Wireless sensor signal detection method based on joint design of review and quantification

Abstract

This invention discloses a wireless sensor signal detection method based on a combined censoring and quantization design. First, a method for transmitting observation values ​​that combines censoring and quantization is designed based on the log-likelihood ratio of the observation values. Next, a maximum likelihood detector based on censoring and quantization is designed for the fusion center. Finally, a method for selecting the optimal censoring and quantization threshold is proposed to maximize detector performance. The technical solution provided by this invention improves the energy efficiency of wireless sensor network systems. Therefore, the technical solution provided by this invention can effectively improve the energy utilization efficiency of sensors while ensuring sufficient signal detection performance.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a wireless sensor signal detection method based on joint design of review and quantization. Background Art

[0002] The Internet of Things (IoT) is a rapidly developing emerging information industry, combining the latest technologies in mobile communications and the internet. Intelligent perception of the real world is crucial in IoT applications. Wireless sensor networks, with their low cost, flexible deployment, and powerful self-organizing capabilities, play a key role in this intelligent perception.

[0003] Distributed signal detection technology is widely used in sensing applications within wireless sensor networks. This technology relies on a group of wireless sensors and a fusion center to detect specific target signals. These sensors, distributed across a detection area, use sensing components to collect observations from the environment, quantify them, and transmit them to the fusion center. The fusion center receives the observations from all sensors and combines them to determine whether the target signal exists.

[0004] Although the fusion center relies on observations collected by sensors, these observations are not always valuable because the target signal is not always present. When the target signal is absent, the collected observations are essentially pure environmental noise and interference. To avoid excessive energy and communication resource consumption caused by transmitting useless observations, optimized sensor transmission schemes have been widely researched. The idea is to transmit only valuable observations while minimizing performance loss, thereby reducing overall sensor energy consumption. Existing control schemes fall into two main categories: sensor selection and sensor review. In the sensor selection scheme, the fusion center is responsible for determining which sensors will transmit observation data. In the sensor review scheme, the sensors first perform a preliminary review of the observations and, if they are deemed valuable, forward them to the fusion center. Compared to the sensor selection scheme, the sensor review scheme allows the sensors to independently determine their transmission status without requiring coordination by the fusion center, making it more flexible.

[0005] Existing sensor review schemes often assume that the observations received by the fusion center are accurate, unquantized observations. This means that in real systems, very high sensor quantization accuracy is required to implement existing review schemes, which runs counter to the requirement for low sensor energy consumption in real perception systems. Summary of the Invention

[0006] To address the limitations and defects of the prior art, the present invention provides a wireless sensor signal detection method based on a joint design of review and quantification, comprising:

[0007] M sensors are dispersedly set in the preset area;

[0008] The sensor observes the preset area, obtains the observation value of the target signal source in the preset area, and transmits the observation value to the corresponding review and quantification module;

[0009] The review and quantification module determines whether the observation value is valuable;

[0010] If the review and quantization module determines that the observed value is valuable, the sensor performs single-bit quantization on the observed value to form a quantized value, and transmits the quantized value back to the fusion center; if the review and quantization module determines that the observed value is worthless, the sensor remains silent;

[0011] The fusion center determines whether the target signal source exists according to the quantized value and a maximum likelihood detector based on audit quantization.

[0012] Optionally, the step of obtaining the observation value of the target signal source in the preset area includes:

[0013] The expression of the observation value obtained by the mth sensor is as follows:

[0014]

[0015] in, is the target signal to be detected, It is subject to the mean of 0 and the variance of Gaussian white noise, the observations obey the Gaussian distribution:

[0016] Optionally, if the review and quantization module determines that the observation value is valuable, performing single-bit quantization on the observation value to form a quantized value, and transmitting the quantized value back to the fusion center; if the review and quantization module determines that the observation value is worthless, the step of the sensor remaining silent includes:

[0017] The log-likelihood ratio of the observed value is calculated, and the expression of the log-likelihood ratio is as follows:

[0018]

[0019] Among them, x m is the observed value, L(x m ) is the log-likelihood ratio of the observations;

[0020] If the log-likelihood ratio of the observation value is greater than or equal to the review threshold, the observation value is single-bit quantized to form a quantized value, and the quantized value is sent back to the fusion center; if the log-likelihood ratio of the observation value is less than the review threshold, the sensor remains silent.

[0021] Optionally, the step of performing single-bit quantization on the observation value to form a quantized value and transmitting the quantized value back to the fusion center includes:

[0022] Comparing the observed value with a quantization threshold;

[0023] If the comparison result shows that the observed value is less than the quantization threshold, the observed value is quantized to "-1" and "-1" is sent back to the fusion center; if the comparison result shows that the observed value is greater than or equal to the quantization threshold, the observed value is quantized to "1" and "1" is sent back to the fusion center; the expression is as follows:

[0024]

[0025] Among them, η 1，m is the review threshold, η 2，m is the quantization threshold;

[0026] The threshold for review quantization is calculated according to expressions (2) and (3), as follows:

[0027]

[0028] Among them, τ 1，m is the equivalent review threshold of the mth sensor, τ 2，m is the equivalent quantization threshold value of the mth sensor.

[0029] Optionally, also include:

[0030] According to the Bayesian criterion, the maximum a posteriori probability detector is equivalently converted into a maximum likelihood detector, and the expression is as follows:

[0031]

[0032] Among them, p0 is the prior probability corresponding to H0, p1 is the prior probability corresponding to H1,

[0033] According to the independence of the observation values ​​of each sensor, the expression of the likelihood ratio is transformed into:

[0034]

[0035] Taking the logarithm of expression (6), the log-likelihood ratio obtained using expression (2) is used to represent the detector, and the expression is as follows:

[0036]

[0037] The fusion center expresses the distribution of received observations according to expression (4) as follows:

[0038]

[0039] Substituting expression (8) into expression (2), we can obtain the log-likelihood ratio of the observed value after censoring and quantization, which is as follows:

[0040] L(x m ∈R i )=lnγ 1,j,i / γ 0,j,i ,i∈{-1,0,1}

[0041] Substituting the expression of the log-likelihood ratio of the observation value after censored quantization into expression (7), the expression of the maximum likelihood detector based on censored quantization is obtained as follows:

[0042]

[0043] in, is the detection threshold, T CQ-ML is the test statistic of the detector.

[0044] Optionally, also include:

[0045] The performance of the detector is expressed using the detection probability and false alarm probability, as follows:

[0046]

[0047] The statistical properties of the test statistic are derived using the central limit theorem, which is expressed as follows:

[0048]

[0049] in, It means that it asymptotically obeys a certain distribution. Indicates that the mean is μ CQ-ML,0 , the variance is Gaussian distribution;

[0050] According to expressions (10) and (11), the detection probability and false alarm probability of the maximum likelihood detector based on audit quantization are obtained as follows:

[0051]

[0052] Establish an optimization problem with the goal of maximizing detection performance, which is expressed as follows:

[0053]

[0054] Among them, P c is the average backhaul ratio of the sensor network, expressed as follows:

[0055]

[0056] According to the optimization problem, the optimal value of the review quantization threshold is as follows:

[0057]

[0058] Among them, τ 1，m，opt is the optimal value of the censorship threshold of the mth sensor, τ 2，m，opt is the optimal value of the quantization threshold of the mth sensor.

[0059] The present invention has the following beneficial effects:

[0060] This invention provides a wireless sensor signal detection method based on a joint design of censoring and quantization. First, a method for transmitting observation values ​​that combines censoring and quantization is designed based on the log-likelihood ratio of the observation values. Next, a maximum likelihood detector based on censoring and quantization is designed for the fusion center. Finally, a method for selecting the optimal censoring and quantization threshold is proposed to maximize detector performance. The technical solution provided by this invention improves the energy efficiency of wireless sensor network systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the system model of the wireless sensor signal detection method based on joint design of review and quantification provided in the first embodiment of the present invention.

[0062] Figure 2a Schematic diagram of the log-likelihood ratio-based review and quantification mechanism provided in Example 1 of the present invention.

[0063] Figure 2b Schematic diagram of the review and quantification mechanism in the observation value domain provided in Example 1 of the present invention.

[0064] Figure 3 This is a flow chart of a wireless sensor signal detection method based on joint design of review and quantification provided in the first embodiment of the present invention.

[0065] Figure 4 This is a diagram of simulation results under different signal-to-noise ratios for the wireless sensor signal detection method based on joint review and quantization design provided in Example 1 of the present invention.

[0066] Figure 5 This is a diagram of simulation results under different sensor quantities of the wireless sensor signal detection method based on joint design of review and quantization provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0067] To enable those skilled in the art to better understand the technical solution of the present invention, the wireless sensor signal detection method based on joint design of review and quantification provided by the present invention is described in detail below with reference to the accompanying drawings.

[0068] Example 1

[0069] 1. Overview

[0070] To improve the energy efficiency of wireless sensor network systems, this embodiment proposes a distributed signal detection method based on a joint design of review and single-bit quantization. This embodiment provides a wireless sensor signal detection method based on a joint design of review and quantization. By reviewing and quantizing the observed values ​​at the sensor end, the sensor's energy consumption is significantly reduced. Combined with a maximum likelihood detector based on review and quantization at the fusion center and a method for selecting the optimal value of the review and quantization threshold designed to optimize detection performance, the technical solution provided by the present invention can effectively improve the energy utilization efficiency of the sensor while ensuring sufficient signal detection performance.

[0071] 2. Technical route

[0072] This embodiment first designs an observation value feedback method that combines censoring and quantization mechanisms based on the log-likelihood ratio of the observation values. Then, a censoring-quantization maximum likelihood (CQ-ML) detector based on censoring and quantization is designed for the fusion center. Finally, a censoring and quantization optimal threshold selection method is proposed with the goal of maximizing detector performance.

[0073] This section first introduces the system model of the distributed signal detection scenario, and then proposes the review quantization transmission mechanism at the sensor end, the maximum likelihood detector at the fusion center, and the optimal value method of the review quantization threshold.

[0074] 2.1 System Model

[0075] The distributed signal detection system model studied in this embodiment is as follows Figure 1 As shown in the figure, M distributed sensors observe areas where target signal sources may be present. The collected observations are then evaluated by the review and quantization module. If an observation is deemed valuable, it is quantized using a single bit and transmitted back to the fusion center. If it is deemed worthless, the sensor remains silent. After receiving all transmitted quantized observations, the fusion center uses the CQ-ML detector to determine whether the target signal source exists, completing the detection process.

[0076] In this model, the observation value collected by the mth sensor at a certain moment can be expressed as:

[0077]

[0078] Where, is the target signal to be detected, It is subject to the mean of 0 and the variance of Gaussian white noise. In the detection scenario of this embodiment, the distribution parameters of the target signal and noise are assumed to be known. Therefore, the observed value should also obey the Gaussian distribution: in

[0079] 2.2 Review of Quantitative Mechanism

[0080] The purpose of reviewing observations is to discard as many worthless observations as possible and return valuable observations. The value of an observation can be measured by its log-likelihood ratio. According to the definition of likelihood ratio, the log-likelihood ratio of an observation can be expressed as:

[0081]

[0082] The likelihood ratio-based review mechanism can be summarized as: discard L(x m ) is smaller than the censoring threshold, and the remaining observations are transmitted back. Because the observations need to be quantized to a single bit before being transmitted back, the returned observations are further divided into two parts: the part smaller than the quantization threshold is quantized to "0" and transmitted back, and the remaining part is quantized to "1" and transmitted back. Therefore, the complete censoring and quantization mechanism on the sensor can be summarized as:

[0083]

[0084] In order to avoid consuming sensor computing power by calculating the log-likelihood ratio, the thresholds for review quantization are calculated by combining expressions (2) and (3) as follows:

[0085]

[0086] Where, τ 1,m ,τ 2,m are the equivalent audit and quantization threshold values ​​of the mth sensor respectively. The optimal value selection method for these two thresholds will be introduced in detail in Section 2.4. Figure 2a and Figure 2b A simplified diagram of this step is given, Figure 2a It represents the three decision domains divided according to the log-likelihood ratio, corresponding to the three states of "keep silent", "echo -1" and "echo 1" respectively. Figure 2b These are the three decision domains simplified based on the log-likelihood ratio formula, corresponding to expression (4).

[0087] 2.3 Maximum Likelihood Detector

[0088] Since the distribution parameters of the target signal are known, the optimal detector is the maximum a posteriori probability detector. Based on the Bayesian criterion, the maximum a posteriori probability detector can be equivalently converted into a maximum likelihood detector:

[0089]

[0090] Where p0 and p1 are the prior probabilities corresponding to H0 and H1 respectively. Taking into account the independence of each sensor observation, the likelihood ratio can be expressed as:

[0091]

[0092] To find the logarithm of this formula, we can use the log-likelihood ratio obtained from expression (2) to represent the detector:

[0093]

[0094] Where, is the detection threshold, T CQ-ML is the test statistic of the detector.

[0095] In order to calculate the above log-likelihood ratio, the distribution of the observations received by the fusion center can be expressed as follows according to expression (4):

[0096]

[0097] Substituting expression (8) into expression (2), we can get the log-likelihood ratio expression of the observed value after censored quantization: L(x m ∈R i )=lnγ 1,j,i / γ 0,j,i ,i∈{-1,0,1}. Substituting this formula into expression (7), the complete CQ-ML detector in this scenario can be expressed as:

[0098]

[0099] This embodiment provides a wireless sensor signal detection method based on a combined censoring and quantization design. By censoring and quantizing observations at the sensor end, it significantly reduces sensor energy consumption. Combined with a maximum likelihood detector based on censoring and quantization at the fusion center and a method for optimizing censoring and quantization thresholds designed to maximize detection performance, the technical solution provided by this embodiment effectively improves sensor energy efficiency while ensuring adequate signal detection performance.

[0100] 2.4 Optimal Review Quantization Threshold

[0101] In order to improve the detector performance, this embodiment further sets the optimal review quantization threshold in Section 2.3: 1,m ,τ 2,m , 1≤m≤M, where the detector performance is expressed by the detection probability and false alarm probability as:

[0102]

[0103] Based on the independence of the observations of each sensor, the statistical characteristics of the test statistic can be derived from the central limit theorem:

[0104]

[0105] Where, represents asymptotically obeying a certain distribution, The representative mean is μ CQ-ML,0 , the variance is Gaussian distribution. Combining expressions (10) and (11), the detection probability and false alarm probability of the CQ-ML detector are:

[0106]

[0107] Based on this, the optimization problem with the goal of maximizing detection performance can be established as follows:

[0108]

[0109] Where, P c is the average backhaul ratio of the entire sensor network and is defined as follows:

[0110]

[0111] Solving this optimization problem, we can get the optimal value of the review quantization threshold as:

[0112]

[0113] Based on the above process, the complete process of the distributed signal detection method proposed in this embodiment based on the review quantization mechanism is as follows: Figure 1 As shown. The entire process can be divided into offline preparation and online work stages. In the offline preparation stage, the fusion center is responsible for calculating the probability distribution of each sensor's observation value and calculating the optimal threshold of the review quantization mechanism according to expression (15) and sending it to each sensor. The system then enters the online work stage, where the sensor observes the signal and determines its own return status according to expression (4). After receiving all the returned observation values, the fusion center determines whether the target signal exists according to expression (9).

[0114] 3. Experimental Results

[0115] To verify the performance of the proposed method, this embodiment simulates the signal detection performance of the following three algorithms under different environments:

[0116] Method 1: The detection method with review and quantification mechanism proposed in this embodiment;

[0117] Method 2: A detection method that only performs single-bit quantization on the observed value without review;

[0118] Method 3: Only the observations are reviewed, and the returned observations are not quantified.

[0119] Figure 3 The detection accuracy of the three methods under different signal-to-noise ratios (the ratio of target signal power to noise power) is shown. The number of sensors is set to 100, and the pass rate of method 1 and method 3 is set to P c =0.6. Figure 3 It can be seen that the higher the power of the target signal, the better the detection performance of each method. Under the same target signal power and signal-to-noise ratio conditions, the method proposed in this embodiment is better than Method 2. The results show that compared with the pure single-bit quantization mechanism with only two states, the proposed method has three transmission states and can transmit more information to the fusion center. Compared with Method 1 and Method 3, which also review the observation values, although Method 3 does not quantize the observation values, its performance is still inferior to Method 1. This is because the method of this embodiment optimizes the CQ-ML detector for the review quantization mechanism, and the performance gain brought about by this optimization exceeds the information loss caused by the quantization itself. This result further proves the effectiveness of the CQ-ML detector proposed in this embodiment.

[0120] Figure 4 The detection accuracy of the three methods with different numbers of sensors is shown. The signal-to-noise ratio is set to 0dB, and the pass rate of method 1 and method 3 is set to P c = 0.6. As the figure shows, the performance of all methods improves with increasing sensor counts, ultimately converging when the number of sensors is sufficiently large. Compared with Methods 2 and 3, Method 1 proposed in this example demonstrates significant performance advantages. This is consistent with simulation results at different signal-to-noise ratios, further demonstrating the effectiveness and advantages of this method.

[0121] 4. Conclusion

[0122] This example investigates a distributed signal detection method for wireless sensor networks based on a joint design of censoring and quantization. By censoring and quantizing observations at the sensor end, this method significantly reduces sensor energy consumption. Combined with the CQ-ML detector developed at the fusion center and a method for optimizing censoring and quantization thresholds designed to maximize detection performance, this method effectively improves sensor energy efficiency while ensuring adequate signal detection performance.

[0123] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A wireless sensor signal detection method based on joint design of review and quantification, characterized in that: include: M sensors are dispersedly set in the preset area; The sensor observes the preset area, obtains the observation value of the target signal source in the preset area, and transmits the observation value to the corresponding review and quantification module; The review and quantification module determines whether the observation value is valuable; If the review and quantization module determines that the observed value is valuable, the sensor performs single-bit quantization on the observed value to form a quantized value, and transmits the quantized value back to the fusion center; if the review and quantization module determines that the observed value is worthless, the sensor remains silent; The fusion center determines whether the target signal source exists according to the quantized value and a maximum likelihood detector based on review quantization; If the review and quantization module determines that the observation value is valuable, the observation value is single-bit quantized to form a quantized value, and the quantized value is returned to the fusion center; If the review and quantification module determines that the observation value is worthless, the step of keeping the sensor silent includes: The log-likelihood ratio of the observed value is calculated, and the expression of the log-likelihood ratio is as follows: Among them, x m is the observed value, L(x m ) is the log-likelihood ratio of the observations; If the log-likelihood ratio of the observation value is greater than or equal to the review threshold, the observation value is single-bit quantized to form a quantized value, and the quantized value is transmitted back to the fusion center; if the log-likelihood ratio of the observation value is less than the review threshold, the sensor remains silent; The step of performing single-bit quantization on the observation value to form a quantized value and transmitting the quantized value back to the fusion center includes: Comparing the observed value with a quantization threshold; If the comparison result shows that the observed value is less than the quantization threshold, the observed value is quantized to "-1" and "-1" is returned to the fusion center; if the comparison result shows that the observed value is greater than or equal to the quantization threshold, the observed value is quantized to "1" and "1" is returned to the fusion center; the expression is as follows: Among them, η 1，m is the review threshold, η 2，m is the quantization threshold; The threshold for review quantization is calculated according to expressions (2) and (3), as follows: Among them, τ 1，m is the equivalent review threshold of the mth sensor, τ 2，m is the equivalent quantization threshold value of the mth sensor.

2. The wireless sensor signal detection method based on joint design of review and quantification according to claim 1 is characterized in that: The step of obtaining the observation value of the target signal source in the preset area includes: The expression of the observation value obtained by the mth sensor is as follows: in, is the target signal to be detected, It is subject to the mean of 0 and the variance of Gaussian white noise, the observations obey the Gaussian distribution:

3. The wireless sensor signal detection method based on joint design of review and quantification according to claim 2 is characterized in that: Also includes: According to the Bayesian criterion, the maximum a posteriori probability detector is equivalently converted into a maximum likelihood detector, and the expression is as follows: Among them, p0 is the prior probability corresponding to H0, p1 is the prior probability corresponding to H1, According to the independence of the observation values ​​of each sensor, the expression of the likelihood ratio is transformed into: Taking the logarithm of expression (6), the log-likelihood ratio obtained using expression (2) is used to represent the detector, and the expression is as follows: The fusion center expresses the distribution of received observations according to expression (4) as follows: Substituting expression (8) into expression (2), we can obtain the log-likelihood ratio of the observed value after censoring and quantization, which is as follows: L(x m ∈R i )=lnγ 1,j,i / c 0,j,i ,i∈{-1,0,1} Substituting the expression of the log-likelihood ratio of the observation value after censored quantization into expression (7), the expression of the maximum likelihood detector based on censored quantization is obtained as follows: in, is the detection threshold, T CQ-ML is the test statistic of the detector.

4. The wireless sensor signal detection method based on joint design of review and quantification according to claim 3 is characterized in that: Also includes: The performance of the detector is expressed using the detection probability and false alarm probability, as follows: The statistical properties of the test statistic are derived using the central limit theorem, which is expressed as follows: in, It means that it asymptotically obeys a certain distribution. Indicates that the mean is μ CQ-ML,0 , the variance is Gaussian distribution; According to expressions (10) and (11), the detection probability and false alarm probability of the maximum likelihood detector based on audit quantization are obtained as follows: Establish an optimization problem with the goal of maximizing detection performance, which is expressed as follows: Among them, P c is the average backhaul ratio of the sensor network, expressed as follows: According to the optimization problem, the optimal value of the review quantization threshold is as follows: Among them, τ 1，m，opt is the optimal value of the censorship threshold of the mth sensor, τ 2，m，opt is the optimal value of the quantization threshold of the mth sensor.

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