) of the signal and lack in-depth analysis of the dynamic behavior of drones. This makes it difficult to effectively distinguish between different motion characteristics (such as stationary, approaching, departing, passing, and circling), resulting in high false alarm rates in complex electromagnetic environments. Furthermore, these methods can only detect signals from known drone models and are unable to discern the behavioral intent of communication signals from unknown drone models.

[0004] Passive detection of unknown drone models often makes it difficult to determine whether they are drones based solely on a single signal feature. However, considering the kinematic characteristics of drones, estimating the radiating source's maneuvering pattern from the temporal sequence of signal intensity, azimuth, and other characteristics (actually, simple electromagnetic behavior characteristics) is an effective method for identifying drone signals. Therefore, a technology that combines high-precision direction finding with time-series signal feature extraction and dynamic behavior modeling is urgently needed to achieve refined identification and evaluation of drone signals. Summary of the Invention

[0005] In order to effectively solve the above-mentioned problems existing in the prior art, the present invention provides a method and device for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics. The method constructs a linear tracking model to filter and correct the tracking chain state defined by the mean and variance of the state quantity, and then distinguishes the electromagnetic behavior characteristics based on the probabilistic characteristics of the electromagnetic behavior, thereby realizing the discrimination of UAV passive detection signals. It not only solves the signal discrimination problem for passive detection of unknown models of UAVs, but also improves the signal discrimination accuracy by improving the observation accuracy of the observation value through the design of a reasonable linear tracking filtering model.

[0006] The present invention provides a method for distinguishing passive detection signals of unmanned aerial vehicles based on electromagnetic behavior characteristics, comprising:

[0007] Step 110: performing direction finding data collection to obtain measurement data of the signal strength and azimuth of the unknown model UAV target; performing dynamic high-frequency time series sampling on the measurement data to obtain observation values ​​of the signal strength and azimuth;

[0008] Step 120: constructing a linear tracking model for tracking and filtering the signal strength and azimuth, including defining a tracking chain state based on a mean and variance of state quantities;

[0009] Step 130, perform initialization settings;

[0010] Step 140: input the observed value and the tracking chain state at the previous moment in the time series into the linear tracking model to update the tracking chain state at the current moment;

[0011] Step 150, using the updated tracking chain state, calculate the likelihood probability of the electromagnetic behavior characteristics at the current moment;

[0012] Step 160 , using the likelihood probability of the electromagnetic behavior characteristics at the current moment, calculate the posterior probability of the electromagnetic behavior characteristics at the current moment, and based on the maximum probability principle, identify the electromagnetic behavior characteristics of the drone at the current moment, completing the passive detection signal identification at the current moment;

[0013] Step 170 , looping through steps 140 to 160 until all time series of passive detection signal discrimination is completed.

[0014] In addition, the present invention provides a device for distinguishing passive detection signals of drones based on electromagnetic behavior characteristics, which is used to implement the steps of the aforementioned method, and includes:

[0015] The first module is used to collect direction-finding data and obtain the signal strength and azimuth measurement data of unknown drone targets. Dynamic high-frequency time series sampling of the measurement data is performed to obtain the observed values ​​of signal strength and azimuth.

[0016] The second module is used to build a linear tracking model for tracking and filtering signal strength and azimuth, including defining the tracking chain state based on the mean and variance of the state quantity;

[0017] The third module: used for initialization settings;

[0018] The fourth module is used to input the observation value and the tracking chain state at the previous moment in the time series into the linear tracking model to update the tracking chain state at the current moment;

[0019] The fifth module is used to calculate the likelihood probability of the electromagnetic behavior characteristics at the current moment using the updated tracking chain state;

[0020] The sixth module is used to calculate the posterior probability of the electromagnetic behavior characteristics at the current moment using the likelihood probability of the electromagnetic behavior characteristics at the current moment, and to identify the electromagnetic behavior characteristics of the drone at the current moment based on the maximum probability principle, thereby completing the passive detection signal identification at the current moment;

[0021] The seventh module is used to cyclically execute the functions of the fourth module to the sixth module until the passive detection signal discrimination of all time series is completed.

[0022] Compared with the existing technology, the method and device for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics provided by the present invention have the following beneficial effects:

[0023] The present invention constructs a linear tracking model to filter and correct the tracking chain state defined by the mean and variance of the state quantity, and then discriminates the electromagnetic behavior characteristics based on the probabilistic characteristics of the electromagnetic behavior, thereby realizing the passive detection signal discrimination of the UAV. It not only solves the signal discrimination problem for the passive detection of UAVs of unknown models, but also improves the observation accuracy of the observation value and the signal discrimination accuracy by designing a reasonable linear tracking filter model. It has good application prospects in the field of passive detection of UAVs of unknown models. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments or example embodiments of the present invention.

[0025] Figure 1 This is a flowchart of the steps of the method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics in the first embodiment of the present invention;

[0026] Figure 2Schematic diagram of measurement data of a single UAV target in the simulation experiment of the present invention during the time periods of four typical electromagnetic behavior characteristics, wherein (a) represents the polar coordinate diagram of signal strength and azimuth change obtained during the time period of the stationary behavior characteristic, (b) represents the polar coordinate diagram of signal strength and azimuth change obtained during the time period corresponding to the away behavior characteristic, (c) represents the polar coordinate diagram of signal strength and azimuth change obtained during the time period corresponding to the approaching behavior characteristic, and (d) represents the polar coordinate diagram of signal strength and azimuth change obtained during the time period corresponding to the passing behavior characteristic; in the four polar coordinates (a), (b), (c), and (d), the circumferential direction represents the azimuth in degrees, and the radial direction represents the signal strength in dB. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] In a first embodiment, the present invention proposes a method for identifying passive detection signals of drones based on electromagnetic behavior characteristics, comprising:

[0030] Step 110: performing direction finding data collection to obtain measurement data of the signal strength and azimuth of the unknown model UAV target; performing dynamic high-frequency time series sampling on the measurement data to obtain observation values ​​of the signal strength and azimuth;

[0031] Step 120: constructing a linear tracking model for tracking and filtering the signal strength and azimuth, including defining a tracking chain state based on a mean and variance of state quantities;

[0032] Step 130, perform initialization settings;

[0033] Step 140: input the observed value and the tracking chain state at the previous moment in the time series into the linear tracking model to update the tracking chain state at the current moment;

[0034] Step 150, using the updated tracking chain state, calculate the likelihood probability of the electromagnetic behavior characteristics at the current moment;

[0035] Step 160 , using the likelihood probability of the electromagnetic behavior characteristics at the current moment, calculate the posterior probability of the electromagnetic behavior characteristics at the current moment, and based on the maximum probability principle, identify the electromagnetic behavior characteristics of the drone at the current moment, completing the passive detection signal identification at the current moment;

[0036] Step 170 , looping through steps 140 to 160 until all time series of passive detection signal discrimination is completed.

[0037] In many application scenarios, when collecting direction finding data for unknown drone signals, the drone may track multiple targets. Situation, in which is the maximum scale setting of the UAV target, and the time series sampling scale of the measurement data is set to Therefore, the observed data obtained at this time is actually a Dimensional matrix data:

[0038] ;

[0039] in, is the target At the sampling time The signal strength, is the target At the sampling time azimuth.

[0040] However, since each UAV target has the same electromagnetic behavior feature recognition method, it is only necessary to consider the electromagnetic behavior feature recognition of a single UAV target. and Respectively Signal strength and azimuth at the time.

[0041] Specifically, in step 110, a radiation source detection and direction finding device may be used to collect direction finding data. For example, a commonly used interferometer direction finding device on the market that meets the accuracy requirements may be used, such as the CRFS RFeye (developed by the UK CRFS company, supporting a frequency band of 20 MHz to 18 GHz and a direction finding accuracy of 100 MHz to 200 GHz) that supports modular design and multi-band direction finding and signal feature extraction. , high sensitivity, high cost) system, lightweight and portable DroneShield RfOne system (developed by Australian DroneShield company, with main frequency bands of 2.4 GHz, 5.8 GHz and 900 MHz, and direction finding accuracy of about , such as the RfOne Mk2, which can be deployed in anti-drone systems at airports), and the low-cost, relatively limited-performance KrakenSDR system (developed by KrakenRF Inc., with a frequency range of 24 MHz to 1.7 GHz and a direction-finding accuracy of approximately 5° to 15°). Another example is the R&S ADD597, an interferometer direction-finding system launched by Rohde & Schwarz, with a frequency range of 20 MHz to 8.5 GHz (vertical polarization) and 20 MHz to 7.5 GHz (horizontal polarization), and a direction-finding accuracy of , the direction finding sensitivity is: to , suitable for fixed, mobile and transportable applications, the system also integrates an omnidirectional monitoring antenna and supports switchable two polarization modes.

[0042] Furthermore, in step 120, the process of constructing the linear tracking model includes:

[0043] Step 121, setting the state quantity to include at least signal strength , signal strength change rate , azimuth and azimuth rate of change , set the signal strength and azimuth As observation quantities, we use and Indicates the first The state and observation quantities at each moment:

[0044] .

[0045] Using the mean and variance of the state quantity, the tracking chain state of the linear tracking model is defined: ,in Take the mean of the state quantity, is the covariance matrix of the state quantity, specifically, and It is given by:

[0046] ;

[0047] in, Indicates signal strength The mean of Indicates the signal strength change rate The mean of Indicates azimuth The mean of Indicates the rate of change of azimuth The mean of represents a diagonal matrix, and They represent signal strength, signal strength change rate, azimuth, and variance of azimuth change rate, respectively.

[0048] Step 122, give the state update equation:

[0049] ,

[0050] in, is the state transfer matrix, which is given by:

[0051] ,

[0052] is the time difference between two observations, and is also the time window length of the time series. Indicates the moment; the above The setting treats signal strength and azimuth as independent processes. is the noise vector of the state transfer process, which is used to describe the dynamic changes and uncertainties of the UAV in intensity and azimuth during the transfer process. The mean is 0 and the variance is Gaussian distribution (normal distribution), They represent the process noise variance of signal strength, signal strength change rate, azimuth and azimuth change rate respectively, reflecting the random fluctuations caused by the maneuverability of the UAV. For example, , , (units are degrees²), (Units are degrees²).

[0053] Step 123, using state quantity Represents the observed quantity , we get the observation equation:

[0054] ,

[0055] in, is the observation matrix, is the observation noise, which has a mean of 0 and a variance of Gaussian distribution, is the variance of the observations determined by the precision of the observations, for example, , (Units are degrees²).

[0056] Step 124, using the structure of the state quantity update equation, establish the state transfer equation of the tracking chain:

[0057] ;

[0058] The state transfer equation uses the previous moment ( The tracking chain state of the moment) is obtained by transfer prediction at the current moment (the The predicted value of the tracking chain state at each moment, where is the predicted mean of the state quantity, is the predicted covariance of the state quantity.

[0059] Step 125: Calculate the probability of the tracking chain state update by using the observed outlier probability judgment.

[0060] Observation probability (obeying Gaussian distribution), where is the mean of the observed predictions of the UAV target, is the observed residual covariance, To observe the noise The covariance matrix of This time, the observation value of the drone target is entered.

[0061] Observation probability , is the observed value Relative to the predicted likelihood probability, it reflects the degree of match between the observed value and the predicted value. Note that due to the 360° wrapping problem in direction measurement, before substituting it into the Gaussian distribution probability formula, the observed value needs to be unwrapped so that the azimuth value falls within the range of 180° around the mean of the azimuth variable distribution.

[0062] set up shows the observation count of the drone target, based on the historical observation probability of all the drone targets ( ) and we get , it means that the UAV target has not been effectively observed, and the corresponding attribution probability is 0.

[0063] The posterior probability is used to calculate the probability of belonging to an observation (measurement data in the time series) when :

[0064] ;

[0065] in, is the prior probability of the outlier (e.g. 0.2); calculated using uniform distribution , indicating the observed value The likelihood distribution of outliers (abnormal data).

[0066] Step 126, using the attribution probability and The observation value at the moment and the predicted value of the tracking chain state are used to correct the tracking chain state and update the Tracking the chain status at each moment is achieved through the following formula:

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] in, is the identity matrix, is the observation matrix.

[0072] Furthermore, in step 130, the process of performing initialization settings includes:

[0073] Set the initial value of the tracking chain state, use the observed value to calculate the sample mean of the state quantity, and obtain the state Initial value of ; Set the initial value of the covariance matrix of the state quantity: ,in, Generally set to , Determined based on the maximum range of the signal strength change rate, for example, if the signal strength change rate Falling ,but , Generally set to , Determined according to the maximum range of azimuth angle change rate, for example, if the azimuth angle change rate falls within ,but .

[0074] Set the initial value of the electromagnetic behavior characteristic probability and the state transition probability matrix, the electromagnetic behavior characteristics include at least stationary, approaching, moving away, passing, and flying around; set these five electromagnetic behavior characteristics, use , , respectively represent the electromagnetic behavior characteristics of stationary, approaching, moving away, passing and flying around, and the corresponding initial probability is expressed as:

[0075] ;

[0076] The state transition probability matrix of electromagnetic behavior characteristics is:

[0077] ;

[0078] in, Represents the probability parameter of state transition, and the diagonal of the state transition probability matrix Indicates that electromagnetic behavior characteristics are maintained The probability of the remaining four Representation characteristics Transfer to four other electromagnetic behavior characteristics The probability of .

[0079] In step 140, all observations and the previous moment (the The tracking chain state of the current moment (the The tracking chain status at a certain moment) is obtained, and the correction value of the tracking chain status at the current moment is obtained.

[0080] Note that according to the setting of the linear tracking model in step 120, if the update in step 140 fails, the subsequent steps at the current moment are terminated and the linear tracking model is used again according to step 140 to execute the next moment (step 140). Filter correction of the tracking chain state at each moment.

[0081] Furthermore, in step 150, the likelihood probability of the electromagnetic behavior feature at the current moment is calculated, specifically based on the significance of the intensity change rate and the azimuth change rate.

[0082] Generally, under stationary features, the signal strength change rate and azimuth change rate are both small; under far-away features, the signal strength has a significant negative change rate, and the azimuth change rate is small; under approaching features, the signal strength has a significant positive change rate, and the azimuth change rate is small; under passing features, the signal strength change rate and azimuth change rate are both significant; under circumventing features, the signal strength change rate is small, while the azimuth has a significant change rate.

[0083] First, the probability of whether the signal strength change rate is significant (further divided into the probability of positive change and negative change) and the probability of significant azimuth change are calculated respectively.

[0084] According to the current moment (the The mean and covariance of the state quantity at each moment are used to extract the signal strength change rate. , uncertainty of signal strength change rate (standard deviation) and azimuth rate of change , uncertainty in azimuth rate of change .

[0085] Calculate the significance probability of the rate of change of signal strength:

[0086] ;

[0087] in, is the aforementioned prior probability of the observed outlier value, is the assumed signal strength rate of change variable Obey Gaussian distribution Conditional Value The probability density of :

[0088] .

[0089] Calculate the significance probability of positive and negative rates of change in signal strength:

[0090] ;

[0091] ;

[0092] .

[0093] Similarly, calculate the significance probability of the azimuth rate of change:

[0094] .

[0095] The likelihood probability of each electromagnetic behavior feature is calculated based on the significant probability of the signal strength change rate (including positive change rate and negative change rate) and the significant probability of the azimuth change rate:

[0096] .

[0097] Specifically, step 160 includes:

[0098] First, use the posterior probability of the previous moment to calculate the predicted probability of each electromagnetic behavior feature at the current moment:

[0099] ;

[0100] in," " represents the vector dot product operation.

[0101] Then, the predicted probability and likelihood probability of the electromagnetic behavior characteristics at the current moment are used to calculate the posterior probability:

[0102] ;

[0103] For the above Normalize to get the final posterior probability of the electromagnetic behavior characteristics at the current moment:

[0104] .

[0105] According to the maximum probability principle, the feature with the largest median of the posterior probability of the electromagnetic behavior feature is selected as the electromagnetic behavior feature discrimination result output at the current moment, and its corresponding posterior probability value is the confidence of the discrimination result.

[0106] The present invention provides a method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics. The method constructs a linear tracking model for tracking and filtering the signal strength and azimuth, performs filtering and correction on the tracking chain state defined by the mean and variance of the state quantity, and then distinguishes the electromagnetic behavior characteristics based on the probabilistic characteristics of the electromagnetic behavior, thereby realizing the discrimination of UAV passive detection signals. It not only realizes the signal discrimination of passive detection of UAVs of unknown models, but also simultaneously improves the observation value and signal discrimination accuracy by designing an optimized linear tracking filtering model, showing good application prospects in the field of passive detection of UAVs of unknown models.

[0107] In view of the above embodiment, the present invention has carried out corresponding simulation experiments, and obtained the simulation data of the signal strength and azimuth of the drone as the measurement data in the experiment, such as Figure 2 As shown, the measurement data of a single UAV target in four typical electromagnetic behavior characteristic time periods, among which, Figure 2 (a) A polar coordinate plot of the signal strength and azimuth obtained during the period of stationary behavior. Visual observation shows that the signal amplitude and azimuth in the polar coordinates are basically stable within a certain range, but there will be some jitter due to observation errors. Figure 2 (b) A polar coordinate diagram showing the changes in signal strength and azimuth obtained during the time period corresponding to the moving behavior feature. In the polar coordinates, the signal strength decreases and the azimuth is basically stable. Figure 2 (c) A polar coordinate diagram showing the changes in signal strength and azimuth obtained during the time period corresponding to the approach behavior characteristics. In the polar coordinates, the signal strength increases and the azimuth is basically stable. Figure 2 (d) represents the polar coordinate diagram of the signal strength and azimuth obtained during the time period corresponding to the behavior feature. In the polar coordinate diagram, the signal strength changes slowly or remains basically unchanged, while the azimuth changes continuously. Figure 2 (a) Figure 2 (b) Figure 2 (c) and Figure 2 In the polar coordinates of (d), the radial direction is the signal strength (dB) and the circumferential direction is the azimuth (°). Using the UAV passive detection signal discrimination method based on electromagnetic behavior characteristics of the aforementioned embodiment of the present invention, the electromagnetic behavior characteristics of the UAV target are identified, and the signal discrimination results are as follows:

[0108] (1) Identification of target’s static behavior characteristics: Figure 2 As shown in (a), the azimuth angle in the polar coordinate array is randomly jittered but stable, and there is no significant signal strength fluctuation, which is consistent with the characteristic of non-directional position offset of the stationary target signal source; the posterior probability of the electromagnetic behavior characteristic can be calculated as:

[0109] ;

[0110] It is thus determined that the electromagnetic behavior characteristics of the drone target are "stationary", and the confidence level of this result is 0.998, which means that this judgment result is believed with a very high probability (close to 1).

[0111] (2) Identification of target's moving away behavior characteristics: Figure 2 (b) shows that the signal strength in the polar array is in a more stable direction (azimuth angle between and Between, deviation The significance of the position) shows a decreasing trend, indicating that the signal source is gradually moving away from the device; the posterior probability of the final electromagnetic behavior feature can be calculated as:

[0112] ;

[0113] It is thus determined that the electromagnetic behavior characteristic of the drone target is "stay away", and the confidence level of this result is 0.999, which means that this judgment result is believed with a very high probability (close to 1).

[0114] (3) Identification of target’s approach behavior characteristics: Figure 2 As shown in (c), the signal azimuth in polar coordinates is stable, but the signal strength is increasing, which is consistent with the characteristic that the received energy increases as the signal source approaches the device. The posterior probability of the final electromagnetic behavior characteristic can be calculated as:

[0115] ;

[0116] It is thus determined that the electromagnetic behavior characteristics of the drone target are "close", and the confidence level of this result is 0.999, which means that this judgment result is believed with a very high probability (close to 1).

[0117] (4) Identification of target’s behavioral characteristics: Figure 2 As shown in (d), in the polar coordinate array, the signal strength is generally stable, indicating that the distance from the signal source to the device has not changed significantly. However, the signal azimuth increases counterclockwise from 150°, which is consistent with the characteristics of the target passing through the detection device. The posterior probability of the final electromagnetic behavior characteristic can be calculated as:

[0118] ;

[0119] It is thus determined that the electromagnetic behavior characteristic of the drone target is "passing by", and the confidence level of this result is 1.000, which means that this judgment result is believed with a very high probability (approximately equal to 1).

[0120] The above experimental results further verify that the drone passive detection signal discrimination method based on electromagnetic behavior characteristics proposed in the present invention has a good target behavior feature discrimination effect in signal discrimination of passive detection of unknown model drones.

[0121] In addition, in a second embodiment, the present invention provides a device for distinguishing passive detection signals of drones based on electromagnetic behavior characteristics. The device is used to implement the steps of the method described in the first embodiment, and the device includes:

[0122] The first module is used to collect direction-finding data and obtain the signal strength and azimuth measurement data of unknown drone targets. Dynamic high-frequency time series sampling of the measurement data is performed to obtain the observed values ​​of signal strength and azimuth.

[0123] The second module is used to build a linear tracking model for tracking and filtering signal strength and azimuth, including defining the tracking chain state based on the mean and variance of the state quantity;

[0124] The third module: used for initialization settings;

[0125] The fourth module is used to input the observation value and the tracking chain state at the previous moment in the time series into the linear tracking model to update the tracking chain state at the current moment;

[0126] The fifth module is used to calculate the likelihood probability of the electromagnetic behavior characteristics at the current moment using the updated tracking chain state;

[0127] The sixth module is used to calculate the posterior probability of the electromagnetic behavior characteristics at the current moment using the likelihood probability of the electromagnetic behavior characteristics at the current moment, and to identify the electromagnetic behavior characteristics of the drone at the current moment based on the maximum probability principle, thereby completing the passive detection signal identification at the current moment;

[0128] The seventh module is used to cyclically execute the functions of the fourth module to the sixth module until the passive detection signal discrimination of all time series is completed.

[0129] In another embodiment, the present invention provides a computer device, which may be a server, comprising a processor, memory, a network interface, and a database connected via a system bus. The processor of the device is configured to provide computing and control capabilities. The memory of the device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the device is configured to store data for distinguishing drone passive detection signals based on electromagnetic behavior characteristics. The network interface of the device is configured to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements the method for distinguishing drone passive detection signals based on electromagnetic behavior characteristics.

[0130] Those skilled in the art will understand that the description of the technical features of the equipment in the above embodiments does not constitute a limitation on all equipment to which the present invention is applied. The specific equipment may include more or fewer components, or combine certain components, or have different component arrangements.

[0131] In another embodiment, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for distinguishing the passive detection signal of a drone based on electromagnetic behavior characteristics provided in any of the above embodiments are implemented.

[0132] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0133] Matters not covered by the present invention are known technologies.

[0134] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The above-described embodiments merely represent several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics, characterized in that: include: Step 110: performing direction finding data collection to obtain measurement data of the signal strength and azimuth of the unknown model UAV target; Perform dynamic high-frequency time series sampling on the measurement data to obtain observation values ​​of signal strength and azimuth; Step 120: constructing a linear tracking model for tracking and filtering the signal strength and azimuth, including defining a tracking chain state based on a mean and variance of state quantities; Step 130, perform initialization settings; Step 140: input the observed value and the tracking chain state at the previous moment in the time series into the linear tracking model to update the tracking chain state at the current moment; Step 150, using the updated tracking chain state at the current moment, and according to the significant probability of the signal strength change rate and the significant probability of the azimuth change rate, calculate the likelihood probability of the electromagnetic behavior feature at the current moment; The likelihood probability of the electromagnetic behavior characteristics is given by the following formula: ； in, represents the current moment in the time series, is the significance probability of the signal strength change rate, is the significant probability of the azimuth rate of change, is the significant probability of a positive rate of change in signal strength, is the significant probability of a negative rate of change of signal strength; Step 160 , using the likelihood probability of the electromagnetic behavior characteristics at the current moment, calculate the posterior probability of the electromagnetic behavior characteristics at the current moment, and based on the maximum probability principle, identify the electromagnetic behavior characteristics of the drone at the current moment, completing the passive detection signal identification at the current moment; Step 170 , looping through steps 140 to 160 until all time series of passive detection signal discrimination is completed.

2. The method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics according to claim 1 is characterized in that: The electromagnetic behavior characteristics at least include stationary, approaching, moving away, passing, and flying around.

3. The method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics according to claim 2, wherein: In step 120, a linear tracking model for tracking and filtering the signal strength and azimuth is constructed, including: Step 121, using the mean and variance of the state quantity, define the tracking chain state of the linear tracking model: , the state quantity at least includes signal strength , signal strength change rate , azimuth and azimuth rate of change ; Set signal strength and azimuth As the observed quantity; among them, is the mean value of the state quantity, is the covariance matrix of the state quantity; Step 122, give the state update equation at the current moment: ， in, Indicates that the current moment is the A moment, is the state transfer matrix, which is given by: ， is the time difference between the two observations, is the noise vector of the state transfer process, which has a mean of 0 and a variance of Gaussian distribution, represent the process noise variance of signal strength, signal strength change rate, azimuth and azimuth change rate respectively; Step 123, using state quantity Represents the observed quantity , we get the observation equation: ， in, is the observation matrix, is the observation noise, which has a mean of 0 and a variance of Gaussian distribution, is the variance of the observation determined by the observation accuracy; Step 124, using the structure of the state quantity update equation, establish the state transfer equation of the tracking chain: ； in is the predicted mean of the state quantity, is the predicted covariance of the state quantity; the state transfer equation is used to obtain the predicted value of the tracking chain state at the current moment.

4. The method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics according to claim 3, wherein: In step 120, a linear tracking model for tracking and filtering the signal strength and azimuth angle is constructed, which also includes: Step 125, using the observed outlier probability judgment, calculates the probability of the tracking chain state update, including: Calculating observation probabilities using Gaussian distribution ; The attribution probability is calculated by summing the observation count obtained by the historical observation probability attributing to the drone target: If the observation count is 0, the attribution probability of the observation value is ; If the observation count is not 0, the posterior probability is used to calculate the probability of the observation value belonging to: ； in, is the prior probability of an outlier; the observed value is calculated using a uniform distribution Likelihood distribution of attributed outliers: ; Step 126, using The probability of belonging, observation value and predicted value of tracking chain state at the moment are used to correct the tracking chain state and update the Tracking chain status at each moment: ； ； ； ； in, is the identity matrix, is the observation matrix, is the observed residual covariance, Observation noise The covariance matrix of .

5. The method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics according to claim 4 is characterized in that: In step 130, initialization settings are performed, including: Set the initial state of the linear tracking model, including: Using the observed value of the observation quantity, calculate the sample mean of the state quantity and obtain the state Initial value of ; Set the initial value of the covariance matrix of the state quantity: ， in, Set as , Determined by the maximum range of signal strength change rate, Set as , Determined according to the maximum range of azimuth angle change rate; Set the probability initial value and state transition probability matrix of electromagnetic behavior characteristics, including: Set 5 electromagnetic behavior characteristics, use , , respectively represent the electromagnetic behavior characteristics of stationary, approaching, moving away, passing and flying around, and the corresponding initial probability is expressed as: ； The state transition probability matrix of electromagnetic behavior characteristics is: ； in, Represents the probability parameter of state transition, and the diagonal of the state transition probability matrix Indicates that electromagnetic behavior characteristics are maintained The probability of the remaining four Representation characteristics Transfer to four other electromagnetic behavior characteristics The probability of .

6. The method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics according to claim 5 is characterized in that: In step 150, the likelihood probability of the electromagnetic behavior feature at the current moment is calculated using the updated tracking chain state at the current moment and the significant probability of the signal strength change rate and the significant probability of the azimuth change rate, including: Extract the signal strength change rate based on the mean and covariance of the current state quantity output by the linear tracking model , standard deviation of signal strength change rate and azimuth rate of change , standard deviation of azimuth angle change rate ; Calculate the significance probability of the rate of change of signal strength: ； in, is the prior probability of an outlier; is the assumed signal strength rate of change variable Obey Gaussian distribution Conditional Value The probability density of : ； Calculate the significance probability of positive and negative rates of change in signal strength: ； ； in, ; Compute the significance probability of the azimuth rate of change: ； in, is the assumed azimuth rate variable Obey Gaussian distribution conditions, Value The probability density of Calculate the likelihood probability of electromagnetic behavior characteristics: 。 7. The method for distinguishing UAV passive detection signals based on electromagnetic behavior characteristics according to claim 6 is characterized in that: Step 160 includes: First, use the posterior probability of the previous moment to calculate the predicted probability of each electromagnetic behavior feature at the current moment: ； in," " represents vector dot multiplication operation; Using the predicted probability and likelihood probability of the electromagnetic behavior characteristics at the current moment, calculate the posterior probability: ； right Normalize to get the final posterior probability of the electromagnetic behavior characteristics at the current moment: ； According to the maximum probability principle, the feature with the largest median of the posterior probability of the electromagnetic behavior feature is selected as the electromagnetic behavior feature discrimination result output at the current moment, and the corresponding posterior probability value is used as the confidence of the discrimination result.

8. A UAV passive detection signal identification device based on electromagnetic behavior characteristics, characterized in that: The device is used to implement the steps of the method according to any one of claims 1 to 7, and the device includes the following modules: The first module is used to collect direction-finding data and obtain the signal strength and azimuth measurement data of unknown drone targets. Dynamic high-frequency time series sampling of the measurement data is performed to obtain the observed values ​​of signal strength and azimuth. The second module is used to build a linear tracking model for tracking and filtering signal strength and azimuth, including defining the tracking chain state based on the mean and variance of the state quantity; The third module: used for initialization settings; The fourth module is used to input the observation value and the tracking chain state at the previous moment in the time series into the linear tracking model to update the tracking chain state at the current moment; The fifth module is used to calculate the likelihood probability of the electromagnetic behavior characteristics at the current moment based on the significant probability of the signal strength change rate and the significant probability of the azimuth change rate using the updated tracking chain status; The sixth module is used to calculate the posterior probability of the electromagnetic behavior characteristics at the current moment using the likelihood probability of the electromagnetic behavior characteristics at the current moment, and to identify the electromagnetic behavior characteristics of the drone at the current moment based on the maximum probability principle, thereby completing the passive detection signal identification at the current moment; The seventh module is used to cyclically execute the functions of the fourth module to the sixth module until the passive detection signal discrimination of all time series is completed.

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