Transmission path construction method, interference analysis method and interference elimination method based on UAV synaesthesia integration

Abstract

The present invention discloses a transmission path construction method, an interference analysis method and an interference elimination method based on the integration of unmanned aerial vehicle interawareness, step 1, establishing an unmanned aerial vehicle interawareness integration scene and a multi-input multi-output communication channel model, which is composed of two unmanned aerial vehicles, a base station, multiple users and a mobile target; step 2, establishing a communication channel model between the unmanned aerial vehicle, the base station and the respective user receiving terminals; step 3, establishing an unmanned aerial vehicle perception channel model. The present invention defines the mathematical formulas of each communication signal and perception signal in the unmanned aerial vehicle interawareness integration scene, clarifies the role of the unmanned aerial vehicle, the base station and the user, and then designs the transmission path of the communication signal and the perception signal. Finally, different interferences are analyzed under this system and path, and two interference elimination methods are provided, and the interference between each user, between unmanned aerial vehicles and between users and unmanned aerial vehicles is represented by a heat map, and the interference elimination method is selected according to the different interference degrees of the heat map.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a path construction method, an interference analysis method and an interference elimination method based on unmanned aerial vehicle synaesthesia integration. Background Art

[0002] With the deep integration of information technology, mobile communication technology, artificial intelligence technology and big data technology, the emergence of emerging mobile Internet services such as smart cities, smart medical care, intelligent transportation, smart production, and extended reality, as well as the rapid growth of wireless devices, have prompted the evolution of the fifth generation of mobile communication systems to the sixth generation of mobile communications systems (6G). Integrated sensing and communication (ISAC) will be one of the hallmarks of 6G. The ISAC system refers to the perception of the direction, distance, speed and other information of the target object through spectrum sharing, hardware sharing, signal sharing and other means in the same system while transmitting information, or detecting, tracking, identifying, imaging and other information of the target object, event or environment. Unmanned aerial vehicle (UAV) communication is a typical application of 6G, with the advantages of flexible deployment and wide-area coverage, and has received widespread attention from academia and industry in recent years. Compared with traditional ground base stations, drones have the advantages of high mobility, strong line of sight, and easy deployment, which can increase capacity and supplement coverage for hot spots and emergency scenarios. The drone is combined with the integrated communication and perception technology to improve the drone's environmental perception ability. Based on the perceived environmental information, the drone provides more accurate coverage for ground users and realizes more intelligent wireless networking. ISAC technology faces complex and changeable interference problems, such as clutter introduced in harsh environments, interference between multiple users caused by multi-base station and multi-cell communication, and self-interference caused by transmitter signal leakage. In addition, in ISAC, communication and perception will also cause interference problems during the transmission process. In response to these problems, the present invention proposes a path construction method, an interference analysis method and an interference elimination method based on drone interaception integration. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the shortcomings of the existing technology, the present invention provides a transmission path construction method, an interference analysis method and an interference elimination method based on the integration of UAV synaesthesia, which solves the problems that the existing communication channel model cannot accurately or quickly reproduce dynamic and complex signal propagation over a very wide bandwidth, and the existing technology faces complex and changeable interference.

[0005] (II) Technical solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: The purpose of the present invention is to provide a transmission path construction method, an interference analysis method and an interference elimination method based on UAV interawareness integration, for a UAV interawareness integration system including two UAVs, multiple users and mobile targets. According to the transmission process of communication and perception signals in this scenario and combined with different channel models, mathematical modeling of the interference generated is performed, and then interference elimination technology is used to suppress interference and improve system performance.

[0007] The technical solution of the present invention is: a method for constructing a transmission path based on the integration of UAV synaesthesia, and the specific implementation steps include the following:

[0008] Step 1: Establish a UAV interawareness integration scenario and a multi-input multi-output communication channel model, which consists of two UAVs, a base station, multiple users, and a mobile target;

[0009] Step 2: Establish the communication channel model between the drone, the base station and the respective user receiving end;

[0010] Step 3: Establish the UAV perception channel model.

[0011] Preferably, in step 1, the input and output relationship of the multi-input multi-output communication channel model is:

[0012]

[0013] Wherein, y represents all communication signals received by the receiving end, H represents the gain of each channel, and n represents Gaussian white noise;

[0014]

[0015] in, represents the channel gain from the first transmitting antenna to the first receiving antenna, represents the channel gain from the second transmitting antenna to the first receiving antenna, represents the channel gain from the first transmitting antenna to the second receiving antenna, represents the channel gain from the second transmitting antenna to the second receiving antenna, so the above multiple-input multiple-output communication channel model is expressed as:

[0016]

[0017] in, Indicates the communication signal received by the first receiving end. Indicates the communication signal received by the second receiving end;

[0018] represents the communication signal transmitted by the first transmitting antenna, represents the communication signal transmitted by the second transmitting antenna, represents the Gaussian white noise at the first receiving end, represents the Gaussian white noise at the second receiving end.

[0019] Preferably, in step 2, there are line-of-sight links and non-line-of-sight links between the transmitter and the user receiving end. In the case of the line-of-sight link, the signal is directly transmitted from the transmitter to the receiving end without reflection, diffraction or penetration of any obstacle. The path loss under the line-of-sight link is modeled as:

[0020]

[0021] Where m represents the user, t represents the transmission time, represents the path loss of the line-of-sight link, represents the path loss factor, represents the free space distance, represents the carrier frequency, c represents the speed of light, Represents the shadow fading loss of the line-of-sight link;

[0022] In the case of a non-line-of-sight link, when there are obstacles between the transmitter and the receiver, the signal will experience reflection, diffraction and penetration loss. The path loss in the non-line-of-sight link is modeled as:

[0023]

[0024] in, represents the path loss of the non-line-of-sight link, represents the path loss factor, Represents the shadow fading loss of the non-line-of-sight link;

[0025] Combining the two cases of line-of-sight link and non-line-of-sight link, the average path loss between the base station and its corresponding user receiving end is modeled as:

[0026]

[0027] in, represents the average path loss model, represents the probability of line-of-sight link transmission between the base station and its corresponding user within time t, represents the probability of non-line-of-sight link transmission between the base station and its corresponding user within time t, and then The line-of-sight link transmission probability is modeled as:

[0028]

[0029] in, represents the distance between the base station and its corresponding user in time t;

[0030] The communication channel link between the base station and the user is:

[0031]

[0032] in, represents the communication link between the base station and its corresponding user receiving end during time t, represents the path loss coefficient, represents the average path loss model;

[0033] The average path loss between the drone and its corresponding user receiver is modeled as:

[0034]

[0035] in, represents the average path loss model, represents the probability of line-of-sight link transmission between the UAV and its corresponding user within time t, represents the probability of non-line-of-sight link transmission between the UAV and its corresponding user within time t, and then The line-of-sight link transmission probability is modeled as:

[0036]

[0037] in, represents the angle between the drone and its corresponding user, , represents a constant related to the surrounding environment, and then the angle between the drone and the user To model:

[0038]

[0039] Where H represents the assumed fixed altitude of the drone, represents the distance between the UAV and its corresponding user receiving end in time t;

[0040] The communication channel link between the drone and its corresponding user is:

[0041]

[0042] in, represents the communication link between the drone and its corresponding user within time t, represents the path loss coefficient, Represents the average path loss model.

[0043] Preferably, in step 3, the perception link between the UAV and the mobile target is modeled as:

[0044]

[0045] in, represents the perception link between the UAV and the mobile target within time t, represents the distance between the UAV and the moving target within time t, and then To model:

[0046]

[0047] in, represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the radar signal. Indicates the radar cross section of the target.

[0048] Preferably, an interference analysis method based on the integration of UAV synaesthesia is provided, in which the UAV transmitting end TX1 and the base station transmitting end TX2 correspond to the user receiving end RX1 and the user receiving end RX2 respectively. Because the UAV is synaesthetically integrated, the transmitting end TX1 also sends a perception signal, and the corresponding receiving end is the UAV receiving end RX3. TX1 transmits a useful communication signal to reach the target receiving end RX1, and TX2 transmits a useful communication signal to reach the target receiving end RX2. In this process, there will be a multipath effect, which is also called small-scale fading. It refers to the reflection, refraction or diffraction of electromagnetic waves during propagation due to obstacles such as large buildings and mountains, causing the signal to arrive at the receiving end along multiple paths. The difference in length of these different paths causes the signal to arrive at the receiving end at different times, and the phase will also be different. Due to the multiple-input and multiple-output communication channel model, each receiving end will receive the communication signal transmitted by each transmitting antenna, so At the user receiving end RX1, not only can it receive the useful communication signal emitted by the corresponding TX1, but also the communication signal emitted by TX2. In addition, because of the same frequency, it can also receive the echo signal reflected by the mobile target. The communication signal and the reflected signal emitted by TX2 are interference signals for the user receiving end RX1. Similarly, in addition to receiving the useful communication signal emitted by the corresponding TX2, the user receiving end RX2 also receives the communication signal emitted by TX1 and the echo signal reflected by the mobile target. These signals are also interference signals for RX2. While receiving the echo signal, the drone RX3 also receives the communication signals emitted by TX1 and TX2. These signals are also interference signals for the drone RX3. All of the above are interference signals for each receiving end, but the communication signal, perception signal and reflection signal will also generate interference in their respective transmission paths, that is, self-interference between communication and perception, and mutual interference between communication and echo.

[0049] Assume that TX1 sends useful communication signals:

[0050]

[0051] The received signal is superimposed at the receiving end after passing through multiple paths and the direct component of the signal is added. The useful communication signal received by RX1 is expressed as:

[0052]

[0053] in, It represents the time-varying attenuation of the amplitude of signals on different paths, determined by path loss and shadow fading; represents the signal path transmission delay of different paths, Represents the Doppler phase shift of different paths; Doppler phase shift refers to the change in signal frequency caused by the movement of the medium or carrier; this change is caused by the Doppler effect, which refers to the change in signal frequency when there is relative motion between the signal source and the receiver; compared with the original signal, the phase change of the nth multipath signal is:

[0054]

[0055] Substituting the above formula into ,get:

[0056]

[0057] Assume that TX2 transmits useful communication signal:

[0058]

[0059] Among them, A represents the amplitude of the useful signal, Indicates the frequency of the useful signal, so the communication signal sent from TX2 to the receiving end of RX2 is:

[0060]

[0061] in, represents the attenuation on the i-th path, represents the delay on the i-th path;

[0062] Secondly, the UAV radar is a pulse Doppler radar system. When the radar transmits a fixed-frequency pulse wave to scan the ground, if it encounters a stationary target, the frequency of the echo is consistent with the frequency of the transmitted wave. If it encounters a moving target, there is a frequency difference between the frequency of the echo and the frequency of the transmitted wave, which is called the Doppler frequency. According to the size of the Doppler frequency, the radial relative motion speed of the target to the radar is measured; according to the time difference between the transmitted pulse and the received pulse, the distance of the target is measured. Assuming that the radar transmits a linear frequency modulation perception signal:

[0063]

[0064] Where t represents time, represents the perceived signal amplitude, represents the width of the rectangular signal, K represents the linear frequency modulation coefficient, represents a rectangular signal,

[0065]

[0066] The target echo signal generated by the radar after encountering a moving target is:

[0067]

[0068] in, Indicates the fluctuation of the target signal. Indicates the distance between the moving target and the radar. represents the Doppler shift,

[0069] All signals received by the RX1 and RX2 receivers and the drone receiver RX3 are:

[0070]

[0071] For RX1, is its interference signal. Similarly, for RX2, It is its interference signal. For RX3, It is its interference signal.

[0072] Preferably, the signal to interference and noise ratio refers to the ratio of the power of the useful signal received in the communication system to the total power of the interference signal and noise received, and is an important indicator for measuring the performance of the communication system. The basic expression of the signal to interference and noise ratio is:

[0073]

[0074] in, Indicates the received useful signal power, represents the received interference power, represents thermal noise;

[0075] Thermal noise is white noise, and its noise power at the receiving end is expressed as:

[0076]

[0077] in, represents the Boltzmann constant, is the temperature of the noise source, represents the noise equivalent of the filter, Indicates the peak gain or loss of the filter;

[0078] For RX1, the useful signal power is the sum of the signal powers of the communication signal sent by TX1 and reaching the receiving end through different paths, that is:

[0079]

[0080] in, It represents the communication signal power from the transmitter TX1 to the receiver RX1 in the line-of-sight path. Indicates the communication signal power from the TX1 transmitter to the RX1 receiver in non-line-of-sight. Modeled as:

[0081]

[0082] in, Indicates the TX1 communication signal transmitting antenna power, represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the communication signal transmitted by TX1. Indicates the distance between the TX1 transmitter and the RX1 receiver. represents the loss not related to propagation, Modeled as:

[0083]

[0084] in, represents the path loss coefficient, Indicates the other path distance of the signal from the TX1 transmitter to the RX1 receiver;

[0085] Similarly, for RX2, the useful communication signal power is:

[0086]

[0087] in, It represents the communication signal power from the TX2 transmitter to the RX2 receiver in the line-of-sight path. Indicates the communication signal power from the TX2 transmitter to the RX2 receiver in non-line-of-sight. Modeled as:

[0088]

[0089] in, Indicates the TX2 communication signal transmitting antenna power, represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the communication signal transmitted by TX2. Indicates the distance between the TX2 transmitter and the RX2 receiver. represents the loss not related to propagation, Modeled as:

[0090]

[0091] in, represents the path loss coefficient, Indicates the other path distance of the signal from the TX2 transmitter to the RX2 receiver;

[0092] The power of the radar signal is expressed as:

[0093]

[0094] in, Expressed as radar signal power, represents the gain of the radar transmitting antenna, represents the gain of the target radar receiving antenna, represents the wavelength of the radar, represents the cross-sectional area of ​​the radar, represents the radar's transmit power, Indicates the distance between the UAV and the moving target. The reflected power of the radar signal after encountering the moving target is:

[0095]

[0096] in, is the reflected power, represents the reflection coefficient, Indicates return loss;

[0097] The self-interference generated by the communication signal and perception signal sent by TX1 during the transmission process:

[0098]

[0099] in, Represents communication signals and sensory signals The interference power;

[0100]

[0101] in, Represents communication signals and sensory signals The interference power;

[0102] Reflected signal and communication signals Mutual interference during transmission:

[0103]

[0104] Reflected signal and communication signals Mutual interference during transmission:

[0105]

[0106] So the signal-to-interference-to-noise ratio at RX1 is:

[0107]

[0108] in, represents the interference signal power at RX1, Represents communication signals and sensory signals The self-interference power of RX2 is:

[0109]

[0110] in, represents the interference signal power at RX2, Represents communication signals and sensory signals The self-interference power at the receiving end of the drone RX3 is:

[0111]

[0112] in, represents the interference signal power at the drone end, represents the mutual interference power between the reflected signal and the two communication signals, and the total signal-to-interference-to-noise ratio of the UAV interawareness integrated system is obtained as follows:

[0113] .

[0114] Preferably, an interference elimination method based on the integration of UAV interawareness includes hardware interference elimination and algorithmic interference elimination, wherein the hardware interference elimination integrates antenna self-interference elimination and digital self-interference elimination technology, separates the transmitting and receiving antennas, and then eliminates residual interference by digital filtering;

[0115] Preferably, the algorithm interference elimination technology adopts an improved variable step size minimum mean square error (LMS) adaptive filtering algorithm. Adaptive filtering is a signal processing method. Its main purpose is to improve the effect of signal processing by dynamically adjusting filter parameters according to the characteristics of the signal. In the communication system, the adaptive filter is used to suppress channel noise and improve the transmission quality of the signal. In the radar system, the adaptive filter is used to suppress ground clutter and interference and improve the performance of target detection. The algorithm flow is as follows: starting with initialization w(n), reading the input signal x(n) and the expected signal d(n), calculating the output signal y(n) of the filter, calculating the error e(n) between the output signal y(n) and the expected signal d(n), and then updating the weight coefficient vector w(n), and at the same time, it is used to update the step size factor of the weight coefficient vector.

[0116] (III) Beneficial effects

[0117] The present invention provides a transmission path construction method, an interference analysis method and an interference elimination method based on the integration of unmanned aerial vehicle synaesthesia. It has the following beneficial effects:

[0118] (1) The present invention defines the mathematical formulas of each communication signal and perception signal in the UAV interawareness integration scenario, clarifies the roles of the UAV, base station and user, and then designs the transmission path of the communication signal and perception signal. Finally, different interferences are analyzed under this system and path.

[0119] (2) Existing communication channel models cannot accurately or quickly reproduce dynamic and complex signal propagation over very wide bandwidths. The advantage of using a hybrid channel model in the present invention is that it can well balance modeling accuracy and computational efficiency while taking into account both communication and perception functions.

[0120] (3) The present invention uses an interference heat map to represent the interference between users and drones. The intensity of the interference between them can be intuitively seen from the heat map. In addition, the present invention also provides two interference elimination methods, and selects appropriate interference elimination technology according to the intensity of interference in different areas. The hardware interference elimination method combines multiple technologies and is more effective than a single technology, so it is suitable for areas with strong interference. The improved LMS adaptive filtering interference elimination algorithm has better convergence speed and error than the LMS algorithm because the step size is time-varying, but it still inherits the low complexity of LMS and has a small amount of calculation, so it is suitable for areas with weak interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1 It is a schematic diagram of the system scenario of the present invention;

[0122] Figure 2 It is a schematic diagram of interference analysis of the present invention;

[0123] Figure 3 A schematic diagram of the distribution of drones, users and targets of the present invention;

[0124] Figure 4 A heat map of communication interference intensity between users of the present invention;

[0125] Figure 5 is a heat map of perceived interference intensity between drones of the present invention;

[0126] Figure 6 A heat map of the communication and perceived interference intensity between the user and the drone of the present invention;

[0127] Figure 7 This is a heat map of the interference intensity between the reflected signal and the user communication after the drone perception signal of the present invention encounters the target. DETAILED DESCRIPTION

[0128] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0129] See also Figure 1-7 The embodiment of the present invention provides a technical solution: a transmission path construction method, an interference analysis method and an interference elimination method based on the integration of UAV synaesthesia.

[0130] Embodiment 1, step 1: establish the drone interawareness integration scenario and multiple input multiple output (MIMO) communication channel model of the present invention. The system consists of two drones, a base station, multiple users and a mobile target. The drone is an interawareness integrated drone that sends communication and perception signals, assuming that the communication and perception signals are of the same frequency. A drone needs to perform a target perception task in the system and communicate data with the user. This drone sends a perception signal and a communication signal. The perception signal generates an echo when it hits a mobile target. Another drone and a nearby user end receive the echo signal, and the communication signal is sent to each user receiving end. Data communication is also performed between drones. After receiving the echo signal, the other drone sends the target parameters to the base station and the original drone. The base station also sends a communication signal to each user end. Under this system, each user will receive the useful signal emitted by its target transmitting antenna and the interference signal emitted by other transmitting antennas. Thus, a MIMO multiple input multiple output communication channel model is established.

[0131] Both the drone and the base station are equipped with transmitting antennas to send useful communication signals to the corresponding user receiving end. In this system, the transmitted data stream is processed by the transmitter MIMO encoding, and then mapped to each transmitting antenna before being sent out. The transmitted communication signal reaches each receiving antenna after the superposition of different channel gains. Each receiving antenna will receive the communication signal sent from all transmitting antennas. The received signal contains both useful signals and interference signals. Then the receiving end of the multi-input multi-output communication channel model recovers the transmitted data stream through decoding, thereby completing the communication process of the entire multi-input multi-output communication channel model. The input and output relationship of the multi-input multi-output communication channel model is:

[0132] (1)

[0133] Wherein, y represents all communication signals received by the receiving end, H represents the gain of each channel, and n represents Gaussian white noise.

[0134] (2)

[0135] in, Indicates the channel gain from the first transmitting antenna to the first receiving antenna. Represents the channel gain from the second transmitting antenna to the first receiving antenna. Indicates the channel gain from the first transmitting antenna to the second receiving antenna. represents the channel gain from the second transmitting antenna to the second receiving antenna. Therefore, the above multi-input multi-output communication channel model is expressed as:

[0136] (3)

[0137] in, Indicates the communication signal received by the first receiving end. Indicates the communication signal received by the second receiving end.

[0138] Represents the communication signal transmitted by the first transmitting antenna. Represents the communication signal transmitted by the second transmitting antenna. represents the Gaussian white noise at the first receiving end. represents the Gaussian white noise at the second receiving end.

[0139] Step 2: Establish the communication channel model between the drone, the base station and the respective user receivers. There are line of sight (LOS) and non-line of sight (NLOS) links between the transmitter and the user receiver. In the case of LOS, the signal is transmitted directly from the transmitter to the receiver without reflection, diffraction or penetration of any obstacles. The path loss under LOS is modeled as:

[0140] (4)

[0141] Where m represents the user and t represents the transmission time. Indicates the path loss of the LOS link. represents the path loss factor. Represents the free space distance. represents the carrier frequency and c represents the speed of light. Represents the shadow fading loss of the LOS link.

[0142] In the case of NLOS, when there are obstacles between the transmitter and the receiver, the signal will experience reflection, diffraction and penetration loss. So the path loss under NLOS is modeled as:

[0143] (5)

[0144] in, Indicates the path loss of the NLOS link. represents the path loss factor. Represents the shadow fading loss of the NLOS link.

[0145] Therefore, combining the two cases of LOS and NLOS, the average path loss between the base station and its corresponding user receiving end is modeled as:

[0146] (6)

[0147] in, Represents the average path loss model. It represents the probability of LOS transmission between the base station and its corresponding user within time t. represents the probability of NLOS transmission between the base station and its corresponding user within time t. The LOS transmission probability is modeled as:

[0148] (7)

[0149] in, It represents the distance between the base station and its corresponding user in time t.

[0150] So the communication channel link between the base station and the user is:

[0151] (8)

[0152] in, Represents the communication link between the base station and its corresponding user receiving end during time t. represents the path loss coefficient. Represents the average path loss model.

[0153] The average path loss between the drone and its corresponding user receiver is modeled as:

[0154] (9)

[0155] in, Represents the average path loss model. It represents the probability of LOS transmission between the UAV and its corresponding user within time t. represents the probability of NLOS transmission between the drone and its corresponding user within time t. The LOS transmission probability is modeled as:

[0156] (10)

[0157] in, Represents the angle between the drone and its corresponding user. , represents a constant related to the surrounding environment. Then the angle between the drone and the user To model:

[0158] (11)

[0159] Where H represents the assumed fixed altitude of the drone. It represents the distance between the UAV and its corresponding user receiving end in time t.

[0160] Therefore, the communication channel link between the drone and its corresponding user is:

[0161] (12)

[0162] in, represents the communication link between the UAV and its corresponding user during time t. represents the path loss coefficient. Represents the average path loss model.

[0163] Step 3: Establish the UAV perception channel model. The perception link between the UAV and the mobile target is modeled as:

[0164] (13)

[0165] in, Represents the perception link between the UAV and the mobile target within time t. represents the distance between the UAV and the moving target within time t. To model:

[0166] (14)

[0167] in, represents the transmit antenna gain, Indicates the receive antenna gain. Indicates the wavelength of the radar signal. Indicates the radar cross section of the target.

[0168] Step 4: Analyze the interference in the UAV interawareness integration scenario. The UAV transmitter TX1 and the base station transmitter TX2 correspond to the user receiver RX1 and the user receiver RX2 respectively. Because the UAV is interaware, the transmitter TX1 also sends a perception signal, and the corresponding receiver is the UAV receiver RX3. TX1 transmits a useful communication signal to the target receiver RX1, and TX2 transmits a useful communication signal to the target receiver RX2. In this process, there will be multipath effects. Multipath effects are also called small-scale fading. It refers to the reflection, refraction or diffraction of electromagnetic waves during propagation due to obstacles such as large buildings and mountains, causing the signal to reach the receiver along multiple paths. The difference in the length of these different paths causes the signal to arrive at the receiver at different times and phases. Since each receiver in the multi-input multi-output communication channel model receives the communication signal transmitted by each transmitting antenna, the user receiver RX1 can not only receive the useful communication signal transmitted by the corresponding TX1, but also receive the communication signal transmitted by TX2. In addition, because of the same frequency, it can also receive the echo signal reflected by the moving target. The communication signal and reflected signal transmitted by TX2 are both interference signals for the user receiving end RX1. Similarly, in addition to receiving the useful communication signal transmitted by the corresponding TX2, the user receiving end RX2 also receives the communication signal emitted by TX1 and the echo signal reflected by the mobile target. These signals are also interference signals for RX2. While receiving the echo signal, the drone RX3 also receives the communication signals emitted by TX1 and TX2. These signals are also interference signals for the drone RX3. The above are all interference signals for each receiving end, but the communication signal, perception signal and reflected signal will also cause interference in their respective transmission paths, that is, self-interference between communication and perception, and mutual interference between communication and echo.

[0169] Assume that TX1 sends useful communication signals:

[0170] (15)

[0171] The received signal is superimposed at the receiving end after passing through multiple paths and the direct component of the signal is added. The useful communication signal received by RX1 is expressed as:

[0172] (16)

[0173] in, Represents the time-varying attenuation of the amplitude of signals along different paths, determined by path loss and shadow fading. Indicates the signal path transmission delay of different paths. Represents the Doppler phase shift of different paths. Doppler phase shift refers to the change in signal frequency caused by the movement of the medium or carrier. This change is caused by the Doppler effect, which refers to the change in signal frequency when there is relative motion between the signal source and the receiver. Compared with the original signal, the phase change of the nth multipath signal is:

[0174] (17)

[0175] Substituting the above formula into ,get:

[0176] (18)

[0177] Assume that TX2 transmits useful communication signal:

[0178] (19)

[0179] Where A represents the amplitude of the useful signal. Indicates the frequency of the useful signal. So the communication signal sent from TX2 to the receiving end RX2 is:

[0180] (20)

[0181] in, represents the attenuation on the i-th path. represents the delay on the i-th path.

[0182] Secondly, the drone radar is a pulse Doppler radar system. When the radar transmits a fixed-frequency pulse wave to scan the ground, if it encounters a stationary target, the frequency of the echo is consistent with the frequency of the transmitted wave. If it encounters a moving target, there is a frequency difference between the frequency of the echo and the frequency of the transmitted wave, which is called the Doppler frequency. According to the size of the Doppler frequency, the radial relative motion speed of the target to the radar is measured; according to the time difference between the transmitted pulse and the received pulse, the distance of the target is measured. Assume that the radar transmits a linear frequency modulation perception signal:

[0183] (twenty one)

[0184] Where t represents time, Represents the perceived signal amplitude. Represents the width of the rectangular signal. K represents the linear frequency modulation coefficient. Represents a rectangular signal.

[0185] (twenty two)

[0186] The target echo signal generated by the radar after encountering a moving target is:

[0187] (twenty three)

[0188] in, Indicates the fluctuation of the target signal. Indicates the distance between the moving target and the radar. Represents the Doppler shift frequency.

[0189] All signals received by the RX1 and RX2 receivers and the drone receiver RX3 are:

[0190] (twenty four)

[0191] But for RX1, is its interference signal. Similarly, for RX2, is its interference signal. For RX3, It is its interference signal.

[0192] Step 5: SINR (Signal to Interference plus Noise Ratio) refers to the ratio of the power of the useful signal received in the communication system to the total power of the interference signal and noise received. It is an important indicator to measure the performance of the communication system. The basic expression of SINR is:

[0193] (25)

[0194] in, Indicates the received useful signal power. Indicates the received interference power. represents thermal noise.

[0195] Thermal noise is white noise, and its noise power at the receiving end is expressed as:

[0196] (26)

[0197] in, represents the Boltzmann constant. Expressed as the temperature of the noise source. Represents the noise equivalent of the filter. Indicates the peak gain or loss of the filter.

[0198] For RX1, the useful signal power is the sum of the signal powers of the communication signal sent by TX1 and reaching the receiving end through different paths, that is:

[0199] (27)

[0200] in, Indicates the communication signal power in the line-of-sight path from the TX1 transmitter to the RX1 receiver. Indicates the communication signal power from the TX1 transmitter to the RX1 receiver in non-line-of-sight. Modeled as:

[0201] (28)

[0202] in, Indicates the TX1 communication signal transmitting antenna power. represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the communication signal transmitted by TX1. Indicates the distance between the TX1 transmitter and the RX1 receiver. represents the losses not related to propagation (antenna losses). Modeled as:

[0203] (29)

[0204] in, represents the path loss coefficient. Indicates the other path distance of the signal from the TX1 transmitter to the RX1 receiver.

[0205] Similarly, for RX2, the useful communication signal power is:

[0206] (30)

[0207] in, Indicates the communication signal power in the line-of-sight path from the TX2 transmitter to the RX2 receiver. Indicates the communication signal power from the TX2 transmitter to the RX2 receiver in non-line-of-sight conditions. Modeled as:

[0208] (31)

[0209] in, Indicates the TX2 communication signal transmitting antenna power. represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the communication signal transmitted by TX2. Indicates the distance between the TX2 transmitter and the RX2 receiver. represents the losses not related to propagation (antenna losses). Modeled as:

[0210] (32)

[0211] in, represents the path loss coefficient. Indicates the other path distance of the signal from the TX2 transmitter to the RX2 receiver.

[0212] The power of the radar signal is expressed as:

[0213] (33)

[0214] in, Expressed as radar signal power. Represents the gain of the radar transmitting antenna. Represents the gain of the target radar receiving antenna. Indicates the wavelength of the radar. Indicates the cross-sectional area of ​​the radar. Indicates the radar's transmit power. Indicates the distance between the drone and the moving target. The reflected power of the radar signal after encountering the moving target is:

[0215] (34)

[0216] in, Indicates the reflected power. Represents the reflection coefficient. Indicates return loss.

[0217] The self-interference generated by the communication signal and perception signal sent by TX1 during the transmission process:

[0218] (35)

[0219] in, Represents communication signals and sensory signals interference power.

[0220] (36)

[0221] in, Represents communication signals and sensory signals interference power.

[0222] Reflected signal and communication signals Mutual interference during transmission:

[0223] (37)

[0224] Reflected signal and communication signals Mutual interference during transmission:

[0225] (38)

[0226] So the signal-to-interference-to-noise ratio at RX1 is:

[0227] (39)

[0228] in, Indicates the interference signal power at RX1. Represents communication signals and sensory signals The self-interference power. Similarly, the signal-to-interference-to-noise ratio at RX2 is:

[0229] (40)

[0230] in, Indicates the interference signal power at RX2. Represents communication signals and sensory signals The signal-to-noise ratio at the receiving end of the drone RX3 is:

[0231] (41)

[0232] in, Indicates the interference signal power at the drone end. represents the mutual interference power between the reflected signal and the two communication signals. The total signal-to-interference-to-noise ratio of the UAV interawareness integrated system is obtained as follows:

[0233] (42)

[0234] Step 6: Based on the specific location distribution of users and drones in the actual drone interawareness integration, use a heat map to represent the various interferences between them. The closer the location, the stronger the interference, and vice versa. The color of the heat map changes from white to gray and then to black, indicating that the interference intensity changes from low to high. According to the different interference intensity, select the appropriate interference elimination technology. If the interference intensity is low, you can choose an interference elimination technology with small calculation amount and low complexity; if the interference intensity is high, you can choose an interference elimination technology with large calculation amount and high complexity.

[0235] Step 7: Find interference elimination technology to eliminate interference. Interference elimination with integrated interrogation includes hardware interference elimination and algorithmic interference elimination. If hardware interference elimination technology is used, it is recommended to integrate antenna self-interference elimination and digital self-interference elimination technology, separate the transmitting and receiving antennas by a certain distance, and then eliminate the residual interference by digital filtering. This interference elimination technology is suitable for interference of various strengths, especially for scenes with strong self-interference. Antenna self-interference elimination technology reduces direct self-interference signals by increasing the physical isolation between the transmitting and receiving antennas (for example, increasing the distance or adding shielding materials). This can weaken the mutual interference between antennas at the hardware level and reduce the strength of the interference signal from the source. Digital self-interference elimination technology uses a digital filtering algorithm to identify and extract residual interference signals at the receiving end, and weaken or eliminate interference through an algorithm. Digital processing is usually used to process residual interference that cannot be completely eliminated by hardware self-interference elimination. The advantage of the fusion of these two technologies is that physically isolating the antenna can significantly reduce most of the strong interference, and the remaining part is further removed by digital filtering. This combination of hardware and software can more effectively deal with complex interference signals in strong interference scenes.

[0236] If algorithmic interference elimination technology is used, it is recommended to use the improved variable step size minimum mean square error (LMS) adaptive filtering algorithm. Adaptive filtering is a signal processing method whose main purpose is to improve the effect of signal processing by dynamically adjusting the filter parameters according to the characteristics of the signal. In communication systems, adaptive filters can be used to suppress channel noise and improve the transmission quality of signals. In radar systems, adaptive filters can be used to suppress ground clutter and interference and improve the performance of target detection.

[0237] The basic principle of adaptive filtering: according to the difference between the input signal and the expected output signal, the weight of the filter is adjusted to minimize the difference between the output signal and the expected output signal. Through continuous iteration of this process, the adaptive filter will automatically adjust the weights to achieve the optimal filtering effect. The LMS algorithm is based on the minimum mean square error criterion to minimize the mean square error between the output signal of the filter and the expected output signal. However, the LMS algorithm has certain limitations in convergence speed and steady-state error, so the improved variable step size LMS algorithm is adopted, using a time-varying step size instead of a fixed step size.

[0238] Algorithm flow: Start with initialization w(n), read the input signal x(n) and the expected signal d(n), calculate the output signal y(n) of the filter, calculate the error e(n) between the output signal y(n) and the expected signal d(n), and then update the weight coefficient vector w(n), which is also used to update the step factor of the weight coefficient vector.

[0239] Compared with the standard LMS, it adds dynamic adjustment of the step size in each iteration, but the overall computational complexity is still low. The overall complexity of the algorithm is slightly higher than that of the traditional LMS, and the hardware implementation needs to consider the control logic of the step size more. However, since the calculation of the step size adjustment is relatively simple, the variable step size LMS maintains the low complexity characteristics of LMS and is suitable for application on devices with limited resources (such as drones).

[0240] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0241] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for constructing a transmission path based on the integration of UAV synaesthesia, characterized in that: Specific implementation steps include: the following: Step 1: Establish a UAV interawareness integration scenario and a multi-input multi-output communication channel model, which consists of two UAVs, a base station, multiple users, and a mobile target; Step 2: Establish the communication channel model between the drone, the base station and the respective user receiving end; Step 3: Establish UAV perception channel model; In step 1, the input and output relationship of the multi-input multi-output communication channel model is: ； Wherein, y represents all communication signals received by the receiving end, H represents the gain of each channel, and n represents Gaussian white noise; ； in, represents the channel gain from the first transmitting antenna to the first receiving antenna, represents the channel gain from the second transmitting antenna to the first receiving antenna, represents the channel gain from the first transmitting antenna to the second receiving antenna, represents the channel gain from the second transmitting antenna to the second receiving antenna, so the above multiple-input multiple-output communication channel model is expressed as: ； in, Indicates the communication signal received by the first receiving end. Indicates the communication signal received by the second receiving end; represents the communication signal transmitted by the first transmitting antenna, represents the communication signal transmitted by the second transmitting antenna, represents the Gaussian white noise at the first receiving end, represents the Gaussian white noise at the second receiving end; In step 2, there are line-of-sight links and non-line-of-sight links between the transmitter and the user receiving end. In the case of line-of-sight links, the signal is transmitted directly from the transmitter to the receiver without reflection, diffraction or penetration of any obstacles. The path loss under the line-of-sight link is modeled as: ； Where m represents the user, t represents the transmission time, represents the path loss of the line-of-sight link, represents the path loss factor, represents the free space distance, represents the carrier frequency, c represents the speed of light, Represents the shadow fading loss of the line-of-sight link; In the case of a non-line-of-sight link, when there are obstacles between the transmitter and the receiver, the signal will experience reflection, diffraction and penetration loss. The path loss in the non-line-of-sight link is modeled as: ； in, represents the path loss of the non-line-of-sight link, represents the path loss factor, Represents the shadow fading loss of the non-line-of-sight link; Combining the two cases of line-of-sight link and non-line-of-sight link, the average path loss between the base station and its corresponding user receiving end is modeled as: ； in, represents the average path loss model, represents the probability of line-of-sight link transmission between the base station and its corresponding user within time t, represents the probability of non-line-of-sight link transmission between the base station and its corresponding user within time t, and then The line-of-sight link transmission probability is modeled as: ； in, represents the distance between the base station and its corresponding user in time t; The communication channel link between the base station and the user is: ； in, represents the communication link between the base station and its corresponding user receiving end during time t, represents the path loss coefficient, represents the average path loss model; The average path loss between the drone and its corresponding user receiver is modeled as: ； in, represents the average path loss model, represents the probability of line-of-sight link transmission between the UAV and its corresponding user within time t, represents the probability of non-line-of-sight link transmission between the UAV and its corresponding user within time t, and then The line-of-sight link transmission probability is modeled as: ； in, represents the angle between the drone and its corresponding user, , represents a constant related to the surrounding environment, and then the angle between the drone and the user To model: ； Where H represents the assumed fixed altitude of the drone, represents the distance between the UAV and its corresponding user receiving end in time t; The communication channel link between the drone and its corresponding user is: ； in, represents the communication link between the drone and its corresponding user within time t, represents the path loss coefficient, represents the average path loss model; In step 3, the perception link between the UAV and the mobile target is modeled as: ； in, represents the perception link between the UAV and the mobile target within time t, represents the distance between the UAV and the moving target within time t, and then To model: ； in, represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the radar signal. Indicates the radar cross section of the target; The interference generated in the construction method of the transmission path is analyzed. The specific method includes that the UAV transmitting end TX1 and the base station transmitting end TX2 correspond to the user receiving end RX1 and the user receiving end RX2 respectively. The UAV is interaware and integrated. The transmitting end TX1 also sends a perception signal. The corresponding receiving end is the UAV receiving end RX3. Suppose the useful communication signal sent by TX1 is: ； The received signal is superimposed at the receiving end after passing through multiple paths and the direct component of the signal is added. The useful communication signal received by RX1 is expressed as: ； in, It represents the time-varying attenuation of the amplitude of signals on different paths, determined by path loss and shadow fading; represents the signal path transmission delay of different paths, Represents the Doppler phase shift of different paths; compared with the original signal, the phase change of the n-th multipath signal is: ； Substitution ,get: ； Assume that TX2 transmits useful communication signal: ； Among them, A represents the amplitude of the useful signal, Indicates the frequency of the useful signal. The communication signal sent from TX2 to the receiving end of RX2 is: ； in, represents the attenuation on the i-th path, represents the delay on the i-th path; Assume that the radar transmits a linear frequency modulation sensing signal: ； Where t represents time, represents the perceived signal amplitude, represents the width of the rectangular signal, K represents the linear frequency modulation coefficient, represents a rectangular signal, ； The target echo signal generated by the radar after encountering a moving target is: ； in, Indicates the fluctuation of the target signal. Indicates the distance between the moving target and the radar. represents the Doppler shift; All signals received by the RX1 and RX2 receivers and the drone receiver RX3 are: ； For RX1, is its interference signal. Similarly, for RX2, It is its interference signal. For RX3, It is its interference signal; It also includes that the signal-to-interference-and-noise ratio refers to the ratio of the power of the useful signal received in the communication system to the total power of the interference signal and noise received. It is an important indicator to measure the performance of the communication system. The basic expression of the signal-to-interference-and-noise ratio is: ； in, Indicates the received useful signal power, represents the received interference power, represents thermal noise; Thermal noise is white noise, and its noise power at the receiving end is expressed as: ； in, represents the Boltzmann constant, Expressed as the temperature of the noise source, represents the noise equivalent of the filter, Indicates the peak gain or loss of the filter; For RX1, the useful signal power is the sum of the signal powers of the communication signal sent by TX1 and reaching the receiving end through different paths, that is: ； in, It represents the communication signal power from the transmitter TX1 to the receiver RX1 in the line-of-sight path. Indicates the communication signal power from the TX1 transmitter to the RX1 receiver in non-line-of-sight. Modeled as: ； in, Indicates the TX1 communication signal transmitting antenna power, represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the communication signal transmitted by TX1. Indicates the distance between the TX1 transmitter and the RX1 receiver. represents the loss not related to propagation, Modeled as: ； in, represents the path loss coefficient, Indicates the other path distance of the signal from the TX1 transmitter to the RX1 receiver; Similarly, for RX2, the useful communication signal power is: ； in, It represents the communication signal power from the TX2 transmitter to the RX2 receiver in the line-of-sight path. Indicates the communication signal power from the TX2 transmitter to the RX2 receiver in non-line-of-sight. Modeled as: ； in, Indicates the TX2 communication signal transmitting antenna power, represents the transmit antenna gain, represents the receiving antenna gain, Indicates the wavelength of the communication signal transmitted by TX2. Indicates the distance between the TX2 transmitter and the RX2 receiver. represents the loss not related to propagation, Modeled as: ； in, represents the path loss coefficient, Indicates the other path distance of the signal from the TX2 transmitter to the RX2 receiver; The power of the radar signal is expressed as: ； in, Expressed as radar signal power, represents the gain of the radar transmitting antenna, represents the gain of the target radar receiving antenna, represents the wavelength of the radar, represents the cross-sectional area of ​​the radar, represents the radar's transmit power, Indicates the distance between the UAV and the moving target. The reflected power of the radar signal after encountering the moving target is: ； in, is the reflected power, represents the reflection coefficient, Indicates return loss; The self-interference generated by the communication signal and perception signal sent by TX1 during the transmission process: ； in, Represents communication signals and sensory signals The interference power; ； in, Represents communication signals and sensory signals The interference power; Reflected signal and communication signals Mutual interference during transmission: ； Reflected signal and communication signals Mutual interference during transmission: ； So the signal-to-interference-to-noise ratio at RX1 is: ； in, represents the interference signal power at RX1, Represents communication signals and sensory signals The self-interference power of RX2 is: ； in, represents the interference signal power at RX2, Represents communication signals and sensory signals The self-interference power of the drone RX3 receiver is: ； in, represents the interference signal power at the drone end, represents the mutual interference power between the reflected signal and the two communication signals, and the total signal-to-interference-to-noise ratio of the UAV interawareness integrated system is obtained as follows: 。 2. An interference elimination method, characterized in that: The interference signal obtained by the analysis in claim 1 is eliminated by using a hardware interference elimination method and an algorithmic interference elimination method. The hardware interference elimination method integrates antenna self-interference elimination and digital self-interference elimination technology to separate the transmitting and receiving antennas, and then eliminates residual interference by digital filtering.

3. The interference elimination method according to claim 2, characterized in that: The algorithm interference elimination method adopts an improved variable step size minimum mean square error (LMS) adaptive filtering algorithm. The algorithm flow is as follows: starting with initialization w(n), reading the input signal x(n) and the expected signal d(n), calculating the output signal y(n) of the filter, calculating the error e(n) between the output signal y(n) and the expected signal d(n), and then updating the weight coefficient vector w(n), and at the same time, the step size factor used to update the weight coefficient vector.

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