A multi-target uav uplink tracking jamming method based on frequency hopping pattern

Abstract

The application provides a multi-target unmanned aerial vehicle uplink tracking interference method based on a frequency hopping pattern; frequency hopping signals present periodic occurrence rules in each subchannel, and the periods are all frequency hopping sequence periods, so that tracking interference is completed on the periodic signals of each subchannel, and the whole tracking interference on the single unmanned aerial vehicle frequency hopping link is realized. Based on this feature, the application divides different subchannels and different tracking and interference threads in the same channel, converts the problem of tracking and interfering on multiple unmanned aerial vehicles into the problem of tracking and interfering on various periodic signals in different subchannels, can track the frequency hopping time of the frequency hopping signal under the condition of complex time-frequency domain distribution of the frequency hopping signal and unknown accurate frequency hopping period parameters, accurately interferes on the communication link of the multiple unmanned aerial vehicles, has short interference response time and high interference efficiency, and thus better completes the multi-unmanned aerial vehicle countermeasure task.

Description

TECHNICAL FIELD

[0001] The present application relates to unmanned aerial vehicle countermeasure technology, in particular to multi-target unmanned aerial vehicle uplink tracking interference technology. BACKGROUND

[0002] In recent years, with the development of unmanned aerial vehicle application in various fields, the safety hazards brought by unmanned aerial vehicles gradually emerged, and the unmanned aerial vehicle countermeasure technology has gradually become a research hotspot. Among them, the suppression interference implemented for the uplink communication link of the unmanned aerial vehicle is an effective way to realize the unmanned aerial vehicle countermeasure, which has the advantages of fast disposal speed, small damage to the surrounding environment, etc.

[0003] The uplink of the unmanned aerial vehicle usually adopts frequency hopping mode and has good anti-interference performance. At present, the main methods for implementing suppression interference for the frequency hopping communication of the unmanned aerial vehicle include full-band noise interference, comb interference, tracking interference, etc. Among them, the tracking interference is to implement accurate interference only for the frequency time range area of each hop signal on the basis of frequency hopping synchronization, so it has the advantages of high interference efficiency, good interference effect, small influence on other communication links, etc. compared with other interference methods. But the tracking interference needs to realize the synchronization of the hop of the non-cooperative jammer and the frequency hopping signal, and the synchronization effect directly affects the final interference effect. The prerequisite for realizing the hop synchronization is to obtain the frequency hopping communication parameters of the unmanned aerial vehicle, but in the actual unmanned aerial vehicle countermeasure scene, the frequency hopping parameters of the unmanned aerial vehicle are obtained by blind estimation of the pre-detection device, and the error in parameter estimation will reduce the synchronization accuracy and stability and then reduce the interference performance. On the other hand, most of the existing unmanned aerial vehicle link tracking interference methods are only suitable for single unmanned aerial vehicle scene. In the multi-unmanned aerial vehicle scene, different frequency hopping links coexist in asynchronous or synchronous networking mode, and the frequency hopping rules of different links are different, so the frequency hopping signal in the time-frequency domain is complex. Therefore, compared with the single unmanned aerial vehicle scene, it is difficult to implement tracking interference for multiple unmanned aerial vehicle links. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a method for realizing accurate and stable synchronization and effective tracking interference for the uplink of the unmanned aerial vehicle under the condition that the prior information such as the pre-detection hop period and hop time has an error.

[0005] The technical scheme adopted by the present application to solve the above technical problem is a multi-target unmanned aerial vehicle uplink tracking interference method based on a frequency hopping pattern, comprising the following steps:

[0006] comprising the following steps:

[0007] s1. Sub-channel establishment

[0008] Sub-channels are created using each signal characteristic parameter in each type of frequency hopping pattern in the frequency hopping pattern set. The signal characteristic parameters include the left and right cutoff frequencies, dwell time, and period of the frequency hopping pattern. The left and right cutoff frequencies of the sub-channels are set to the left and right cutoff frequencies of the corresponding signal characteristic parameters, and the channel signal table of the sub-channels is initialized.

[0009] s2. Multi-UAV frequency hopping signal synchronization steps: The specific implementation steps for synchronizing the periodic signals within each sub-channel are as follows:

[0010] 2-1 Creating Synchronous Threads

[0011] (1) Set up the thread table for the synchronization thread, which includes the signal characteristic parameters of the target being tracked by the thread, the thread prediction value at the start time of the next signal, the number of captures and the number of lost trackings; input the real-time signal parameter vector, which includes the left and right cutoff frequencies of the real-time signal and the start and end times of the real-time signal; then set the frequency threshold, time threshold and number threshold.

[0012] (2) Determine whether the synchronization thread matches the real-time signal that is the target. If yes, proceed to step 2-2; otherwise, update the synchronization thread.

[0013] 2-2 Capture Phase of Synchronized Threads

[0014] Update the synchronization thread information table, keep the signal characteristic parameters of the thread tracking target unchanged, set the thread prediction value at the start time of the next signal to be the sum of the period of the signal start time and the frequency hopping pattern, set the number of lost tracking to 0, and increment the number of captures by 1; determine whether the difference between the thread prediction value at the start time of the next signal and the current time is greater than the time threshold. If so, consider the capture to have failed, destroy the thread, and return to step (1). Otherwise, further determine whether the number of captures is greater than or equal to the number threshold. If so, consider the capture to be completed, update the synchronization thread information table, keep the signal characteristic parameters of the thread tracking target and the thread prediction value at the start time of the next signal unchanged, set the number of captures and the number of lost tracking to 0. If not, return to step 2-2.

[0015] 2-3 The tracing phase of synchronized threads

[0016] (1) Set the tracking time threshold and set the synchronization time t for the k-th tracking. k Measured values t s At the start time of the signal, update the k-th jump period U. k Measured values

[0017] (2) Calculate the jump period U k Prior prediction value for:

[0018]

[0019] Among them, E k Indicates the middle value. The mean square error of the k-th jump cycle The prior prediction value, Q u The variance of the measurement noise with skipped cycles follows a normal distribution;

[0020] Update jump cycle U k posterior estimate for:

[0021]

[0022] Among them, M k To ensure that the mean square error of the k-th jump cycle is Minimum optimal gain, Q t Let R be the variance of the normal distribution that the system noise follows at the current moment. t Let be the variance of the normal distribution that the measurement noise at the current moment follows. Let be the mean square error of the k-th jump cycle;

[0023] Calculate the synchronization time t k Prior prediction value for:

[0024]

[0025] Among them, Z k Indicates the middle value. Let be the variance of the prior estimate at the k-th synchronization time. Let K be the mean square error between the (k-1)th synchronization time and the kth jump cycle. k-1 To ensure the mean square error at the k-1 synchronization times The minimum optimal gain;

[0026] Update the k-th synchronization time t k posterior estimate for:

[0027]

[0028] Among them, K k To ensure that the mean square error at the k-th synchronization time is The minimum optimal gain;

[0029] (3) Update the thread prediction value t for the next signal start time in the synchronization thread information table of the k-th tracking. pre Let t be the time of the kth synchronization. kposterior estimate With jump period U k posterior estimate The sum; determine the sum of the current time and t. pre If the difference is less than or equal to the tracking time threshold, then the k-th tracking is considered successful. The tracking count k is then incremented by 1, and the result is updated. Update the number of lost tracking attempts to 0 and proceed to step 3-1; otherwise, consider the k-th tracking attempt to have failed and update... Increment the number of lost tracking attempts by 1, and determine whether the number of lost tracking attempts is greater than the threshold. If yes, return to step 2-2; otherwise, return to step 3-2.

[0030] S3. Steps for interfering with frequency-hopping signals from multiple drones:

[0031] 3-1 Set the interference delay time t delay And according to the k-th synchronization time t k posterior estimate Set the interference time period to Among them, t ad To allow for interference time, t hL To determine the dwell time in the signal characteristic parameters of the thread tracking target, proceed to step 3-3;

[0032] 3-2 Set the interference delay time t delay And based on the thread prediction value t at the start time of the next signal in the synchronization thread information table of the kth tracking, pre Set the interference time period to [t] pre +t delay , t pre +t hL +t delay +t ad Proceed to step 3-3;

[0033] 3-3 Perform interference according to the set interference time period and interference waveform.

[0034] Preferably, after initializing the channel signal table of the sub-channel in step s1, the following steps are performed:

[0035] Sub-channels with overlapping frequencies are merged. The left and right cutoff frequencies of the merged sub-channel are reset to the minimum left cutoff frequency and the maximum right cutoff frequency of the two overlapping sub-channels. The channel signal table of the merged sub-channel is the union of the channel signal tables of the two overlapping sub-channels. All sub-channels are traversed until the cutoff frequencies of any two sub-channels no longer overlap. At this point, the sub-channel division is complete, and the channel signal tables of all sub-channels form the channel signal library.

[0036] Preferably, step (2) of step 2-1 is specifically:

[0037] The difference between the left and right cut-off frequencies in the signal characteristic parameters of the thread tracking target and the left and right cut-off frequencies of the real-time signal is determined, and if the difference is less than or equal to a frequency threshold, the thread cut-off frequency matches the real-time signal frequency, and then target signal judgment is performed, otherwise, the real-time signal is filtered out; the target signal judgment is specifically that the difference between the dwell time in the signal characteristic parameters of the thread tracking target and the signal duration determined according to the start and end time of the real-time signal is determined, and if the difference is less than or equal to a time threshold, the real-time signal is considered as a target signal, and then thread matching judgment is performed, otherwise the real-time signal is filtered out; the matching judgment is specifically that the difference between the next signal start time of the thread table and the end time of the real-time signal is determined, and if the difference is less than or equal to a time threshold, the thread is considered to match the target, and step 2-2 is entered, otherwise the synchronization thread information table is updated, the signal characteristic parameters of the thread tracking target are kept unchanged, the thread prediction value of the next signal start time is set as the sum of the signal start time and the period of the frequency hopping pattern, the capture times and the loss tracking times are both set as 0, and step (2) is returned.

[0038] Optionally, step (2) can also combine the judgment conditions of thread matching with the target.

[0039] The frequency hopping signal presents a periodic appearance rule in each subchannel, and the period is the frequency hopping sequence period t FHP Therefore, tracking and jamming each subchannel periodic signal can realize tracking and jamming the whole single unmanned aerial vehicle frequency hopping link. Based on this feature, the tracking and jamming technology proposed in the application can convert the problem of tracking and jamming multiple unmanned aerial vehicles into the problem of tracking and jamming each type of periodic signal in different subchannels, so that the method of the application can be applied to the multiple unmanned aerial vehicle countermeasure scene. After the subchannels are divided, the synchronization of the signals is performed in each channel and thread, thereby reducing the time required for synchronization.

[0040] The application has the advantages that it is suitable for multiple target unmanned aerial vehicle scenes, combines the jump period and jump time with the kalman filter model, can track the jump time of the frequency hopping signal under the condition that the time-frequency domain distribution of the frequency hopping signal is complex and the accurate jump period parameter is unknown, accurately interferes with the communication link of the multiple unmanned aerial vehicles, has short interference response time and high interference efficiency, and thus can better complete the multiple unmanned aerial vehicle countermeasure task. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 System implementation block diagram

[0042] Figure 2 Frequency hopping tracking effect diagram

[0043] Figure 3 Real-time debugging results of the FPGA-based interference tracking platform;

[0044] Figure 4 A diagram illustrating the effect of a tracking and interference test using two drones. Detailed Implementation

[0045] (1) Construct a joint Kalman filter model based on period jump and time jump.

[0046] In practical drone countermeasure scenarios, the detection of drone frequency hopping parameters by forward detection equipment is prone to errors. Directly using these parameters for frequency hopping synchronization will reduce synchronization accuracy and stability. Therefore, this embodiment establishes a joint Kalman filter model based on the hopping cycle and synchronization time to achieve stable and accurate tracking of signal synchronization time.

[0047] In frequency hopping communication, the time t when the current hop signal occurs... k From the time t when the jump signal occurs k-1 and the skip period U k The decision. The process of tracking the moment the frequency-hopping signal appears can be viewed as finding t. k The optimal estimate The process of establishing the time t when the jump signal occurs. k Kalman filter equation:

[0048]

[0049] Where, ω k Let ω be the system noise at the time of occurrence. k It follows a pattern with a mean of zero and a variance of Q. t The normal distribution of v k The measurement noise occurs at the time of occurrence, and v k It follows a pattern with a mean of zero and a variance of R. t The distribution follows a normal pattern. However, in real-world scenarios, there are measurement errors in the observation of skipped cycles, meaning the observed values ​​of skipped cycles may differ. It can be represented as:

[0050]

[0051] Where γ k For the measurement noise of the skipped cycle, and γ k Follows a pattern with a mean of 0 and a variance of R. unormal distribution. If the sampling values of several periods are directly used as the period estimation results, the estimation results have deviation compared with the ideal results due to the limitation of sampling times. Moreover, the skip period itself has tiny changes due to the influence of clock jitter, console movement and other factors, i.e. the skip period also has process noise λ k i.e.

[0052] U k = U k-1 + λ k (3)

[0053] Therefore, the fixed period estimation value is used for synchronization, and after time accumulation, the synchronization effect of the skip signal occurrence time will gradually become poor. Therefore, kalman filtering can be considered for the skip period and the signal occurrence time to obtain the optimal skip period estimation value and the synchronization time estimation value Combining the above analysis, the joint state equation of the skip period and the synchronization time is established as follows:

[0054]

[0055] wherein the measurement values of the synchronization time and the skip period satisfy Combining the state equation, the measurement noise relationship of the skip period and the synchronization time is obtained as follows:

[0056] γ k = ω k + v k - v k-1 (5)

[0057] According to the state equation, the prior prediction value of the skip period and the synchronization time is as follows:

[0058]

[0059] The posterior estimation value of the skip period and the synchronization time is as follows:

[0060]

[0061] In order to stabilize the synchronization clock, the optimal gains M k and K k should be selected to make the mean square error of the skip period and the synchronization time minimum, i.e. are minimum respectively, E represents expectation, and the gains M k and K k corresponding to the skip period and the synchronization time are obtained under the condition that the kalman gains of the skip period and the synchronization time are as follows:

[0062]

[0063] Finally, the Kalman filtering relationship between the skip period U and the synchronization time t is obtained through the recursive relationship of the state equation. Period U k The prediction equation is:

[0064]

[0065] Period U k The update equation is:

[0066]

[0067] Secondly, synchronization time t k The prediction equation is:

[0068]

[0069] in The variance of the prior estimate at the synchronization time, i.e. E represents the expectation.

[0070] Synchronization time t k The update is as follows:

[0071]

[0072] (2) Implement tracking interference based on the constructed model.

[0073] like Figure 1 As shown, it includes (1) the sub-channel establishment process, (2) the multi-UAV frequency hopping signal synchronization process based on the sub-channel, and (3) the multi-UAV frequency hopping signal interference process.

[0074] The input to the entire system is the frequency hopping pattern set Φ = {φ} of the I pre-detected UAV communication links. i The frequency hopping pattern set is obtained by the front-end reconnaissance equipment, where the i-th type of frequency hopping pattern φ is defined as follows: (i = 1 to I) ... i ={R ij The characteristic parameters R of the J signals within the complete frequency hopping sequence period of the frequency hopping sequence are contained in the range j = 1 to J. ij =[f lij f rij , t holdij , t FHPij ] T Where the superscript T indicates transpose, f lij f rij These are the left and right cutoff frequencies of the j-th signal in the i-th frequency hopping pattern, respectively, t holdij t represents the duration of each hop of the j-th signal in the i-th frequency hopping pattern, i.e., the dwell time.FHPij The period of the frequency hopping pattern of the ith frequency hopping pattern of the jth signal. The time-frequency parameters are also obtained by the front-end reconnaissance device, and the expression of the input time-frequency parameter vector is v in = [t s , t e , f l , f r ] T , where t s , t e are the start and end times of the signal, and f l , f r are the left and right cutoff frequencies of the signal. The pre-detected signal frequency hopping pattern set is used to complete the process of sub-channel division and channel signal library establishment. The real-time detected time-frequency parameters are used as the real-time synchronization process of the frequency hopping signal.

[0075] s1. Sub-channel establishment

[0076] The sub-channel establishment process is to divide the sub-channels according to the frequency of the signal, and to establish the signal library corresponding to the sub-channels according to the possible signals in each frequency band, for subsequent frequency hopping synchronization.

[0077] 1) Create a sub-channel according to each frequency hopping signal point

[0078] Create a sub-channel C using the characteristic parameter R ij of each signal in the frequency hopping pattern set, and set the cutoff frequencies f lp , f rp of the sub-channel C as the cutoff frequencies [f lp , f rp ] T = [f lij , f rij ] T of the corresponding subset, initialize the channel signal table R p = {R p1 = R ij}, the number of channel signals n p = 1, and R p1 represents the characteristic parameter vector of the first signal in the pth channel;

[0079] 2) Merge the sub-channels with partially overlapping frequencies, if two sub-channels C u and C v , the left cutoff frequencies of the two sub-channels are f lCu , f lCv , the right cutoff frequencies are f rCu , f rCu , the channel signal tables are R u , R v , and the numbers of channel signals are n u , n v. meet f lCv ≤ f lCu ≤ f rCv or f lCv ≤ f rCu ≤ f rCv then the corresponding sub-channel is merged, and the start and end frequencies of the channel are reset to [f lp′ , f rp′ ] T = [min{f lu , f lv}, max{f ru , f rv}] T , and the channel signal table is updated to R' p = R u ∪ R v , the number of signals n p ' = n u + n v , and all channels are traversed until the end frequencies of any two channels do not overlap, then the sub-channel division is complete, and the target signal table is established.

[0080] s2. Multi-UAV frequency hopping signal synchronization based on sub-channels

[0081] The tracking jamming process includes synchronization of the synchronization time of each hop signal and transmission of the jamming waveform, where accurate synchronization of the synchronization time of multiple hops is the core link to complete tracking jamming.

[0082] The synchronization process is performed under each thread of the sub-channel, and each thread is used to synchronize a single target signal under the sub-channel. The synchronization is divided into capture and tracking stages, and the thread table L = [R L , t pre , s, n hunt , n ltrack ] T is set when the thread is created. R L represents the characteristic parameter vector of the target signal tracked by the thread, including the left cutoff frequency f lL , the right cutoff frequency f rL , the residence time t hL , and the frequency hopping pattern period t FHPL . If the target signal is located in the pth channel and the qth signal, then R L = R pq , and R p is the characteristic parameter vector of the qth signal in the pth channel tracked by the thread. t pre is the thread prediction value of the next signal start time, s is the current synchronization state, s = 0 indicates that the thread is in the signal capture state, s = 1 indicates that the thread is in the signal tracking state, n hunt is the capture number, and n ltrackFor the lost tracking times, the specific implementation steps for the periodic signal synchronization in each sub-channel C are as follows:

[0083] 1) Create a synchronization thread

[0084] Input real-time signal parameter vector v in =[t s , t e , f l , f r ] T , set frequency threshold f th and time threshold t th , if the thread cutoff frequency matches the signal frequency, that is, |[f lC , f rC ] T -|[f l , f r ]| T |≤|f th , f th |, then continue to match with the signal in the channel signal table, otherwise the signal is not in the channel, and the real-time signal is filtered out. In the signal and channel matching state, if the parameter vector R pq (k-1~n p ) in the signal table meets |[f l , f r , t e -t s ] T -[f lpq , f rpq , t hpq ] T ≤[f th , f th , t th ], then the input v in is the target signal, otherwise it is filtered out as an interference signal. If the thread L and the new target signal meet |[f lL , f rL , t hL , t pre ] T -[f l , f r , t e -t s , t s ] T |≤[f th , f th , t th , t th ] T , the thread matches the input target, otherwise a new synchronization thread is created, and the thread information table is initialized as L=R L , ts +t FHPL , 0, 0, 0] T .

[0085] 2) Capture phase of synchronous thread

[0086] The coarse synchronization of the signal is executed in the capture state, and it is confirmed whether the signal meets the periodic rule. If the thread matches the real-time input parameter vector v in in the capture state, the thread information L = [R L , t s +t FHPL , 0, n hunt +1, 0] is updated. T If t pre -t > t th , t is the current time, which indicates that the target signal does not appear in the prediction time range of the thread, the capture fails, and the thread is destroyed; if n hunt ≥ 3, it is determined that the signal capture is completed, the tracking phase is entered, and the thread information table L = [R L , t s +t FHPL , 1, 0, 0] is updated. T .

[0087] 3) Tracking phase of synchronous thread

[0088] The accurate synchronization of the signal starting time is executed in the tracking phase. The invention proposes a joint estimation method of the period and the synchronization time based on kalman filtering, which completes the accurate synchronization of the signal under the condition that the measurement error of the signal appearance period and the synchronization time is large.

[0089] Based on the joint kalman filtering model of the period and the synchronization time, if the thread matches the real-time input parameter vector v in , the input synchronization time measurement value is input, the signal period measurement value is First, the kalman filtering equation of the iterative period estimation is used according to formula (9) and formula (10), and then the kalman filtering equation of the iterative synchronization time estimation is used according to formula (11) and formula (12). When the kth tracking is completed, the prediction value of the k+1th tracking is given by the prior estimation value of the joint kalman filtering But the U k+1 needs to obtain the next measurement value The estimation value of the kth period is used as an approximation . Therefore, the thread prediction value is updated as The kth thread prediction value t pre judges whether the k+1th tracking is successful, and the thread information table is updated when the synchronous thread completes a signal tracking or fails. The tracking time threshold tloc If the thread is performing the k-th tracing, and the current time t exceeds the predicted value t... pre The error range is tt pre >t loc If no new signal matches the current thread, the k-th tracking attempt is considered a failure, as the measurement value at the start time cannot be obtained due to the tracking failure. Therefore, the posterior estimate at the k-th initial time Unable to obtain, use the posterior estimate of the (k-1)th period. Replace the posterior estimate of the (k-1)th period Using thread-predicted value t pre The posterior estimation results of the replacement final joint Kalman filter The predicted value of the update thread at this time is... When tracing fails, n ltrack =n ltrack +1, if n ltrack >3 threads return to the capture phase. When within the error range (tt) pre ≤t loc If a signal matches the current thread, the k-th tracking attempt is considered successful, and the tracking prediction value is given by the prior estimate from the joint Kalman filter. n ltrack =0, clears the number of missed steps.

[0090] s3. Frequency hopping signal interference from multiple drones

[0091] The interference phase is based on clock synchronization and performs interference on the UAV link. When the system is in the tracking phase, the thread-based interference mode is activated.

[0092] When the thread is in a tracing state and an interference command is issued, set the interference delay time t. delay , t delay >t loc When the thread completes the k-th signal tracking, the posterior estimate of the starting time obtained using equation (12) is used. Set the interference time period to Among them, t ad To allow for interference time so that the interference ends later than the signal ends, t hL This refers to the dwell time of the target signal for the thread. When the thread fails to track the signal on the kth attempt but has not exceeded the maximum number of failed attempts, the posterior estimate of the start time on the kth attempt cannot be calculated. Then, the prior prediction value t of the thread is used. pre Set the interference time period to t∈[t] pre +t delay , t pre +t hL +tdelay +t ad ]。

[0093] The interference waveform can be selected from a tone interference, a co-modulation signal interference, a frequency sweep interference and the like, and can be adjusted according to a real-time interference effect.

[0094] After determining the interference time and the interference waveform for each thread, each thread generates a baseband interference data stream J bbL When not in the interference time, J bbL = 0. The interference signal modulation module receives the baseband data streams J bbL1 , J bbL2 … of each thread in real time, and synthesizes a final baseband interference signal J bb = ∑J bbL . The baseband interference signal is up-converted to a radio frequency and transmitted through an antenna to achieve a communication link suppression interference effect on multiple unmanned aerial vehicles in a target airspace.

[0095] Figure 2 For a frequency hopping tracking actual effect diagram, in the case that the hopping period is dithered and the hopping time is measured with Gaussian white noise, the frequency hopping rule of the unmanned aerial vehicle can be tracked stably and accurately. It is shown that the tracking method of the embodiment can effectively improve the accuracy and stability of synchronization.

[0096] The parallel processing of the frequency channel signal is realized by combining hardware devices such as FPGA, Figure 3 For the real-time debugging result of the tracking interference platform based on FPGA, the processing delay is greatly reduced by parallel processing, and the interference response time is reduced. In addition, like single unmanned aerial vehicle tracking interference, multi-unmanned aerial vehicle tracking interference only performs accurate interference on the frequency time range area where different frequency hopping signals are located, so that the interference efficiency is high. Finally, the tracking interference test effect of two unmanned aerial vehicles is shown in Figure 4 , a single-tone interference signal with frequency offset successfully tracks the frequency hopping signals of the two links, proving the effectiveness of the interference method proposed in the embodiment.

Claims

1. A multi-target UAV uplink tracking jamming method based on frequency hopping patterns, characterized in that, Including the following steps: s1. Sub-channel establishment Sub-channels are created using each signal characteristic parameter in each type of frequency hopping pattern in the frequency hopping pattern set. The signal characteristic parameters include the left and right cutoff frequencies, dwell time, and period of the frequency hopping pattern. The left and right cutoff frequencies of the sub-channels are set to the left and right cutoff frequencies of the corresponding signal characteristic parameters, and the channel signal table of the sub-channels is initialized. s2. Multi-UAV frequency hopping signal synchronization steps: The specific implementation steps for synchronizing the periodic signals within each sub-channel are as follows: 2-1 Creating Synchronous Threads (1) Set up a thread table for the synchronization thread, the thread table including the signal characteristic parameters of the thread tracking target, the thread prediction value at the next signal start time, the number of captures and the number of lost trackings; Input a real-time signal parameter vector, which includes the left and right cutoff frequencies of the real-time signal and the start and end times of the real-time signal; then set the frequency threshold, time threshold, and number threshold. (2) Determine whether the synchronization thread matches the real-time signal that is the target. If yes, proceed to step 2-2; otherwise, update the synchronization thread. 2-2 Capture Phase of Synchronized Threads Update the synchronization thread information table, keep the signal characteristic parameters of the thread tracking target unchanged, set the thread prediction value at the start time of the next signal to be the sum of the period of the signal start time and the frequency hopping pattern, set the number of lost tracking to 0, and increment the number of captures by 1; determine whether the difference between the thread prediction value at the start time of the next signal and the current time is greater than the time threshold. If so, consider the capture to have failed, destroy the thread, and return to step (1). Otherwise, further determine whether the number of captures is greater than or equal to the number threshold. If so, consider the capture to be completed, update the synchronization thread information table, keep the signal characteristic parameters of the thread tracking target and the thread prediction value at the start time of the next signal unchanged, set the number of captures and the number of lost tracking to 0. If not, return to step 2-2. 2-3 The tracing phase of synchronized threads (1) Set the tracking time threshold and set the synchronization time t for the k-th tracking. k Measured values t s At the start time of the signal, update the k-th jump period U. k Measured values (2) Calculate the jump period U k Prior prediction value for: Among them, E k Indicates the middle value. The mean square error of the k-th jump cycle The prior prediction value, Q u The variance of the measurement noise with skipped cycles follows a normal distribution; Update jump cycle U k posterior estimate for: Among them, M k To ensure that the mean square error of the k-th jump cycle is Minimum optimal gain, Q t Let R be the variance of the normal distribution that the system noise follows at the current moment. t Let be the variance of the normal distribution that the measurement noise at the current moment follows. Let be the mean square error of the k-th jump cycle; Calculate the synchronization time t k Prior prediction value for: Among them, Z k Indicates the middle value. Let be the variance of the prior estimate at the k-th synchronization time. Let K be the mean square error between the (k-1)th synchronization time and the kth jump cycle. k-1 To ensure the mean square error at the k-1 synchronization times The minimum optimal gain; Update the k-th synchronization time t k posterior estimate for: Among them, K k To ensure that the mean square error at the k-th synchronization time is The minimum optimal gain; (3) Update the thread prediction value t for the next signal start time in the synchronization thread information table of the k-th tracking. pre Let t be the time of the kth synchronization. k posterior estimate With jump period U k posterior estimate The sum; determine the sum of the current time and t. pre If the difference is less than or equal to the tracking time threshold, then the k-th tracking is considered successful. The tracking count k is then incremented by 1, and the result is updated. Update the number of lost tracking attempts to 0 and proceed to step 3-1; otherwise, consider the k-th tracking attempt to have failed and update... Increment the number of lost tracking attempts by 1, and determine whether the number of lost tracking attempts is greater than the threshold. If yes, return to step 2-2; otherwise, proceed to step 3-2. S3. Steps for interfering with frequency-hopping signals from multiple drones: 3-1 Set the interference delay time t delay And according to the k-th synchronization time t k posterior estimate Set the interference time period to Among them, t ad To allow for interference time, t hL To determine the dwell time in the signal characteristic parameters of the thread tracking target, proceed to step 3-3; 3-2 Set the interference delay time t delay And based on the thread prediction value t at the start time of the next signal in the synchronization thread information table of the kth tracking, pre Set the interference time period to [t] pre +t delay ,t pre +t hL +t delay +t ad Proceed to step 3-3; 3-3 Perform interference according to the set interference time period and interference waveform.

2. The method as described in claim 1, characterized in that, s1. After initializing the channel signal table for the sub-channel, the following steps are performed: Sub-channels with overlapping frequencies are merged. The left and right cutoff frequencies of the merged sub-channel are reset to the minimum left cutoff frequency and the maximum right cutoff frequency of the two overlapping sub-channels. The channel signal table of the merged sub-channel is the union of the channel signal tables of the two overlapping sub-channels. All sub-channels are traversed until the cutoff frequencies of any two sub-channels no longer overlap. At this point, the sub-channel division is complete, and the channel signal tables of all sub-channels form the channel signal library.

3. The method as described in claim 1, characterized in that, Step 2-1, step (2) specifically refers to: Determine whether the difference between the left and right cutoff frequencies in the signal characteristic parameters of the thread tracking target and the left and right cutoff frequencies of the real-time signal is less than or equal to the frequency threshold. If so, the thread cutoff frequency matches the real-time signal frequency, and then the target signal is judged. Otherwise, the real-time signal is filtered out. Specifically, the target signal judgment is to determine whether the difference between the dwell time in the signal characteristic parameters of the thread tracking target and the signal duration determined according to the start and end times of the real-time signal is less than or equal to the time threshold. If so, the real-time signal is considered to be the target signal, and then the thread matching judgment is performed. Otherwise, the real-time signal is filtered out. Specifically, the matching judgment is to determine whether the difference between the start time of the next signal in the thread table and the end time of the real-time signal is less than or equal to the time threshold. If so, the thread is considered to match the target, and step 2-2 is entered. Otherwise, the synchronization thread information table is updated, the signal characteristic parameters of the thread tracking target remain unchanged, the thread prediction value of the start time of the next signal is set to the sum of the signal start time and the period of the frequency hopping pattern, the number of captures and the number of lost trackings are both set to 0, and the process returns to step (2).

4. The method as described in claim 1, characterized in that, The interference waveforms include tone interference, co-modulation signal interference, and frequency sweep interference.

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