Remote intrusion detection method based on RSS
By using ratio method and Rayleigh filter in WiFi intrusion detection to remove noise and perform fast Fourier transform, the maximum amplitude value except for the 0 frequency component is found, which solves the problem of insufficient sensitivity to long-distance detection and achieves more Wide range of intrusion detection.
Patent Information
- Application Number
- CN202411213628.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-30
- Publication Date
- 2024-12-20
AI Technical Summary
The existing RSS-based intrusion detection method has a small dynamic component when the target is far away from the WiFi device, resulting in insufficient detection sensitivity and inability to achieve long-distance intrusion detection.
By collecting wireless signal data transmitted by the WiFi transmitter, amplitude noise is removed by using the ratio method, the Rayleigh filter removes the measurement noise, performs fast Fourier transform, finds the maximum amplitude value except the 0 frequency component as the threshold, and uses the sliding window Continuously calculate whether the maximum amplitude value of RSS data exceeds the threshold to detect intrusion.
The sensitivity of RSS data to dynamic components is improved, the perceived range of intrusion detection is expanded, and effective intrusion detection is achieved under long distances.
Smart Images
Figure CN119172015A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of intrusion detection, and in particular relates to a long-distance intrusion detection method based on RSS. Background Art
[0002] In recent years, with the rapid development of wireless technology, intrusion detection has attracted more and more attention and has great application potential in many application fields such as border protection, smart home, and elderly healthcare. Various intrusion detection technologies have been proposed, such as vision-based, acoustic-based, and sensor-based methods. Vision-based methods are greatly affected by light, have limited detection effects in dim scenes, and cause serious privacy issues for home users. Although acoustic-based methods can solve privacy issues, the detection accuracy is easily affected by environmental noise. Sensor-based methods require additional hardware equipment, which increases costs. Therefore, people strongly hope to adopt ubiquitous, low-cost, and privacy-first passive intrusion detection methods.
[0003] WiFi-based intrusion detection methods can meet the above needs. So far, the received WiFi signal measurement methods include received signal strength (RSS) and channel state information (CSI), which passively detect moving targets by identifying changes. The basic principle of WiFi-based methods is that the key characteristics of sensitive wireless signals change with nearby intrusions. At present, WiFi-based methods can effectively detect human motion by utilizing changes in RSS measurements. When more smart devices (such as refrigerators, TVs, air conditioners, etc.) are connected, the RSS-based intrusion detection system can expand the monitoring range. Compared with RSS, CSI contains the amplitude and phase of each subcarrier, is more sensitive to the path changes of WiFi signals, and has a higher detection rate for human motion. However, CSI requires more complex hardware support and occupies a lot of computing resources, which limits the application of CSI in daily home monitoring.
[0004] In recent years, many RSS-based contactless human activity recognition systems have been proposed. For example, Heba Abdelnasser Awad et al. from Imperial College London used pattern matching to construct and recognize independent gesture families around the receiver in a paper titled A ubiquitous wifi-based fine-grained gesture recognition system published in IEEE Transactions on Mobile Computing. Zicheng Chi et al. from Cleveland State University used RSS as input data for their designed human activity recognition system in a paper titled Harmony: Exploiting coarse-grained received signal strength from iot devices for human activity recognition published in International Conference on Network Protocols, and used three pairs of IoT devices to recognize nine coarse-grained activities. Majid GhosianMoghaddam et al. from the University of Ottawa used RSS and random forests to detect seven types of human-to-human interactions in a paper titled A wifi-based method for recognizing fine-grained multiple-subject human activities published in IEEE Transactions on Instrumentation and Measurement.
[0005] However, RSS data is less sensitive to the path changes of WiFi signals. When the research object is far away from the WiFi device and the channel noise is large, the effective range of the above-mentioned prior art methods for detecting intrusion is short. In actual home environments, due to the complex environment and cheap equipment, long-distance intrusion detection cannot be achieved in most cases. Summary of the invention
[0006] In order to overcome the defects of the prior art and realize non-contact intrusion detection at a long distance, the present invention first collects wireless signal data transmitted by a WiFi transmitter in an indoor environment, extracts RSS raw data from the WiFi signal, and intercepts a section of RSS raw data in a static environment, then uses a ratio method to remove the amplitude noise of the RSS data and the RSS data in the static environment, uses a Rayleigh filter to remove the measurement noise of the RSS data and the RSS data in the static environment, then performs a fast Fourier transform on the denoised RSS data in the static environment, finds the maximum amplitude value excluding the 0 frequency component as a threshold, and finally calculates the maximum amplitude value of the denoised RSS data excluding the 0 frequency component. When the maximum amplitude value is greater than the threshold, intrusion is detected.
[0007] In order to achieve the above object, the present invention is achieved through the following technical solutions:
[0008] The present invention is a long-distance intrusion detection method based on RSS, which specifically comprises the following steps:
[0009] Step 1: In an indoor environment, collect the wireless signal data transmitted by the WiFi transmitter through communication equipment, extract the received signal strength (RSS) raw data from the WiFi signal and intercept a section of RSS data in a static environment;
[0010] Step 2: Use the ratio method to remove the amplitude noise of the RSS data and the RSS data in the static environment, and use the Rayleigh filter to remove the measurement noise of the RSS data and the RSS data in the static environment;
[0011] Step 3: Perform fast Fourier transform on the RSS data in a static environment after denoising, and find the maximum amplitude value except the 0 frequency component as the threshold;
[0012] Step 4: intercept the denoised RSS data and calculate the maximum amplitude value except the 0 frequency component. When the maximum amplitude value is greater than the threshold, intrusion is detected.
[0013] A further improvement of the present invention is that in step 1, the wireless signal data transmitted by the WiFi transmitter is collected by a communication device in an indoor environment, the received signal strength (RSS) raw data is extracted from the WiFi signal, and a section of RSS data in a static environment is intercepted, specifically:
[0014] Step 1-1, the device includes two commercial WiFi devices equipped with Intel 5300 network cards to collect RSS data, and each device has an external antenna. The frequency is set to 5.24GHz, the bandwidth is set to 20MHz, the transmitter sends 200 data packets per second, and the transmission power is 15dBm. The receiver router extracts the original received signal strength RSS data from the collected WiFi signal, and the calculation formula is:
[0015]
[0016] Where f represents the subcarrier frequency, t represents time, p represents the pth antenna pair, H(f,t) represents the channel frequency response of the signal at frequency f and time t, and A noise is the amplitude noise, P t is the transmission power, RSS(f,f) is the original received signal strength RSS data;
[0017] Step 1-2: Extract a section of RSS data in a static environment from the original received signal strength RSS data. S (f, t).
[0018] A further improvement of the present invention is that in step 2, a ratio method is used to remove the amplitude noise of the RSS data and the RSS data in a static environment, and a Rayleigh filter is used to remove the measurement noise of the RSS data and the RSS data in a static environment, specifically:
[0019] Step 2-1: Select the received signal strength data at a certain moment as the reference data, and divide the original RSS data by the reference data to remove the amplitude noise. The calculation formula is:
[0020]
[0021] Wherein, t0 represents the time point of the selected reference data, and R(f,t) is the RSS data after removing the amplitude noise;
[0022] Step 2-2: Use Rayleigh filter to filter the RSS data with amplitude noise removed to remove measurement noise. express;
[0023] Step 2-3: Use the ratio method and Rayleigh filter to remove the amplitude noise and measurement noise of the RSS data in a static environment. express.
[0024] A further improvement of the present invention is that in step 3, a fast Fourier transform is performed on the RSS data in a static environment after denoising, and the maximum amplitude value except the 0 frequency component is found as the threshold, specifically:
[0025] Step 3-1: Perform fast Fourier transform on the RSS data in a static environment after denoising. The calculation formula is:
[0026]
[0027] Among them, FFT is fast Fourier transform, S S (w) is The spectrum obtained after fast Fourier transform in the time dimension, w is the frequency in the spectrum;
[0028] Step 3-2, find the maximum amplitude value except the 0 frequency component as the threshold, and its calculation formula is:
[0029] TH=max(S S (w)|w≠0)
[0030] Among them, TH is the threshold value.
[0031] A further improvement of the present invention is that in step 4, the denoised RSS data is intercepted and the maximum amplitude value except the 0 frequency component is calculated. When the maximum amplitude value is greater than the threshold, an intrusion is detected, specifically:
[0032] Step 4-1: Use a sliding window to continuously capture the denoised RSS data. The size of the sliding window is consistent with the size of the RSS data captured in the static environment. express;
[0033] Step 4-2: Perform fast Fourier transform on the intercepted RSS data. The calculation formula is:
[0034]
[0035] Where S(w) is The spectrum obtained after fast Fourier transform in the time dimension;
[0036] Step 4-3, find the maximum amplitude value except the 0 frequency component, the calculation formula is:
[0037] A max =max(S(w)|w≠0)
[0038] Among them, A max It is the maximum amplitude value of the intercepted RSS data except the 0 frequency component;
[0039] Step 4-4: When the maximum amplitude value of the intercepted RSS data except the zero frequency component is greater than the threshold, an intrusion is detected.
[0040] The beneficial effects of the present invention are as follows: the present invention removes the amplitude noise of RSS data by a ratio method, removes the measurement noise of RSS data by a Rayleigh filter, improves the sensitivity of RSS data to dynamic components, and uses the denoised RSS data in a static environment to perform a fast Fourier transform, finds the maximum amplitude value except the 0-frequency component as a threshold, and uses a sliding window to continuously calculate whether the maximum amplitude value of RSS data except the 0-frequency component exceeds the threshold. If the threshold is exceeded, an intrusion behavior is detected. The problem that the target is far away from the WiFi device, resulting in the RSS data with a small dynamic component being not sensitive enough to realize long-distance intrusion detection, is solved, and the perception range of using RSS to detect intrusion is improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 It is a schematic diagram of the detection method of the present invention.
[0042] Figure 2 It is the spectrum diagram of RSS in a static environment and under intrusion behavior of the present invention.
[0043] Figure 3 It is a schematic diagram of the intrusion detection effect of the present invention. DETAILED DESCRIPTION
[0044] The following will disclose the embodiments of the present invention with drawings. For the purpose of clear description, many practical details will be described together in the following description. However, it should be understood that these practical details should not be used to limit the present invention. That is to say, in some embodiments of the present invention, these practical details are not necessary.
[0045] like Figure 1 As shown, after WiFi transmits and receives reflected signals to the human body, it generates RSS raw data. The present invention first intercepts a section of RSS raw data in a static environment and uses a ratio method to remove the amplitude noise of the RSS data and the RSS data in the static environment. Then the present invention uses a Rayleigh filter to remove the measurement noise of the RSS data and the RSS data in the static environment. Next, the present invention performs a fast Fourier transform on the denoised RSS data in a static environment, and finds the maximum amplitude value excluding the 0-frequency component as a threshold. Finally, the present invention calculates the maximum amplitude value of the denoised RSS data excluding the 0-frequency component. When the maximum amplitude value is greater than the threshold, an intrusion is detected. The intrusion detection method of the present invention specifically includes the following steps:
[0046] Step 1: In an indoor environment, complete the collection of wireless signal data transmitted by the WiFi transmitter through communication equipment, extract the received signal strength (RSS) raw data from the WiFi signal and intercept a section of RSS data in a static environment. The specific steps are as follows:
[0047] Step 1-1: The equipment includes two commercial WiFi devices equipped with Intel 5300 network cards to collect RSS data. Each device has an external antenna. The frequency is set to 5.24GHz, the bandwidth is set to 20MHz, the transmitter sends 200 data packets per second, and the transmission power is 15dBm. The receiver router extracts the original received signal strength RSS data from the collected WiFi signal, and the calculation formula is:
[0048]
[0049] Where f represents the subcarrier frequency, t represents time, p represents the pth antenna pair, H(f,t) represents the channel frequency response of the signal at frequency f and time t, and A noise is the amplitude noise, P t is the transmission power, RSS(f,t) is the original received signal strength RSS data;
[0050] Step 1-2: Extract a section of RSS data in a static environment from the original received signal strength RSS data. S (f, t).
[0051] Step 2: Use the ratio method to remove the amplitude noise of the RSS data and the RSS data in the static environment, and use the Rayleigh filter to remove the measurement noise of the RSS data and the RSS data in the static environment. The specific steps are:
[0052] Step 2-1: Select the received signal strength data at a certain moment as the reference data, and divide the original RSS data by the reference data to remove the amplitude noise. The calculation formula is:
[0053]
[0054] Wherein, t0 represents the time point of the selected reference data, and R(f,t) is the RSS data after removing the amplitude noise;
[0055] Step 2-2: Use Rayleigh filter to filter the RSS data with amplitude noise removed to remove measurement noise. The filtered RSS data is used express;
[0056] Step 2-3: Use the ratio method and Rayleigh filter to remove the amplitude noise and measurement noise of the RSS data in a static environment. express.
[0057] Step 3: Perform fast Fourier transform on the RSS data in a static environment after denoising, and find the maximum amplitude value except the 0 frequency component as the threshold. The specific steps are as follows:
[0058] Step 3-1: Perform fast Fourier transform on the RSS data in a static environment after denoising. The calculation formula is:
[0059]
[0060] Among them, FFT is fast Fourier transform, S S (w) is The spectrum obtained after fast Fourier transform in the time dimension, w is the frequency in the spectrum;
[0061] Step 3-2: Find the maximum amplitude value except the 0 frequency component as the threshold, and its calculation formula is:
[0062] TH=max(S S (w)|w≠0)
[0063] Among them, TH is the threshold value.
[0064] Step 4: intercept the denoised RSS data and calculate the maximum amplitude value except the 0 frequency component. When the maximum amplitude value is greater than the threshold, intrusion is detected, such as Figure 2 As shown in the figure, the spectrum diagram of RSS in a static environment and with intrusion behavior. When there is intrusion behavior, the maximum amplitude value of RSS except the 0 frequency component is much larger than the maximum amplitude value of RSS except the 0 frequency component in a static environment. The specific steps are:
[0065] Step 4-1: Use a sliding window to continuously intercept the denoised RSS data. The size of the sliding window is consistent with the size of the RSS data intercepted in the static environment. express;
[0066] Step 4-2: Perform fast Fourier transform on the intercepted RSS data. The calculation formula is:
[0067]
[0068] Where S(w) is The spectrum obtained after fast Fourier transform in the time dimension;
[0069] Step 4-3: Find the maximum amplitude value except the 0 frequency component. The calculation formula is:
[0070] A max =max(S(w)|w≠0)
[0071] Among them, A max It is the maximum amplitude value of the intercepted RSS data except the 0 frequency component;
[0072] Step 4-4: When the maximum amplitude value of the intercepted RSS data except the 0 frequency component is greater than the threshold, intrusion is detected.
[0073] In order to verify the effect of the present invention, Figure 3 As shown, the present invention uses RSS to achieve the effect of intrusion detection. The environment in which the present invention collects data is an indoor environment. During the experiment, the experimental subject gradually approaches the WiFi device from 10m away. Figure 3 The intrusion detection effect of four subjects is shown in FIG. 1 , where the solid line represents the subject, the dotted line represents the threshold, and the intrusion can be detected when the threshold is above the dotted line. When the distance between the experimental subject and the WiFi transceiver is 8m, the present invention can still achieve intrusion detection.
[0074] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims
1. A long-distance intrusion detection method based on RSS, characterized in that: The intrusion detection method specifically comprises the following steps: Step 1: In an indoor environment, wireless signal data transmitted by a WiFi transmitter is collected through a communication device, raw received signal strength (RSS) data is extracted from the WiFi signal, and RSS data in a static environment is intercepted; Step 2: using a ratio method to remove the amplitude noise of the RSS data and the RSS data in a static environment, and using a Rayleigh filter to remove the measurement noise of the RSS data and the RSS data in a static environment; Step 3: Perform fast Fourier transform on the RSS data in the static environment after denoising, and find the maximum amplitude value except the 0 frequency component as the threshold; Step 4: intercept the denoised RSS data and calculate the maximum amplitude value except the 0 frequency component. When the maximum amplitude value is greater than the threshold, intrusion is detected.
2. The RSS-based long-distance intrusion detection method according to claim 1, characterized in that: The step 1 specifically includes the following steps: Step 1-1, the device includes two commercial WiFi devices equipped with Intel 5300 network cards to collect RSS data. Each device has an external antenna, the frequency is set to 5.24GHz, the bandwidth is set to 20MHz, the transmitter sends 200 data packets per second, the transmission power is 15dBm, and the receiver router extracts the original received signal strength RSS data from the collected WiFi signal. The calculation formula is: Where f represents the subcarrier frequency, t represents time, p represents the pth antenna pair, H(f,t) represents the channel frequency response of the signal at frequency f and time t, and A noise is the amplitude noise, P t is the transmission power, RSS(f,t) is the original received signal strength RSS data; Step 1-2: Extract a section of RSS data in a static environment from the original received signal strength RSS data. S (f, t).
3. The RSS-based long-distance intrusion detection method according to claim 1, characterized in that: The step 2 specifically includes the following steps: Step 2-1: Select the received signal strength data at a certain moment as the reference data, and divide the original RSS data by the reference data to remove the amplitude noise. The calculation formula is: Wherein, t0 represents the time point of the selected reference data, and R(f,t) is the RSS data after removing the amplitude noise; Step 2-2: Use Rayleigh filter to filter the RSS data with amplitude noise removed to remove measurement noise. express; Step 2-3: Use the ratio method and Rayleigh filter to remove the amplitude noise and measurement noise of the RSS data in a static environment. express.
4. The RSS-based long-distance intrusion detection method according to claim 1, characterized in that: The step 3 specifically includes the following steps: Step 3-1: Perform fast Fourier transform on the RSS data in a static environment after denoising. The calculation formula is: Among them, FFT is fast Fourier transform, S S (w) is The spectrum obtained after fast Fourier transform in the time dimension, w is the frequency in the spectrum; Step 3-2, find the maximum amplitude value except the 0 frequency component as the threshold, and its calculation formula is: TH=max(S S (w)|w≠0) Among them, TH is the threshold value.
5. The RSS-based long-distance intrusion detection method according to claim 1, characterized in that: The step 4 specifically includes the following steps: Step 4-1: Use a sliding window to continuously capture the denoised RSS data. The size of the sliding window is consistent with the size of the RSS data captured in the static environment. express; Step 4-2: Perform fast Fourier transform on the intercepted RSS data. The calculation formula is: Where S(w) is The spectrum obtained after fast Fourier transform in the time dimension; Step 4-3, find the maximum amplitude value except the 0 frequency component, the calculation formula is: A max =max(S(w)|w≠0) Among them, A max It is the maximum amplitude value of the intercepted RSS data except the 0 frequency component; Step 4-4: When the maximum amplitude value of the intercepted RSS data except the zero frequency component is greater than the threshold, an intrusion is detected.