Single-channel ambient backscatter-assisted multi-sensor distributed detection method

Abstract

Single channel environment backscatter assisted multi-sensor distributed detection method, the radio frequency source in the environment randomly generates binary information bits a After being transmitted through the channel I Observed by one single antenna local environment backscatter sensor, and configured I The fusion center of the antenna receives. The source generates binary information bits u Observed by I After being observed by one single antenna local environment backscatter sensor, hard decision is made, and the radio frequency signal is reflected according to the decision result; the reflected signal of each link is transmitted through a binary symmetric channel, and then the radio frequency signal received by the fusion center is subjected to modulo-2 addition operation. The calculation result of each link reaches the fusion center through a binary symmetric channel, the fusion center obtains a low-complexity decision metric value according to the received signal, and obtains a detection result after comparing the decision metric value with a decision threshold. The present application has the characteristics of low calculation complexity, strong robustness and high bandwidth utilization rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, in particular to a single-channel environment backscattering assisted multi-sensor distributed detection method. BACKGROUND

[0002] The widely covered information perception network is the key infrastructure for the transformation and upgrading of equipment manufacturing to "digitalization", "networking" and "intelligentization". The perception layer at the bottom of the network is mainly responsible for the wireless access of local intelligent terminal equipment in equipment manufacturing. Under the premise of accurate and timely data transmission in the network layer, the accuracy of data processing in the application layer and the accuracy of data mining conclusions will depend on the quality of the data in the perception layer. Therefore, ensuring reliable transmission of data in the perception layer of ubiquitous perception network is the most effective way to realize reliable perception of time and space information in equipment manufacturing from the "information source", and it is extremely important to study it.

[0003] The data provided by a single sensor cannot meet the needs of the development of digitalization and intelligentization of equipment manufacturing. Multiple sensors must be used to provide multi-feature observation information for comprehensive, efficient, accurate and reasonable fusion decision, estimation or decision-making, and multi-source information fusion technology becomes an inevitable choice. However, most of the traditional multi-sensor information fusion research does not consider the use of relay nodes for cooperative communication, and when the transmission distance is far, normal information transmission cannot be carried out, that is, the communication distance is greatly restricted, and the network coverage ability is insufficient. Only a small amount of research on multi-relay decision fusion exists, but it needs to perfectly estimate the instantaneous channel state information (CSI) of each relay channel in each transmission link, which has high implementation complexity and is not easy to be applied in engineering. Moreover, most of the existing research is carried out under parallel topology, which consumes a lot of system bandwidth, and when there are more sensor data, the bandwidth resource consumption is greater.

[0004] In a wireless sensor network with strict resource constraints, each sensor node cannot be connected to the power network due to its wireless characteristics, and the volume of the node is usually small, which cannot carry a large capacity battery. For some nodes in a complex environment, replacing the battery for the node will bring more cost. If the relay node is not supplied with enough energy, the entire wireless sensor network is likely to face a paralysis problem. When decision fusion is made at the fusion center, if complex nonlinear operations such as summation and multiplication are required, the transmission delay will be increased, and the energy consumption of information transmission will be increased. Therefore, how to reduce the computational complexity of the fusion center becomes particularly important. Secondly, in the traditional wireless sensor network, when decision fusion is made at the fusion center, the CSI from the local sensor to the fusion center needs to be obtained. In practical applications, due to the real-time dynamic change of the channel state, the estimation process of the CSI involves high implementation complexity and high energy consumption. This is contrary to the concept of low complexity, low cost and low power consumption of the wireless sensor network. Moreover, when there is an error in the estimation of the CSI, the performance of the entire system will be sharply decreased. That is, the detection system using accurate CSI is not robust to CSI. Moreover, most of the existing researches are carried out under parallel topology, which needs to allocate a dedicated channel to each local sensor to transmit the local observation information to the fusion center. When the number of local sensors is huge, more bandwidth resources need to be consumed. The above technical deficiencies limit the application depth and breadth of multi-routing multi-relay wireless sensor networks in equipment manufacturing perception data reliable transmission to some extent. SUMMARY

[0005] To solve the above technical problems, the present application provides a multi-sensor distributed detection method assisted by environmental backscattering under a single channel, which uses multi-sensor detection under a single channel, is a multiple access channel, and is based on environmental backscattering technology, so as to make the local sensor have low energy consumption, the fusion center has low computational complexity, the decision fusion has strong robustness, and the bandwidth utilization rate is high.

[0006] To achieve the above technical purpose, the technical scheme adopted is as follows: a multi-sensor distributed detection method assisted by environmental backscattering under a single channel, comprising the following steps:

[0007] Step S1: a radio frequency source in the environment randomly generates a binary information bit a, which is observed by I single-antenna local environmental backscattering sensors after channel transmission, and is received by a fusion center configured with I antennas, I being an odd number; the received signal of the ith single-antenna environmental backscattering sensor is The received signal of the ith antenna of the fusion center is E i represents the transmission error of the BSC between the radio frequency source and the ith single-antenna local environmental backscattering sensor, Λ iThe transmission error of the BSC between the RF source and the i-th antenna of the fusion center is represented by 1≤i≤I;

[0008] Step S2: The binary information bit u generated by the source is simultaneously and independently observed by I single-antenna local environment backscatter sensors. Based on the maximum likelihood criterion, the i-th single-antenna local environment backscatter sensor performs a hard decision on the observed data sample to obtain x. i x i =0 or x i =1, based on the judgment result x i The i-th single-antenna local environment backscatter sensor determines how to process the RF received signal b received in step S1. i Reflection occurs when x i When = 0, b is left unchanged. i Reflected out; when x i When = 1, b i After bit flipping, it is reflected out;

[0009] Step S3: The reflected signal generated in step S2 is forwarded by J-1 relay nodes and then received by the i-th antenna of the fusion center, denoted as y. i y i =0 or y i =1, J is the number of BSC signals, y i With the received signal c in step S1 i After performing modulo 2 operation, z is obtained. i z of all transmission links i Then, perform a modulo-2 operation with the channel error vector e at the fusion center to obtain the received information r used for decision-making, where r = 0 or r = 1;

[0010] Step S4: The fusion center extracts a low-complexity decision metric that does not contain channel state information based on the received information r;

[0011] Step S5: Compare the low-complexity decision metric extracted in step S4 with the decision threshold to obtain the final detection result.

[0012] Furthermore, in step S3, the relay node uses amplified forwarding as the forwarding method, and the amplification factor is 1.

[0013] Furthermore, the method for extracting low-complexity decision metrics from the fusion center in step S4 is as follows:

[0014] Λ=2r-1

[0015] Here, Λ represents a low-complexity decision metric that does not contain channel state information.

[0016] Further, the specific method of comparing the low-complexity decision metric value with the decision threshold in step S5 is that when the low-complexity decision metric value is greater than or equal to the decision threshold, the estimation value of the information u generated by the source is 1; and when the low-complexity decision metric value is less than or equal to the decision threshold, the estimation value of the information u generated by the source is 0.

[0017] The present application has the advantages that: the present application provides a multi-access channel environment backscattering assisted multi-sensor distributed detection method, which is calculated through the established system detection model, based on the environment backscattering technology, so that the local sensor has low energy consumption, the received information is obtained through special calculation in the fusion center, and then the low-complexity decision metric value without any channel state information is calculated through a simple method, and finally, comparison and detection are performed, and the method has the characteristics of low calculation complexity, strong robustness and high reliability.

[0018] Compared with the optimal decision fusion decision method, the detection method has a small performance loss, and does not require any channel state information CSI, so that the method has the characteristics of low complexity, low cost and easy implementation. It is very suitable for wireless sensor networks with strict power consumption and calculation ability. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a flow chart of the data fusion decision method of the present application;

[0020] Figure 2 is a transmission model diagram of a communication system in the embodiment of the present application;

[0021] Figure 3 is an equivalent system transmission model diagram in the embodiment of the present application;

[0022] Figure 4 is an equivalent channel model diagram from the source to the received value z in the embodiment of the present application; i

[0023] Figure 5 is a BER performance influence diagram of the routing quantity based on the accurate decision metric value extraction method in the case that the channels between the source and the local environment backscattering sensor, the environment radio frequency source and the local environment backscattering sensor, and the radio frequency source and each antenna of the fusion center are all ideal;

[0024] Figure 6 is a FER performance influence diagram of the routing quantity based on the accurate decision metric value extraction method in the case that the channels between the source and the local environment backscattering sensor, the environment radio frequency source and the local environment backscattering sensor, and the radio frequency source and each antenna of the fusion center are all ideal;

[0025] Figure 7 ​is the BER performance impact graph of the number of relays based on the exact decision metric extraction method under the ideal channel condition between the source and the local environment backscatter sensor, the environment RF source and the local environment backscatter sensor, and the RF source and the antenna of the fusion center in the embodiment;

[0026] Figure 8 is the FER performance impact graph of the number of relays based on the exact decision metric extraction method under the ideal channel condition between the source and the local environment backscatter sensor, the environment RF source and the local environment backscatter sensor, and the RF source and the antenna of the fusion center in the embodiment;

[0027] Figure 9 is the BER performance impact graph of the number of relays based on the simplified decision metric extraction method under the ideal channel condition between the source and the local environment backscatter sensor, the environment RF source and the local environment backscatter sensor, and the RF source and the antenna of the fusion center in the embodiment;

[0028] Figure 10 is the FER performance impact graph of the number of relays based on the simplified decision metric extraction method under the ideal channel condition between the source and the local environment backscatter sensor, the environment RF source and the local environment backscatter sensor, and the RF source and the antenna of the fusion center in the embodiment;

[0029] Figure 11 is the BER performance impact graph of the number of routes based on the simplified decision metric extraction method under the ideal channel condition between the source and the local environment backscatter sensor, the environment RF source and the local environment backscatter sensor, and the RF source and the antenna of the fusion center in the embodiment;

[0030] Figure 12 is the FER performance impact graph of the number of routes based on the simplified decision metric extraction method under the ideal channel condition between the source and the local environment backscatter sensor, the environment RF source and the local environment backscatter sensor, and the RF source and the antenna of the fusion center in the embodiment;

[0031] Figure 13 is the BER performance comparison graph between the optimal decision metric extraction method and the simplified decision metric extraction method under the ideal channel condition between the source and the local environment backscatter sensor, the environment RF source and the local environment backscatter sensor, and the RF source and the antenna of the fusion center in the embodiment;

[0032] Figure 14is the FER impact figure of performance comparison between the optimal decision metric value extraction method and the simplified decision metric value extraction method based on the assumption that the channel between the source and the local environment backscatter sensor, the channel between the radio frequency source and the local environment backscatter sensor, and the channel between the radio frequency source and each antenna of the fusion center are ideal. DETAILED DESCRIPTION

[0033] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0034] A decision fusion method assisted by environment backscatter in a multiple access channel, which is realized based on a local single-antenna environment backscatter sensor, a relay node and a fusion center, as shown in Figure 1 Step S1: A radio frequency source in an environment randomly generates a binary information bit a, a=0 or a=1. After a is transmitted through a channel, it is observed by I single-antenna local environment backscatter sensors, and is received by a fusion center configured with I antennas, I being an odd number. Let the reception value of the i(th) (1≤i≤I) single-antenna environment backscatter sensor be The reception value of the i(th) antenna of the fusion center is E i represents a transmission error of the BSC between the radio frequency source and the i(th) single-antenna local environment backscatter sensor, Λ i represents a transmission error of the BSC between the radio frequency source and the i(th) (1≤i≤I) antenna of the fusion center.

[0035] The environment backscatter technology can well cope with the problem of insufficient power supply in traditional wireless sensor networks. The specific implementation process is to use a passive tag to load the observed source information onto the widely existing radio frequency signals in space and reflect them to the fusion center. In this process, the node part only consumes a small amount of energy. The single-antenna local environment backscatter sensor in the method is configured with a single antenna and carries a passive tag. The local sensor consumes less energy in data transmission.

[0036] Step S2: The binary information bit u (u=0 or u=1) generated by the source is simultaneously and independently observed by I single-antenna local environment backscatter sensors. Based on the maximum likelihood criterion, the i(th) single-antenna local environment backscatter sensor obtains x i after performing hard decision on the observed data samples. i x i= 1. The decision result x i The ith single-antenna local environment backscatter sensor determines how to reflect the radio frequency received signal b i received in step S1. When x i = 0, b i is reflected intact; when x i = 1, b i is reflected after bit flipping.

[0037] S21: After the ith local environment backscatter sensor observes the binary data H0 or H1 sent by the source, the information x i is obtained by hard decision according to the maximum likelihood criterion.

[0038] Step S3: The reflected signal generated by the ith single-antenna local environment backscatter sensor in step S2 is forwarded by J-1 relay nodes and received by the ith antenna of the fusion center, denoted as y i , y i = 0 or y i = 1, and J is the number of BSC signals. Due to the inherent superposition characteristics of the multiple access channel (multiple sensor access single channel), y i and the environment radio frequency received signal c i in step S1 are subjected to modulo-2 operation to obtain z i , and all transmission links z i (1≤i≤I) are subjected to modulo-2 operation with the channel error vector e at the fusion center to obtain the received information r for decision, r = 0 or r = 1.

[0039] S31: The information reflected by the ith single-antenna local environment backscatter sensor is forwarded by J-1 relay nodes to obtain y i .

[0040] The relationship between x i and y i is:

[0041]

[0042] where e i,j represents the channel transmission error of the jth BSC of the ith transmission link, represents modulo-2 addition.

[0043] S32: y i and the environment radio frequency received signal c i in step S1 are subjected to modulo-2 operation to obtain z i , and all z i(1≤i≤I) and channel error vector e at the fusion center to obtain the received information r for decision.

[0044] In step S31, the relay node adopts an amplify-and-forward strategy, and the amplification coefficient is 1.

[0045] Step S4: The fusion center extracts a low-complexity decision metric value without channel state information from the received information r.

[0046] The method for the fusion center to extract the decision metric value in step S4 is:

[0047] When the channel between the source and each single-antenna local environment backscatter sensor is an ideal channel and the number of transmission paths I is odd, there is

[0048] Λ = 2r-1 (2)

[0049] Wherein, Λ represents the decision metric value extracted by the fusion center based on the received value r in the case that the channel between the source and the local environment backscatter sensor is ideal and the number of transmission paths I is odd, which does not contain any instantaneous CSI.

[0050] S5: Compare the low-complexity decision metric value extracted in step S4 with a decision threshold to obtain the final detection result (decision result).

[0051] Further, the method for comparing the low-complexity decision metric value with the decision threshold in step S5 is:

[0052]

[0053] Wherein, τ represents the decision threshold, represents the estimated value of the information u generated by the source, because the low-complexity decision metric value itself does not contain any instantaneous CSI, the decision implementation is simple, and detection and decision can be performed by 0 and 1.

[0054] The channels between the environmental radio frequency source and each single-antenna local environment backscatter sensor, between the environmental radio frequency source and each antenna of the fusion center, between the local environment backscatter sensor and the relay node, between the relay nodes, and between the relay node and the fusion center are all BSC (Binary Symmetric Channel).

[0055] The extraction and processing method of the low-complexity decision metric value by the fusion center in step S4 includes the following steps:

[0056] A1: z at the fusion center i Can be represented as

[0057]

[0058] A2: Let α i = P(E i = 1) be the error transition probability of the BSC between the radio frequency source and the ith local environment backscatter sensor, β i = P(A i = 1) be the error transition probability of the BSC between the radio frequency source and the ith antenna of the fusion center, ε i,j = P(e i,j = 1) be the error transition probability of the jth BSC of the ith transmission link, then from equation (4) we have:

[0059]

[0060] Without loss of generality, let α i = ε i,0 , β i = ε i,J+1 , then we have

[0061]

[0062] A3: From the results of equation (5), the statistical characteristics of the data transmission process from the binary data H0 or H1 sent by the source to the z i at the fusion center can be expressed as:

[0063]

[0064]

[0065] A3: At the fusion center, the received value r used for decision can be expressed as: where e is the error vector introduced by the MAC at the fusion center. The task of the fusion center is to determine whether the binary data sent by the source is H0 or H1 according to the received value r. According to the maximum likelihood criterion in logarithmic form, the expression of the best decision metric can be derived as:

[0066]

[0067] Let P(e = 1) = γ, then when the received value r = 1 at the fusion center, combining equations (6) and (7), equation (8) can be further expressed as

[0068]

[0069] When the received value r = 0 at the fusion center, combining equations (6) and (7), equation (8) can be further expressed as

[0070]

[0071] Then, combining equation (9) and equation (10), the final expression of the optimal decision metric value of the fusion center can be obtained.

[0072]

[0073] Thus far, the final expression of the optimal decision metric value of the fusion center is obtained.

[0074] A4: In the implementation process of the optimal decision metric value, i.e., equation (11), the fusion center needs to perfectly obtain two parameters representing the decision performance characteristics of each local environment backscatter sensor: the detection probability P di and the false alarm probability P fi , the instantaneous CSI of each relay BSC of each transmission link, i.e., the error transfer probability ε i,j , the error transfer probability α i between the radio frequency source and each local environment backscatter sensor, the error transfer probability β i between the radio frequency source and the fusion center, and the error transfer probability value γ of the MAC; and the implementation process of equation (11) involves a large number of logarithmic and multiplication operations, and has high implementation complexity and large energy consumption, so it is necessary to obtain a fusion detection method with low complexity and without instantaneous CSI.

[0075] A5: On the basis of the optimal decision metric value, the suboptimal decision metric value (the low complexity decision metric value of the present application) is given, i.e., the theoretical derivation process of equation (2).

[0076] A6: Considering that the channel from the signal source to the local environment backscatter sensor is in an ideal case, at this time, P fi = 0 and P di = 1.

[0077] When the fusion center receives the value r = 1 and the number of transmission paths I is odd, equation (9) can be written as

[0078]

[0079] When the fusion center receives the value r = 0 and the number of transmission paths I is odd, equation (10) can be written as

[0080]

[0081] From equations (12) and (13), the decision metric value in the ideal case of the local sensor can be written as

[0082]

[0083] A7: In order to facilitate analysis, let ε I+1 = γ, then the decision metric value in equation (14) can be changed to

[0084]

[0085] A8: Using the relationship of hyperbolic tangent function tanhθ and inverse hyperbolic tangent function arctanhθ, i.e.

[0086]

[0087] Let then the logarithmic term in equation (15) can be changed to:

[0088]

[0089] A9: When the number of relays J-1 is large, from equation (5), ε i → 0.5, since then δ i → 0, equation (17) can be changed to

[0090]

[0091] Equation (15) can be approximately expressed as

[0092]

[0093] A10: For 1≤i≤I, we have:

[0094]

[0095] Let then we have

[0096]

[0097] A11: When the number of relays J-1 is large, ε i,j → 0.5, η i,j → 0, equation (21) can be changed to

[0098]

[0099] A12: Combining equation (19) and (22), equation (15) can be written as

[0100]

[0101] Note that,

[0102] A13: Since η i,j and δ i,j are positive, the positive value in equation (23) is Therefore, when the decision threshold τ is set to 0, the positive value in equation (23) is discarded The item does not affect the final decision result. Then the final suboptimal decision metric value is

[0103] Λ=2r-1 (24)

[0104] As shown in Figure 1 , the workflow of each transmission link of the system is as follows: the binary information bits a randomly generated by the radio frequency source in the environment are received by the I single-antenna local environment backscatter sensors and the fusion center configured with I antennas. The source generates binary bit information u, which is observed by the above I local environment backscatter sensors through a wireless channel at the same time, and a hard decision is made according to the maximum likelihood criterion. When the decision result is 0, the received radio frequency signal is reflected as it is; when the decision result is 1, the received radio frequency signal is reflected after bit flipping. The reflection signal of each link is transmitted through J-1 independent binary symmetric channels and then subjected to modulo-2 addition operation with the radio frequency signal received by the fusion center. The calculation result of each link finally reaches the fusion center through a binary symmetric channel, and the fusion center obtains the low-complexity decision metric value required for decision according to the received signal, and obtains the final decision result after comparing the decision metric value with the decision threshold.

[0105] The method is suitable for wireless sensor networks with strict power consumption and computing capability constraints, such as target detection under multiple sensors, defect detection under multiple sensors, fault detection under multiple sensors, etc. The corresponding comparison result is obtained according to the method, and it is finally judged whether there is a target, a fault or a defect. According to the comparison method of formula (3), it is determined according to the actual situation, for example, there is a target, and 1 is finally detected, and there is no target, that is, 0.

[0106] The system bit error rate performance and frame error rate performance when using the optimal decision metric extraction method are given in Figures 5 to 8 , and the decision threshold τ is set to 0. In Figure 5 and Figure 6 , the number of relays is set to 2, and the number of routes I is uniformly changed from 3 to 7. As can be seen from the figure, there is a threshold phenomenon, when the error transfer probability of BSC is less than the threshold, the performance of the system changes more obviously, and when it is greater than the threshold, the change is very slow. Moreover, with the increase of the number of routes, the performance of the system presents a decreasing trend. In Figure 7 and Figure 8 , the detection performance under the relay number change scenario is given. With the increase of the number of relays, the system performance also decreases.

[0107] The bit error rate performance and frame error rate performance of the system when using the suboptimal decision metric extraction method are given in Figures 9 to 12The decision threshold τ is set to 0 in the simulation results shown in Figs. 6 and 7. The influence of the number of routes and the number of relays on the system detection performance is shown in Figs. 6 and 7, respectively. It can be observed that the curve trend of the sub-optimal decision metric is very similar to that of the optimal decision metric.

[0108] The optimal and sub-optimal decision metrics are compared in the case that the channel from the source to the local sensor is ideal, and the simulation results are shown in Figs. 8 and 9. Figure 13 and Figure 14 The decision threshold τ is set to 0 in the simulation results shown in Figs. 6 and 7. The influence of the number of routes and the number of relays on the system detection performance is shown in Figs. 6 and 7, respectively. It can be observed that the curve trend of the sub-optimal decision metric is very similar to that of the optimal decision metric. Figure 13 and Figure 14 It can be observed that the performance curves of the optimal and sub-optimal decision metrics almost coincide, which means that the performance loss of the sub-optimal decision metric is only in a very small range, while the complexity is greatly reduced.

[0109] In summary, the low complexity decision fusion method under the multi-access channel proposed in the application has the characteristics of high reliability, strong robustness, high bandwidth utilization and low complexity.

[0110] The above description of disclosed embodiments allows those skilled in the art to make or use the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-sensor distributed detection method with single channel down environment backscatter assistance, characterized in that, The method comprises the following steps: Step S1: a binary information bit a is randomly generated by a radio frequency source in the environment, and is observed by I single-antenna local environment backscatter sensors and received by a fusion center with I antennas after being transmitted through a channel, where I is an odd number; the received signal of the ith single-antenna environment backscatter sensor is The received signal of the ith antenna of the fusion center is E i represents a transmission error of the BSC between the radio frequency source and the ith single-antenna local environment backscatter sensor, i represents a transmission error of the BSC between the radio frequency source and the ith antenna of the fusion center, 1≤i≤I. Step S2: The binary information bits u generated by the source are observed by I single-antenna local environment backscatter sensors respectively and simultaneously. Based on the maximum likelihood criterion, the ith single-antenna local environment backscatter sensor makes a hard decision on the observed data samples to obtain x i , x i = 0 or x i = 1. According to the decision result x i , the ith single-antenna local environment backscatter sensor decides how to reflect the radio frequency received signal b i received in step S1. When x i = 0, b i is reflected intact; when x i = 1, b i is reflected after bit flipping. Step S3: the reflection signal generated in step S2 is received by the i-th antenna of the fusion center after being forwarded by J-1 relay nodes, denoted as y i , y i = 0 or y i = 1, J is the number of BSC signals, y i is subjected to modulo-2 operation with the received signal c i in step S1 to obtain z i , and z i of all transmission links is subjected to modulo-2 operation with the channel error vector e at the fusion center to obtain the received information r for decision, r = 0 or r = 1; Step S4: the fusion center extracts low-complexity decision metric values without channel state information according to the received information r; Step S5: the low-complexity decision metric values extracted in step S4 are compared with a decision threshold to obtain a final detection result.

2. The single channel, backscatter-assisted, multi-sensor distributed detection method of claim 1, wherein: The forwarding mode adopted by the relay node in step S3 is amplify-forward, and the amplification coefficient is 1.

3. The single channel, backscatter-assisted, multi-sensor distributed detection method of claim 1, wherein, The method for the fusion center to extract low-complexity decision metric values in step S4 is: Λ = 2r-1 Wherein, Λ represents low-complexity decision metric values without channel state information.

4. The single channel, down-range environment backscatter-assisted, multi-sensor distributed detection method of claim 1, wherein: The specific method for the low-complexity decision metric values to be compared with the decision threshold in step S5 is that when the low-complexity decision metric value is greater than or equal to the decision threshold, the estimation value of the information u generated by the signal source is 1; and when the low-complexity decision metric value is less than or equal to the decision threshold, the estimation value of the information u generated by the signal source is 0.

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