Multi-sensor distributed signal detection system based on reconfigurable intelligent surface

Abstract

The invention provides a multi-sensor distributed signal detection system based on a reconfigurable intelligent surface, which belongs to the field of wireless communication and comprises a plurality of sensor nodes, a fusion center and at least one external radiation source. After each sensor node independently completes target detection, a public radio frequency signal emitted by the same external radiation source is directly used as an information carrier, the phase and direction of a reflected signal are regulated and controlled in real time through the reconfigurable intelligent surface module, and modulation and transmission of information are naturally completed in a wireless channel. According to the architecture, the node structure is greatly simplified, and the hardware cost is remarkably reduced. The fusion center only needs to execute matched filtering based on known signal waveforms and constant false alarm rate threshold judgment on the received superposed signals to complete global decision, a complex signal fusion algorithm and a mass data processing link in a traditional distributed detection system are omitted, and millisecond-level processing delay is shortened to microsecond-level processing delay. And the real-time performance of the system is obviously improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a multi-sensor distributed signal detection system based on a reconfigurable smart surface. Background Technology

[0002] The widespread application of Wireless Sensor Networks (WSNs) has long been constrained by node power. Traditional sensor nodes rely on battery power, which leads to high replacement costs, poor environmental adaptability, and environmental pollution. Furthermore, signal fusion in multi-sensor distributed detection often requires complex algorithms, increasing the hardware cost and computational load of the fusion center (FC), thus limiting its application. Summary of the Invention

[0003] This invention provides a multi-sensor distributed signal detection system based on a reconfigurable smart surface. To address the shortcomings of the prior art, it processes radio frequency signals from the same external radiation source and loads local decision results to achieve coherent superposition of wireless signals in the air, thereby greatly simplifying the complexity of sensor nodes and fusion centers.

[0004] This invention provides a multi-sensor distributed signal detection system based on a reconfigurable smart surface, comprising: multiple sensor nodes, a fusion center, and at least one external radiation source; Each of the sensor nodes includes an environmental energy harvesting and storage module, which is used to capture radio frequency energy, solar energy and / or wind energy from the surrounding environment, and store the energy to provide a continuous power supply to other modules of the sensor node to achieve battery-free self-powering; The perception module is used to detect targets in the monitoring area and generate local decision information, which indicates whether the target exists or does not exist. A reconfigurable smart surface module is used to receive radio frequency signals emitted by the external radiation source, control the reflection direction of the radio frequency signals according to the local decision information generated by the sensing module, and adjust the phase of the radio frequency signals in real time to generate a phase-corrected reflected signal. The fusion center is used to receive the reflected signals from multiple sensor nodes, and to perform matched filtering and threshold judgment on the reflected signals to generate a global decision result; The external radiation source is used to transmit a common radio frequency signal, which serves as an information carrier, to the multiple sensor nodes.

[0005] Furthermore, the environmental energy harvesting and storage module includes: An environmental energy harvesting unit is used to harvest radio frequency energy, solar energy, and / or wind energy from the surrounding environment. A supercapacitor is used to store the energy captured by the environmental energy harvesting unit and to power the sensor node; The supercapacitor is configured to utilize its rapid charge and discharge characteristics to smooth out instantaneous fluctuations in the output power of the environmental energy harvesting unit and maintain the continuous operation of the sensor node during periods of interruption in environmental energy supply.

[0006] Furthermore, the specific configuration of the reconfigurable smart surface module includes: Based on the local decision information output by the sensing module, when the existence of the target is determined, the common radio frequency signal is modulated into a reflected signal toward the fusion center. When the target is determined not to exist, the common radio frequency signal is modulated into a reflected signal that deviates from the direction of the fusion center or the reflection action is suppressed. The reconfigurable smart surface module can load and transmit the local decision information without the need for a traditional radio frequency transceiver unit by adjusting the phase offset and reflection characteristics of its multiple reflective units.

[0007] Furthermore, the reconfigurable smart surface module is also used to calculate phase correction parameters based on the difference in signal propagation path length between the sensor nodes and the fusion center, and to adjust the phase of each reflected signal, so that the reflected signals of multiple sensor nodes achieve coherent aerial superposition at the receiving antenna of the fusion center.

[0008] Furthermore, the step of the reconfigurable smart surface module calculating the phase correction parameters includes: Obtain the signal propagation path length between the sensor node and the fusion center; Calculate the phase delay caused by the path difference based on the signal propagation path length and the carrier frequency of the common radio frequency signal; The phase correction parameters are generated based on the phase delay, and the phase of the reflected signal is compensated accordingly so that the reflected signals from different sensor nodes have the same phase when they reach the fusion center.

[0009] Furthermore, the common radio frequency signal emitted by the external radiation source is a periodic signal, which has a fixed carrier frequency, repetition period, and known modulation waveform. The fusion center designs a matched filter kernel based on the known characteristics of the periodic signal. The matched filter kernel is used to perform matched filtering on the signal after coherent superposition in the air, so as to maximize the extraction of useful signal energy and suppress noise and non-target interference.

[0010] Furthermore, the fusion center includes: The matched filtering module is used to construct a matched filter based on the known waveform characteristics of the common radio frequency signal emitted by the external radiation source, and to perform joint matched filtering on the reflected signals from multiple sensor nodes, and output the filtered signal. The constant false alarm rate (CFAR) decision module is used to dynamically calculate a decision threshold based on a preset CFAR requirement, and compare the peak value of the filtered signal with the decision threshold. If the peak value exceeds the decision threshold, the target is determined to exist. If the peak value does not exceed the decision threshold, the target is determined to not exist; wherein, the constant false alarm rate decision module adaptively adjusts the decision threshold to maintain a constant false alarm rate when the background noise or interference level changes.

[0011] Furthermore, when the local decision information of the N sensor nodes all indicate the existence of the target, the aerial coherent superposition makes the signal-to-noise ratio of the superimposed signal received by the fusion center N times that of the single-channel signal-to-noise ratio, where N is an integer greater than 1.

[0012] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: After each sensor node independently completes target detection, it eliminates the need for traditional high-power RF transceiver units and the need for digitizing and modulating raw data or local decision results. Instead, it directly utilizes a common RF signal emitted from the same external radiation source as the information carrier. The phase and direction of the reflected signal are adjusted in real time by a reconfigurable smart surface module, naturally completing information modulation and transmission in the wireless channel. This architecture significantly simplifies the node structure and reduces hardware costs. Furthermore, by completely eliminating reliance on batteries, node lifespan is no longer limited by battery capacity and replacement cycles, fundamentally preventing node failures due to battery depletion or failure, resulting in a qualitative leap in system reliability. More importantly, each node's reconfigurable smart surface module controls the reflection direction of the RF signal based on the local decision information generated by the sensing module and adjusts the phase of the RF signal in real time to generate a phase-corrected reflected signal. The fusion center only needs to perform matched filtering based on known signal waveforms and constant false alarm rate threshold judgment on the received superimposed signals to complete the global decision. This eliminates the complex signal fusion algorithms and massive data processing steps in traditional distributed detection systems, reducing the millisecond-level processing latency to the microsecond level and significantly improving the system's real-time performance.

[0013] This invention enables the offloading and distributed processing of computational load, which not only reduces the requirements for the hardware performance of the fusion center and reduces the system deployment and maintenance costs, but also constructs a green, long-life, low-cost, and highly reliable new Internet of Things sensing system through environmental energy self-sufficiency and passive reflection communication mechanism. It is especially suitable for large-scale, wide-area coverage and difficult-to-maintain smart agriculture, environmental monitoring and security monitoring application scenarios. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of an optional multi-sensor distributed signal detection system based on a reconfigurable smart surface provided by the present invention; Figure 2 This is a schematic diagram of an optional sensor node provided by the present invention; Figure 3 This is a schematic diagram illustrating the performance loss of a coherent superposition scheme under different optional time delay conditions compared to the ideal superposition scheme provided by the present invention; Figure 4 This is a schematic diagram of an optional power gain of coherent superposition relative to incoherent superposition provided by the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0017] It should be noted that, in the description of the embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0018] This invention discloses a multi-sensor distributed signal detection system based on a reconfigurable intelligent surface. This system fundamentally solves the two core technical challenges of limited energy of traditional wireless sensor network nodes and high computational complexity of fusion centers by combining environmental energy harvesting technology, the beam manipulation capability of reconfigurable intelligent surfaces (RIS), and the principle of coherent superposition in the air.

[0019] Figure 1 This is a schematic diagram of a multi-sensor distributed signal detection system based on a reconfigurable smart surface provided in an embodiment of the present invention, as shown below. Figure 1 As shown, a multi-sensor distributed signal detection system based on a reconfigurable smart surface includes: multiple sensor nodes, a fusion center, and at least one external radiation source; Each of the sensor nodes includes an environmental energy harvesting and storage module, which is used to capture radio frequency energy, solar energy and / or wind energy from the surrounding environment, and store the energy to provide a continuous power supply to other modules of the sensor node to achieve battery-free self-powering; The perception module is used to detect targets in the monitoring area and generate local decision information, which indicates whether the target exists or does not exist. A reconfigurable smart surface module is used to receive radio frequency signals emitted by the external radiation source, control the reflection direction of the radio frequency signals according to the local decision information generated by the sensing module, and adjust the phase of the radio frequency signals in real time to generate a phase-corrected reflected signal. The fusion center is used to receive the reflected signals from multiple sensor nodes, and to perform matched filtering and threshold judgment on the reflected signals to generate a global decision result; The external radiation source is used to transmit a common radio frequency signal, which serves as an information carrier, to the multiple sensor nodes.

[0020] In this embodiment, the external radiation source is deployed at the center of the monitoring area, and can be an existing communication base station or a dedicated signal transmitting device, with a continuous transmission power of [missing information]. The periodic radio frequency signal, after beamforming, forms a stable electromagnetic field environment covering the monitoring area, providing a unified information carrier for the entire system.

[0021] Each sensor node is independently deployed within the monitoring area, with the spacing between them determined by monitoring requirements and signal coverage. For example, they can be deployed on a grid of nodes ranging from 50 to 200 meters. All sensor nodes collaboratively observe the same target. Notably, the sensor nodes completely abandon the traditional battery-powered mode. Their environmental energy harvesting and storage module converts stray radio frequency energy, solar energy, and wind energy in the environment into electrical energy through the integration of at least one device, including a radio frequency energy harvester, a micro solar panel, and a small wind turbine. This electrical energy can then be stored in a supercapacitor.

[0022] The sensing module includes, but is not limited to, image sensors, infrared detectors, vibration sensors, or chemical sensors, used to locally detect targets such as crop pests and diseases, human intrusion, and air quality, and output binary local decision information, i.e., whether the target exists or does not exist, based on the built-in detection algorithm.

[0023] The reconfigurable smart surface module consists of an array of M active reflective units, each with a size approximately 1 / 10 to 1 / 5 of the operating wavelength. For example, for a 2.4 GHz signal, the unit spacing is approximately 12.5 mm, and the overall RIS panel size can be controlled to within 10 cm × 10 cm. The reconfigurable smart surface module and its controller include a microprocessor and phase driving circuitry, enabling independent control of the phase offset of each reflective unit. , The control accuracy should ideally be within 5 degrees, thereby achieving precise phase control of the incident signal.

[0024] Optionally, when the sensing module determines that the target exists, the reconfigurable smart surface module generates a first control command to configure the phase of all reflecting units to a beamforming mode oriented towards the fusion center, so that the main lobe of the reflected signal is aligned with the receiving antenna of the fusion center; conversely, when the target does not exist, the reconfigurable smart surface module generates a second control command to guide the reflected beam to the null region of the antenna pattern of the fusion center or to reflect it in a low-gain direction, ensuring that the fusion center does not receive a valid signal.

[0025] The fusion center is used to receive the reflected signals from multiple sensor nodes and perform matched filtering and threshold judgment on the reflected signals to generate a global decision result.

[0026] In an optional embodiment, the fusion center is deployed at the edge of the monitoring area or on a central high ground, equipped with a high-gain receiving antenna and a signal processing unit. Its receiving bandwidth covers the frequency band of the external radiation source signal, and the sampling rate is at least twice the signal bandwidth to ensure signal integrity.

[0027] In this embodiment, the fusion center receiver simultaneously receives M valid reflected signals, where M ≤ N, N is the total number of sensors, and M is determined by the local detection probability of each sensor when the target is present. Decision, for example when And when N=8, the average value of M is approximately 7.

[0028] The fusion center performs matched filtering on the received signal. The filter coefficients are designed based on the known modulation waveform s(t) of the external radiation source signal. Specifically, it is implemented as a finite impulse response filter with an order ranging from 64 to 256. The filtered signal is aligned with the signal period at the time of signal period. A peak appears at that location.

[0029] Based on the content disclosed in the above embodiments, in an optional embodiment of the present invention, Figure 2 This is a schematic diagram of an optional sensor node structure provided by the present invention, such as... Figure 2 As shown, each of the sensor nodes includes an environmental energy harvesting and storage module, which is used to capture radio frequency energy, solar energy and / or wind energy from the surrounding environment, and store the energy to provide a continuous power supply to other modules of the sensor node to achieve battery-free self-powering.

[0030] The specific configuration of the environmental energy harvesting and storage module is as follows: An environmental energy harvesting unit is used to harvest radio frequency energy, solar energy, and / or wind energy from the surrounding environment. A supercapacitor is used to store the energy captured by the environmental energy harvesting unit and to power the sensor node; The supercapacitor is configured to utilize its rapid charge and discharge characteristics to smooth out instantaneous fluctuations in the output power of the environmental energy harvesting unit and maintain the continuous operation of the sensor node during periods of interruption in environmental energy supply.

[0031] In this embodiment, the environmental energy harvesting unit employs a multi-source collaborative harvesting architecture. For example, the antenna of the radio frequency energy harvester is designed as a wideband microstrip antenna, covering the 0.8GHz to 6GHz frequency band, to capture stray energy from radiation sources such as surrounding base stations and Wi-Fi routers. The solar energy harvesting unit uses flexible amorphous silicon thin-film batteries with a photoelectric conversion efficiency of approximately 8% to 10%, providing continuous power of 50 milliwatts to 200 milliwatts under standard illumination conditions. The wind energy harvesting unit uses a miniature piezoelectric wind energy harvester, generating approximately 10 milliwatts of power at a wind speed of 2 m / s.

[0032] As a core energy storage component, the capacity configuration of supercapacitors must meet the energy requirements of nodes to operate continuously for more than 72 hours without any ambient energy input. For example, double-layer supercapacitors with a capacity range of 10 to 100 farads and a rated voltage of 2.7 volts should be selected, with an equivalent series resistance of less than 50 milliohms, to cope with the working conditions where the instantaneous power of the reconfigurable smart surface module may exceed the average collection power by more than 10 times, while ensuring the energy autonomy of the nodes in scenarios where energy supply is interrupted, such as at night or in severe weather.

[0033] In this embodiment, a hybrid supercapacitor is selected, which combines the characteristics of double-layer capacitance and pseudocapacitance. The energy density can reach more than 10 Wh / kg, the power density exceeds 5000 W / kg, the cycle life is greater than 1 million cycles, and the operating temperature range covers -30℃ to +70℃ to adapt to harsh outdoor environments.

[0034] The environmental energy harvesting unit is equipped with a maximum power point tracking (MPPT) circuit and overvoltage and overcurrent protection circuits between itself and the supercapacitor to ensure maximum energy conversion efficiency. The module can be implemented in a compact package, with a volume of less than 50 cubic centimeters and a weight of less than 100 grams, allowing for easy integration into sensor nodes. Under different environmental conditions, it can adaptively switch energy sources; for example, it primarily uses solar energy during the day and automatically switches to radio frequency energy harvesting at night, supplementing with wind energy when there is wind, and storing excess energy in the supercapacitor, achieving a dynamic spatiotemporal balance of energy.

[0035] It should be noted that the voltage status of the supercapacitor is monitored in real time by an analog-to-digital converter. When the voltage is lower than a preset threshold, such as 1.8 volts, the sensing module and the reconfigurable smart surface module automatically enter a low-power sleep mode, reducing power consumption to the microwatt level until the supercapacitor voltage recovers to the normal operating range, such as above 2.2 volts, thereby ensuring the long-term autonomous operation capability of the node.

[0036] Based on the content disclosed in the above embodiments, in an optional embodiment of the present invention, the reconfigurable smart surface module is specifically configured as follows: Based on the local decision information output by the sensing module, when the existence of the target is determined, the common radio frequency signal is modulated into a reflected signal toward the fusion center. When the target is determined not to exist, the common radio frequency signal is modulated into a reflected signal that deviates from the direction of the fusion center or the reflection action is suppressed. The reconfigurable smart surface module can load and transmit the local decision information without the need for a traditional radio frequency transceiver unit by adjusting the phase offset and reflection characteristics of its multiple reflective units.

[0037] In this embodiment, the reconfigurable smart surface module has a built-in local decision encoder that maps the binary local decision information output by the sensing module into two discrete states of the reflected signal.

[0038] Specifically, when the local judgment information indicates the presence of a target, the encoder generates a first modulation pattern, which includes two key parameters: the reflected beam pointing angle. and phase compensation amount The pointing angle Based on the GPS coordinates of the fusion center antenna and the position coordinates of the sensor nodes themselves, it is calculated that the main lobe center of the reflected beam is aligned with the fusion center receiving antenna, and the beamwidth is controlled within the range of 10 to 30 degrees to improve energy concentration.

[0039] The phase compensation amount can be generated by periodically receiving beacon signals broadcast by the fusion center, measuring the signal arrival time or received signal strength, estimating the distance from the sensor node to the fusion center, and then combining the distance from the external radiation source to the sensor node to calculate the total path length.

[0040] When the local decision information indicates that the target does not exist, the encoder generates a second modulation mode, sets the pointing angle of the reflected beam to an angle at least 60 degrees away from the direction of the fusion center, or configures it as an absorption mode. In this case, the amplitude of the reflection coefficient is less than 0.1, so that the power of the reflected signal is lower than the receiving sensitivity threshold of the fusion center.

[0041] The reconfigurable smart surface module can communicate with the reflective unit array driver chip via a high-speed SPI interface, ensuring rapid response to dynamic targets. Each reflective unit is controlled by a PIN diode or varactor diode, with a phase adjustment step of 5.625 degrees (i.e., 6-bit resolution) and an amplitude adjustment range of 0 to -3dB, thereby achieving joint control of the amplitude and phase of the reflected signal. This eliminates the need for independent RF oscillators, mixers, and power amplifiers in the sensor node, allowing it to directly utilize the incident signal from an external radiation source as a carrier and load information by controlling the reflection characteristics, reducing node power consumption to below the milliwatt level.

[0042] Based on the content disclosed in the above embodiments, in an optional embodiment of the present invention, the reconfigurable smart surface module is further configured to calculate phase correction parameters according to the difference in signal propagation path length between the sensor nodes and the fusion center, and adjust the phase of each reflected signal, so that the reflected signals of multiple sensor nodes achieve coherent superposition in the air at the receiving antenna of the fusion center, and the coherent superposition in the air enables the signal-to-noise ratio of the effective reflected signal to be linearly improved relative to the single signal.

[0043] In this embodiment, each sensor is equipped with an active RIS (Reflection Array) for transmitting local decision results to the fusion center. The RIS contains M active reflective elements, precisely controlled by the sensor through a RIS controller to ensure accurate and efficient communication. The RIS can apply an additional phase shift to the reflected signal; by jointly optimizing the phase shift and reflection characteristics of each scattering element, coherent signal superposition can be achieved. To achieve the target performance, the signal can be adjusted towards the desired receiver direction, and nulls can be formed in other directions.

[0044] By optimizing the phase control of the RIS (Resource Identifier), and combining this with transceiver parameters (such as resource allocation, power allocation, and transmit beamforming), the performance of the RIS-assisted wireless network can be analyzed. For the link between the nth sensor and the fusion center, the phase offset of the RIS can be actively controlled. This allows the reflected signals from each RIS to be coherently fused at the fusion center. The phase correction amount can be calculated based on the signal propagation path length.

[0045] Each sensor is equipped with a RIS that receives signals emitted from the same radiation source. In this embodiment, the signal duration is... It contains L carrier cycles, that is (in (where is the carrier period). According to the coherent superposition scheme of the reflected signals from each sensor, due to differences in the transmission paths, the arrival times of the signals from each sensor at the fusion center cannot be completely identical. Let the time difference between the earliest and latest arriving signals be . , Figure 3 This is a schematic diagram illustrating the performance loss of a coherent superposition scheme under different selectable time delay conditions compared to the ideal superposition scheme, as provided by the present invention. The following section will analyze the performance loss under different time delay conditions. The effect of the value on the coherent superposition performance is shown in the following results. Figure 3 As shown in the figure (L=100, N=10).

[0046] When coherent superposition is performed at the fusion center, only signals of (L - k - 1) cycles can be coherently superimposed, and the remaining signals are discarded. In this case, the signal power loss can be minimized. Characterization. With As the number of elements increases, the performance loss of the coherent superposition scheme relative to the ideal superposition scheme also increases.

[0047] It should be noted that, in one scenario, coherent superposition performance may be inferior to traditional methods. Specifically, when using traditional fusion methods, the fusion center receives the observation signals from each sensor separately, performs reception and matched filtering on the signals, and then performs fusion processing. In this case, the power of the fused signal is equal to the sum of the power of the individual sensor signals received by the fusion center.

[0048] To simplify the analysis, it is assumed that the power of the transmitted signals from each sensor received by the fusion center is equal, and all are... The signal power received by the traditional method is .

[0049] After adopting the coherent superposition scheme, the signal power received by the fusion center is: ; From the perspective of signal power, when At that time, the proposed solution was inferior to traditional methods in terms of signal power. .

[0050] Figure 4 This demonstrates the performance gain of coherent superposition compared to incoherent superposition (number of sensors N=10 in the figure). When performance loss... At times, the performance of coherent stacking schemes is inferior to that of incoherent stacking schemes; and in real-world scenarios, The value is usually less than 0.1, therefore the coherent superposition scheme proposed in this invention has better performance.

[0051] In an optional embodiment, the method for calculating the phase correction parameters of the reconfigurable smart surface module is further disclosed as follows: the sensor node has a built-in path length measurement submodule, which employs a round-trip time measurement method, specifically implemented as follows: The fusion center periodically broadcasts narrow-pulse beacon signals, and the sensor nodes record the received timestamps. It immediately triggers the reconfigurable smart surface module to reflect the beacon signal after a preset delay τ, and the fusion center records the timestamp of the reflected beacon's return. By calculating the total round-trip time Subtracting the known delay τ, we obtain the signal propagation time. Then calculate the signal propagation path length. , where c is the speed of light.

[0052] It is worth noting that, to eliminate clock synchronization errors, a differential measurement mechanism can be used, which involves measuring the change in RTT over two consecutive cycles and then applying the formula... The phase correction parameters are dynamically updated based on changes in the calculated path length. The phase correction parameter calculation unit can be integrated into the controller of the reconfigurable smart surface module. It employs a lookup table method for rapid phase calculation, pre-stores a mapping table between path length and phase offset, achieves a phase resolution better than 1 degree, and has a calculation latency of less than 1 millisecond.

[0053] In practical deployments, considering the impact of multipath effects, sensor nodes can also be configured with RSSI (Received Signal Strength Indication) monitoring units. By measuring the intensity ratio of the direct wave to the multipath components, channel quality is assessed. When the RSSI value is below -80dBm, the phase correction tolerance range is automatically increased to ±10 degrees to compensate for phase jitter caused by multipath. Furthermore, for moving target monitoring scenarios, where the path length change rate can reach 10 meters per second, the system updates the phase correction parameters in real time at a frequency of 100Hz to ensure that coherent superposition performance is unaffected by movement. Measured phase locking accuracy is within 3 degrees, and coherent superposition efficiency is above 90%.

[0054] In an optional embodiment, the common radio frequency signal emitted by the external radiation source is a periodic signal, which is designed to use a linear frequency modulated continuous wave (FMCW) or a phase-coded pulse train, with the center frequency selected from an unlicensed frequency band, such as the 2.4 GHz ISM band or the 5.8 GHz band, the signal bandwidth B set to 10 MHz to 20 MHz, and the period T set to 1 ms to 10 ms.

[0055] The modulation waveform s(t) can employ a pseudo-random code with good autocorrelation characteristics (such as a 13-bit Barker code or a 1023-bit Gold code), whose autocorrelation function has a main peak to sidelobe ratio greater than 20dB, facilitating matched filtering at the fusion center. The peak transmit power of the signal is controlled within the range of 20dBm to 30dBm to ensure that the received power at each sensor node within the monitoring area is higher than -70dBm, meeting the minimum operating level requirements of the reconfigurable smart surface module and complying with radio transmit power limits.

[0056] Based on the content disclosed in the above embodiments, in an optional embodiment of the present invention, the reconfigurable smart surface module, the fusion center, includes: The matched filtering module is used to construct a matched filter based on the known waveform characteristics of the common radio frequency signal emitted by the external radiation source, and to perform joint matched filtering on the reflected signals from multiple sensor nodes, and output the filtered signal. The constant false alarm rate (CFAR) decision module is used to dynamically calculate a decision threshold based on a preset CFAR requirement, and compare the peak value of the filtered signal with the decision threshold. If the peak value exceeds the decision threshold, the target is determined to exist. If the peak value does not exceed the decision threshold, the target is determined to not exist; wherein, the constant false alarm rate decision module adaptively adjusts the decision threshold to maintain a constant false alarm rate when the background noise or interference level changes.

[0057] In this embodiment, the matched filtering module of the fusion center is implemented using a matched filter.

[0058] The fusion center receiver simultaneously receives M valid reflected signals (of which, N is the total number of sensors, and M is determined by the local detection probability of each sensor when the target is present.

[0059] For example, when the local detection probability of the sensor is 0.9 and N=8, the average value of M is approximately 7. Each signal satisfies the coherence conditions of "same frequency, same phase (after correction), and same modulation format".

[0060] Assume the amplitude of the reflected signal from a single sensor is A, and the noise power is... After coherent superposition of M signals, the total signal amplitude is MA (energy and...). (proportional), total noise power is (The noise is an incoherent superposition, and the power is proportional to M).

[0061] Therefore, the signal-to-noise ratio (SNR) after superposition is: It is M times the signal-to-noise ratio of a single-channel signal, providing signal gain for subsequent detection.

[0062] The fusion center is based on the known characteristics of external radiation source signals (such as the carrier frequency of periodic signals). Given the period T and the modulation waveform s(t), design a matched filter kernel. (in (This is the complex conjugate of s(t)). This ensures that the useful signal energy is extracted to the maximum extent after filtering, while suppressing noise and non-target interference.

[0063] The coherently superimposed signal x(t) is input into a matched filter, and the output signal is y(t) = x(t) * h(t) (where * denotes convolution). When the target is present, y(t) will have a significant peak at the signal period alignment time (e.g., t = kT, k is a positive integer), and the peak amplitude is proportional to MA; when the target is not present, y(t) is only generated by filtering noise, and the output amplitude is random and much lower than the peak when the target is present.

[0064] The constant false alarm rate (CFAR) decision module uses the CFAR criterion to set the decision threshold. This ensures that in a "no-target" scenario, the probability of mistakenly judging "target existence" (false alarm rate) is minimized. Maintain at the preset level (e.g.) This meets the low false alarm requirement. Threshold The calculation needs to take into account the noise power. The quantiles of the noise statistical distribution are calculated by integrating with the output characteristics of the matched filter. For example, when the noise follows a Gaussian distribution, (in (This is the inverse function of the Gaussian Q-function).

[0065] If the matched filter output peak value If the target is found to exist, then the probability of making the correct decision is determined to be the detection probability. ( Positively correlated with M and single-channel signal-to-noise ratio: the larger M is, the higher the signal-to-noise ratio. The closer to 1); if If the signal-to-noise ratio (SNR) is insufficient, then the target is determined to be non-existent. It should be noted that two error scenarios need to be excluded at this point: first, the target actually exists, but the signal-to-noise ratio is insufficient due to a small M value (e.g., most sensors fail to detect the target); second, noise occasionally generates high amplitude values, causing false alarms. However, through the CFAR criterion, the false alarm rate has been controlled within an acceptable range.

[0066] Based on the content disclosed in the above embodiments, in an optional embodiment of the present invention, the reconfigurable smart surface module, when the local decision information of the N sensor nodes are all targets, the aerial coherent superposition makes the signal-to-noise ratio of the superimposed signal received by the fusion center N times the signal-to-noise ratio of the single signal, where N is an integer greater than 1.

[0067] In this embodiment, when the local decision information of N sensor nodes all indicate the existence of the target, the aerial coherent superposition makes the signal-to-noise ratio of the superimposed signal received by the fusion center N times that of the single-channel signal-to-noise ratio, where N is an integer greater than 1.

[0068] The linear improvement in signal-to-noise ratio (SNR) can be further verified using the following experimental data: Assume that N=10 sensor nodes simultaneously detect pests and diseases, and their reflected signals are coherently superimposed onto a central base station via a reconfigurable smart surface module. If the SNR of a single node's reflected signal reaching the base station is 10dB, then after coherent superposition of these 10 nodes, the SNR of the superimposed signal received by the base station will reach 10×10dB = 20dB. This 20dB SNR is significantly higher than that of a single node, enabling the base station to more clearly and accurately determine whether there are widespread pests and diseases in the farmland, maintaining high detection performance even under strong interference during signal transmission.

[0069] From a power gain perspective, after adopting the coherent superposition scheme, the signal power received by the fusion center is: Where M is the number of effective reflected signals, The signal power is for a single channel; while the received power of the traditional incoherent superposition scheme is... When M=10, the power gain of coherent superposition is 10 times that of incoherent superposition, i.e., a 10dB gain. Figure 4 This performance advantage is clearly demonstrated: when the number of sensors N=10 and the performance loss factor k<0.1, the power gain of the coherent superposition scheme is consistently higher than that of the incoherent scheme. This linear improvement in signal-to-noise ratio significantly enhances the target detection capability of the fusion center in low signal-to-noise ratio environments, enabling the system to extend the monitoring radius from 100 meters in traditional schemes to over 300 meters while maintaining low transmission power, and simultaneously keeping the detection probability high. This provides a practical technical path for large-scale IoT applications.

[0070] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0071] The above provides a detailed description of the adaptive modulation method, apparatus, electronic device, and computer-readable storage medium for backscattered signals provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-sensor distributed signal detection system based on reconfigurable intelligent surface, characterized in that, The system comprises: a plurality of sensor nodes, a fusion center and at least one external radiation source; each of the sensor nodes comprises: an ambient energy harvesting and storage module, configured to capture radio frequency energy, solar energy and / or wind energy from the surrounding environment, and store the energy to provide a continuous power supply to other modules of the sensor node, so as to realize self-power supply without battery; a perception module, configured to detect a target in a monitoring area and generate local decision information indicating the presence or absence of the target; a reconfigurable intelligent surface module, configured to receive a radio frequency signal emitted by the external radiation source, control a reflection direction of the radio frequency signal according to the local decision information generated by the perception module, and adjust a phase of the radio frequency signal in real time to generate a reflected signal after phase correction; the fusion center is configured to receive the reflected signal from the plurality of sensor nodes, and perform matched filtering and threshold decision on the reflected signal to generate a global decision result; 2. The reconfigurable intelligent surface based multi-sensor distributed signal detection system of claim 1, wherein, the external radiation source is configured to emit a common radio frequency signal as an information carrier to the plurality of sensor nodes. The ambient energy harvesting and storage module comprises: an ambient energy harvesting unit, configured to collect radio frequency energy, solar energy and / or wind energy from the surrounding environment; a super capacitor, configured to store the energy captured by the ambient energy harvesting unit and supply power to the sensor node; 3. The reconfigurable intelligent surface based multi-sensor distributed signal detection system of claim 1, wherein, wherein the super capacitor is configured to smooth instantaneous fluctuations in the output power of the ambient energy harvesting unit by using its fast charge and discharge characteristics, and maintain continuous operation of the sensor node during intermittent supply of environmental energy. The reconfigurable intelligent surface module specifically comprises: when it is determined that a target exists according to the local decision information output by the perception module, the common radio frequency signal is modulated into a reflected signal directed towards the fusion center; when it is determined that a target does not exist, the common radio frequency signal is modulated into a reflected signal deviating from the direction of the fusion center or a reflection suppression action is performed; 4. The reconfigurable intelligent surface based multi-sensor distributed signal detection system of claim 1, wherein, wherein the reconfigurable intelligent surface module adjusts the phase offset and reflection characteristics of a plurality of reflection units thereof to load and transmit the local decision information without using a traditional radio frequency transceiver.

5. The reconfigurable intelligent surface based multi-sensor distributed signal detection system of claim 4, wherein, The reconfigurable intelligent surface module is further configured to calculate a phase correction parameter according to a difference in signal propagation path length between the sensor node and the fusion center, and adjust the phase of each reflected signal, so that the reflected signals of the plurality of sensor nodes are coherently superimposed in the air at a receiving antenna of the fusion center. The reconfigurable intelligent surface module calculates the phase correction parameter by: obtaining the signal propagation path length between the sensor node and the fusion center; calculating a phase delay caused by the path difference according to the signal propagation path length and the carrier frequency of the common radio frequency signal; generating the phase correction parameter based on the phase delay, and compensating the phase of the reflected signal accordingly, so that the reflected signals from different sensor nodes have the same phase when they arrive at the fusion center. 6.The reconfigurable intelligent surface based multi-sensor distributed signal detection system of claim 1, wherein, the common radio frequency signal emitted by the external radiation source is a periodic signal having a fixed carrier frequency, a repetition period and a known modulation waveform. the fusion center designs a matched filter kernel based on the known characteristics of the periodic signal, the matched filter kernel being used for matched filtering processing of the airborne coherent superposition signal to maximize the extraction of useful signal energy and suppress noise and non-target interference.

7. The reconfigurable intelligent surface based multi-sensor distributed signal detection system of claim 1, wherein, the fusion center comprises: a matched filtering module configured to construct a matched filter based on the known waveform characteristics of the common radio frequency signal emitted by the external radiation source, and perform joint matched filtering on the reflected signals from the plurality of sensor nodes to output filtered signals; a constant false alarm rate decision module configured to dynamically calculate a decision threshold according to a preset false alarm rate requirement, and compare a peak value of the filtered signals with the decision threshold, if the peak value exceeds the decision threshold, it is determined that a target exists; if the peak value does not exceed the decision threshold, it is determined that a target does not exist; wherein the constant false alarm rate decision module adaptively adjusts the decision threshold to keep the false alarm rate constant when the background noise or interference level changes.

8. The reconfigurable intelligent surface based multi-sensor distributed signal detection system of claim 4, wherein, when the local decision information of N sensor nodes is that a target exists, the airborne coherent superposition makes the signal-to-noise ratio of the superposition signal received by the fusion center N times that of a single channel signal, wherein N is an integer greater than 1.

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