Indoor positioning method and system based on acoustic metasurface

Abstract

The present invention discloses an indoor positioning method and system based on a sound metasurface, which relates to the field of indoor positioning. The method comprises: step 1, broadcasting a sound signal with embedded position features through a single sound base station in a deployment environment; step 2, detecting the sound signal emitted by the sound base station through a receiving end, extracting the position information therein, and calculating the specific position of the receiving end to achieve indoor positioning; the step 1 comprises: step 1.1, emitting an original sound signal without position features through a sound source module in the sound base station; step 1.2, converting the original sound signal into a sound signal with position features through a sound metasurface module. The step 2 comprises: step 2.1, detecting and continuously collecting sound signals with position features through a signal detection module in the receiving end; step 2.2, processing the sound signals collected in step 2.1 through a position solution module in the receiving end, and calculating the specific position of the receiving end with the help of a neural network.

Description

Technical Field

[0001] The present invention relates to the field of indoor positioning, and in particular to an indoor positioning method and system based on a sound metasurface. Background Art

[0002] Indoor positioning is in great demand for both humans and various indoor robots in indoor scenarios where GPS signals cannot be obtained. Indoor positioning technology based on sound signals has attracted extensive attention and research in recent years due to its advantages such as high positioning accuracy, robustness to lighting conditions, and easy acquisition of hardware equipment.

[0003] Existing positioning technologies based on sound signals can be divided into methods based on time of arrival (ToA) and angle of arrival (AoA). The ToA-based method obtains the distance between the base station and the node by measuring the time it takes for the base station signal to reach the mobile node signal, and then calculates the position of the mobile node through geometric operations. This method requires strict time synchronization between the base station and the node, which introduces additional hardware costs or communication burdens. The AoA-based method locates the node by measuring the angle of the node relative to the base station. It requires a microphone array composed of multiple microphones to obtain the angle of arrival of the signal. The microphones need to be spaced a certain distance apart. The microphone array solution increases the hardware cost and size. At the same time, these two methods also have the disadvantages of requiring the deployment of multiple base stations and being easily interfered by multipath effects in the environment.

[0004] Liu Jiaojiao et al. proposed a positioning system based on ultrasonic waves in their Chinese invention patent application "Asynchronous indoor positioning method based on intelligent terminal and ultrasonic communication" (application number CN201911061028.5). By arranging multiple fixed ultrasonic base stations, the ultrasonic base stations send ultrasonic waves to mobile nodes. The mobile nodes calculate the distance of the stage relative to the base station according to the flight time of the ultrasonic waves, and locate the mobile nodes according to the distance. This method requires the deployment of multiple ultrasonic base stations, and also requires strict time synchronization between the base stations and the receivers. Wang Mei et al. proposed a positioning method based on microphone arrays in their Chinese invention patent application "Indoor positioning and mapping method based on microphone arrays" (application number CN201811631579.6). The microphone arrays and single sound sources are arranged at different positions indoors, and then the positioning and mapping are completed according to the geometric relationship of the acoustic signal in the indoor environment propagation model with respect to the first-order reflection of the wall. This method requires the use of microphone arrays.

[0005] Therefore, technicians in this field are committed to developing a new indoor positioning method and system based on sound signals to solve the above-mentioned defects in the prior art. Summary of the invention

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to overcome the defects in the prior art, such as the need to deploy multiple base stations, the requirement for strict time synchronization between base stations and nodes in ToA-based methods, and the requirement for microphone arrays in AoA-based methods, and to find a new indoor positioning system and method that can reduce the requirements on the number of base stations and microphone arrays, avoid dependence on time synchronization and interference from multipath effects, thereby improving the final positioning accuracy.

[0007] To achieve the above object, the present invention provides an indoor positioning method based on sound metasurface, comprising the following steps:

[0008] Step 1: broadcasting a sound signal embedded with location features through a single sound base station in a deployment environment;

[0009] Step 2: Detect the sound signal sent by the sound base station through the receiving end, extract the position information therein, and calculate the specific position of the receiving end to achieve indoor positioning;

[0010] Wherein, the step 1 includes the following sub-steps:

[0011] Step 1.1, sending an original sound signal without position characteristics through the sound source module in the sound base station;

[0012] Step 1.2: Convert the original sound signal into a sound signal having the position characteristics through a sound metasurface module.

[0013] Furthermore, the step 2 includes the following sub-steps:

[0014] Step 2.1, detecting the sound signal with the location characteristics through the signal detection module in the receiving end and continuously collecting it;

[0015] Step 2.2: Process the sound signal collected in step 2.1 through the position calculation module in the receiving end, and calculate the specific position of the receiving end with the help of a neural network.

[0016] Furthermore, the original sound signal in step 1.1 is a linear frequency modulation signal, and its mathematical expression is as follows:

[0017]

[0018] in, is the starting frequency of the linear frequency modulation signal, is the slope of the linear FM signal frequency change, is the amplitude of the linear frequency modulation signal.

[0019] Furthermore, the acoustic metasurface module in step 1.2 is a passive structure, comprising A linear arrangement of sound units, each of which changes the phase of the incident sound wave, producing a phase lag value ,but The sound unit is equivalent to sound sources with different phases; The sound emitted by the sound unit for:

[0020]

[0021] Said The sound waves emitted by the sound sources with different phases propagate in the deployment environment, and the sound signals received at each location are The sound signals of the sound sources are superimposed after propagation. is an imaginary unit;

[0022] The position coordinates are point The received sound signal is expressed as follows:

[0023]

[0024] in, It is Sound units to point The distance is the attenuation of the sound signal amplitude caused by distance, is the phase delay caused by distance, Is an imaginary unit.

[0025] Furthermore, the phase lag value of each sound unit in the sound metasurface module is The construction is carried out by the following method: First, the deployment environment is uniformly Points ; Then, calculate the sound signal at each point ; Finally, in Among the points, the cosine similarity between each two is calculated, and all the cosine similarities are superimposed to form the following optimization problem:

[0026]

[0027]

[0028] By solving the above optimization problem, the optimal phase lag value of each sound unit is obtained. .

[0029] Furthermore, the signal detection module in step 2.1 uses a correlation test method to determine the starting time of the sound signal; the correlation test method includes the following steps: first, using the original sound signal and the sound signal collected by the signal detection module to perform correlation calculation, specifically as follows:

[0030]

[0031] in, Indicates relevance, Indicates the actual sound signal received, represents the original sound signal;

[0032] Then, the time when the correlation value is the largest is taken as the starting time , specifically:

[0033]

[0034] in, It means finding the parameter value that makes the function reach its maximum value.

[0035] Furthermore, the step 2.2 includes the following sub-steps:

[0036] Step 2.2.1, preprocessing the sound signal collected in step 2.1, using fast Fourier transform to convert the received sound signal from the time domain to the frequency domain, and extracting the frequency domain signal within the target frequency band;

[0037] Step 2.2.2, using the processed frequency domain signal as the input of a convolutional neural network, and the output of the convolutional neural network is the specific position of the receiving end;

[0038] The sound signal with the position feature is used as a data set, and the position information is used as a label to perform supervised learning training on the convolutional neural network, so as to train a neural network model that can predict the position.

[0039] The present invention also provides an indoor positioning system based on a sound metasurface, comprising a single sound base station and a receiving end; the sound base station comprises a sound source module and a sound metasurface module connected front and back, wherein the sound source module emits an original sound signal without position characteristics, and the sound metasurface module converts the original sound signal into a sound signal with position characteristics; the receiving end is connected to the sound base station, comprising a signal detection module and a position solution module connected front and back, wherein the signal detection module is responsible for detecting the sound signal with position characteristics emitted by the sound base station and continuously collecting it, and the position solution module is responsible for processing the collected sound signal and inferring the predicted position of the receiving end through a neural network;

[0040] The system also includes any of the previous indoor positioning methods based on sound metasurfaces.

[0041] Furthermore, the sound source module is equipped with a speaker responsible for emitting the original sound signal.

[0042] Furthermore, the signal detection module collects sound signals using a microphone.

[0043] The indoor positioning method and system based on acoustic metasurface provided by the present invention have at least the following technical effects:

[0044] 1. The technical solution provided by the present invention can achieve accurate positioning through a single sound base station, reducing the number of base stations required, solving the defect that multiple base stations are usually required for positioning in the existing positioning method, and avoiding the dependence on time synchronization in the positioning method based on arrival time (ToA);

[0045] 2. The technical solution provided by the present invention uses the acoustic metasurface to act on the sound source, generating a differentiated sound signal at each location in the environment, so that positioning can be achieved with only a single microphone, reducing the cost and volume of the sensor, and avoiding the problem of using a microphone array in the positioning method based on the angle of arrival (AoA);

[0046] 3. The technical solution provided by the present invention is a position estimation method based on a neural network. The sound features of each position are extracted through a convolutional neural network, and then the position is estimated based on the characteristics of the input signal, thereby avoiding the problem that the existing technical solutions are easily interfered by the multipath effect.

[0047] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1is a method flow chart of a preferred embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of a sound metasurface in a preferred embodiment of the present invention;

[0050] Figure 3 This is a convolutional neural network structure diagram in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0052] Example 1

[0053] An embodiment of the present invention provides an indoor positioning method based on a sound metasurface, comprising the following steps:

[0054] Step 1: broadcasting a sound signal embedded with location features through a single sound base station in a deployment environment;

[0055] Step 2: The receiving end detects the sound signal emitted by the sound base station, extracts the location information therein, and calculates the specific location of the receiving end to achieve indoor positioning;

[0056] Wherein, step 1 includes the following sub-steps:

[0057] Step 1.1, sending out an original sound signal without position characteristics through a sound source module in a sound base station;

[0058] Step 1.2: Convert the original sound signal into a sound signal with position characteristics through the sound metasurface module.

[0059] In particular, step 2 includes the following sub-steps:

[0060] Step 2.1, detecting sound signals with location characteristics through a signal detection module in the receiving end and continuously collecting them;

[0061] Step 2.2: Process the sound signal collected in step 2.1 through the position calculation module in the receiving end, and calculate the specific position of the receiving end with the help of a neural network.

[0062] Example 2

[0063] Based on Example 1, the original sound signal in step 1.1 is a linear frequency modulation signal, and its mathematical expression is as follows:

[0064]

[0065] in, is the starting frequency of the linear FM signal, is the slope of the frequency change of the linear FM signal, is the amplitude of the linear FM signal.

[0066] like Figure 2 As shown, the acoustic metasurface module in step 1.2 is a passive structure, including Linearly arranged sound units, each sound unit changes the phase of the incident sound wave, resulting in a phase lag value ,but The sound unit is equivalent to sound sources with different phases; The sound produced by the sound unit for:

[0067]

[0068] The sound waves emitted by the sound sources with different phases propagate in the deployment environment, and the sound signals received at each location are The sound signals of the sound sources are superimposed after propagation. is an imaginary unit;

[0069] The position coordinates are point The received sound signal is expressed as follows:

[0070]

[0071] in, It is Sound units to point The distance is the attenuation of the sound signal amplitude caused by distance, is the phase delay caused by distance, Is an imaginary unit.

[0072] Example 3

[0073] Based on Example 1 or 2, the phase lag value of each sound unit in the sound metasurface module is It is constructed in the following way: First, evenly distribute Points ; Then, calculate the sound signal at each point ; Finally, in Among the points, the cosine similarity between each two is calculated, and all the cosine similarities are superimposed to form the following optimization problem:

[0074]

[0075]

[0076] By solving the above optimization problem, the optimal phase lag value of each sound unit is obtained. .

[0077] Example 4

[0078] Based on Embodiment 1, 2 or 3, the signal detection module in step 2.1 uses a correlation test method to determine the starting time of the sound signal; the correlation test method includes the following steps: first, the original sound signal and the sound signal collected by the signal detection module are used to perform correlation calculation, which is specifically as follows:

[0079]

[0080] in, Indicates relevance, Indicates the actual sound signal received, represents the original sound signal;

[0081] Then, the time when the correlation value is the largest is taken as the starting time , specifically:

[0082]

[0083] in, It means finding the parameter value that makes the function reach its maximum value.

[0084] In particular, if Figure 3 As shown, step 2.2 includes the following sub-steps:

[0085] Step 2.2.1, pre-processing the sound signal collected in step 2.1, using fast Fourier transform (FFT) to convert the received sound signal from the time domain to the frequency domain, and extracting the frequency domain signal within the target frequency band;

[0086] Step 2.2.2, the processed frequency domain signal is used as the input of the convolutional neural network, and the output of the convolutional neural network is the specific position of the receiving end;

[0087] Among them, the sound signal with position characteristics is used as the data set, and the position information is used as the label to train the convolutional neural network for supervised learning, and a neural network model that can predict the position is trained.

[0088] Example 5

[0089] like Figure 1As shown, an embodiment of the present invention provides an indoor positioning system based on sound metasurface, including a single sound base station and a receiving end; the sound base station is responsible for broadcasting a sound signal with embedded location characteristics in a deployment environment, including a sound source module and a sound metasurface module connected front and back, wherein the sound source module emits an original sound signal without location characteristics, and the sound metasurface module converts the original sound signal into a sound signal with location characteristics; the receiving end is connected to the sound base station, for detecting the sound signal emitted by the sound base station, extracting the location information therein, and calculating the specific location of the receiving end, including a signal detection module and a position solution module connected front and back, wherein the signal detection module is responsible for detecting the sound signal with location characteristics emitted by the sound base station and continuously collecting it, and the position solution module is responsible for processing the collected sound signal and inferring the predicted position of the receiving end through a neural network.

[0090] The system also includes an indoor positioning method based on sound metasurface as described in any of the previous embodiments.

[0091] Example 6

[0092] Based on Example 5, the sound source module is equipped with a speaker responsible for emitting the original sound signal. The original signal emitted by the sound source does not have significant position information at different positions. A linear frequency modulation signal is selected as the original sound signal, and its mathematical expression is as follows:

[0093]

[0094] in, is the starting frequency of the linear FM signal, is the slope of the frequency change of the linear FM signal, is the amplitude of the linear FM signal.

[0095] The acoustic metasurface module is a passive structure consisting of Linearly arranged sound units, each sound unit changes the phase of the incident sound wave, resulting in a phase lag value ,but The sound unit is equivalent to sound sources with different phases; The sound produced by the sound unit for:

[0096]

[0097] The sound waves emitted by the sound sources with different phases propagate in the deployment environment, and the sound signals received at each location are The sound signals of the sound sources are superimposed after propagation. is an imaginary unit;

[0098] The position coordinates are point The received sound signal is expressed as follows:

[0099]

[0100] in, It is Sound units to point The distance is the attenuation of the sound signal amplitude caused by distance, is the phase delay caused by distance, Is an imaginary unit.

[0101] The differences in sound signals between different locations determine the accuracy of the location prediction algorithm.

[0102] In particular, the signal detection module is responsible for detecting the sound signal emitted by the base station and collecting the sound signal using a microphone. The signal detection module uses a correlation test method to determine the starting time of the sound signal. The correlation test method includes the following steps: First, the original sound signal and the sound signal collected by the signal detection module are used to perform a correlation calculation, as shown in the following formula:

[0103]

[0104] in, Indicates relevance, Indicates the actual sound signal received, represents the original sound signal;

[0105] Then, the time when the correlation value is the largest is taken as the starting time , specifically:

[0106]

[0107] in, It means finding the parameter value that makes the function reach its maximum value.

[0108] In particular, the position calculation module is responsible for processing the received signal and inferring the final position through a neural network. After detecting the base station signal, the position calculation module first pre-processes the signal, uses the fast Fourier transform (FFT) to convert the received sound signal from the time domain to the frequency domain, and extracts the effective frequency domain signal in the target frequency band. For position calculation, we use a convolutional neural network. The processed frequency domain signal is used as the input of the convolutional neural network, and the output of the neural network is the position of the receiving end. For network training, during the deployment phase, sound signals with location information are collected as a data set, and the location information is used as a label for supervised learning, so as to train a neural network model that can predict the location.

[0109] Example 7

[0110] Taking an indoor room of 4m×4m as a specific example, the specific implementation steps of an indoor positioning method based on a sound metasurface provided by an embodiment of the present invention are as follows:

[0111] Step 1. Build a sound source module. Use a Bluetooth speaker as the sound source. Use a computer to generate a 16kHz-20kHz linear frequency modulation signal with a duration of 0.1s and a sampling rate of 48kHz as the original sound signal, and control the speaker to broadcast the original sound signal through Bluetooth connection;

[0112] Step 2: Construct a sound metasurface module. The sound metasurface consists of 10 linearly arranged phase modulation units, and the room is modeled as an x,y plane. Assume that the speaker is placed at ,Will Units are placed at position , set The phase lag of each point is ;

[0113] Step 3: Solve the phase lag value of each unit through the optimization method. In the optimization method, 100 points are uniformly selected in the environment. The coordinates of the points are , construct the sound signal of each point ,in , construct the optimization problem , the optimization problem can be solved by the minimum gradient method to obtain the phase lag value of each unit;

[0114] Step 4: Detection of sound signals. The microphone continuously receives real-time sound signals and detects the start time of the signals. The received signals are continuously correlated with the original signals. After the correlation value is obtained, the time when the correlation value is the largest is used as the start time of the signal. Then, 0.1s of the sound signal is intercepted to infer the position.

[0115] Step 5: Preprocessing of received signals. Perform FFT processing on the intercepted 0.1s signal to obtain The complex spectrum vector of The real amplitude vector is then intercepted from the 16kHz-20kHz part of the spectrum to obtain A vector of

[0116] Step 6: Position prediction based on neural network. Construct a convolutional neural network, the input of the network is the preprocessed data, and the output of the network is the position coordinates. The training set of the neural network is collected in advance during the deployment phase.

[0117] After experiments, the method provided by the embodiment of the present invention can achieve a positioning accuracy of 0.3m.

[0118] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An indoor positioning method based on acoustic metasurface, characterized in that: The method comprises the following steps: Step 1: broadcasting a sound signal embedded with location features through a single sound base station in a deployment environment; Step 2: Detect the sound signal sent by the sound base station through the receiving end, extract the position information therein, and calculate the specific position of the receiving end to achieve indoor positioning; Wherein, the step 1 includes the following sub-steps: Step 1.1, sending an original sound signal without position characteristics through the sound source module in the sound base station; Step 1.2: transforming the original sound signal into a sound signal having the position characteristics through a sound metasurface module; Step 2 includes the following sub-steps: Step 2.1, detecting the sound signal with the location characteristics through the signal detection module in the receiving end and continuously collecting it; Step 2.2, processing the sound signal collected in step 2.1 by the position calculation module in the receiving end, and calculating the specific position of the receiving end by means of a neural network; The acoustic metasurface module in step 1.2 is a passive structure comprising Linearly arranged sound units, each sound unit changes the phase of the incident sound wave, resulting in a phase lag value ,but The sound unit is equivalent to sound sources with different phases; The sound emitted by the sound unit for: Said The sound waves emitted by the sound sources with different phases propagate in the deployment environment, and the sound signals received at each location are The sound signals of the sound sources are superimposed after propagation. is an imaginary unit; The position coordinates are point The received sound signal is expressed as follows: in, It is Sound units to point The distance is the attenuation of the sound signal amplitude caused by distance, is the phase delay caused by distance, is an imaginary unit; The phase lag value of each sound unit in the sound metasurface module The construction is carried out by the following method: First, the deployment environment is uniformly Points ; Then, calculate the sound signal at each point ; Finally, in Among the points, the cosine similarity between each two is calculated, and all the cosine similarities are superimposed to form the following optimization problem: By solving the above optimization problem, the optimal phase lag value of each sound unit is obtained. .

2. The indoor positioning method based on acoustic metasurface according to claim 1, characterized in that: The original sound signal in step 1.1 is a linear frequency modulation signal, and its mathematical expression is as follows: in, is the starting frequency of the linear frequency modulation signal, is the slope of the linear FM signal frequency change, is the amplitude of the linear frequency modulation signal.

3. The indoor positioning method based on acoustic metasurface according to claim 1, characterized in that: The signal detection module in step 2.1 uses a correlation test method to determine the starting time of the sound signal; the correlation test method includes the following steps: first, using the original sound signal and the sound signal collected by the signal detection module to perform correlation calculation, specifically as follows: in, Indicates relevance, Indicates the actual sound signal received, represents the original sound signal; Then, the time when the correlation value is the largest is taken as the starting time , specifically: in, It means finding the parameter value that makes the function reach its maximum value.

4. The indoor positioning method based on acoustic metasurface according to claim 3, characterized in that: The step 2.2 includes the following sub-steps: Step 2.2.1, preprocessing the sound signal collected in step 2.1, using fast Fourier transform to convert the received sound signal from the time domain to the frequency domain, and extracting the frequency domain signal within the target frequency band; Step 2.2.2, using the processed frequency domain signal as the input of a convolutional neural network, and the output of the convolutional neural network is the specific position of the receiving end; The sound signal with the position feature is used as a data set, and the position information is used as a label to perform supervised learning training on the convolutional neural network, so as to train a neural network model that can predict the position.

5. An indoor positioning system based on acoustic metasurface, characterized in that: It comprises a single sound base station and a receiving end; the sound base station comprises a sound source module and a sound metasurface module connected front and back, wherein the sound source module emits an original sound signal without position characteristics, and the sound metasurface module converts the original sound signal into a sound signal with position characteristics; the receiving end is connected to the sound base station, and comprises a signal detection module and a position solution module connected front and back, wherein the signal detection module is responsible for detecting the sound signal with position characteristics emitted by the sound base station and continuously collecting it, and the position solution module is responsible for processing the collected sound signal and inferring the predicted position of the receiving end through a neural network; The system also includes the indoor positioning method based on sound metasurface as described in any one of claims 1 to 4.

6. The indoor positioning system based on acoustic metasurface according to claim 5, characterized in that: The sound source module is equipped with a speaker, which is responsible for emitting the original sound signal.

7. The indoor positioning system based on acoustic metasurface according to claim 6, characterized in that: The signal detection module collects sound signals using a microphone.

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