A method for indoor multi-target visible light positioning based on compressed sensing

Abstract

The present invention discloses a method for indoor multi-target visible light positioning based on compressed sensing, which relates to the technical field of visible light communication. The present invention converts visible light signals into sparse vectors, aggregates autocorrelation signal vectors in multiple visible light signals to obtain the total power of the signals; when measuring indoor visible light transmission, a compressed sensing algorithm CS forms an observation matrix, assigns measurement power to the observation matrix based on the total power, and uses the measurement power to recover multiple sparse vectors; aggregates the cross-correlation measurement vectors in the multiple visible light signals, and reconstructs the sparse vectors to obtain multiple positioning sparse vectors, identifies the positions of non-zero elements in the positioning sparse vectors, and determines the positions of multiple targets; the process exploits the sparsity and cross-correlation in the multi-target visible light signals, utilizes the compressed sensing method CS to convert the multi-target positioning problem in a complex environment into a sparse recovery problem, and utilizes the sparse weak signals to distinguish and identify the signals of the multiple targets, so as to achieve high-precision multi-target positioning.

Description

Technical Field

[0001] The present invention relates to the field of visible light communication technology, and in particular to a method, device, equipment and medium for indoor multi-target visible light positioning based on compressed sensing. Background Art

[0002] With the rapid development of the Industrial Internet of Things (IIoT) and smart manufacturing, indoor positioning systems (RTLS) are playing an increasingly important role in smart factories, automated warehousing, and logistics management. Accurate multi-target positioning technology is crucial for improving production efficiency, optimizing equipment scheduling, and ensuring operational safety. However, existing indoor positioning technologies (such as those based on RSS, TOA, and AOA) suffer from insufficient positioning accuracy, high computational complexity, and poor robustness in complex environments. Especially in scenarios where multiple targets are simultaneously positioned, existing methods often fail to meet the requirements of high precision and real-time performance.

[0003] At present, indoor positioning systems mainly rely on traditional methods based on received signal strength (RSS), time of arrival (TOA) and angle of arrival (AOA). These technologies can provide high accuracy in single-target positioning, but face significant challenges and limitations in scenarios where multiple targets are positioned simultaneously. These include: ① Decreased positioning accuracy due to environmental complexity; in complex indoor environments, traditional positioning methods are susceptible to interference from factors such as multipath effects, non-line-of-sight propagation (NLOS) and environmental noise; these problems cause the received signal characteristics to be distorted, thereby significantly reducing positioning accuracy; ② Limitations in multi-target scenarios; in the case of simultaneous multi-target positioning, existing methods usually exhibit high computational complexity and low efficiency; ③ Signal sparsity problem; although compressed sensing (CS) technology has been The sparse characteristics of LED signals are introduced to achieve efficient positioning, but in complex environments, the sparsity assumption of signals may not be fully established, thus affecting the positioning effect; ④ The impact of noise and interference: Background noise and signal interference in complex indoor environments will still significantly affect the positioning accuracy, and existing methods lack effective robustness mechanisms to deal with these challenges; ⑤ Real-time and multi-target positioning accuracy: The existing VLP system has insufficient real-time processing capabilities in multi-target scenarios, and it is difficult to simultaneously achieve high-precision and low-latency positioning requirements in highly dynamic environments; ⑥ Insufficient technology integration: Although generative adversarial networks (GANs) as a deep learning method have shown significant advantages in image processing and signal recovery, how to effectively apply GAN technology to VLP systems is still in the initial exploration stage.

[0004] Therefore, in complex environments, the received signals of multiple targets are often affected by geometric structures and signal interference, resulting in distortion of signal characteristics of the received signals of multiple targets, accompanied by the problem of signal overlap. The current methods are difficult to distinguish and identify the signals of multiple targets, making it difficult to achieve high-precision simultaneous positioning of multiple targets. Summary of the Invention

[0005] The embodiment of the present invention provides an indoor multi-target visible light positioning method based on compressed sensing, which can solve the problem in the existing technology that the current method is difficult to distinguish and identify the signals of multiple targets, making it difficult to achieve high-precision simultaneous positioning of multiple targets.

[0006] An embodiment of the present invention provides a method for indoor multi-target visible light positioning based on compressed sensing, comprising the following steps:

[0007] Build an indoor multi-target visible light positioning VLP system, where multiple targets receive visible light signals simultaneously to form a visible light signal data set;

[0008] Convert multiple visible light signals into multiple sparse vectors, where the non-zero element positions of the sparse vectors correspond to the positions of the targets; aggregate the autocorrelation signal vectors within the multiple visible light signals to obtain the total power of the multiple visible light signals; when measuring visible light transmission in a room, a compressed sensing algorithm (CS) forms an observation matrix and assigns a measurement power to each element in the observation matrix based on the total power; and using the measurement power assigned by the observation matrix, recover the multiple sparse vectors to form multiple preliminary positioning sparse vectors.

[0009] Based on the collaborative relationship between different emitting light sources in the system, the cross-correlation measurement vectors in multiple visible light signals are aggregated, and the preliminary positioning sparse vector is reconstructed based on the cross-correlation measurement vectors to obtain multiple positioning sparse vectors;

[0010] The positions of the multiple targets are determined according to the positions of the non-zero elements in the multiple positioning sparse vectors.

[0011] Preferably, obtaining the total power of the plurality of visible light signals includes:

[0012] Aggregate the received signal vectors of multiple visible light signals to form an autocorrelation signal vector y, which is expressed as:

[0013]

[0014] Where: ω represents the total background noise vector; θ represents the target positioning vector on the grid, θ=[θ1,...,θ j ,...,θ N ] T is an indicator vector with N elements;

[0015] The measurement matrix Φ in the compressed sensing algorithm CS is expressed as:

[0016]

[0017] By aggregating the autocorrelation signal vector y of multiple visible light signals, the total power P of multiple visible light signals is obtained. rx , expressed as:

[0018]

[0019] Where: E{·} represents the statistical expectation operator; ⊙ represents the Hadamard product operator; (·) * represents the complex conjugate operator; represents the variance background noise; 1 M Represents an M-length vector of full values.

[0020] Preferably, the recovering of multiple sparse vectors to form multiple preliminary positioning sparse vectors includes:

[0021] When the compressed sensing algorithm CS measures the indoor visible light channel and propagation environment, it forms an observation matrix J, which is expressed as:

[0022]

[0023] Based on the total power, each element in the observation matrix J is assigned a measurement power, and the measurement power assigned by the observation matrix is used to restore multiple sparse vectors to form multiple preliminary positioning sparse vectors.

[0024] Preferably, the reconstructing the preliminary positioning sparse vector includes:

[0025] According to the cooperative relationship between different emitting light sources in the indoor multi-target visible light positioning VLP system, the cross-correlation measurement vector P in multiple visible light signals is aggregated. corr , expressed as:

[0026]

[0027] in:(·) H represents the conjugate transpose operator; represents the Kronecker product operator; represents the Khatri-Rao product; I M Represents the identity matrix of size M×M; the cross-correlation measurement vector P corr is length-M 2 ;

[0028] Define an M(M+1) / 2×M 2 The selection matrix S is of size M×M; each row of the selection matrix S corresponds to P corrThere are M independent diagonal elements and M(M-1) / 2 upper diagonal elements. After the selection process is completed, a cross-correlation measurement model based on the compressed sensing algorithm CS is formulated, which is expressed as:

[0029]

[0030] Among them, the size of matrix Ψ is m(m+1) / 2×N, which represents the observation matrix; the matrix Ψ is expressed as:

[0031]

[0032] The number of independent linear equations used in the cross-correlation measurement model based on the compressed sensing algorithm CS increases from M to M(M+1) / 2, and the measurement data available for sparse recovery based on the compressed sensing algorithm CS gradually increases;

[0033] A preliminary positioning sparse vector is reconstructed according to the cross-correlation measurement model to obtain multiple positioning sparse vectors.

[0034] Preferably, it also includes:

[0035] Using a generative adversarial network (GAN) to identify a received signal vector in a plurality of aggregated visible light signals, generating a positioning vector, and using the positioning vector to correct a plurality of positioning sparse vectors to obtain a plurality of final positioning sparse vectors; the method comprises the following steps:

[0036] The received signal vector y of the aggregated multiple visible light signals is used as the initial input of the generator of the generative adversarial network (GAN), replacing the random variable z in the GAN. Two generators are used to gradually establish an explicit mapping relationship from the received signal vector y to the target positioning vector θ.

[0037] Generator G1(·) generates the corresponding potential channel matrix H according to the input y, and inputs the potential channel matrix H into the pre-trained generator G2(·) to generate a sparse positioning vector containing position information

[0038] A regularization term is added to the loss function of the Generative Adversarial Network (GAN) to reduce the error between the generated vector and the true vector, forming a composite loss function. Expressed as:

[0039]

[0040] in:

[0041] Generate sparse positioning vectors based on Correct multiple positioning sparse vectors to obtain multiple final positioning sparse vectors.

[0042] An embodiment of the present invention further provides an indoor multi-target visible light positioning device based on compressed sensing, comprising:

[0043] The acquisition module is used to build an indoor multi-target visible light positioning (VLP) system. Multiple targets receive visible light signals simultaneously to form a visible light signal data set.

[0044] The recognition module is configured to convert multiple visible light signals into multiple sparse vectors, wherein the positions of non-zero elements of the sparse vectors correspond to the positions of the targets; aggregate the autocorrelation signal vectors within the multiple visible light signals to obtain the total power of the multiple visible light signals; use the compressed sensing algorithm CS to form an observation matrix when measuring visible light transmission in the room, and assign a measurement power to each element in the observation matrix based on the total power; and use the measurement power assigned by the observation matrix to recover the multiple sparse vectors to form multiple preliminary positioning sparse vectors.

[0045] The positioning module is used to aggregate the cross-correlation measurement vectors in multiple visible light signals based on the cooperative relationship between different emitting light sources in the system, and reconstruct the preliminary positioning sparse vector based on the cross-correlation measurement vectors to obtain multiple positioning sparse vectors.

[0046] An embodiment of the present invention further provides an electronic device, including a memory and a processor;

[0047] The memory is used to store computer programs;

[0048] The processor is used to implement the steps of the above-mentioned method for indoor multi-target visible light positioning based on compressed sensing when executing the computer program stored in the memory.

[0049] An embodiment of the present invention further provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for indoor multi-target visible light positioning based on compressed sensing.

[0050] The embodiment of the present invention provides a method for indoor multi-target visible light positioning based on compressed sensing. Compared with the existing technology, its beneficial effects are as follows:

[0051] The present invention converts multiple visible light signals into multiple sparse vectors, aggregates the autocorrelation signal vectors within the multiple visible light signals to obtain the total power of the multiple visible light signals; when measuring visible light transmission in a room, a compressed sensing algorithm (CS) forms an observation matrix and assigns a measurement power to each element in the observation matrix based on the total power; uses the measurement power assigned by the observation matrix to restore multiple sparse vectors; aggregates the cross-correlation measurement vectors within the multiple visible light signals, and reconstructs the sparse vectors based on the cross-correlation measurement vectors to obtain multiple positioning sparse vectors, thereby identifying the positions of non-zero elements in the positioning sparse vectors and determining the positions of multiple targets; the process converts multiple visible light signals into multiple sparse vectors and aggregates the total power of the autocorrelation signal vectors within the multiple visible light signals to form the measurement power of the observation matrix to restore the sparse vectors, while aggregating the cross-correlation measurement vectors within the multiple visible light signals and reconstructing the sparse vectors based on the cross-correlation measurement vectors. The process described above exploits the sparsity and cross-correlation within the multi-target visible light signals and uses the compressed sensing method (CS) to transform the multi-target positioning problem in a complex environment into a sparse recovery problem. The sparse weak signals can be used to distinguish and identify the signals of multiple targets to achieve high-precision multi-target positioning.

[0052] In addition, the present invention uses a generative adversarial network (GAN) to identify the positioning vector within the received signal vector to correct multiple sparse positioning vectors; the generative adversarial network (GAN) uses an adversarial training mechanism to enable the generator to extract sparse location information directly from the received signal while suppressing background noise and interference signals; this adversarial learning framework not only improves the robustness of the positioning system, but also significantly enhances its adaptability in complex dynamic environments, thereby further optimizing the sparse recovery process of compressed sensing. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of the overall process of a method for indoor multi-target visible light positioning based on compressed sensing provided by an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of a typical multi-target VLP system for an indoor multi-target visible light positioning method based on compressed sensing provided by an embodiment of the present invention;

[0055] Figure 3 A schematic diagram illustrating a visible light position estimation technology based on collaborative compressed sensing for an indoor multi-target visible light positioning method based on compressed sensing provided by an embodiment of the present invention;

[0056] Figure 4 A schematic diagram of the CS-GAN technology for a compressed sensing-based indoor multi-target visible light positioning method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] See also Figure 1 The embodiments of the present invention provide a method for indoor multi-target visible light positioning based on compressed sensing. This method addresses the shortcomings of existing visible light positioning (VLP) technology in terms of accuracy, noise immunity, and computational efficiency in multi-target positioning. In particular, in complex environments affected by geometric structures and signal interference, existing methods are difficult to achieve high-precision simultaneous positioning of multiple targets. Current technology cannot guarantee centimeter-level positioning accuracy in environments with dense targets or high noise levels, and exhibits poor robustness and efficiency in applications with high real-time requirements. Specifically, this method is:

[0059] Decreased positioning accuracy due to environmental complexity: In complex indoor environments, traditional positioning methods are susceptible to interference from factors such as multipath, non-line-of-sight (NLOS) propagation, and ambient noise. These issues distort received signal characteristics, significantly reducing positioning accuracy. Furthermore, target movement and signal variations in dynamic environments make it difficult for traditional methods to quickly adapt and adjust, limiting their robustness and reliability in practical applications.

[0060] Limitations in multi-target scenarios: Existing methods often exhibit high computational complexity and low efficiency when simultaneously locating multiple targets. For example, RSS-based positioning methods cannot effectively distinguish the signal characteristics of each target in multi-target scenarios. This is especially true in environments with overlapping signals and high noise levels, where computational complexity increases further and positioning accuracy and real-time performance decrease significantly. Even with RF fingerprinting, similar signal characteristics or device heterogeneity can easily lead to confusion and misjudgment, making it difficult to meet the needs of real-world industrial scenarios.

[0061] Signal sparsity problem: Although compressed sensing (CS) technology has been introduced to utilize the sparse characteristics of LED signals to achieve efficient positioning, in complex environments, the signal sparsity assumption may not be completely valid, thus affecting the positioning effect.

[0062] Impact of noise and interference: Background noise and signal interference in complex indoor environments will still significantly affect positioning accuracy. Existing methods lack effective robustness mechanisms to deal with these challenges.

[0063] Real-time and multi-target positioning accuracy: Existing VLP systems lack real-time processing capabilities in multi-target scenarios and find it difficult to simultaneously achieve high-precision and low-latency positioning requirements in highly dynamic environments.

[0064] Insufficient integration of new technologies: Although generative adversarial networks (GANs) as a deep learning method have demonstrated significant advantages in image processing and signal recovery, the effective application of GAN technology to VLP systems is still in its early stages of exploration. Specifically, while the adversarial training framework of GANs can enhance anti-interference capabilities, its computational complexity is high, and its adaptability and stability in positioning tasks have not been fully verified. In addition, the potential of the collaborative optimization of GAN and CS technologies in multi-target rapid positioning has not been fully explored, making it difficult for existing systems to achieve significant performance breakthroughs.

[0065] The present invention designs a multi-target collaborative VLP framework, which utilizes the existing indoor lighting system as the positioning signal source. By multi-point reception of LED signals and combining the compressed sensing theory, the high-precision positioning of multiple indoor targets is achieved. Within this framework, the present invention transforms the positioning problem into a sparse recovery problem and optimizes the recovery of sparse matrices through the CS algorithm, thereby improving positioning accuracy, especially in complex indoor environments.

[0066] Secondly, in order to solve the problem that positioning accuracy is affected by noise and signal distortion in the existing technology, the present invention introduces generative adversarial network (GAN) technology; by training the GAN model, we can significantly improve the accuracy of sparse feature recovery of the signal and enhance the anti-interference ability of the positioning system in low signal-to-noise ratio environments; the introduction of GAN can not only optimize the accuracy of sparse recovery, but also accurately identify and locate the positions of multiple targets in dynamic and complex environments, thereby improving the stability and reliability of the system.

[0067] Finally, by implementing the system in an indoor environment, the present invention further introduces a collaborative positioning mechanism between targets; this mechanism ensures the real-time positioning of multiple targets, which not only improves the positioning accuracy but also optimizes the real-time response capability of the system; through the technical integration of compressed sensing and generative adversarial networks, the present invention can effectively overcome the limitations of traditional VLP systems in multi-target positioning and provide a fast, accurate and stable indoor positioning solution; the application of this technology provides strong support for the real-time positioning of multiple targets in fields such as intelligent manufacturing, automated warehousing and industrial Internet of Things.

[0068] The present invention proposes a multi-target rapid visible light positioning method based on the combination of compressed sensing (CS) and generative adversarial network (GAN), which makes innovative improvements to the problems of insufficient multi-target positioning accuracy, anti-interference ability and real-time performance of existing technologies in complex indoor environments; the technical solution of the present invention includes the design of a multi-target collaborative VLP framework, a sparse recovery algorithm based on CS, a collaborative mechanism between multiple targets, and the application of GAN in optimizing signal sparsity characteristics and enhancing anti-interference ability; by effectively integrating CS and GAN technologies, the present invention significantly improves the positioning accuracy and efficiency of the system in low signal-to-noise ratio and high interference environments, and optimizes the collaboration between targets to achieve real-time dynamic positioning.

[0069] Specifically:

[0070] 1. Multi-target visible light positioning under the collaborative compressed sensing framework.

[0071] 1. Introduce compressed sensing theory into visible light multi-target positioning.

[0072] In a typical downlink visible light communication (VLP) system, such as Figure 2 As shown in the figure, a multi-objective VLP framework based on compressed sensing (CS) is introduced and defined as CSM-VLP.

[0073] Specifically, a generalized indoor VLP scenario is considered here, assuming that the number of target terminals is K, where K is much smaller than the number of grids N (i.e., K << N). The pilot signals of the i-th LED sent from all K target terminals can be aggregated through cooperation between targets (e.g., wireless communication links). After aggregating the K terminal signals, the following formula is used to define:

[0074]

[0075] Where: i represents the sum of the background noise at the terminal of the transmission link from the i-th to the K-th target.

[0076] Then, formulate a total reception by combining all aggregated received signal vectors ysignals Corresponding to all M LEDs, y can be rewritten into matrix form as:

[0077]

[0078] Where: ω represents the total background noise vector.

[0079] The multi-target visible light communication (VLP) framework based on compressed sensing (CS) can be expressed as the above formula; this formula takes advantage of the sparsity of the target location in the indoor area relative to the entire grid point, and is specifically expressed as follows:

[0080]

[0081] Among them, the target positioning vector on the grid θ=[θ1,...,θ j ,...,θ N ] T Is an indicator vector with N elements, each element corresponds to an area in the grid, and the value of each element indicates whether there is a target at the corresponding grid point; therefore, most elements are mostly zero, except for the elements corresponding to the K target positions to be located, which are 1; since the proportion of non-zero elements in vector θ is much smaller than the length of the vector, θ is a sparse vector and can be recovered by sparse recovery methods. In the measurement model based on compressed sensing, the observation matrix Φ is defined as:

[0082]

[0083] In order to improve the model based on compressed sensing (CS) and make it more universal in different scenarios, the present invention obtains the total received power by aggregating the autocorrelation signal vector y of the received signal, which is expressed as the received power P rx , expressed as:

[0084]

[0085] Where: E{·} represents the statistical expectation operator; ⊙ represents the Hadamard product operator; (·) * represents the complex conjugate operator; represents the variance background noise; 1 M Represents an M-length vector of full values.

[0086] Then, an improved power measurement model based on compressed sensing (CS) is established, in which the observation matrix J can also be regarded as a database of indoor visible light channels and propagation environments for positioning, after the following processing:

[0087]

[0088] It is observed that the elements in J are related to the channel gain. In the positioning phase, an improved power measurement model based on compressed sensing (CS) is obtained through a downlink VLP system with inter-target cooperation; it can be seen that this is actually a sparse recovery problem, which aims to measure the power through a given observation matrix J and recover the online grid target positioning sparse vector θ; the grid points corresponding to the positions of non-zero elements are the estimated target terminal positions; the sparse recovery problem can be solved using It is efficiently solved by minimization methods, or greedy algorithms based on compressed sensing such as Orthogonal Matching Pursuit (OMP).

[0089] In fact, the accuracy of the proposed scheme is closely related to the grid point step size. A denser grid segmentation results in a smaller position quantization error; appropriately increasing the number of grid points can improve positioning resolution. Furthermore, increasing N also makes θ a sparser vector, making it easier to recover using compressed sensing (CS)-based algorithms. However, a smaller grid step size does not necessarily lead to better CS-based positioning performance. According to CS theory, sparse recovery problems can only guarantee the recovery of positive constants when the condition M ≥ μK log (N / K) is satisfied. Therefore, the values of M and N should be selected based on the actual situation.

[0090] 2. CS-GAN theory.

[0091] Compressed sensing (CS) is a method for reconstructing signals using a small amount of measurement data, and is particularly suitable for sparse signals. Its basic principle is to use the sparsity of the signal and an appropriate measurement matrix to recover the complete signal using a small number of linear measurements. Typically, this involves solving an optimization problem to find the signal that best matches the measurement results and satisfies the sparsity constraints.

[0092] In recent years, generative adversarial networks (GANs) have been introduced into the compressed sensing framework due to their powerful capabilities in generative modeling and data distribution learning. GANs consist of two neural networks: a generator and a discriminator. The generator aims to generate samples similar to the real data distribution, while the discriminator is responsible for distinguishing between real samples and generated samples. Through this adversarial training, GANs are able to learn complex distributions and generate high-quality data.

[0093] CS-GAN combines the advantages of compressed sensing and generative adversarial networks (GANs) to improve the accuracy and robustness of signal reconstruction. This method relies on signal sparsity, with the generator learning a mapping of input data to generate outputs corresponding to the sparse signal, achieving efficient reconstruction. GAN's adversarial training mechanism enables the generator to continuously improve during training, better simulating the distribution of real signals, thus overcoming the limitations of traditional CS algorithms under harsh conditions. Furthermore, the introduction of a composite loss function helps to simultaneously optimize the adversarial loss and the reconstruction loss, ensuring the high quality and accuracy of the generated signals. CS-GAN is particularly well-suited for tasks such as multi-target localization, which require recovering high-dimensional information from sparse observations and can effectively combine the spatial and temporal characteristics of the signal to adapt to a variety of complex application scenarios.

[0094] 2. Specific plan design.

[0095] 1. Multi-target visible light positioning based on enhanced collaborative compressed sensing.

[0096] Although the CSM-VLP scheme has exploited inter-target information to formulate aggregated measurement data, the potential geometric information of the indoor environment and the channel corresponding to the information of different anchors, i.e., LEDs, has not been fully utilized to enhance the robustness of localization; therefore, the present invention further designs a cooperative CSMVLP (CoCSM-VLP) scheme through cross-correlation, such as Figure 3 As shown, the inter-anchor collaboration is exploited between the corresponding aggregated received signals to achieve better multi-target joint localization performance.

[0097] Specifically, the cross-correlation P is given by the following formula: corr To obtain the aggregated received signal provided by the inter-anchor correlation:

[0098]

[0099] in:(·) H Represents the conjugate transpose operator.

[0100] Then, both sides of the above equation are vectorized to derive the cross-correlation measurement vector. The calculation process is expressed as:

[0101]

[0102] in: represents the Kronecker product operator; represents the Khatri-Rao product; I M Represents the identity matrix of size M×M; the cross-correlation measurement vector P corr is length-M 2 .

[0103] In fact, the problem given by the above formula is a system of linear equations, in which the independent mathematical equations have a direct impact on the sparse recovery performance based on compressed sensing (CS); this is actually determined by the CS theory based on the coherence of the measurement matrix; since the cross-correlation matrix P corr is symmetric, then we can define an M(M+1) / 2×M 2 The selection matrix S is of size M×M; each row of the selection matrix corresponds to P corr There are M independent diagonal elements and M(M-1) / 2 upper diagonal elements; after the selection process is completed, the CS-based cross-correlation measure can be formulated to satisfy the given model. The model is expressed as:

[0104]

[0105] Among them, the size of the matrix Ψ m(m+1) / 2×N represents the observation matrix, that is, the fingerprint database CoCSM-VLP scheme based on the compressed sensing cross-correlation measurement model in the above formula; compared with the observation matrix J, the number of rows of the matrix Ψ is much higher, which means that more independent equations can be used to solve the support; that is, more independent equations can be used to determine the position of the unknown sparse non-zero elements in the positioning vector θ; all elements in the matrix Ψ are also related to the channel gain {h ij} is related, so through the pre-estimated indoor channel, the information required to construct the matrix Ψ can be collected, which is given; the matrix Ψ is expressed as:

[0106]

[0107] The number of independent linear equations available in the cross-correlation measurement model based on compressed sensing increases from M to M(M+1) / 2 compared to the measurement model; therefore, the measurement data available for sparse recovery based on compressed sensing gradually increases, which will directly benefit N-based multi-target localization. Since the amount of measurement data in the cross-correlation measurement model, i.e., M(M+1) / 2, is much larger than M, it is easier to meet the requirements; this will improve the accuracy and reliability of the proposed scheme, especially under harsh conditions with a large number of unknowns, such as a large number of targets (large K), dense grid networks (large N), and strong background noise (low signal-to-noise ratio).

[0108] After this, the sparse localization vector can be reconstructed via an efficient sparse recovery problem, similar to CSM-VLP; this can be achieved by using Norm minimization methods or greedy algorithms based on compressed sensing, such as Orthogonal Matching Pursuit (OMP) implementation.

[0109] 2. Introduce GAN network.

[0110] Based on the enhanced collaborative compressed sensing anchor multi-target visible light positioning cooperation, a GAN-based sparse learning framework is further constructed to recover the vector θ. First, the aggregated received signal vector y is directly used as the initial input of the generator network, replacing the random variable z in the traditional GAN. Two generators are used to gradually establish an explicit mapping relationship from y to θ. The generator G1(·) generates the corresponding latent channel matrix H according to the input y. The latent channel matrix H is then used as input to the pre-trained generator G2(·). Its purpose is to generate a corresponding sparse positioning vector containing position information based on the input channel information. In addition, the present invention adds a regularization term on the basis of the traditional loss function to reduce the error between the generated vector and the true vector, and designs a composite loss function Defined as:

[0111]

[0112] in:

[0113] like Figure 4 The figure shows the processing diagram after the introduction of CS and GAN. The GAN-MVLP scheme proposed in this invention mainly consists of two stages: training stage and inference stage. First, the discriminator network is trained to correctly distinguish the real positioning vector θ from the generated positioning vector θ. Finally, the simultaneous positioning results of multiple targets are obtained through sparse recovery.

[0114] This paper proposes a multi-target rapid visible light positioning solution based on a collaborative compressed sensing framework (CSM), aiming to address the high time complexity of simultaneous multi-target positioning in existing visible light positioning systems. By exploiting the sparsity and cross-correlation in multi-target positioning, this solution uses compressed sensing technology to transform the multi-target positioning problem into a sparse recovery problem, significantly reducing the computational complexity of positioning. Furthermore, by combining the compressed sensing-generative adversarial network (CS-GAN) model, the efficiency and accuracy of simultaneous multi-target positioning are further optimized. By training the generative adversarial network, the model can rapidly map from aggregated received signal vectors to sparse positioning vectors, ensuring efficient sparse recovery and improved positioning accuracy in multi-target scenarios.

[0115] To address the high time complexity of simultaneous multi-target positioning in existing visible light positioning (VLP) systems, this paper proposes a fast multi-target visible light position estimation scheme based on a collaborative compressed sensing framework. The core steps are as follows: First, by fully exploiting the inherent sparsity of a small number of target positions relative to all grid points in the indoor environment, a visible light position estimation technique based on collaborative compressed sensing (CSM) is proposed. This technique aims to effectively address the sparsity and cross-correlation problems of multi-target positioning in visible light positioning, transforming the multi-target positioning problem into a sparse recovery problem. Second, a compressed sensing-generative adversarial network model (CS-GAN) is used as a research entry point to address the high time complexity of simultaneous multi-target positioning. The core idea of this model is to utilize the sparsity of positioning targets and the cross-correlation between received signals under a compressed sensing framework to achieve low-time-complexity sparse recovery by solving underdetermined equations. Finally, the CS-GAN is trained so that the generator network can directly learn the mapping from aggregated received signal vectors to sparse positioning vectors. By effectively integrating the collaborative information of multiple targets, the aim is to improve the multi-target positioning accuracy and computational efficiency, thereby reducing the time complexity required for simultaneous multi-target positioning.

[0116] The present invention assumes that the spatial position of the target terminal obeys a random distribution function and the spatial direction information of the target obeys a truncated Laplace distribution, and takes typical visible light positioning boundary conditions as the basis (5m×5m×3m, and the emitting light source is 4×4 LEDs); combined with the proposed multi-target fast visible light position estimation technology based on the collaborative compressed sensing framework, CS-GAN is introduced to transform the multi-target positioning problem into a sparse recovery problem, and GAN is further used to learn the mapping from the aggregated received signal vector to the sparse positioning vector, so as to achieve the purpose of reducing the time complexity required for simultaneous multi-target positioning; thus, when the plane grid points are divided into 20cm×20cm and the number of targets is ≤5, the average position estimation error AE (Average Error) is ≤8cm, and the total calculation time for multiple targets is no more than 100ms.

[0117] The present invention aims to solve the high time complexity problem of existing visible light positioning systems when simultaneously positioning multiple targets. By exploiting the sparsity between target positions and indoor environment grid points, the method of the present invention effectively transforms the multi-target positioning task into a sparse recovery problem. The present invention adopts the compressed sensing-generative adversarial network model (CS-GAN) to achieve efficient mapping of aggregated received signal vectors to sparse positioning vectors, significantly improving positioning accuracy and computational efficiency. Compared with the typical compressed sensing method Block-OMP algorithm multi-target positioning, which takes 490ms, the computation time of the proposed algorithm is shortened by 59.18%.

[0118] In terms of improving positioning accuracy, the present invention uses compressed sensing technology to extract sparse signals and restore target positions, effectively improving positioning accuracy, especially in noisy and interfering environments; in terms of enhancing system robustness, the generative adversarial network (GAN) optimizes signal processing through adversarial training, significantly improving anti-interference ability and ensuring stable positioning performance in complex environments; in terms of optimizing computational efficiency, multi-target positioning is converted into a sparse recovery problem, reducing computational complexity and improving the system's real-time response capability; in terms of adapting to complex environments, based on visible light positioning (VLP), radio frequency band signal attenuation and multipath interference are avoided, which is particularly suitable for complex indoor environments, providing high-precision, low-interference positioning solutions; the present invention combines compressed sensing with generative adversarial networks, optimizes signal processing through sparse recovery and adversarial training, and significantly improves the accuracy, efficiency and robustness of multi-target positioning; it not only provides new multi-target positioning solutions for smart manufacturing, industrial Internet of Things and other fields, but also promotes the development of smart factories and automated production systems.

[0119] Creative aspects:

[0120] The creativity of this invention is mainly reflected in the redefinition and optimization of traditional positioning technology; by innovatively combining compressed sensing (CS) and generative adversarial network (GAN) technology, this invention has pioneered a new design idea for a multi-target indoor visible light positioning (VLP) system; specifically, compressed sensing technology exploits signal sparsity to transform the complex multi-target positioning problem into a sparse signal recovery task, achieving efficient processing from signal acquisition to positioning calculation; this technical advantage enables the system to ensure high-precision and high-efficiency positioning performance even in scenarios with dense distribution of multiple targets or severe signal aliasing.

[0121] On the other hand, the introduction of generative adversarial networks (GANs) further optimizes the sparse recovery process of compressed sensing. Through an adversarial training mechanism, GAN enables the generator to directly extract sparse location information from the received signal while suppressing background noise and interference signals. This adversarial learning framework not only improves the robustness of the positioning system, but also significantly enhances its adaptability in complex dynamic environments. In particular, it provides an innovative solution that can operate stably under conditions such as high noise, non-line-of-sight (NLOS) propagation, and signal multipath effects, which are easily limited by traditional VLP systems.

[0122] Novelty:

[0123] The novelty of this invention lies in its dual aspects of technological innovation and application expansion. It proposes for the first time the combination of generative adversarial networks (GANs) and compressed sensing (CS) and applies them to indoor multi-target visible light positioning (VLP) systems. This invention technically breaks the limitations of traditional VLP systems and proposes an efficient and stable solution for multi-target positioning scenarios in complex environments.

[0124] Traditional VLP systems exhibit obvious deficiencies when faced with signal interference, environmental noise, and densely distributed targets. For example, multipath interference can lead to a significant decrease in positioning accuracy, and the signal recovery process has a high computational complexity. However, this invention uses compressed sensing technology and the sparse characteristics of LED signals to abstract the multi-target positioning problem into a sparse signal recovery task, significantly optimizing computational efficiency. On this basis, the introduction of GAN further enhances the system's anti-interference capability and signal recovery quality. Through adversarial training of the generator and the discriminator, GAN can not only accurately restore the target position, but also dynamically adjust the system to adapt to complex environmental changes, such as non-line-of-sight propagation conditions or signal aliasing scenarios.

[0125] Practicality:

[0126] The practicality of this invention is reflected in its broad application prospects in the field of multi-target positioning, especially its practical value in industrial manufacturing and Internet of Things related scenarios; the multi-target visible light positioning method based on compressed sensing-GAN can break through the accuracy bottleneck of traditional positioning systems in complex environments, and provide efficient and reliable positioning support for intelligent manufacturing, industrial Internet of Things and automated production.

[0127] Compressed sensing technology greatly improves the system's computing efficiency by reducing the amount of signal collection and calculation, while significantly reducing the response delay caused by the high computational complexity of traditional methods. This feature enables the present invention to quickly complete positioning tasks in densely distributed or dynamic environments with multiple targets and meet real-time requirements. In addition, the introduction of GAN further optimizes system performance. Its strong anti-interference ability enables the system to maintain stable high-precision positioning effects even in high-noise environments or complex interference conditions.

[0128] In terms of application scenarios, the present invention is particularly suitable for situations in industrial environments where high-precision positioning is required, such as intelligent robots, automatic guided vehicles (AGVs), dynamic asset tracking, and automatic scheduling of workshop equipment. By using visible light as a positioning signal, the present invention effectively avoids the problems of limited wireless spectrum resources, signal congestion and interference, while having the characteristics of energy saving and environmental protection, and can be seamlessly connected with intelligent lighting systems.

[0129] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for indoor multi-target visible light positioning based on compressed sensing, characterized in that: The following steps are involved: Build an indoor multi-target visible light positioning VLP system, where multiple targets receive visible light signals simultaneously to form a visible light signal data set; Convert multiple visible light signals into multiple sparse vectors, where the non-zero element positions of the sparse vectors correspond to the positions of the targets; aggregate the autocorrelation signal vectors within the multiple visible light signals to obtain the total power of the multiple visible light signals; when measuring visible light transmission in a room, a compressed sensing algorithm (CS) forms an observation matrix and assigns a measurement power to each element in the observation matrix based on the total power; and using the measurement power assigned by the observation matrix, recover the multiple sparse vectors to form multiple preliminary positioning sparse vectors. Based on the collaborative relationship between different emitting light sources in the system, the cross-correlation measurement vectors in multiple visible light signals are aggregated, and the preliminary positioning sparse vector is reconstructed based on the cross-correlation measurement vectors to obtain multiple positioning sparse vectors; Determine the positions of multiple targets according to the positions of non-zero elements in the multiple positioning sparse vectors; The method further includes: using a generative adversarial network (GAN) to identify a received signal vector in the aggregated multiple visible light signals, generating a positioning vector, and using the positioning vector to correct the multiple positioning sparse vectors to obtain multiple final positioning sparse vectors; including the following steps: Using a generative adversarial network (GAN) to identify a received signal vector in a plurality of aggregated visible light signals, generating a positioning vector, and using the positioning vector to correct a plurality of positioning sparse vectors to obtain a plurality of final positioning sparse vectors; the method comprises the following steps: The received signal vector y of the aggregated multiple visible light signals is used as the initial input of the generator of the generative adversarial network (GAN), replacing the random variable z in the GAN. Two generators are used to gradually establish an explicit mapping relationship from the received signal vector y to the target positioning vector θ. Generator G1(·) generates the corresponding potential channel matrix H according to the input y, and inputs the potential channel matrix H into the pre-trained generator G2(·) to generate a sparse positioning vector containing position information A regularization term is added to the loss function of the Generative Adversarial Network (GAN) to reduce the error between the generated vector and the true vector, forming a composite loss function. Expressed as: in: θ represents the target positioning vector on the grid; E{·} represents the statistical expectation operator; Generate sparse positioning vectors based on Correct multiple positioning sparse vectors to obtain multiple final positioning sparse vectors.

2. The method for indoor multi-target visible light positioning based on compressed sensing according to claim 1, characterized in that: The obtaining of the total power of the plurality of visible light signals comprises: Aggregate the received signal vectors of multiple visible light signals to form an autocorrelation signal vector y, which is expressed as: Where: ω represents the total background noise vector; θ represents the target positioning vector on the grid, θ=[θ1,...,θ j ,...,θ N ] T is an indicator vector with N elements; M represents the number of LEDs; N represents the number of grids; The measurement matrix Φ in the compressed sensing algorithm CS is expressed as: By aggregating the autocorrelation signal vector y of multiple visible light signals, the total power P of multiple visible light signals is obtained. rx , expressed as: Where: E{·} represents the statistical expectation operator; ⊙ represents the Hadamard product operator; (·) * represents the complex conjugate operator; represents the variance background noise; 1 M represents an M-length vector of full values; J represents the observation matrix.

3. The method for indoor multi-target visible light positioning based on compressed sensing according to claim 2, characterized in that: The recovering of the plurality of sparse vectors to form a plurality of preliminary positioning sparse vectors includes: When the compressed sensing algorithm CS measures the indoor visible light channel and propagation environment, it forms an observation matrix J, which is expressed as: Based on the total power, each element in the observation matrix J is assigned a measurement power, and the measurement power assigned by the observation matrix is used to restore multiple sparse vectors to form multiple preliminary positioning sparse vectors.

4. The method for indoor multi-target visible light positioning based on compressed sensing according to claim 3, characterized in that: The reconstructing of the preliminary positioning sparse vector includes: According to the cooperative relationship between different emitting light sources in the indoor multi-target visible light positioning VLP system, the cross-correlation measurement vector P in multiple visible light signals is aggregated. corr , expressed as: in:(·) H represents the conjugate transpose operator; represents the Kronecker product operator; represents the Khatri-Rao product; I M Represents the identity matrix of size M×M; the cross-correlation measurement vector P corr is length-M 2 ; Define an M(M+1) / 2×M 2 The selection matrix S is of size M×M; each row of the selection matrix S corresponds to P corr There are M independent diagonal elements and M(M-1) / 2 upper diagonal elements. After the selection process is completed, a cross-correlation measurement model based on the compressed sensing algorithm CS is formulated, which is expressed as: Among them, the size of matrix Ψ is m(m+1) / 2×N, which represents the observation matrix; the matrix Ψ is expressed as: The number of independent linear equations used in the cross-correlation measurement model based on the compressed sensing algorithm CS increases from M to M(M+1) / 2, and the measurement data available for sparse recovery based on the compressed sensing algorithm CS gradually increases; A preliminary positioning sparse vector is reconstructed according to the cross-correlation measurement model to obtain multiple positioning sparse vectors.

5. An indoor multi-target visible light positioning device based on compressed sensing, characterized in that: include: The acquisition module is used to build an indoor multi-target visible light positioning (VLP) system. Multiple targets receive visible light signals simultaneously to form a visible light signal data set. The recognition module is configured to convert multiple visible light signals into multiple sparse vectors, wherein the positions of non-zero elements of the sparse vectors correspond to the positions of the targets; aggregate the autocorrelation signal vectors within the multiple visible light signals to obtain the total power of the multiple visible light signals; use the compressed sensing algorithm CS to form an observation matrix when measuring visible light transmission in the room, and assign a measurement power to each element in the observation matrix based on the total power; and use the measurement power assigned by the observation matrix to recover the multiple sparse vectors to form multiple preliminary positioning sparse vectors. A positioning module is used to aggregate the cross-correlation measurement vectors in multiple visible light signals based on the cooperative relationship between different emitting light sources in the system, and reconstruct a preliminary positioning sparse vector based on the cross-correlation measurement vectors to obtain multiple positioning sparse vectors; The method further includes: using a generative adversarial network (GAN) to identify a received signal vector in the aggregated multiple visible light signals, generating a positioning vector, and using the positioning vector to correct the multiple positioning sparse vectors to obtain multiple final positioning sparse vectors; including the following steps: Using a generative adversarial network (GAN) to identify a received signal vector in a plurality of aggregated visible light signals, generating a positioning vector, and using the positioning vector to correct a plurality of positioning sparse vectors to obtain a plurality of final positioning sparse vectors; the method comprises the following steps: The received signal vector y of the aggregated multiple visible light signals is used as the initial input of the generator of the generative adversarial network (GAN), replacing the random variable z in the GAN. Two generators are used to gradually establish an explicit mapping relationship from the received signal vector y to the target positioning vector θ. Generator G1(·) generates the corresponding potential channel matrix H according to the input y, and inputs the potential channel matrix H into the pre-trained generator G2(·) to generate a sparse positioning vector containing position information A regularization term is added to the loss function of the Generative Adversarial Network (GAN) to reduce the error between the generated vector and the true vector, forming a composite loss function. Expressed as: in: θ represents the target positioning vector on the grid; E{·} represents the statistical expectation operator; Generate sparse positioning vectors based on Correct multiple positioning sparse vectors to obtain multiple final positioning sparse vectors.

6. An electronic device, characterized in that: include: memory and processor; The memory is used to store computer programs; The processor is configured to implement the steps of a method for indoor multi-target visible light positioning based on compressed sensing as described in any one of claims 1 to 4 when executing the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that Used to store a computer program, which, when executed by a processor, implements the steps of the indoor multi-target visible light positioning method based on compressed sensing as described in any one of claims 1 to 4.

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