Passive coded device positioning and tracking method and apparatus

Abstract

The application provides a passive coding device positioning and tracking method and device, and relates to the technical field of computers, and comprises the following steps: acquiring original data of radio frequency signals reflected by all passive coding devices arranged in a monitoring area; performing feature separation according to the original data of the radio frequency signals to obtain a signal baseband feature set; performing propagation path derivation according to the baseband feature set to obtain channel impulse responses describing environmental reflection characteristics; performing device position probability field construction according to the channel impulse responses to obtain a position probability distribution of the device in the monitoring area; performing motion trajectory function fitting according to the position probability distribution to obtain a space-time trajectory function of the device; and performing trajectory optimization under physical constraints according to the space-time trajectory function to obtain a positioning and tracking result. The application realizes high-precision and stable and continuous positioning and tracking of passive coding devices in a complex electromagnetic environment.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and apparatus for locating and tracking passive coding devices. Background Technology

[0002] Passive coding device positioning and tracking technology has significant application value in fields such as the Internet of Things (IoT) and smart warehousing. This technology typically relies on parameter analysis of the device's reflected signals to calculate its spatial location. However, in complex indoor environments, factors such as dense multipath effects, dynamic environmental changes, and weak reflected signals severely limit the accuracy and robustness of existing positioning methods. Achieving continuous, stable, and high-precision tracking of devices under conditions of high interference and low signal-to-noise ratio is a core technical challenge. Existing solutions mainly focus on physical layer information such as signal arrival time, angle of arrival, or signal strength, employing filtering, data fusion, or geometric positioning. However, these methods generally have the following limitations: First, they typically employ suppression or elimination strategies for multipath signals, failing to effectively utilize the environmental structural information contained within the multipath signals. Second, the positioning process often relies on relatively ideal propagation models or a large number of preset environmental parameters, resulting in significant performance degradation when the model mismatches or the environment changes. Furthermore, trajectory generation largely depends on smooth fitting of discrete positioning points, lacking a mechanism for overall constraint and optimization from the signal level and physical propagation laws, leading to problems such as trajectory jumps or deviations from the actual physical path in complex scenarios.

[0003] Based on the shortcomings of the existing technology, there is an urgent need for a passive coding device positioning and tracking method and apparatus. Summary of the Invention

[0004] The purpose of this invention is to provide a passive coding device positioning and tracking method to improve the above-mentioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a passive coding device positioning and tracking method, including:

[0006] Acquire raw data of radio frequency signals reflected by all passive coding devices deployed in the monitoring area. The raw data of radio frequency signals includes signal amplitude, phase, time difference of arrival, and unique coding identifier of the device.

[0007] Based on the original radio frequency signal data, feature separation is performed to obtain the signal baseband feature set;

[0008] Based on the baseband feature set, the propagation path is deduced. By utilizing the phase and amplitude constraint relationship between each projection component, the geometric structure of the electromagnetic wave reflection path of the generated component is deduced in reverse, and the channel impulse response describing the environmental reflection characteristics is obtained.

[0009] The device location probability field is constructed based on the channel impulse response. By treating the end point of each reflection path as a potential source transmission location, and calculating the joint probability of the existence of all potential source transmission locations based on path delay and attenuation, the device location probability distribution within the monitoring area is obtained.

[0010] The spatiotemporal trajectory function of the device is obtained by fitting the motion trajectory function based on the position probability distribution;

[0011] The trajectory is optimized under physical constraints based on the spatiotemporal trajectory function to obtain the positioning and tracking results.

[0012] Secondly, this application also provides a passive coding device positioning and tracking device, comprising:

[0013] The acquisition module is used to acquire raw data of radio frequency signals reflected by all passive coding devices deployed in the monitoring area. The raw data of radio frequency signals includes signal amplitude, phase, time difference of arrival, and unique coding identifier of the device.

[0014] The separation module is used to perform feature separation based on the original RF signal data to obtain a signal baseband feature set;

[0015] The derivation module is used to derive the propagation path based on the baseband feature set, and to reverse derive the geometric structure of the electromagnetic wave reflection path of the generated component by utilizing the phase and amplitude constraint relationship between each projection component, thereby obtaining the channel impulse response describing the environmental reflection characteristics.

[0016] The construction module is used to construct the device location probability field based on the channel impulse response. By treating the end point of each reflection path as a potential source transmission location, and calculating the joint probability of the existence of all the potential source transmission locations based on path delay and attenuation, the device location probability distribution within the monitoring area is obtained.

[0017] The fitting module is used to fit the motion trajectory function according to the position probability distribution to obtain the spatiotemporal trajectory function of the device.

[0018] The optimization module is used to optimize the trajectory under physical constraints based on the spatiotemporal trajectory function to obtain the positioning and tracking results.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention transforms multipath signal interference into effective information that can construct environmental reflection characteristics, and realizes signal separation and probability field construction based on device coding features. Finally, it optimizes the trajectory under strict physical propagation laws, thereby achieving high-precision, stable and continuous positioning and tracking of passive coding devices in complex electromagnetic environments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of a passive coding device positioning and tracking method according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of the structure of a passive coding device positioning and tracking device according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a passive coding device for positioning and tracking, as described in an embodiment of the present invention.

[0025] The markings in the figure are as follows: 800, a passive coding device for positioning and tracking; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, separation module; 903, derivation module; 904, construction module; 905, fitting module; 906, optimization module. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] Example 1:

[0029] This embodiment provides a method for locating and tracking passive coding devices.

[0030] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0031] Step S100: Obtain raw data of radio frequency signals reflected by all passive coding devices deployed in the monitoring area. The raw data of radio frequency signals includes signal amplitude, phase, time difference of arrival, and unique coding identifier of the device.

[0032] Understandably, in complex indoor monitoring scenarios, the radio frequency signals reflected by passive devices carry all information, including device identity, propagation distance, and interaction with the environment. Acquiring raw data containing signal amplitude, phase, time difference of arrival, and the device's unique coded identifier is crucial to avoid introducing any potential information loss during the initial preprocessing stage, ensuring that subsequent processing is based on the most accurate and complete data foundation. Especially in automated bulk material management scenarios, such as in a mineral warehouse, the basic data acquisition in step S100 is achieved through the following specific method: Multiple RF reader nodes are systematically deployed on the warehouse ceiling and walls, forming a comprehensive monitoring network. When passive coded devices attached to the material pile or transport equipment enter this area, the RF signals continuously emitted by the readers excite these passive devices, causing them to reflect back signals carrying their unique coded identifiers. Each reader node synchronously receives these reflected signals and uses coherent reception technology to accurately measure the signal amplitude and phase information. By calculating the time difference of signal arrival at different reader nodes and combining it with known node coordinates, basic time delay information is provided for subsequent positioning. This process directly acquires raw observation data containing device identity (encoding), distance (phase, time delay), and signal strength (amplitude), laying a data foundation for accurate tracking in complex bulk material environments filled with obstructions and reflections.

[0033] Step S200: Perform feature separation based on the original RF signal data to obtain the signal baseband feature set;

[0034] The core task of feature separation in step S200 is to extract essential features closely related to each individual device from the mixed raw radio frequency signal data. In typical scenarios with multiple concurrent devices and multipath interference, it is extremely difficult to perform location calculations directly using the raw signals. This step transforms the signal from the original time-frequency domain to a feature domain that better highlights the individual differences of each device by utilizing the structured information contained in the unique device identifier. This creates conditions for subsequent fine-grained processing by device and path, essentially completing the purification from "mixed signal" to "device features".

[0035] Step S300: Based on the baseband feature set, the propagation path is derived. Using the phase and amplitude constraint relationship between each projection component, the geometric structure of the electromagnetic wave reflection path of the generated component is derived in reverse to obtain the channel impulse response describing the environmental reflection characteristics.

[0036] It should be noted that the propagation path derivation process in step S300 represents a shift in perspective, moving from analyzing the characteristics of the signal itself to retrieving the signal's propagation history in space. This step recognizes that the phase and amplitude constraints between the projected components in the signal's baseband feature set are not random, but determined by the physical laws governing electromagnetic wave propagation in space. By using these constraints to inversely derive the electromagnetic wave's reflection path, this method links the interpretation of signal characteristics with specific spatial geometry, transforming multipath effects, which are difficult to utilize directly, into valuable information for constructing environmental channel models.

[0037] Step S400: Construct the device location probability field based on the channel impulse response. By treating the end point of each reflection path as a potential source transmission location, and calculating the joint probability of the existence of all potential source transmission locations based on path delay and attenuation, the device location probability distribution within the monitoring area is obtained.

[0038] Specifically, the device location probability field construction in step S400 introduces a spatial positioning concept based on probability theory. By treating the endpoint of the path as the potential source transmission location and integrating the delay and attenuation information of all paths to calculate the joint probability, this process transforms the positioning problem into an estimation problem of a spatial probability distribution. The result (location probability distribution) better reflects the inherent uncertainty of location information in complex environments.

[0039] Step S500: Fit the motion trajectory function according to the position probability distribution to obtain the spatiotemporal trajectory function of the device;

[0040] Understandably, the goal of step S500, which involves fitting the motion trajectory function, is to transform discrete, potentially abrupt, instantaneous position estimates into a continuous, smooth spatiotemporal trajectory. This step takes into account the inertial constraints of equipment motion in the physical world, meaning that its position changes should be continuous in the time dimension. By fitting the position probability distribution at discrete moments into a continuous spatiotemporal trajectory function, a leap from static "point" estimation to dynamic "path" description is achieved, providing a mathematical expression for analyzing the continuous motion behavior of equipment.

[0041] Step S600: Optimize the trajectory under physical constraints based on the spatiotemporal trajectory function to obtain the positioning and tracking results.

[0042] The trajectory optimization under physical constraints in step S600 is a crucial step in ensuring that the final result conforms to the realities of the physical world. This step introduces a closed-loop verification and correction mechanism: the initially fitted spatiotemporal trajectory function is substituted into the electromagnetic wave propagation model to calculate the theoretically observable signal, and then compared with the channel impulse response derived in practice. By minimizing the difference between the two, the trajectory is optimized, ensuring that the final positioning and tracking result is not only mathematically smooth, but more importantly, intrinsically consistent with the reflection characteristics and wave propagation laws of the physical environment.

[0043] Further, step S200 includes steps S210 to S230.

[0044] Step S210: Based on the original data of the radio frequency signal, the coding feature space is constructed by resolving the unique coding identifier of the device into orthogonal basis functions and constructing a low-dimensional subspace by linear combination of the basis functions to obtain the device coding feature space.

[0045] Step S220: Perform signal interference component stripping processing based on the device coding feature space and the original radio frequency signal data. Calculate the projection residual of the received signal in the coding feature space and model the multipath scattering-related components in the residual as environmental structured noise to obtain a preliminarily purified signal projection.

[0046] Step S230: Based on the preliminary purified signal projection, perform baseband feature extraction processing. By solving the optimization problem that maximizes the energy of the projection components, separate the signal components that uniquely correspond to each coded identifier to obtain the signal baseband feature set.

[0047] Specifically, the coding feature space construction process in step S210 first resolves the unique identifier of each passive coding device into a set of mutually orthogonal basis functions. These basis functions are similar to constructing a mathematical coordinate system specific to the device coding. The linear combination of all possible coding sequences constitutes a low-dimensional subspace, namely the device coding feature space. Next, the signal interference component stripping process in step S220 projects the received mixed RF signal raw data onto this feature space. The core of this process is that the signal components matching the device coding will mainly fall within this subspace, while the residual generated after projection mainly contains interference related to environmental multipath scattering. The key processing here is that the residual is not simply discarded, but modeled as a type of environmental structured noise with specific statistical characteristics. This reflects that in a bulk material environment filled with metal machinery and densely stacked materials, multipath interference is not completely random, but exhibits a certain describable regularity, thus obtaining a preliminarily purified signal projection. Based on this, the baseband feature extraction process in step S230 solves an optimization problem that maximizes the energy of the projected components. The purpose is to further filter out the signal components that best represent the target device and have the highest signal-to-noise ratio from the initially purified signal projection. This optimization process is essentially finding the direction of the strongest energy concentration of the signal in the feature space, thereby finally separating out the pure signal baseband feature set that clearly and uniquely corresponds to each coded identifier. This series of progressive processes effectively overcomes the challenge of severe aliasing and distortion of signals from multiple devices in a bulk material environment.

[0048] The orthogonal formula for the basis functions of the device coding feature space is expressed as follows:

[0049] ;

[0050] The formula for maximizing the energy of feature space projection is expressed as:

[0051] ;

[0052] In the formula, It serves as a unique identifier for equipment. In bulk material management scenarios, it can be represented as an ID number, similar to an RFID tag, used to uniquely distinguish different equipment or material units. and They represent the first The and the first Each basis function is encoded Generated through a specific parsing function; Represent the Kronecker function, when The value is 1 when the condition is met and 0 otherwise, ensuring that the basis functions are pairwise orthogonal, thereby avoiding information redundancy in the feature space; Representation encoding The corresponding feature subspace, spanned by basis functions, is used to focus on the signal characteristics of a specific device; For feature space The unit vector in the vector represents the direction of the signal projection; This represents the raw data vector of the received radio frequency signal, which includes information such as amplitude and phase. This represents the transpose operation of a vector.

[0053] Further, step S300 includes steps S310 to S330.

[0054] Step S310: Perform multipath component clustering based on the signal baseband feature set. By analyzing the phase change rate and amplitude attenuation ratio of each projection component under the same coding identifier, the components that satisfy the same spherical wavefront constraint relationship are classified as signals from the same reflection path, thus obtaining a directional multipath signal cluster.

[0055] Step S320: Perform path geometry parameter solving based on the multipath signal clusters, convert the arrival time difference and phase information of each signal cluster into a set of equations for the direction of arrival and relative time delay, and determine the incident angle and path length difference of the signal cluster relative to the receiving node array by solving the set of equations to obtain the reflection path set.

[0056] This set of equations relates the observations (time difference of arrival and phase difference) of the same multipath signal cluster at different receiving nodes to the geometric parameters to be determined (direction of arrival vector and path length difference). The expression is:

[0057] Observation equation:

[0058] ;

[0059] Geometric constraint equations:

[0060] ;

[0061] In the formula, Indicates the signal has arrived at the first The and the first The time difference between each receiving node, measured by signal processing, reflects the difference in the length of the signal propagation path. Indicates the signal has arrived at the first The and the first The received carrier phase difference, measured by signal processing, provides path length difference information with higher precision than the time difference. The speed at which electromagnetic waves propagate in the environment; The carrier wavelength of the radio frequency signal; and This indicates that the multipath signal travels from the virtual source (i.e., the reflection point or device) to the first... The and the first The absolute path length of each receiving node; and The residual term in the observation equation represents the measurement uncertainty caused by factors such as noise and scattering in the bulk material environment; This represents the three-dimensional coordinate vector of the virtual information source in space. Indicates the first The known fixed coordinate vectors of each receiving node in space; Indicates the first in the environment Coordinates of a virtual reflection point on a main reflecting surface; The coordinates of the reference point for the receiving node array; This refers to integer ambiguity in phase difference observations.

[0062] Step S330: Perform environmental scatterer position inversion processing based on the reflection path set. Treat each path as a ray from the device to the receiving node, and use the path incident angle and length difference to jointly calculate the virtual position of the reflecting surface in the environment, and obtain the channel impulse response including the direct path and the reflection path.

[0063] Specifically, the multipath component clustering process in step S310 takes the signal baseband feature set separated in step S200 as input. Its core operation is to analyze the phase change law and amplitude attenuation mode of multiple signal projection components encoded by the same device at multiple receiving nodes. In practical applications, the phase change and amplitude attenuation of signal components from the same physical reflection path (e.g., reflected by warehouse walls or fixed mechanical surfaces) will satisfy specific spherical wavefront propagation constraints. That is, the relationship between them conforms to a spherical wave model with the reflection point or emission source as the center. Through this constraint, components with consistent behavior are grouped into one category, thereby separating the mixed signals into several multipath signal clusters with directional characteristics corresponding to different propagation paths. The path geometry parameter solution process in step S320 further mines the spatial information contained in each multipath signal cluster. It transforms the arrival time difference and precise phase information between different receiving nodes within a signal cluster into a set of mathematical equations about the signal wave direction of arrival and relative transmission delay. Solving this set of equations can quantitatively determine the spatial incident angle of each signal cluster (i.e., each path) relative to the array of receiving nodes and the length difference between the path and the reference path, thereby converting the signal characteristics into a set of reflection paths that describes the geometric properties of the reflection path. The environmental scatterer position inversion process in step S330 abstracts each path in the reflection path set into a ray from the device to the receiving node. Based on the path incident angle and length difference obtained in step S320, the virtual position of the medium in the environment (such as walls, large equipment, and other fixed obstacles) that caused the reflection is jointly calculated using the ray tracing concept in geometric optics. This process essentially uses multipath signals to "infer" the environmental structure and finally outputs a channel impulse response that not only includes the direct path but also describes the main reflection path more completely, thus laying the environmental perception foundation for accurate positioning in complex scenes.

[0064] Further, step S400 includes steps S410 to S430.

[0065] Step S410: Perform virtual source space mapping processing based on the channel impulse response, convert the arrival time delay of each reflection path into an ellipsoid with the receiving node as the focus, the sum of the distances from each point on the ellipsoid to the focus is equal to the path length, and map the path into an ellipsoidal positioning region in space to obtain a set of ellipsoids representing the potential source location.

[0066] Step S420: Perform source location hypothesis conflict resolution processing based on the ellipsoid set. Calculate the intersection of different ellipsoids in the spatial regions formed by the pairwise receiving nodes, and weight the source existence summary in the intersection region based on the preset signal attenuation model to filter out candidate source location points.

[0067] Step S430: Perform device location probability field synthesis processing based on candidate information source location points. Treat each candidate point and its weight as a kernel function, and synthesize a continuous probability distribution in the monitoring area using a non-parametric density estimation method to obtain the device location probability distribution in the monitoring area.

[0068] Specifically, the virtual source space mapping process in step S410 transforms the arrival time delay information of each reflection path in the channel impulse response into geometric positioning constraints. The basic principle is that the total propagation path length of the electromagnetic wave from the source to the receiving node is fixed. This definition constitutes an ellipsoid with the receiving node as the focus in space. The sum of the distances from any point on this ellipsoid to the two foci is equal to the measured path length. Through this transformation, each path is no longer just time delay data, but is mapped to an ellipsoidal positioning region in space. All possible source locations are located on this ellipsoid, thus obtaining a set composed of multiple ellipsoids, which together characterize the potential spatial distribution of device locations. The source location hypothesis conflict resolution process in step S420 aims to address the uncertainty of these ellipsoidal intersections. In environments with significant multipath effects, such as bulk material warehouses, a single ellipsoid may contain a large number of invalid location points. This step calculates the intersection curve or region of the ellipsoids corresponding to each pair of receiving nodes in three-dimensional space and applies a probability weight to these intersection regions based on the signal attenuation model during propagation (e.g., path loss model). The logic is that intersection points where the signal attenuation level matches the propagation distance are more likely to be the true source locations, thereby selecting a set of candidate source location points with the highest weighted scores from a large number of spatial intersections. The device position probability field synthesis processing in step S430 ultimately transforms the discrete position estimate into a continuous probability distribution. It treats each weighted candidate point obtained in the previous step as the kernel center of a probability density function. The higher the weight, the greater the probability density contribution at that point. Through a non-parametric density estimation method, the probability contributions of all candidate points are superimposed and smoothed over the entire monitoring space to synthesize a continuous probability distribution field. This probability distribution intuitively reflects the probability of the device existing at any position in space, providing a probabilistic basis for subsequent trajectory tracking.

[0069] Further, step S500 includes steps S510 to S530.

[0070] Step S510: Perform spatiotemporal correlation field construction processing based on the position probability distribution. By performing tensor product operation along the time axis on the position probability distribution of multiple consecutive time moments, a probability field describing the possibility of the device existing in the joint spatial and temporal domain is constructed to obtain spatiotemporal probability volume data.

[0071] Step S520: Path flow extraction processing is performed based on spatiotemporal probability volume data. By solving the spatial curve in the volume data that satisfies the local maximum of the probability distribution and the minimum change of the path tangent direction, the trajectory fitting problem is transformed into solving the geodesic problem of the curve on the spatiotemporal manifold, and the initial spatiotemporal path of the device is obtained.

[0072] Step S530: Perform trajectory function regularization processing based on the initial spatiotemporal path. By introducing the inertial constraint of the device motion as a regularization term, the initial path is smoothed and corrected so that the first and second derivatives of the trajectory function conform to physical laws, and the spatiotemporal trajectory function of the device is obtained.

[0073] In dynamic tracking scenarios, step S510, the spatiotemporal correlation field construction process, incorporates the discrete time dimension into the positioning model. It fuses the position probability distributions of multiple consecutive moments into a four-dimensional probability field composed of three-dimensional space and one-dimensional time through tensor product operations, forming spatiotemporal probability volume data. This data structure fully preserves the spatiotemporal evolution characteristics of the equipment's positional uncertainty during bulk material transportation, such as the signal obstruction changes caused by material pile movement. Step S520, the path flow extraction process, optimizes for the characteristics of the spatiotemporal probability volume data. The core innovation of this method lies in transforming the trajectory finding problem into solving the optimal path flow in the probability field: finding a spatial curve that simultaneously satisfies the constraints of local maxima of spatial probability (high probability position) and minimum path direction change (motion smoothness). By establishing a spatiotemporal manifold model and transforming the trajectory fitting problem into a geodesic solution problem, it effectively overcomes the trajectory breakage problem caused by temporary obstruction (such as burying the equipment in a material pile) in traditional methods. Step S530, the trajectory function regularization process, introduces physical motion constraints as a mathematical correction mechanism. In transportation scenarios, equipment typically moves smoothly along conveyor belts or material flows, and its acceleration has a physical upper limit. This step performs variational optimization on the initial path through inertial constraint regularization terms, forcing the first derivative (velocity) and second derivative (acceleration) of the trajectory function to satisfy the typical motion characteristics of material conveying equipment. For example, it eliminates non-physical position jumps that may occur when unloading from hoppers, ultimately generating a spatiotemporal trajectory function that conforms to the motion laws of bulk material automation systems.

[0074] Further, step S600 includes steps S610 to S630.

[0075] Step S610: Perform virtual signal forward generation processing based on the spatiotemporal trajectory function. By treating each point on the trajectory as a signal emission source, calculate the theoretical waveform that the signal emission source should generate, including direct waves and multipath signals reflected by fixed obstacles in the environment, based on the electromagnetic wave propagation principle, and obtain the theoretical channel impulse response set corresponding to the trajectory.

[0076] Step S620: Based on the theoretical channel impulse response set and the channel impulse response, a difference field is constructed. By comparing the residuals of the theoretical waveform and the actual signal in amplitude, phase and time delay point by point, a spatiotemporal difference distribution field representing the degree of agreement between the trajectory and physical reality is generated.

[0077] Step S630: Perform variational correction processing on the trajectory function based on the spatiotemporal difference distribution field to obtain a positioning and tracking result that conforms to the wave propagation law.

[0078] In the physical verification phase of positioning and tracking, step S610, the virtual signal forward generation process, treats each spatiotemporal point on the spatiotemporal trajectory function as a virtual signal emission source. Based on the propagation characteristics of electromagnetic waves in bulk material environments (such as reflection from metal silo walls, material obstruction attenuation, etc.), the theoretical signal waveform that the virtual source should generate is calculated, including direct waves and multipath signals reflected by known fixed obstacles. This process essentially transforms the geometric trajectory into a quantifiable physical signal model, generating a theoretical channel impulse response set that perfectly matches the current trajectory assumption. Step S620, the difference field construction process, establishes the physical verification mechanism. By comparing the theoretical channel impulse response set with the actual channel impulse response derived from the signal (output of step S300) at each spatiotemporal point, the residuals in the three dimensions of signal amplitude, phase, and arrival delay are calculated. In bulk material transportation scenarios, changes in the shape of the material pile can cause dynamic changes in the reflection path. The spatiotemporal difference distribution field generated in this step accurately quantifies the degree of deviation between the trajectory assumption and the actual physical propagation, especially capturing abnormal signal offsets caused by material pile movement. Step S630's variational correction of the trajectory function achieves dynamic closed-loop optimization. Using the spatiotemporal difference distribution field as the guiding field, a target function is constructed through a variational method: minimizing the global residual between the theoretical and actual signals while maintaining trajectory smoothness. This process forces the trajectory correction result to simultaneously satisfy motion continuity constraints and electromagnetic wave propagation constraints, ultimately eliminating trajectory distortion caused by temporary material obstruction or dynamic reflective surfaces, and outputting a high-confidence positioning and tracking result consistent with the warehouse's physical environment.

[0079] Further, step S630 includes steps S631 to S633.

[0080] Step S631: Based on the spatiotemporal difference distribution field, construct the vector field. By calculating the negative gradient of the difference field along the spatial and temporal directions, obtain the trajectory correction vector field. The direction and amplitude of the trajectory correction vector field aim to minimize the signal residual.

[0081] Step S632: Perform spatiotemporal manifold evolution processing based on the trajectory correction vector field. By treating the original spatiotemporal trajectory function as a one-dimensional manifold embedded in the spatiotemporal coordinate system, and allowing the one-dimensional manifold to evolve along the direction of the correction vector field, a new spatiotemporal manifold is obtained.

[0082] Step S633: Extract the optimal trajectory under physical constraints based on the new spatiotemporal manifold. Solve for the smooth curve that satisfies the wave equation constraint and is closest to the evolved manifold. Use the smooth curve as the final positioning and tracking result.

[0083] In the final stage of trajectory optimization, step S631, the vector field construction process, transforms the spatiotemporal difference distribution field into a mathematical tool that can drive trajectory deformation. By calculating the negative gradient of the difference field in the three spatial dimensions and the time dimension, a trajectory correction vector field with clear physical meaning is generated: its spatial component indicates which spatial direction each point on the trajectory should move in to reduce signal residuals, while the time component guides the trajectory timescale adjustment, and the overall magnitude is proportional to the magnitude of residual reduction. Step S632, the spatiotemporal manifold evolution process, realizes the geometric adaptive adjustment of the trajectory. The original spatiotemporal trajectory is regarded as an elastic curve (one-dimensional manifold) in four-dimensional spacetime, and it is continuously deformed along the direction of the correction vector field. This process is similar to allowing the trajectory to naturally unfold in the "gravitational field" of the signal residuals while maintaining the topological continuity of the curve. Especially for situations where equipment is temporarily buried in bulk material environments, this evolution can automatically correct trajectory breaks caused by signal interruption, forming a physically more coherent new spatiotemporal manifold. Step S633, the optimal trajectory extraction process, applies decisive physical constraints. A smooth curve satisfying two conditions is extracted from the evolved spatiotemporal manifold: it must conform to the propagation law of electromagnetic waves in a bulk material environment (achieved through embedding wave equation constraints) and maintain minimal geometric deviation from the manifold. This process effectively solves the positioning drift problem caused by strong reflection from metal silo walls and changes in the dielectric properties of materials, ultimately outputting a highly reliable tracking trajectory that simultaneously satisfies the continuity of equipment movement and the physical interpretation of electromagnetic propagation.

[0084] Example 2:

[0085] like Figure 2 As shown, this embodiment provides a passive coding device positioning and tracking device, the device including:

[0086] The acquisition module 901 is used to acquire raw data of radio frequency signals reflected by all passive coding devices deployed in the monitoring area. The raw data of radio frequency signals includes signal amplitude, phase, time difference of arrival, and unique coding identifier of the device.

[0087] The separation module 902 is used to perform feature separation based on the original RF signal data to obtain the signal baseband feature set;

[0088] The derivation module 903 is used to derive the propagation path based on the baseband feature set. By utilizing the phase and amplitude constraint relationship between each projection component, the geometric structure of the electromagnetic wave reflection path of the generated component is derived in reverse, and the channel impulse response describing the environmental reflection characteristics is obtained.

[0089] The construction module 904 is used to construct the device location probability field based on the channel impulse response. By treating the end point of each reflection path as a potential source transmission location, and calculating the joint probability of the existence of all potential source transmission locations based on path delay and attenuation, the device location probability distribution within the monitoring area is obtained.

[0090] The fitting module 905 is used to fit the motion trajectory function according to the position probability distribution to obtain the spatiotemporal trajectory function of the device.

[0091] The optimization module 906 is used to optimize the trajectory under physical constraints based on the spatiotemporal trajectory function to obtain the positioning and tracking results.

[0092] In one specific embodiment of this application, the separation module 902 includes:

[0093] The first separation unit is used to construct the coding feature space based on the original radio frequency signal data. It obtains the device coding feature space by resolving the unique device coding identifier into orthogonal basis functions and constructing a low-dimensional subspace by linear combination of basis functions.

[0094] The second separation unit is used to perform signal interference component stripping processing based on the device coding feature space and the original radio frequency signal data. By calculating the projection residual of the received signal in the coding feature space, and modeling the multipath scattering-related components in the residual as environmental structured noise, a preliminary purified signal projection is obtained.

[0095] The third separation unit is used to perform baseband feature extraction processing based on the preliminary purified signal projection. By solving the optimization problem that maximizes the energy of the projection components, the signal components that uniquely correspond to each coded identifier are separated to obtain the signal baseband feature set.

[0096] In one specific embodiment of this application, the derivation module 903 includes:

[0097] The first derivation unit is used to perform multipath component clustering based on the signal baseband feature set. By analyzing the phase change rate and amplitude attenuation ratio of each projection component under the same coding identifier, the components that satisfy the same spherical wavefront constraint relationship are classified as signals from the same reflection path, thus obtaining a directional multipath signal cluster.

[0098] The second derivation unit is used to solve the path geometry parameters based on the multipath signal clusters. It converts the arrival time difference and phase information of each signal cluster into a set of equations for the direction of arrival and relative time delay. By solving the set of equations, it determines the incident angle and path length difference of the signal cluster relative to the receiving node array, and obtains the reflection path set.

[0099] The third derivation unit is used to perform environmental scatterer position inversion processing based on the set of reflection paths. Each path is regarded as a ray from the device to the receiving node, and the virtual position of the reflecting surface in the environment is calculated by jointly using the path incident angle and length difference, so as to obtain the channel impulse response including the direct path and the reflection path.

[0100] Example 3:

[0101] Corresponding to the above method embodiments, this embodiment also provides a passive coding device positioning and tracking device. The passive coding device positioning and tracking device described below and the passive coding device positioning and tracking method described above can be referred to each other.

[0102] Figure 3 This is a block diagram illustrating a passive coding device positioning and tracking device 800 according to an exemplary embodiment. Figure 3 As shown, the passive coding device positioning and tracking device 800 may include a processor 801 and a memory 802. The passive coding device positioning and tracking device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0103] The processor 801 controls the overall operation of the passive coding device location tracking device 800 to complete all or part of the steps in the passive coding device location tracking method described above. The memory 802 stores various types of data to support the operation of the passive coding device location tracking device 800. This data may include, for example, instructions for any application or method operating on the passive coding device location tracking device 800, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical buttons. Communication component 805 is used for wired or wireless communication between the passive coding device positioning and tracking device 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0104] In an exemplary embodiment, a passive coding device positioning and tracking device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the passive coding device positioning and tracking method described above.

[0105] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the passive coding device location tracking method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by a processor 801 of a passive coding device location tracking device 800 to complete the passive coding device location tracking method described above.

[0106] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for locating and tracking a passive coding device, characterized in that, include: Acquire raw data of radio frequency signals reflected by all passive coding devices deployed in the monitoring area. The raw data of radio frequency signals includes signal amplitude, phase, time difference of arrival, and unique coding identifier of the device. Based on the original radio frequency signal data, feature separation is performed to obtain the signal baseband feature set; Based on the baseband feature set, the propagation path is deduced. By utilizing the phase and amplitude constraint relationship between each projection component, the geometric structure of the electromagnetic wave reflection path of the generated component is deduced in reverse, and the channel impulse response describing the environmental reflection characteristics is obtained. The device location probability field is constructed based on the channel impulse response. By treating the end point of each reflection path as a potential source transmission location, and calculating the joint probability of the existence of all potential source transmission locations based on path delay and attenuation, the device location probability distribution within the monitoring area is obtained. The spatiotemporal trajectory function of the device is obtained by fitting the motion trajectory function based on the position probability distribution; The trajectory is optimized under physical constraints based on the spatiotemporal trajectory function to obtain the positioning and tracking results.

2. The passive coding device positioning and tracking method according to claim 1, characterized in that, Feature separation is performed based on the raw data of the radio frequency signal, including: The original radio frequency signal data is processed to construct the coding feature space. The device coding feature space is obtained by resolving the unique device coding identifier into orthogonal basis functions and constructing a low-dimensional subspace by linear combination of the basis functions. Based on the device coding feature space and the original radio frequency signal data, signal interference component stripping is performed. By calculating the projection residual of the received signal on the coding feature space, and modeling the multipath scattering-related components in the residual as environmental structured noise, a preliminary purified signal projection is obtained. Based on the preliminary purified signal projection, baseband feature extraction is performed. By solving an optimization problem that maximizes the energy of the projection components, the signal components that uniquely correspond to each coded identifier are separated to obtain the signal baseband feature set.

3. The passive coding device positioning and tracking method according to claim 1, characterized in that, The propagation path is derived based on the baseband feature set, including: Multipath component clustering is performed based on the signal baseband feature set. By analyzing the phase change rate and amplitude attenuation ratio of each projection component under the same coding identifier, components that satisfy the same spherical wavefront constraint relationship are classified as signals from the same reflection path, thus obtaining a directional multipath signal cluster. The path geometry parameters of the multipath signal clusters are solved, and the arrival time difference and phase information of each signal cluster are transformed into a set of equations for the direction of arrival and relative time delay. The incident angle and path length difference of the signal cluster relative to the receiving node array are determined by solving the set of equations, and the reflection path set is obtained. The location inversion of the environmental scatterer is performed based on the set of reflection paths. Each path is regarded as a ray from the device to the receiving node, and the virtual position of the reflecting surface in the environment is calculated by using the path incident angle and length difference. The channel impulse response including the direct path and the reflection path is obtained.

4. The passive coding device positioning and tracking method according to claim 1, characterized in that, Constructing the device location probability field based on the channel impulse response includes: Based on the channel impulse response, a virtual source space mapping process is performed, converting the arrival time delay of each reflection path into an ellipsoid with the receiving node as the focus. The sum of the distances from each point on the ellipsoid to the focus is equal to the path length, and the path is mapped to an ellipsoidal positioning region in space, thus obtaining a set of ellipsoids characterizing the potential source location. The source location hypothesis conflict resolution process is performed based on the set of ellipsoids. By calculating the intersection of different ellipsoids in the spatial regions formed by each pair of receiving nodes, and weighting the source existence summary in the intersection region based on the preset signal attenuation model, candidate source location points are obtained. Based on the candidate information source locations, the device location probability field is synthesized. Each candidate point and its weight are regarded as a kernel function. A continuous probability distribution is synthesized in the monitoring area using a non-parametric density estimation method to obtain the device location probability distribution in the monitoring area.

5. The passive coding device positioning and tracking method according to claim 1, characterized in that, Fitting the motion trajectory function based on the said position probability distribution includes: Based on the location probability distribution, a spatiotemporal correlation field is constructed. By performing tensor product operation along the time axis on the location probability distribution at multiple consecutive times, a probability field describing the possibility of the device existing in the joint spatial and temporal domain is constructed, and spatiotemporal probability volume data is obtained. The path flow extraction process is performed based on the spatiotemporal probability volume data. By solving the spatial curve in the volume data that satisfies the local maximum of the probability distribution and the minimum change of the path tangent direction, the trajectory fitting problem is transformed into solving the geodesic problem of the curve on the spatiotemporal manifold, and the initial spatiotemporal path of the device is obtained. The trajectory function is regularized based on the initial spatiotemporal path. By introducing the inertial constraint of the device motion as a regularization term, the initial path is smoothed and corrected so that the first and second derivatives of the trajectory function conform to physical laws, thus obtaining the spatiotemporal trajectory function of the device.

6. The passive coding device positioning and tracking method according to claim 1, characterized in that, Trajectory optimization under physical constraints based on the spatiotemporal trajectory function includes: The virtual signal forward generation process is performed based on the spatiotemporal trajectory function. By treating each point on the trajectory as a signal emission source, the theoretical waveform that the signal emission source should generate, including direct waves and multipath signals reflected by fixed obstacles in the environment, is calculated based on the electromagnetic wave propagation principle. The theoretical channel impulse response set corresponding to the trajectory is obtained. Based on the theoretical channel impulse response set and the channel impulse response, a difference field is constructed. By comparing the residuals of the theoretical waveform and the actual signal in amplitude, phase and time delay point by point, a spatiotemporal difference distribution field characterizing the degree of agreement between the trajectory and physical reality is generated. Based on the spatiotemporal difference distribution field, variational correction processing of the trajectory function is performed to obtain positioning and tracking results that conform to the wave propagation law.

7. The passive coding device positioning and tracking method according to claim 6, characterized in that, Based on the spatiotemporal difference distribution field, the trajectory function variational correction process is performed, including: Based on the spatiotemporal difference distribution field, a vector field is constructed. By calculating the negative gradient of the difference field along the spatial and temporal directions, a trajectory correction vector field is obtained. The direction and amplitude of the trajectory correction vector field aim to minimize the signal residual. The spatiotemporal manifold evolution process is performed based on the trajectory correction vector field. The original spatiotemporal trajectory function is regarded as a one-dimensional manifold embedded in the spatiotemporal coordinate system, and the one-dimensional manifold is evolved along the direction of the correction vector field to obtain the new spatiotemporal manifold. The optimal trajectory extraction process under physical constraints is performed based on the new spatiotemporal manifold. By solving for the smooth curve that satisfies the wave equation constraint and is closest to the evolved manifold, the smooth curve is used as the final positioning and tracking result.

8. A passive coding device for positioning and tracking, characterized in that, include: The acquisition module is used to acquire raw data of radio frequency signals reflected by all passive coding devices deployed in the monitoring area. The raw data of radio frequency signals includes signal amplitude, phase, time difference of arrival, and unique coding identifier of the device. The separation module is used to perform feature separation based on the original RF signal data to obtain a signal baseband feature set; The derivation module is used to derive the propagation path based on the baseband feature set, and to reverse derive the geometric structure of the electromagnetic wave reflection path of the generated component by utilizing the phase and amplitude constraint relationship between each projection component, thereby obtaining the channel impulse response describing the environmental reflection characteristics. The construction module is used to construct the device location probability field based on the channel impulse response. By treating the end point of each reflection path as a potential source transmission location, and calculating the joint probability of the existence of all the potential source transmission locations based on path delay and attenuation, the device location probability distribution within the monitoring area is obtained. The fitting module is used to fit the motion trajectory function according to the position probability distribution to obtain the spatiotemporal trajectory function of the device. The optimization module is used to optimize the trajectory under physical constraints based on the spatiotemporal trajectory function to obtain the positioning and tracking results.

9. The passive coding device positioning and tracking device according to claim 8, characterized in that, The separation module includes: The first separation unit is used to perform coding feature space construction processing based on the original data of the radio frequency signal. By parsing the unique coding identifier of the device into orthogonal basis functions, and constructing a low-dimensional subspace by linear combination of the basis functions, the device coding feature space is obtained. The second separation unit is used to perform signal interference component stripping processing based on the device coding feature space and the original radio frequency signal data. By calculating the projection residual of the received signal on the coding feature space, and modeling the multipath scattering-related components in the residual as environmental structured noise, a preliminary purified signal projection is obtained. The third separation unit is used to perform baseband feature extraction processing based on the preliminary purified signal projection. By solving the optimization problem of maximizing the energy of the projection components, the signal components that uniquely correspond to each coded identifier are separated to obtain the signal baseband feature set.

10. The passive coding device positioning and tracking device according to claim 8, characterized in that, The derivation module includes: The first derivation unit is used to perform multipath component clustering processing based on the signal baseband feature set. By analyzing the phase change rate and amplitude attenuation ratio of each projection component under the same coding identifier, the components that satisfy the same spherical wavefront constraint relationship are classified as signals from the same reflection path, thus obtaining a directional multipath signal cluster. The second derivation unit is used to solve the path geometry parameters based on the multipath signal clusters, convert the arrival time difference and phase information of each signal cluster into a set of equations for the direction of arrival and relative time delay, and determine the incident angle and path length difference of the signal clusters relative to the receiving node array by solving the set of equations, thereby obtaining the reflection path set. The third derivation unit is used to perform environmental scatterer position inversion processing based on the set of reflection paths. Each path is regarded as a ray from the device to the receiving node, and the virtual position of the reflecting surface in the environment is calculated by jointly using the path incident angle and length difference to obtain the channel impulse response including the direct path and the reflection path.

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