System for automatically identifying content through wireless signal positioning

Abstract

The invention discloses a system for automatically identifying content through wireless signal positioning, and particularly relates to the field of wireless positioning, which comprises a signal base station deployment module, an antenna angle distribution detection module, a wireless signal feature analysis module, a shadow object detection and anomaly positioning verification module and a wireless signal propagation mapping module. According to the scheme, through shadow object modeling, historical track comparison and spatial consistency verification, the problem of shadow object misjudgment caused by a multi-path effect, signal reflection and shielding in the wireless signal positioning process is solved, consistency verification is performed based on the historical motion track and the physical law of the target equipment, and the positioning accuracy is improved. If it is detected that the motion trail or the signal propagation angle of the target equipment does not conform to the physical law, the system judges that the motion trail or the signal propagation angle is abnormal, a warning signal is triggered to conduct data correction, and compared with the prior art, the scheme is suitable for reducing misjudgment in a complex environment, and the stability and accuracy of wireless signal recognition are relatively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless positioning, and more specifically, to a system for automatically identifying content through wireless signal positioning. Background Art

[0002] In the prior art, automatically identifying content through wireless signal positioning refers to using a wireless signal base station to obtain the position information, identity characteristics, or status changes of a target, and realizing automatic identification through signal feature analysis, positioning algorithms, and data processing;

[0003] During the process of automatically identifying content through wireless signal positioning, due to signal reflection, multipath effects, and occlusion, a shadow object phenomenon may occur, that is, the system misidentifies a false target or incorrectly calculates the true position. In the prior art, when processing wireless signal positioning data, there is a lack of an effective detection mechanism for shadow objects, which is prone to misjudgment and affects the recognition accuracy and system stability. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a system for automatically identifying content through wireless signal positioning, which solves the problems raised in the above background art through shadow object modeling, historical trajectory comparison, and spatial consistency verification.

[0005] To achieve the above object, the present invention provides the following technical solution: A system for automatically identifying content through wireless signal positioning, including a signal base station deployment module, an antenna angle distribution detection module, a wireless signal feature analysis module, a shadow object detection and abnormal positioning verification module, and a wireless signal propagation mapping module;

[0006] The signal base station deployment module is used to deploy signal base stations in the target environment so that the signals of the signal base stations cover the target environment; the signal base stations include Wi-Fi signal base stations, UWB signal base stations, and BLE signal base stations;

[0007] The antenna angle distribution detection module is used to obtain the angle distribution data of the antennas of each signal base station. The angle distribution data includes directivity parameters and spatial coverage parameters. If the angle distribution data meets the system preset threshold, the wireless signal features are obtained and analyzed, otherwise a warning signal 1 is issued;

[0008] The wireless signal feature analysis module is used to calculate the relative positions of the target device or tag and multiple signal base stations using the time difference of arrival when obtaining and analyzing the wireless signal features; analyze the signal propagation direction using the method of arrival angle; capture the amplitude characteristics and phase characteristics of the wireless signal using the channel state information, analyze the signal feature changes of different objects, and input the data into a filter for preliminary denoising;

[0009] The shadow object detection and abnormal positioning verification module is used in the data analysis process. The system builds a shadow object model based on the historical motion trajectory comparison and spatial consistency rule verification, detects abnormal positioning results in the target environment based on the shadow object model, and verifies the consistency of physical laws based on the abnormal positioning results. If an abnormal positioning result is identified, a warning signal 2 is issued;

[0010] The wireless signal propagation mapping module is used to construct a wireless signal propagation map by synchronous positioning and mapping methods when the shadow object model fails to identify abnormal positioning results, and dynamically update the reflection information, attenuation information and occlusion information in the environment.

[0011] In a preferred embodiment, the directivity parameters include main beam width and directional gain; the spatial coverage parameters include forward and backward radiation power ratio and sidelobe suppression ratio.

[0012] In a preferred embodiment, the angle optimization of the antenna of the signal base station is calculated based on the main beam width, directional gain, forward and backward radiation power ratio, and sidelobe suppression ratio in the angle distribution data;

[0013] Main beam width θ B Used to determine the angular spread of the signal;

[0014] Directional gain G θ Used to determine the power gain of a signal from a base station in a desired direction;

[0015] The forward and backward radiated power ratio F / B is used to determine the directional contrast of the antenna;

[0016] The sidelobe suppression ratio SLL is used to reduce interference signals and optimize energy concentration;

[0017]

[0018]

[0019] Where λ is the signal wavelength; D eff is the effective aperture of the antenna; β is the environmental interference factor; d loss is the equivalent propagation distance lost by the signal during propagation;

[0020] in is the medium absorption factor, δ mat is the material absorption coefficient; θ is the angle correction factor; α loss is the path loss factor; d path is the signal propagation path length;

[0021] Among them G front is the gain of the antenna in the main direction; Gback is the gain of the antenna in the reverse direction; μ dir is the antenna directivity enhancement factor; θ front is the main direction angle; θ back is the angle in the opposite direction;

[0022] Among them G main is the main beam direction gain; G side is the sidelobe directional gain; κ main is the main beam energy enhancement factor; κ side is the sidelobe energy enhancement factor; θ main is the main beam direction angle; θ side is the sidelobe direction angle; m is the sidelobe suppression index.

[0023] In a preferred embodiment, based on θ B , G θ , F / B, SLL define the comprehensive antenna optimization objective function;

[0024]

[0025] Where O is the comprehensive antenna optimization objective function; G max is the directional gain peak; θ opt is the target beam width; (F / B) opt is the preset target forward and backward power ratio; is the target sidelobe suppression ratio valley value; ω 1 ,ω 2 ,ω 3 ,ω 4 is a weight parameter used to adjust the contribution of each indicator to the optimization goal.

[0026] In a preferred embodiment, the target position is calculated based on the arrival time difference and the arrival angle;

[0027]

[0028]

[0029] Where D i is the calculated distance from the target device to base station i; v prop is the signal propagation speed; τ i Indicates the time when the target device signal reaches the i-th signal base station; τ 0 It represents the time when the target device signal reaches the reference base station; ζ is the refractive index correction factor; n med is the refractive index of the ambient medium; θ i is the arrival angle of the target device relative to base station i; (x i ,y i )、(x0 ,y 0 ) are the coordinates of the signal base station i and the target device respectively; ψ is the signal fluctuation correction factor; ω i is the signal oscillation frequency of signal base station i; t is the current time; ρ is the signal attenuation coefficient; d i is the measured distance from signal base station i to the target device.

[0030] In a preferred embodiment, the amplitude and phase characteristics of the wireless signal are captured through the channel state information, and the signal characteristic changes of different objects are analyzed to perform identity recognition and dynamic detection;

[0031]

[0032] Among them I obj (t) represents the comprehensive channel feature vector of the target object at time t; M is the number of subcarriers in the channel; A m is the amplitude characteristic of the mth subcarrier; m is the original phase information of the mth subcarrier; Δ m (t) is the phase drift correction term; κ m is the multipath reflection correction factor; is the signal attenuation factor; β m is the path loss coefficient of the mth subcarrier; d m It is the measured distance from the target object to the signal receiving end.

[0033] In a preferred embodiment, a shadow object model is constructed based on historical motion trajectory comparison and spatial consistency rule verification;

[0034]

[0035] Where P shadow (t) represents the probability of the shadow object at time t; T is the size of the historical time window; x i is the spatial coordinate of the target device at time i; x i-1 is the spatial coordinate of the target device at time i-1; Δt is the adjacent time step; v max is the peak threshold of reasonable speed; N is the number of historical angle samples of the target device; θ j is the target signal propagation angle of historical sample j; θ expected represents the reasonable signal propagation angle predicted based on historical environment; σ θ is the error correction factor of the historical signal propagation angle;

[0036] Detecting anomaly location results in the target environment based on the shadow object model, and calculating anomaly location probability in combination with the shadow object model;

[0037]

[0038] where P anom is the abnormal location probability of the target device; τ is the abnormal location sensitivity factor.

[0039] In a preferred embodiment, based on the abnormal location result, the consistency verification of physical laws is performed, and R phy represents the physical law consistency score;

[0040]

[0041] where K is the physical rule check window; x k is the spatial coordinate of the device at time k; x k-1 is the spatial coordinate of the device at time k - 1; v limit is the maximum speed allowed by the physical law; w k is the weighting factor;

[0042] If the abnormal location result is recognized, a warning signal two is issued;

[0043] S alert = H(P anom - P thresh )

[0044] where S alert is the warning signal two; H(P anom - P thresh ) is a step function, which takes the value of 1 when P anom - P thresh ≥ 0, otherwise it takes the value of 0; P thresh is the trigger threshold for abnormal location; if P anom exceeds the preset P thresh , the warning signal two is triggered.

[0045] In a preferred embodiment, in the case where the abnormal location result is not recognized, the simultaneous localization and mapping method is used to construct a wireless signal propagation map, and the reflection information, attenuation information, and occlusion information are dynamically updated. It is assumed that M signal (x, y, z) represents the wireless signal propagation model at the position (x, y, z), which is used to describe the signal strength and environmental impact at this point;

[0046]

[0047] where S is the number of signal measurement points; G s is the signal gain factor; P s is the transmit power at the measurement point s; is the signal attenuation factor; η s is the path loss coefficient; ds is the distance from the measuring point s to the target position; f(θ s , s ) is the direction correction function; θ s is the signal incident angle; s is the reflection angle of the signal relative to the environment; ξ is the signal loss coefficient related to the material; k is the directivity index; R s (x, y, z) is the reflection information factor, which indicates the reflection effect in the environment; α s is the reflection gain factor; β s is the reflection attenuation coefficient; Z s (x, y, z) is the occlusion information factor; γ s is the occlusion influence coefficient; O s is the obstacle density at that location.

[0048] Technical effects and advantages of the present invention:

[0049] 1. This solution solves the problem of misjudgment of shadow objects caused by multipath effects, signal reflection and occlusion during wireless signal positioning by modeling shadow objects, comparing historical trajectories and checking spatial consistency. It performs consistency verification based on the historical motion trajectory and physical laws of the target device. If the motion trajectory or signal propagation angle of the target device is detected to be inconsistent with the physical laws, the system determines it as abnormal and triggers a warning signal for data correction. Compared with the existing technology, this solution is suitable for complex environments (such as metal reflection areas, glass curtain walls, and around large equipment), reduces misjudgment, and relatively improves the stability and accuracy of wireless signal recognition.

[0050] 2. This solution optimizes the antenna directivity parameters (main beam width, directional gain) and spatial coverage parameters (forward and backward radiation power ratio, sidelobe suppression ratio) of the signal base station to ensure the reasonable distribution of the signal in the target environment;

[0051] 3. This solution extracts the amplitude and phase characteristics of wireless signals based on channel state information to achieve target object identification and motion state monitoring. By analyzing the signal phase drift, signal strength change and multipath reflection characteristics, the system can distinguish different objects, equipment or personnel, and detect whether the target is stationary, moving or rotating. It is more flexible than traditional tag-based identification methods (such as RFID);

[0052] 4. Based on shadow object detection, this solution further introduces a physical law consistency verification mechanism to ensure that the recognition results comply with physical constraints such as speed, direction, and acceleration. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Refer to the instruction manual Figure 1 , a system for automatically identifying content by wireless signal positioning according to an embodiment of the present invention includes a signal base station deployment module, an antenna angle distribution detection module, a wireless signal feature analysis module, a shadow object detection and abnormal positioning verification module, and a wireless signal propagation mapping module;

[0056] The signal base station deployment module is used to deploy signal base stations in the target environment so that the signal of the signal base station covers the target environment; the signal base stations include Wi-Fi signal base stations, UWB signal base stations, and BLE signal base stations; among them, the Wi-Fi signal base station is used to provide a wide range of wireless coverage and support coarse-grained positioning and content recognition based on signal strength and channel state information; the UWB signal base station is used for high-precision positioning, and realizes spatial positioning through arrival time difference and arrival angle; the BLE signal base station is used for low-power short-distance positioning, mainly relying on Bluetooth beacons for regional perception and device interaction;

[0057] The antenna angle distribution detection module is used to obtain the angle distribution data of the antenna of each signal base station. The angle distribution data includes directivity parameters and spatial coverage parameters. If the angle distribution data meets the threshold preset by the system, the wireless signal characteristics are obtained and analyzed, otherwise a warning signal is issued;

[0058] The wireless signal feature analysis module is used to calculate the relative position of the target device or tag and multiple signal base stations using the arrival time difference when acquiring and analyzing the wireless signal features; analyze the signal propagation direction using the arrival angle method; capture the amplitude and phase characteristics of the wireless signal using the channel state information, analyze the signal feature changes of different objects, and input the data into the filter for preliminary denoising;

[0059] The shadow object detection and abnormal positioning verification module is used in the data analysis process. The system builds a shadow object model based on the historical motion trajectory comparison and spatial consistency rule verification, detects abnormal positioning results in the target environment based on the shadow object model, and verifies the consistency of physical laws based on the abnormal positioning results. If an abnormal positioning result is identified, a warning signal 2 is issued;

[0060] The wireless signal propagation mapping module is used to construct a wireless signal propagation map by synchronous positioning and mapping methods when the shadow object model fails to identify abnormal positioning results, and dynamically update the reflection information, attenuation information and occlusion information in the environment.

[0061] The directivity parameters include main beam width and directional gain; the spatial coverage parameters include forward and backward radiation power ratio and sidelobe suppression ratio.

[0062] The angle optimization of the antenna of the signal base station is calculated based on the main beam width, directional gain, forward and backward radiation power ratio, and sidelobe suppression ratio in the angle distribution data;

[0063] Main beam width θ B Used to determine the angular expansion range of the signal; define θ B It is the angle when the power drops to half of the maximum value (-3dB point), expressed based on Fourier transform and directivity function;

[0064] Directional gain G θ Used to determine the power gain of a signal from a base station in a desired direction;

[0065] The forward / backward radiation power ratio F / B is used to determine the directional contrast of the antenna; F / B represents the power ratio of the antenna in the main direction (forward) and the relative direction (backward);

[0066] The sidelobe suppression ratio SLL is used to reduce interference signals and optimize energy concentration;

[0067]

[0068] Where λ is the signal wavelength; D eff is the effective aperture of the antenna; β is the environmental interference factor, which is used to correct the propagation error of the antenna in a complex environment; d loss is the equivalent propagation distance lost by the signal during propagation; 70 in the formula represents an empirical coefficient, which comes from the classic antenna directivity calculation formula and is used to estimate parabolic antennas and directional antennas; at the same time, the above formula introduces the environmental interference correction term β·d loss / D eff To compensate for propagation errors in real environments, such as signal broadening caused by obstacle reflections;

[0069] in is the medium absorption factor, δ mat is the material absorption coefficient; θ is the angle correction factor, γ θ Used to determine the change trend of gain in different directions; α loss is the path loss factor, α loss Used to influence signal propagation in free space and confined environments; dpath is the length of the signal propagation path; θ is the polar angle of the signal propagation direction; the above formula is based on the antenna gain equation G = 4πD 2 / λ 2 , add environmental loss correction; dielectric absorption factor Used to consider the attenuation of the signal by the material; the angle correction term (1+γ θ cos 2 (θ)) is used to reflect the uneven energy distribution in the antenna direction;

[0070] Among them G front is the gain of the antenna in the main direction; G back is the gain of the antenna in the reverse direction; μ dir is the antenna directivity enhancement factor; θ front is the main direction angle; θ back is the reverse angle; the above formula is formed by the logarithmic ratio of the forward and backward gains, and is calculated in dB to adapt to the antenna design standard; in addition, μ dir Control the gain variation in the main rear direction to make it suitable for different types of antennas;

[0071] Among them G main is the main beam direction gain; G side is the sidelobe directional gain; κ main is the main beam energy enhancement factor; κ side is the sidelobe energy enhancement factor; θ main is the main beam direction angle; θ side is the sidelobe angle; m is the sidelobe suppression index, which is used to affect the energy leakage of the sidelobe; in the above formula, the sidelobe suppression ratio is calculated by the ratio of the main lobe to the sidelobe gain, and κ is introduced side To simulate the changing characteristics of the side lobes so that they are relatively consistent with the radiation characteristics of the actual antenna.

[0072] Based on θ B , G θ , F / B, SLL define the comprehensive antenna optimization objective function;

[0073]

[0074] Where O is the comprehensive antenna optimization objective function, which is used to measure the optimization degree of the current antenna parameters. The larger the value, the better the signal distribution. max is the directional gain peak; θ opt is the target beam width, that is, the preset optimal beam angle; (F / B) opt is the preset target forward and backward power ratio, (F / B) opt Used to ensure signal concentration; is the target sidelobe suppression ratio valley value, that is, the minimum value, used to reduce sidelobe interference; ω 1 ,ω 2 ,ω 3 ,ω 4 is a weight parameter used to adjust the contribution of each indicator to the optimization goal;

[0075] The first term in the above formula represents directional gain optimization. By normalizing the directional gain, the main energy of the signal is concentrated in the target area, improving the communication quality. The higher the gain, the larger O is. The second item - Constrain the beam width, the goal is to make the main beam width as close to the preset value θ as possible opt , to prevent the signal from spreading too wide or too narrow. The more the beam width deviates from the target value, the lower O will be. The third item The purpose of constraining the forward and backward power ratio is to make the antenna radiate mainly in the main direction, reduce backward energy leakage, and improve directional performance. The more the forward and backward ratio deviates from the target value, the lower O will be. The fourth item Constraining the sidelobe suppression ratio is used to ensure that the sidelobe suppression is strong enough to reduce interference and improve signal concentration. The more the sidelobe suppression ratio deviates from the target value, the lower O will be.

[0076] Calculate the target position based on the arrival time difference and arrival angle;

[0077]

[0078] Where D i is the calculated distance from the target device to base station i; v prop is the signal propagation speed; τ i Indicates the time when the target device signal reaches the i-th signal base station; τ 0 It indicates the time when the target device signal reaches the reference base station; ζ is the refractive index correction factor, which is used to correct the influence of different media on the signal speed; n med is the refractive index of the ambient medium, such as air, glass, or metal surface; θ i is the arrival angle of the target device relative to base station i; (x i ,y i )、(x 0 ,y 0 ) are the coordinates of the signal base station i and the target device respectively; ψ is the signal fluctuation correction factor, which is used to correct the impact of the dynamic environment; ω i is the signal oscillation frequency of signal base station i; t is the current time; ρ is the signal attenuation coefficient; d i is the measured distance from signal base station i to the target device.

[0079] The amplitude and phase characteristics of wireless signals are captured through channel state information, and the changes in signal characteristics of different objects are analyzed to perform identity recognition and dynamic detection;

[0080]

[0081] Among them I obj (t) represents the comprehensive channel feature vector of the target object at time t; M is the number of subcarriers in the channel, and each subcarrier provides signal information of different frequencies; A m is the amplitude characteristic of the mth subcarrier, representing the signal strength of the subcarrier; m is the original phase information of the mth subcarrier, which is used to reflect the propagation path of the wireless signal; Δ m (t) is the phase drift correction term, which is used to represent the phase shift that changes with time and is affected by the movement of the object or the change of the environment; κ m is the multipath reflection correction factor, which indicates the effect of secondary reflection of different materials in the environment on the signal; is the signal attenuation factor, which indicates that the signal strength decreases with d. m Law of change; m is the path loss coefficient of the mth subcarrier, which is used to consider the attenuation characteristics of signals of different frequencies in the medium; d m The measured distance from the target object to the signal receiving end;

[0082] In the above formula Using Euler's formula e jθ = cos(θ)+jsin(θ) represents the comprehensive phase information of the target object at time t as a complex number, where e is the base of the exponential operation to ensure that the amplitude and phase changes of the signal can be smoothly described by the exponential function, and j is the imaginary unit used to distinguish the real part (amplitude information) and the imaginary part (phase information); the amplitude change of the signal is expressed by A m Reflects the material properties of objects, such as the different absorption and scattering characteristics of metals, plastics, liquids, etc. to wireless signals; the phase change of the signal is m +Δ m (t) reflecting the movement or angle change of an object, such as whether the target object is stationary, rotating or in other motion states; Used to reflect the signal attenuation and reflection of the environment, such as whether there are walls or metal obstacles around that affect signal propagation.

[0083] Construct a shadow object model based on historical motion trajectory comparison and spatial consistency rule verification;

[0084]

[0085] Where Pshadow (t) represents the probability of the shadow object at time t; T is the size of the historical time window; x i is the spatial coordinate of the target device at time i; x i-1 is the spatial coordinate of the target device at time i-1; Δt is the adjacent time step; v max is the peak threshold of reasonable speed; N is the number of historical angle samples of the target device; θ j is the target signal propagation angle of historical sample j; θ expected represents the reasonable signal propagation angle predicted based on historical environment; σ θ is the error correction factor of the historical signal propagation angle;

[0086] The first part of the above formula Used to calculate the speed deviation of the target device within T. If the moving speed of the target device does not conform to the laws of physics, it may be a shadow object; Part 2 Used to calculate the stability of the signal propagation direction. If the signal angle deviates abnormally, it may be a shadow object;

[0087] Detecting anomaly location results in the target environment based on the shadow object model, and calculating anomaly location probability in combination with the shadow object model;

[0088]

[0089] Where P anom is the abnormal location probability of the target device; τ is the abnormal location sensitivity factor; the above formula uses an exponential decay function to convert the shadow object probability into the abnormal location probability, and controls the sensitivity of anomaly detection through τ.

[0090] Based on the anomaly location results, the consistency of physical laws is verified through R phy It represents the consistency score of physical laws;

[0091]

[0092] Where K is the physical rule checking window; x k is the spatial coordinate of the device at time k; x k-1 is the spatial coordinate of the device at time k-1; v limit is the maximum speed allowed by physical laws; w k is a weighting factor used to smooth the abnormality score; if R phy If it exceeds the system preset threshold, it means that the movement of the device does not conform to the laws of physics;

[0093] If an abnormal positioning result is identified, a second warning signal is issued;

[0094] S alert =H(Panom -P thresh )

[0095] Where S alert It is the second warning signal; H(P anom -P thresh ) is a step function, when P anom -P thresh When ≥0, the value is 1, otherwise the value is O; P thresh is the trigger threshold for abnormal location; if P anom Exceeding the preset P thresh , then the warning signal 2 is triggered.

[0096] In the case of no abnormal positioning results, the synchronous positioning and mapping method is used to build a wireless signal propagation map, and dynamically update the reflection information, attenuation information and occlusion information. signal (x, y, z) represents the wireless signal propagation model at the position (x, y, z), which is used to describe the signal strength and environmental impact at that point;

[0097]

[0098] Where S is the number of signal measurement points, that is, the number of reference points used to construct the propagation map; G s is the signal gain factor, which represents the antenna directional gain at the sth measurement point; P s is the transmission power at the measurement point s; is the signal attenuation factor; η s is the path loss coefficient, which is used to determine the attenuation rate of the signal with distance; d s is the distance from the measuring point s to the target position; f(θ s , s ) is the direction correction function, which is used to adjust the propagation characteristics of the signal at different angles; θ s is the signal incident angle; s is the reflection angle of the signal relative to the environment; ξ is the signal loss coefficient related to the material; k is the directivity index, which is used to control the intensity distribution of the signal at different angles; R s (x, y, z) is the reflection information factor, which indicates the reflection effect in the environment; α s is the reflection gain factor, which is used to describe the reflection intensity of the environment; β s is the reflection attenuation coefficient, which indicates the degree of multiple reflection loss of the signal during propagation; Z s (x, y, z) is the occlusion information factor, which describes the occlusion of the signal at different positions; γ s is the occlusion influence coefficient, which is used to determine the influence of occlusion on the signal;s is the obstacle density at that location, O s The higher the value, the denser the obstruction and the more serious the signal attenuation.

[0099] The above formula is combined with the signal G s , P s and Calculate the basic signal strength; use f(θ s , s ) Correct the change of the signal with the incident angle and improve the accuracy of the signal propagation direction; use R s (x, y, z) calculates the effect of signal reflection on propagation, which is used to reflect the enhancement or interference of environmental reflectors such as metal surfaces and glass curtain walls on the signal; s (x,y,z) calculates the occlusion effect of obstacles, such as concrete walls, which severely attenuate the signal.

[0100] It is necessary to further describe that the core purpose of this solution is to achieve automatic identification through wireless signals in complex environments and optimize common signal interference problems to ensure that the system can work stably in different environments;

[0101] In the signal base station deployment phase, the selection of three wireless signals, Wi-Fi, UWB, and BLE, as signal sources is based on their respective technical characteristics and application requirements; Wi-Fi base stations provide wide-area coverage and perform coarse-grained positioning in combination with signal strength information, which is suitable for preliminary screening of targets in a large range; UWB signal base stations have good time synchronization capabilities and can perform positioning through arrival time difference TDOA and arrival angle AOA, which is suitable for high-precision industrial, medical, warehousing and other scenarios; BLE signal base stations are characterized by low power consumption, short distance, and high adaptability, relying on Bluetooth beacons for regional perception and device interaction, and are suitable for IoT devices or personnel tracking; the core logic of this stage is to complement the advantages of different signal sources based on their characteristics, to achieve a combination of long and short distances, wide-area coverage and local positioning, rather than relying on a single signal type, so as to ensure that the system can work normally in various environments;

[0102] Secondly, in the antenna angle distribution detection stage, the system obtains the antenna angle data of each signal base station, including directivity parameters and spatial coverage parameters; since the coverage range and signal propagation stability of wireless signals are closely related to the directivity of the antenna, if the antenna angle distribution is unreasonable, it may lead to insufficient signal coverage in some areas, or strong signal reflection and interference due to unreasonable antenna directivity; therefore, if the detected angle distribution data meets the requirements, the signal analysis will continue; otherwise, the system will issue a warning signal 1, indicating that the base station direction or gain needs to be adjusted to optimize the wireless coverage range; the role of this stage is to optimize the signal propagation environment in advance and reduce errors in subsequent data analysis;

[0103] In the wireless signal feature analysis stage, the system calculates the relative position of the target device through TDOA, analyzes the signal propagation direction through AOA, and extracts amplitude and phase features in combination with channel state information CSI to identify the target object.

[0104] In the shadow object detection and abnormal positioning verification stage, the system introduces historical motion trajectory comparison and spatial consistency rule verification to build a shadow object model, which is used to detect abnormal positioning results in the target environment. Since wireless signals are subject to strong reflection and diffraction in complex environments, some "ghost targets" may be fictitious, leading to system misidentification. The shadow object model determines whether a target may be a forged reflection signal by analyzing historical motion trajectories and spatial consistency. For example, if a device "teleports" in a short period of time that does not conform to the laws of physics, or is detected at multiple different locations at the same time, the system will consider the target to be a shadow object and trigger abnormal positioning analysis. After detecting the anomaly, the system verifies the consistency of physical laws, that is, checks whether the target meets physical constraints such as speed, direction, and acceleration. If an anomaly is found, the second warning signal is triggered to prompt the system to perform further signal correction or data elimination. The necessity of this process lies in that the elimination of shadow objects can greatly improve the relative accuracy of the positioning system and avoid misleading decisions caused by incorrect identification.

[0105] During the wireless signal propagation mapping stage, if the shadow object model does not detect anomalies, the system will use the simultaneous localization and mapping (SLAM) method to build a wireless signal propagation map and dynamically update the reflection information, attenuation information, and occlusion information in the environment. Since the wireless environment is dynamically changing, the movement of buildings, equipment, and personnel will affect the signal propagation characteristics. If a static signal propagation model is used, the system's recognition accuracy will gradually decrease. Therefore, through SLAM technology, the system can continuously learn signal changes in the current environment and adjust the signal propagation model in real time to optimize wireless signal coverage and target recognition accuracy.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A system for automatically identifying content through wireless signal positioning, comprising a signal base station deployment module, an antenna angle distribution detection module, a wireless signal feature analysis module, a shadow object detection and abnormal positioning verification module, and a wireless signal propagation mapping module, characterized in that: The signal base station deployment module is used to deploy signal base stations in the target environment so that the signal of the signal base station covers the target environment; the signal base station includes Wi-Fi signal base station, UWB signal base station, and BLE signal base station; The antenna angle distribution detection module is used to obtain the angle distribution data of the antenna of each signal base station. The angle distribution data includes directivity parameters and spatial coverage parameters. If the angle distribution data meets the threshold preset by the system, the wireless signal characteristics are obtained and analyzed, otherwise a warning signal is issued; The wireless signal feature analysis module is used to calculate the relative position of the target device or tag and multiple signal base stations using the arrival time difference when acquiring and analyzing the wireless signal features; analyze the signal propagation direction using the arrival angle method; capture the amplitude and phase characteristics of the wireless signal using the channel state information, analyze the signal feature changes of different objects, and input the data into the filter for preliminary denoising; The shadow object detection and abnormal positioning verification module is used in the data analysis process. The system builds a shadow object model based on the historical motion trajectory comparison and spatial consistency rule verification, detects abnormal positioning results in the target environment based on the shadow object model, and verifies the consistency of physical laws based on the abnormal positioning results. If an abnormal positioning result is identified, a warning signal 2 is issued; The wireless signal propagation mapping module is used to construct a wireless signal propagation map by synchronous positioning and mapping methods when the shadow object model fails to identify abnormal positioning results, and dynamically update the reflection information, attenuation information and occlusion information in the environment.

2. The system for automatically identifying content through wireless signal positioning according to claim 1, characterized in that: The directivity parameters include main beam width and directional gain; the spatial coverage parameters include forward and backward radiation power ratio and sidelobe suppression ratio.

3. The system for automatically identifying content through wireless signal positioning according to claim 2, characterized in that: The angle optimization of the antenna of the signal base station is calculated based on the main beam width, directional gain, forward and backward radiation power ratio, and sidelobe suppression ratio in the angle distribution data; Main beam width θ B Used to determine the angular spread of the signal; Directional gain G θ Used to determine the power gain of a signal from a base station in a desired direction; The forward and backward radiated power ratio F / B is used to determine the directional contrast of the antenna; The sidelobe suppression ratio SLL is used to reduce interference signals and optimize energy concentration; Where λ is the signal wavelength; D eff is the effective aperture of the antenna; β is the environmental interference factor; d loss is the equivalent propagation distance lost by the signal during propagation; in is the medium absorption factor, δ mat is the material absorption coefficient; θ is the angle correction factor; α loss is the path loss factor; d path is the signal propagation path length; Among them G front is the gain of the antenna in the main direction; G back is the gain of the antenna in the reverse direction; μ dir is the antenna directivity enhancement factor; θ front is the main direction angle; θ back is the angle in the opposite direction; Among them G main is the main beam direction gain; G side is the sidelobe directional gain; κ main is the main beam energy enhancement factor; κ side is the sidelobe energy enhancement factor; θ main is the main beam direction angle; θ side is the sidelobe direction angle; m is the sidelobe suppression index.

4. The system for automatically identifying content through wireless signal positioning according to claim 3, characterized in that: Based on θ B , G θ , F / B, SLL define the comprehensive antenna optimization objective function; Where O is the comprehensive antenna optimization objective function; G max is the directional gain peak; θ opt is the target beam width; (F / B) opt is the preset target forward and backward power ratio; is the target sidelobe suppression ratio valley value; ω1, ω2, ω3, ω4 are weight parameters used to adjust the contribution of each index to the optimization target.

5. The system for automatically identifying content through wireless signal positioning according to claim 4, characterized in that: Calculate the target position based on the arrival time difference and arrival angle; Where D i is the calculated distance from the target device to base station i; v prop is the signal propagation speed; τ i represents the time when the target device signal reaches the i-th signal base station; τ0 represents the time when the target device signal reaches the reference base station; ζ is the refractive index correction factor; n med is the refractive index of the ambient medium; θ i is the arrival angle of the target device relative to base station i; (x i ,y i ), (x0, y0) are the coordinates of the signal base station i and the target device respectively; ψ is the signal fluctuation correction factor; ω i is the signal oscillation frequency of signal base station i; t is the current time; ρ is the signal attenuation coefficient; d i is the measured distance from signal base station i to the target device.

6. The system for automatically identifying content through wireless signal positioning according to claim 5, characterized in that: The amplitude and phase characteristics of wireless signals are captured through channel state information, and the changes in signal characteristics of different objects are analyzed to perform identity recognition and dynamic detection; Among them I obj (t) represents the comprehensive channel feature vector of the target object at time t; M is the number of subcarriers in the channel; A m is the amplitude characteristic of the mth subcarrier; m is the original phase information of the mth subcarrier; Δ m (t) is the phase drift correction term; κ m is the multipath reflection correction factor; is the signal attenuation factor; β m is the path loss coefficient of the mth subcarrier; d m is the measured distance from the target object to the signal receiving end.

7. The system for automatically identifying content through wireless signal positioning according to claim 6, characterized in that: Construct a shadow object model based on historical motion trajectory comparison and spatial consistency rule verification; Where P shadow (t) represents the probability of the shadow object at time t; T is the size of the historical time window; x i is the spatial coordinate of the target device at time i; x i-1 is the spatial coordinate of the target device at time i-1; Δt is the adjacent time step; v max is the peak threshold of reasonable speed; N is the number of historical angle samples of the target device; θ j is the target signal propagation angle of historical sample j; θ expected represents the reasonable signal propagation angle predicted based on historical environment; σ θ is the error correction factor of the historical signal propagation angle; Detecting anomaly location results in the target environment based on the shadow object model, and calculating anomaly location probability in combination with the shadow object model; Where P anom is the abnormal location probability of the target device; τ is the abnormal location sensitivity factor.

8. The system for automatically identifying content through wireless signal positioning according to claim 7, characterized in that: Based on the anomaly location results, the consistency of physical laws is verified through R phy It represents the consistency score of physical laws; Where K is the physical rule checking window; x k is the spatial coordinate of the device at time k; x k-1 is the spatial coordinate of the device at time k-1; v limit is the maximum speed allowed by physical laws; w k is the weighting factor; If an abnormal positioning result is identified, a second warning signal is issued; S alert =H(P anom -P thresh ) Where S alert It is the second warning signal; H(P anom -P thresh ) is a step function, when P anom -P thresh When ≥0, the value is 1, otherwise the value is O; P thresh is the trigger threshold for abnormal location; if P anom Exceeding the preset P thresh , then the warning signal 2 is triggered.

9. The system for automatically identifying content through wireless signal positioning according to claim 8, characterized in that: In the case of no abnormal positioning results, the synchronous positioning and mapping method is used to build a wireless signal propagation map, and dynamically update the reflection information, attenuation information and occlusion information. signal (x, y, z) represents the wireless signal propagation model at the position (x, y, z), which is used to describe the signal strength and environmental impact at that point; Where S is the number of signal measurement points; G s is the signal gain factor; P s is the transmission power at the measurement point s; is the signal attenuation factor; η s is the path loss coefficient; d s is the distance from the measuring point s to the target position; f(θ s , s ) is the direction correction function; θ s is the signal incident angle; s is the reflection angle of the signal relative to the environment; ξ is the signal loss coefficient related to the material; k is the directivity index; R s (x, y, z) is the reflection information factor, which indicates the reflection effect in the environment; α s is the reflection gain factor; β s is the reflection attenuation coefficient; Z s (x, y, z) is the occlusion information factor; γ s is the occlusion influence coefficient; O s is the obstacle density at that location.

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