JP7572004B2 - OBJECT DETECTION SYSTEM, OBJECT DETECTION DEVICE, OBJECT DETECTION METHOD, AND OBJECT DETECTION PROGRAM - Google Patents

Description

This disclosure relates to a technology for detecting objects by acquiring information indicating the state of the wireless propagation path between a transmitting antenna and a receiving antenna.

Technology for detecting objects using wireless signals is known. One example of such technology is disclosed in Non-Patent Document 1. In the system disclosed in Non-Patent Document 1, antennas are installed diagonally or at the corners of an area in which objects are to be detected, surrounding the area.

Shinya Otsuki, Tomonori Murakami, Tomoaki Ogawa, “Evaluation of Advanced Wireless Sensing Technology for the Creation of Intelligent Spaces,” B5-24, 2020 IEICE Society Conference, September 2020

Non-Patent Document 1 describes the results of measuring the rate at which objects were correctly detected when object detection antennas were set at the diagonal corners of the area to be detected and when they were set at the four corners. As can be seen from these measurement results, simply using multiple antennas does not necessarily lead to an improvement in the detection rate. How the antennas are set up is important in order to improve the detection rate.

The present disclosure aims to provide technology that can improve object detection accuracy by optimizing the installation position of the antenna.

To achieve the above object, the present disclosure provides an object detection system. The object detection system of the present disclosure is a system including a transmitting antenna with a fixed installation position, one or more receiving antennas with adjustable installation positions, and an object detection device. The first antenna is configured to transmit a predetermined known signal. The one or more receiving antennas are configured to transmit propagation path status information indicating the status of the propagation path to the transmitting antenna derived from the received known signal. The object detection device is configured to capture information included in wireless communication between the transmitting antenna and one or more receiving antennas to detect objects within the communication area.

In the object detection system of the present disclosure, the object detection device is configured to execute the following information acquisition process, information storage process, derivation process, selection process, and output process. In the information acquisition process, propagation path state information transmitted by one or more receiving antennas is acquired. In the information storage process, the acquired propagation path state information is stored in association with the installation positions of one or more receiving antennas and the arrangement state of the detection target object. In the derivation process, for each installation position of one or more receiving antennas, a cross-correlation value is derived between the propagation path state information acquired when the detection target object is in a first arrangement state and the propagation path state information acquired when the detection target object is in a second arrangement state. In the selection process, an optimal installation position for one or more receiving antennas is selected based on the derived cross-correlation value. Then, in the output process, the selection result of the optimal installation position for one or more receiving antennas is output.

The present disclosure provides an object detection device to achieve the above object. The object detection device of the present disclosure is a device that captures information included in wireless communication performed between a transmitting antenna whose installation position is fixed and one or more receiving antennas whose installation positions are variable, and detects an object within a communication area. The object detection device of the present disclosure is configured to execute the following information acquisition process, information storage process, derivation process, selection process, and output process. In the information acquisition process, propagation path state information indicating the state of the propagation path between one or more receiving antennas and the transmitting antenna is acquired. In the information storage process, the acquired propagation path state information is stored in association with the installation positions of one or more receiving antennas and the arrangement state of the detection target object. In the derivation process, a cross-correlation value between the propagation path state information acquired when the detection target object is in a first arrangement state and the propagation path state information acquired when the detection target object is in a second arrangement state is derived for each installation position of one or more receiving antennas. In the selection process, an optimal installation position of one or more receiving antennas is selected based on the derived cross-correlation value. Then, in the output process, the selection result of the optimal installation position for one or more receiving antennas is output.

The present disclosure provides an object detection method to achieve the above object. The object detection method of the present disclosure is a method for detecting an object within a communication area by capturing information included in wireless communication performed between a transmitting antenna whose installation position is fixed and one or more receiving antennas whose installation positions are variable. The object detection method of the present disclosure includes the following first to sixth steps. In the first step, a predetermined known signal is transmitted from the transmitting antenna. In the second step, one or more receiving antennas are transmitted to transmit propagation path state information indicating the state of the propagation path to the transmitting antenna derived from the received known signal. In the third step, the propagation path state information transmitted by one or more receiving antennas is acquired. In the fourth step, the acquired propagation path state information is stored in association with the installation positions of one or more receiving antennas and the arrangement state of the detection target object. In the fifth step, a cross-correlation value between the propagation path state information acquired when the detection target object is in a first arrangement state and the propagation path state information acquired when the detection target object is in a second arrangement state is derived for each installation position of one or more receiving antennas. Then, in the sixth step, the optimal installation positions of one or more receiving antennas are selected based on the cross-correlation values.

To achieve the above object, the present disclosure provides an object detection program. The object detection program of the present disclosure includes a program for causing a computer to execute the processing performed by the above object detection device. In other words, the above object detection device can be realized by a computer and an object detection program. The object detection program may be recorded on a computer-readable recording medium. The object detection program may be provided via a network.

The object detection system, object detection device, object detection method, and object detection program disclosed herein can improve the accuracy of object detection within a communication area by optimizing the installation position of one or more variable-position receiving antennas.

FIG. 1 is a diagram illustrating an example of the configuration of an object detection system common to each embodiment of the present disclosure. FIG. 2 is a diagram showing an example of a sequence in wireless communication between an AP and a STA. FIG. 13 is a diagram illustrating an example of a VHT NDP frame transmitted from an AP. FIG. 2 illustrates an example of compressed CSI. FIG. 13 is a diagram illustrating an example of a method for converting polar coordinates into Cartesian coordinates. FIG. 1 is a diagram illustrating an example of the configuration of an object detection device common to each embodiment of the present disclosure. 1 is a diagram showing examples of installation positions of an AP antenna (ANT), potential installation positions of a STA, and detection target locations. 1A to 1C are diagrams illustrating a method for selecting an installation position of one STA according to the first embodiment of the present disclosure. 1A to 1C are diagrams illustrating a method for selecting installation positions of multiple STAs according to the first embodiment of the present disclosure. 11A and 11B are diagrams illustrating a method for selecting an installation position of one STA according to a second embodiment of the present disclosure. 11A and 11B are diagrams illustrating a method for selecting installation positions of multiple STAs according to a second embodiment of the present disclosure. 13A and 13B are diagrams illustrating a method for selecting an installation position of one STA according to a third embodiment of the present disclosure. 13A and 13B are diagrams illustrating a method for selecting installation positions of multiple STAs according to a third embodiment of the present disclosure. 13A and 13B are diagrams illustrating a method for selecting an installation position of one STA according to a fourth embodiment of the present disclosure. 13A and 13B are diagrams illustrating a method for selecting installation positions of multiple STAs according to a fourth embodiment of the present disclosure. 13A to 13C are diagrams illustrating a method for selecting installation positions of multiple STAs according to a fifth embodiment of the present disclosure.

Below, embodiments of the object detection system, object detection device, object detection method, and object detection program disclosed herein will be described with reference to the drawings.

1. Configuration of an object detection system common to each embodiment FIG. 1 shows a configuration example of an object detection system 100 common to each embodiment of the present disclosure. The object detection system 100 uses a wireless LAN (Local Area Network) system having an AP (Access Point) 101 having two or more antennas 104 and at least one STA (Station) 102. Then, an object detection device 103 detects an object within the communication area of the wireless LAN system. In FIG. 1, the AP 101 has three antennas 104. However, if the AP 101 has two or more antennas 104, this embodiment can be applied. Note that, in this embodiment, the STA 102 only needs to have at least one antenna. Here, each antenna 104 of the AP 101 corresponds to a "transmitting antenna", and the STA 102 corresponds to a "receiving antenna".

The wireless LAN system shown in FIG. 1 is compatible with the wireless LAN standard 802.11ac, and AP101 performs MIMO communication using three antennas 104. STA102 is a wireless LAN terminal that communicates with AP101. STA102 receives wireless signals transmitted from the three antennas 104 of AP101. Conversely, STA102 transmits wireless signals to the three antennas 104 of AP101. In this way, wireless communication compatible with 802.11ac is performed between AP101 and STA102.

In 802.11ac, the STA 102 measures the state of the wireless propagation path between each antenna 104 and the device itself based on the measurement data transmitted from each of the three antennas 104 of the AP 101. The STA 102 transmits propagation path state information indicating the state of the measured wireless propagation path to the AP 101.

The object detection device 103 monitors the wireless signals communicated between the AP 101 and the STA 102 to detect objects such as humans within the communication area of the wireless LAN system. In particular, in this embodiment, the object detection device 103 captures propagation path state information transmitted from the STA 102 to the AP 101. Then, the object detection device 103 detects objects within the communication area of the wireless LAN system based on the captured propagation path state information.

Note that the position of the object detection device 103 shown in FIG. 1 is just an example. The position of the object detection device 103 may be anywhere as long as it can receive a wireless signal transmitted from the STA 102. In addition, in the example of FIG. 1, the object detection device 103 is placed separately from the AP 101 and the STA 102, but the object detection device 103 may be integrated into the AP 101 or the STA 102 without using an independent object detection device 103.

In addition, in FIG. 1, one AP 101 and one STA 102 are provided, but at least one of AP 101 and STA 102 may be provided in multiple units. Furthermore, when there are multiple APs 101, this embodiment may be applied to wireless communication between the APs 101. In this case, one of the APs 101 has the same functions as the STA 102 in this embodiment.

In addition, in FIG. 1, only AP 101 has multiple antennas 104, but STA 102 may have multiple antennas. Furthermore, in FIG. 1, antenna 104 is directly connected to AP 101, but it may be extended using a coaxial cable or the like. This allows antenna 104 to be installed with a wide extension, making it possible to expand the detection area.

In this way, the object detection system 100 according to this embodiment detects objects within the communication area between the AP 101 and the STA 102 by using communication in a wireless LAN system that complies with 802.11ac.

2. Overview of an object detection method using communication in a wireless LAN system Figure 2 shows an example of a sequence in wireless communication between an AP 101 and a STA 102. The AP 101 broadcasts a VHT NDP Announcement frame as a start signal of a sounding protocol for acquiring propagation path state information, i.e., CSI. Immediately after that, the AP 101 transmits a VHT NDP frame including measurement data to the destination STA 102. VHT stands for Very High Throughput, and 802.11ac is based on a VHT frame for ultra-high speed communication. In addition, NDP stands for Null Data Packet, and the VHT NDP frame is a frame that does not include communication data. The VHT NDP Announcement frame includes the address of the AP 101 and the destination STA 102, and is a frame for notifying the STA 102 in advance of the transmission of the VHT NDP frame. The VHT NDP Announcement frame is transmitted from one or more specific antennas 104, but even when the frame is transmitted from two or more antennas 104, the same data signal is transmitted from each antenna 104.

Here, an example of a VHT NDP frame transmitted from AP 101 is shown in FIG. 3. In FIG. 3, the VHT NDP frame is composed of a header 151 (storing frame type, etc.), measurement data 250, and a tailor 156 (storing error detection, etc.). The header 151 and tailor 156 are transmitted in the same way as the VHT NDP Announcement, but the measurement data 250 is transmitted individually from each antenna 104. In the example shown in FIG. 3, there are four antennas 104, and measurement data 152, measurement data 153, measurement data 154, and measurement data 155 are transmitted from the four antennas 104 in a time-division manner. The measurement data 152, measurement data 153, measurement data 154, and measurement data 155 transmitted from each antenna 104 are received by STA 102 as one VHT NDP frame.

Returning to FIG. 2, the sequence of wireless communication between AP 101 and STA 102 will be described. STA 102 receives a VHT NDP frame from AP 101 and derives a compressed CSI value using the method specified in IEEE 802.11ac (see H. Yu and T. Kim, “Beamforming transmission in IEEE 802.11ac under time-varying channels,” The Scientific World J., vol. 2014, pp. 1-11, Jul. 2014, article ID 920937.).

Here, an example of compressed CSI is shown in FIG. 4. In FIG. 4, the number of transmitting antennas × the number of receiving antennas, the number of compressed CSI, and an example of compressed CSI are listed in order from the left column. φij indicates the phase difference at the antenna of STA 102 of the signal transmitted from the i-th antenna 104 and the j-th antenna 104. Here, φij ∈ [0, 2π). Also, ψij indicates a value (tan ⁻¹ value of the ratio of the absolute values of the amplitudes) of the signal transmitted from the i-th antenna 104 and the j-th antenna 104 at the antenna of STA 102 expressed as an angle. Here, ψij ∈ [0, π/2).

According to the example of FIG. 4, for example, when the number of transmitting antennas is two and the number of receiving antennas is one (described as 2×1), the number of compressed CSI is two, and the compressed CSI is φ11, ψ21. Similarly, in the case of 2×2, the number of compressed CSI is two, and the compressed CSI is φ11, ψ21. Similarly, compressed CSI is obtained according to the combination of the number of transmitting antennas and the number of receiving antennas. In the object detection system 100 shown in FIG. 1, the number of transmitting antennas is the number of antennas 104 of the AP 101 (three), and the number of receiving antennas is the number of antennas of the STA 102 (one). In this case, corresponding to 3×1 in the example of FIG. 4, the number of compressed CSI is four, and the compressed CSI is φ11, φ21, ψ21, ψ31.

Returning to FIG. 2, the sequence of wireless communication between AP101 and STA102 will be described. STA102 stores the derived compressed CSI in a VHT Compressed Beamforming Report frame and transmits it. As described above, the measurement data of the VHT NDP frame is transmitted individually from each antenna 104 of AP101, so STA102 can obtain CSI for each antenna 104 of AP101. As the number of antennas increases, the amount of CSI information fed back from STA102 to AP101 increases. Therefore, CSI (compressed CSI) selected from all CSI according to predetermined conditions is fed back to AP101.

Based on the compressed CSI information fed back from the STA102, the AP101 can obtain the state of the wireless propagation path between each antenna 104 as a transmitting antenna and the STA102 as a receiving antenna, and perform MIMO communication.

The object detection device 103 monitors and captures the VHT Compressed Beamforming Report frame transmitted to the AP 101 by the STA 102 as a receiving antenna. The object detection device 103 then extracts the compressed CSI stored in the captured VHT Compressed Beamforming Report and performs preprocessing on the extracted data. In the preprocessing, for example, a conversion from polar coordinates to Cartesian coordinates is performed. If an object is present within the communication area, the CSI fluctuates. The object detection device 103 detects objects within the communication area by machine learning using the preprocessed data as features.

An example of a method for converting from polar coordinates to Cartesian coordinates is shown in FIG. 5. In FIG. 5, the angle φij from the x-axis can be converted to the corresponding x and y coordinates on a circumference of a radius of 1. Note that although angles 0 and 2π have the same value, they are not suitable as inputs to machine learning because they are discontinuous numerically. Therefore, by converting the angle φij into numerical values of the x and y coordinates as shown in the following formula, the continuity of the numerical values is maintained.
xij = cosφij
yij = sinφij

3. Configuration of object detection device common to each embodiment FIG. 6 shows a configuration example of the object detection device 103 common to each embodiment. However, FIG. 6 also shows the configuration of the AP 101 and the STA 102 limited to the configuration related to object detection. The AP 101 has M (M>1) antennas 104, and the STA 102 has N (N≧1) antennas 301. The AP 101 and the STA 102 transmit and receive signals using the OFDM method. The STA 102 performs FFT processing on the signals received by each antenna 301 in the FFT unit 302 to regenerate multiple subcarrier signals. The STA 102 performs channel estimation and CSI generation in the channel estimation/CSI generation unit 303 based on the regenerated subcarrier signals. The estimated value obtained by the channel estimation is used to demodulate the subcarrier signal. Then, the signal including the compressed CSI is fed back from the STA 102 to the AP 101.

In FIG. 6, the object detection device 103 includes an antenna 201, a CSI extraction unit 202, a data preprocessing unit 203, a machine learning unit 204, a judgment unit 205, a CSI information storage unit 206, a cross-correlation derivation unit 207, and an installation position selection unit 208. The object detection device 103 can be configured with an integrated circuit such as a computer or an FPGA (Field Programmable Gate Array), and each function from the CSI extraction unit 202 to the installation position selection unit 208 can be realized by a program executable by a computer. In addition, the program may be provided by being recorded on a storage medium, or may be provided via a network.

The radio waves communicated between AP101 and STA102 are received by antenna 201, converted into a received signal, and output to CSI extraction unit 202. CSI extraction unit 202 captures wireless LAN frames demodulated from the received signal, and selects only frames that meet preset conditions from the captured wireless LAN frames. The preset conditions are that the source and destination of the frame are the target AP101 and STA102, respectively, and that the type of frame is a VHT Compressed Beamforming Report frame. CSI extraction unit 202 extracts compressed CSI data from the selected VHT Compressed Beamforming Report frame.

The data pre-processing unit 203 performs coordinate conversion on the compressed CSI extracted by the CSI extraction unit 202. Specifically, the data pre-processing unit 203 converts φij, which indicates the phase difference of the compressed CSI, and ψij, which indicates the amplitude ratio of the compressed CSI, from polar coordinates to Cartesian coordinates. The output destination of the compressed CSI pre-processed by the data pre-processing unit 203 is selected from the machine learning unit 204, the determination unit 205, and the CSI information storage unit 206 according to the operation phase of the object detection device 103.

The object detection device 103 has three operational phases: a learning phase, a detection phase, and an installation position selection phase. In the learning phase, the compressed CSI preprocessed by the data preprocessing unit 203 is input to the machine learning unit 204. The machine learning unit 204 learns combinations of labels (teacher data) indicating the presence or absence of an object and the CSI, and accumulates the learning data.

In the detection phase, the compressed CSI preprocessed by the data preprocessing unit 203 is input to the determination unit 205. The determination unit 205 determines the presence or absence of an object within the communication area based on a comparison between the learning data accumulated in the machine learning unit 204 and the actually obtained CSI. The object detection device 103 outputs the determination result by the determination unit 205 as the detection result of an object within the communication area.

In this way, the object detection device 103 can detect objects within the communication area by utilizing communication of a wireless LAN system compliant with 802.11ac. However, the accuracy of object detection by the object detection device 103 depends on the positional relationship between the antenna 104 of the AP 101 and the STA 102 and the environment of the communication area. In particular, when the installation position of the antenna 104 of the AP 101 is fixed, the accuracy of object detection by the object detection device 103 is determined by the installation position of the receiving antenna, STA 102. The installation position selection phase is provided to select an installation position of the STA 102 that can improve the accuracy of object detection within the communication area.

In the installation position selection phase, the compressed CSI preprocessed by the data preprocessing unit 203 is input to the CSI information storage unit 206. In the following description of the installation position selection phase, the compressed CSI preprocessed by the data preprocessing unit 203 is simply referred to as CSI. The CSI information storage unit 206 stores the input CSI in association with the installation position of the STA102 in the communication area and the arrangement state of the object to be detected. The information stored in the CSI information storage unit 206 is processed by the cross-correlation derivation unit 207, and the installation position of the STA102 is selected by the installation position selection unit 208 based on the processing result of the cross-correlation derivation unit 207. The installation position of the STA102 selected by the installation position selection unit 208 is output from the object detection device 103 as the optimal installation position of the STA102.

This specification discloses five embodiments each characterized by a method for selecting the installation location of STA102. In the cross-correlation derivation unit 207 and the installation location selection unit 208, processing that is unique to each embodiment is performed based on a technical concept common to the embodiments. Below, the processing by the cross-correlation derivation unit 207 and the installation location selection unit 208, and the method for selecting the installation location of STA102 that is realized thereby are explained for each embodiment.

In each embodiment described below, it is assumed that the installation position of the antenna 104 of the AP 101, the candidate installation positions of the STA 102, and the detection target location are determined within a closed communication area as shown in FIG. 7. In the example shown in FIG. 7, the antennas 104 of the AP 101 are installed at the positions indicated by double circles 1 to 3. The installation positions of the antennas 104 are fixed. The detection target object is placed at one of the positions indicated by white circles 1 to 20. The STA 102 is placed at at least one of the positions indicated by black circles 1 to 15 depending on the placement of the detection target object. Note that even if the STA 102 has multiple antennas, they are placed in approximately the same location, so the entire STA 102 is considered to be one receiving antenna.

In each embodiment described below, the CSI is used that is acquired when the STA102 is installed at one of the candidate installation positions with the detection target object being located at one of the positions. However, as described above, the CSI acquired here is the CSI that is obtained by converting the compressed CSI from polar coordinates to Cartesian coordinates through preprocessing.

Here, as a convention common to all the embodiments described below, the CSI acquired when the STA 102 is installed at position i while the detection target object is located at position n is described as CSI _n (i). n is a natural number from 1 to 20 corresponding to the number of the white circle shown in Fig. 7. However, if the detection target object is not located anywhere, n = 0. i is a natural number from 1 to 15 corresponding to the number of the black circle shown in Fig. 7.

In each embodiment described below, a cross-correlation value between the CSI acquired when the detection target object is in a first position state and the CSI acquired when the detection target object is in a second position state is derived for each installation position of the STA 102. As a convention common to all embodiments, the cross-correlation value between CSI _n (i) when the STA 102 is installed at position i and the detection target object is installed at position n, and CSI m (i) when the STA 102 is installed at position i and the detection target object is installed at position m is denoted as COR _{(n,m) (} i).

CSI _n (i) and CSI _m (i) are vectors having the same dimension. Therefore, as an example, the cross-correlation value COR _(n,m) (i) can be calculated by the following formula 1. In the following formula 1, A·B represents the inner product of vector A and vector B, and |A| represents the absolute value of vector A.

As a convention common to all the embodiments, the CSI acquired when the STA 102 is installed at positions i and j with the detection target object located at position n is written as CSI _n (i, j). As shown in the following formula 2, CSI _n (i, j) can be expressed as CSI _n (i) when the STA 102 is installed at position i and the detection target object is located at position n, and CSI _n (j) when the STA 102 is installed at position j and the detection target object is located at position n. Here, CSI _n (i) = (a, b, c, ...) and CSI _n (j) = (d, e, f, ...).

Furthermore, as a convention common to all embodiments, the cross-correlation value between CSI _n (i, j) when STA 102 is installed at position i and position j and a detection target object is located at position n, and CSI _m (i, j) when STA 102 is installed at position i and position j and a detection target object is located at position m, is denoted as COR _{(n, m)} (i, j). Since CSI _n (i, j) and CSI _m (i, j) are vectors having the same dimension, for example, the cross-correlation value COR _{(n, m)} (i, j) can be calculated by the following formula 3.

Even when STA102 is installed at three or more positions i, j, k, ..., CSI _n (i, j, k, ...) as propagation path state information can be calculated in the same manner as in the above formula 2. Also, COR _{(n, m)} (i, j, k, ...) as a cross-correlation value can be calculated in the same manner as in the above formula 3.

4. Method for selecting an installation position of an STA according to the first embodiment 4-1. When one STA is installed The first embodiment aims to determine an optimal installation position of the STA 102 for simply detecting the presence or absence of an object at a specific position. The number of STAs 102 installed in the first embodiment may be one or more. Below, a method for selecting an installation position when one STA 102 is installed and a method for selecting an installation position when multiple STAs 102 are installed will be described in order. Note that the method described below may be performed by simulation using a computer model such as ray tracing, or may be performed by experiments in an actual space.

The method of selecting an installation position when one STA102 is installed can be explained with reference to FIG. 8. As an example, let us assume that an object is to be detected at the position of the white circle number 13.

First, as shown in Fig. 8A, in a state where no detection target object is located anywhere, the STA 102 is installed at one of the installation position candidates, and CSI is acquired in that state. In the example shown in Fig. 8A, the STA 102 is installed at position 1, and CSI ₀ (1) is acquired in that state.

8B , the installation position of STA 102 is not changed from position 1, and the detection target object is placed at position 13, and CSI ₁₃ (1) is acquired in this state. When CSI ₀ (1) and CSI ₁₃ (1) are acquired, cross-correlation derivation section 207 derives cross-correlation value COR _(0,13) (1) between CSI ₀ (1) and CSI ₁₃ (1) using equation 1 above.

The cross-correlation derivation unit 207 performs the above calculations for all installation position candidates of STA 102 from No. 1 to No. 15, and derives the cross-correlation value COR _(0,13) (i) for all installation position candidates. Then, the installation position selection unit 208 selects the installation position of STA 102 that provides the smallest cross-correlation value COR _(0,13) (i) as the optimal installation position, that is, the installation position that can detect the object at position No. 13 with the highest accuracy.

4-2. When Multiple STAs are Installed Next, a method for selecting installation positions when multiple STAs 102 are installed will be described with reference to FIG. 9. As an example, an object is to be detected at the position of the white circle number 13. Also, it is assumed that two STAs 102 are installed. Of course, more STAs 102 may be installed, but the number of STAs installed is limited to two here for simplicity of explanation.

First, as shown in Fig. 9A, in a state where no detection target object is located anywhere, STA 102 is installed at any two installation position candidates, and CSI is acquired in that state. In the example shown in Fig. 9A, STA 102 is installed at positions 1 and 3, and CSI ₀ (1) and CSI ₀ (3) are acquired in that state, respectively. Then, CSI ₀ (1,3) is calculated from CSI ₀ (1) and CSI ₀ (3) using the above formula 2.

9B , the installation positions of STA 102 are not changed from positions 1 and 3, and the detection target object is placed at position 13, and in this state, CSI ₁₃ (1) and CSI ₁₃ (3) are acquired. Then, CSI ₁₃ (1,3) is calculated from CSI ₁₃ (1) and CSI ₁₃ (3) using the above formula 2. When CSI ₀ (1,3) and CSI ₁₃ (1,3) are acquired, cross-correlation derivation section 207 derives cross-correlation value COR _(0,13) (1,3) between CSI ₀ (1,3) and CSI ₁₃ (1,3) using the above formula 3.

The cross-correlation deriving unit 207 performs the above calculation for all combinations of candidate installation positions for the STAs 102, and derives the cross-correlation value COR _(0,13) (i,j) for all combinations. The installation position selecting unit 208 then selects the installation position for two STAs 102 that minimizes the cross-correlation value COR _(0,13) (i,j) as the optimal installation position, i.e., the installation position that can detect the object at position 13 with the highest accuracy.

The above-mentioned method for selecting the installation positions of STAs 102 can also be applied when the number of STAs 102 is three or more. The cross-correlation derivation unit 207 calculates the cross-correlation value COR _(0,13) (i,j,k,...) for all combinations of the number of installed STAs. Then, the installation position selection unit 208 selects the installation positions i, j, k,... of STAs 102 that minimize the cross-correlation value COR _(0,13) (i,j,k,...) as the optimal installation positions.

5. Method for selecting an installation position of an STA according to the second embodiment 5-1. When one STA is installed The second embodiment aims to determine an optimal installation position of the STA 102 for detecting the presence or absence of an object at two points. The number of STAs 102 installed in the second embodiment may be one or more. Below, a method for selecting an installation position when one STA 102 is installed and a method for selecting an installation position when multiple STAs 102 are installed will be described in order. Note that the method described below may be performed by simulation using a computer model such as ray tracing, or may be performed by experiments in an actual space.

The method of selecting an installation position when one STA102 is installed can be explained with reference to FIG. 10. As an example, let us assume that objects are to be detected at the positions of the white circles numbered 13 and 14.

First, as shown in Fig. 10A, the STA 102 is installed at one of the installation position candidates with the detection target object placed at position 13, and CSI is acquired in that state. In the example shown in Fig. 8A, the STA 102 is installed at position 1, and CSI ₁₃ (1) is acquired in that state.

10B , the installation position of STA 102 is not changed from position 1, and the detection target object is placed at position 14, and CSI ₁₄ (1) is acquired in this state. When CSI ₁₃ (1) and CSI ₁₄ (1) are acquired, cross-correlation derivation section 207 derives cross-correlation value COR _(13,14) (1) between CSI ₁₃ (1) and CSI ₁₄ (1) using equation 1 above.

The cross-correlation derivation unit 207 performs the above calculations for all installation position candidates of STA 102 from No. 1 to No. 15, and derives the cross-correlation value COR _(13,14) (i) for all installation position candidates. Then, the installation position selection unit 208 selects the installation position of STA 102 that minimizes the cross-correlation value COR _(13,14) (i) as the optimal installation position, that is, the installation position that can detect the object at position No. 13 and the object at position No. 14 with the highest accuracy.

5-2. When Multiple STAs are Installed Next, a method for selecting installation positions when multiple STAs 102 are installed will be described with reference to FIG. 11. As an example, it is assumed that objects are to be detected at the positions indicated by the white circles at numbers 13 and 14. It is also assumed that two STAs 102 are installed. Of course, more STAs 102 may be installed, but the number of STAs installed is limited to two here for ease of explanation.

First, as shown in Fig. 11A, with a detection target object placed at position 13, STA 102 is installed at any two of the installation position candidates, and CSI is acquired in that state. In the example shown in Fig. 9A, STA 102 is installed at positions 1 and 3, and CSI ₁₃ (1) and CSI ₁₃ (3) are acquired in that state, respectively. Then, CSI ₁₃ ( _1,3 ) is calculated from CSI 13 (1) and CSI ₁₃ (3) using the above formula 2.

11B , the installation positions of STA 102 are not changed from positions 1 and 3, and the detection target object is placed at position 14, and in this state, CSI ₁₄ (1) and CSI ₁₄ (3) are acquired. Then, CSI _{14 (1,3) is calculated from CSI 14 (1) and CSI 14 (3) using the above formula 2. When CSI 14 ( 1,3 )} and CSI ₁₄ (1,3) are acquired, cross-correlation derivation section 207 derives cross-correlation value COR _(13,14) (1,3) between CSI ₁₃ (1,3) and CSI ₁₄ (1,3) using the above formula 3.

The cross-correlation deriving unit 207 performs the above calculation for all combinations of candidate installation positions for the STAs 102, and derives the cross-correlation value COR _(13,14) (i,j) for all combinations. The installation position selecting unit 208 then selects the installation positions of the two STAs 102 that minimize the cross-correlation value COR _(13,14) (i,j) as the optimal installation positions, i.e., the installation positions that can detect the object at position 13 and the object at position 14 with the highest accuracy.

The above-mentioned method for selecting the installation positions of STAs 102 can also be applied when the number of STAs 102 is three or more. The cross-correlation derivation unit 207 calculates the cross-correlation value COR _(13,14) (i,j,k,...) for all combinations of the number of installed STAs. Then, the installation position selection unit 208 selects the installation positions i, j, k,... of STAs 102 that minimize the cross-correlation value COR _(13,14) (i,j,k,...) as the optimal installation positions.

6. Method for selecting an installation position of an STA according to the third embodiment 6-1. When one STA is installed The third embodiment aims to determine an optimal installation position of the STA 102 for detecting an object at any position within the notification area. The number of STAs 102 installed in the third embodiment may be one or more. Below, a method for selecting an installation position when one STA 102 is installed and a method for selecting an installation position when multiple STAs 102 are installed will be described in order. Note that the method described below may be performed by simulation using a computer model such as ray tracing, or may be performed by experiments in an actual space.

The method for selecting an installation location when one STA102 is installed can be explained with reference to Figure 12.

First, as shown in Fig. 12A, in a state where no detection target object is located anywhere, the STA 102 is installed at one of the installation position candidates, and CSI is acquired in that state. In the example shown in Fig. 12A, the STA 102 is installed at position 1, and CSI ₀ (1) is acquired in that state.

12B, the installation position of STA102 is not changed from position 1, the detection target object is placed at position 1, and CSI ₁ (1) is acquired in this state. Furthermore, CSI 2 (1) to CSI ₂₀ (1) are acquired for the remaining positions of the detection target objects from position ₂ to 20 without changing the installation position of STA102 from position 1.

The cross-correlation derivation unit 207 derives cross-correlation values COR(0,1)(1 _{) to COR(0,20)} (1) between _CSI0 (1) and each of CSI1(1) to _CSI20 (1) from No. ₁ to No. 20, using the above formula 1. Next, the cross-correlation derivation unit 207 calculates a representative value _CORrep (1) of these 20 cross-correlation values COR _{( 0,1)} (1) to COR _(0,20) (1). Note that in this embodiment, the representative value _CORrep (i) may be, for example, an average value _CORave (i) or a maximum value _CORmax (i).

The cross-correlation derivation unit 207 performs the above calculations for all installation position candidates of STA 102 from No. 1 to No. 15, and calculates the representative value COR _rep (i) of the cross-correlation values for all installation position candidates. The installation position selection unit 208 then selects the installation position of STA 102 that provides the smallest representative value COR _rep (i) of the cross-correlation values as the optimal installation position, that is, the installation position that can detect an object at any of positions No. 1 to No. 20 with the highest accuracy.

6-2. When multiple STAs are installed Next, a method for selecting installation positions when multiple STAs 102 are installed will be described with reference to Fig. 13. It is assumed that the number of STAs 102 to be installed is two. Of course, more STAs 102 may be installed, but here the number of STAs to be installed is limited to two for simplicity of explanation.

First, as shown in Fig. 13A, in a state where no detection target object is located anywhere, STA 102 is installed at any two installation position candidates, and CSI is acquired in that state. In the example shown in Fig. 13A, STA 102 is installed at positions 1 and 3, and CSI ₀ (1) and CSI ₀ (3) are acquired in that state, respectively. Then, CSI ₀ (1,3) is calculated from CSI ₀ (1) and CSI ₀ (3) using the above formula 2.

13B, the installation position of STA 102 is not changed from positions 1 and 3, and the detection target object is placed at position 1, and in this state, CSI ₁ (1) and CSI ₁ (3) are acquired, respectively. Furthermore, the installation position of STA 102 is not changed from positions 1 and 3, and CSI ₂ (1) to CSI ₂₀ (1) and CSI ₂ (3) to CSI ₂₀ (3) are acquired, respectively, for the placement positions of all the remaining detection target objects from positions 2 to 20. Then, using the above formula 2, CSI ₁ (1,3) to CSI ₂₀ (1,3) are calculated from CSI ₁ (1) to CSI ₂₀ (1) and CSI ₁ (3) to CSI ₂₀ (3).

The cross-correlation derivation unit 207 derives cross-correlation values COR _(0,1)(1,3) to COR(0,20)(1,3) between _CSI0 (1,3) and each of CSI1(1,3) to _CSI20 (1,3) from No. ₁ to No. 20 using the above formula 3. Next, the cross-correlation derivation unit 207 calculates a representative value _CORrep (1,3) of these 20 cross-correlation values COR( _{0,1 )} (1,3) to COR _(0,20) (1,3). Note that in this embodiment, the representative value _CORrep (i,j) may be, for example, an average value _CORave (i,j) or a maximum value _CORmax (i,j).

The cross-correlation derivation unit 207 performs the above calculation for all combinations of candidate installation positions for the STAs 102, and calculates the representative value COR _rep (i, j) of the cross-correlation values for all combinations. The installation position selection unit 208 then selects the installation position for two STAs 102 that minimizes the representative value COR _rep (i, j) of the cross-correlation values as the optimal installation position, that is, the installation position that can detect an object at any of positions 1 to 20 with the highest accuracy.

The above-mentioned method for selecting the installation positions of the STAs 102 can also be applied when the number of STAs 102 is three or more. The cross-correlation derivation unit 207 calculates the representative value COR _rep (i, j, k, ...) of the cross-correlation values for all combinations of the number of installed STAs. Then, the installation position selection unit 208 selects the installation positions i, j, k, ... of the STAs 102 that have the smallest representative value COR _rep (i, j, k, ...) of the cross-correlation values as the optimal installation positions.

7. Method for selecting an installation position of an STA according to the fourth embodiment 7-1. When one STA is installed The fourth embodiment aims to determine an optimal installation position of the STA 102 for discriminating between adjacent detection target locations in a situation where there are many locations in a notification area where an object is to be detected. The number of STAs 102 installed in the fourth embodiment may be one or more. Below, a method for selecting an installation position when one STA 102 is installed and a method for selecting an installation position when multiple STAs 102 are installed will be described in order. Note that the method described below may be performed by simulation using a computer model such as ray tracing, or may be performed by experiments in an actual space.

The method for selecting an installation location when one STA102 is installed can be explained with reference to Figure 14.

First, as shown in Fig. 14A, when a detection target object is located at one of the locations, STA 102 is installed at one of the installation location candidates, and CSI is acquired in that state. In the example shown in Fig. 14A, when a detection target object is located at position 6, STA 102 is installed at position 1, and CSI ₆ (1) is acquired in that state.

Next, as shown in FIG. 14B , the installation position of STA102 is not changed from position 1, and the detection target objects are placed in positions 2, 5, 7, and 10, adjacent to position 6, in that order, and CSI ₂ (1), CSI ₅ (1), CSI ₇ (1), and CSI ₁₀ (1) are obtained in each state.

The cross-correlation deriving unit 207 derives cross-correlation values _COR ( _6,2) (1), COR( _6,5 )(1), COR _{( 6,7)(1), and COR(6,10 )} (1) between CSI6(1) and CSI2(1), CSI5(1), _CSI7 (1), and _CSI10 (1), respectively, using the above formula 1. Next, the cross-correlation deriving unit 207 calculates a representative value of these cross-correlation values COR _{( 6,2)} (1), COR _(6,5) (1), COR _(6,7) (1), and COR _(6,10) (1) as a representative cross-correlation value COR ₍₆₎ (1) corresponding to the detection target location No. 6. The representative value may be, for example, an average value or a maximum value. In the same manner, the cross-correlation deriving unit 207 calculates representative cross-correlation values COR ₍₁₎ (1) to COR ₍₂₀₎ (1) for all detection target locations No. 1 to No. 20.

Next, the cross-correlation derivation unit 207 calculates a representative value COR _rep (1) of the obtained 20 representative cross-correlation values COR ₍₁₎ (1) to COR ₍₂₀₎ (1). Note that in this embodiment, the representative value COR _rep (i) may be, for example, the average value COR _ave (i) or the maximum value COR _max (i).

The cross-correlation derivation unit 207 performs the above calculations for all installation position candidates of STA 102 from No. 1 to No. 15, and calculates the representative value COR _rep (i) of the representative cross-correlation values for all installation position candidates. Then, the installation position selection unit 208 selects the installation position of STA 102 that provides the smallest representative value COR _rep (i) of the representative cross-correlation values as the optimal installation position, i.e., the installation position that allows for the most accurate discrimination of objects in adjacent detection target locations.

7-2. When multiple STAs are installed Next, a method for selecting installation positions when multiple STAs 102 are installed will be described with reference to Fig. 15. It is assumed that the number of STAs 102 to be installed is two. Of course, more STAs 102 may be installed, but here the number of STAs to be installed is limited to two for simplicity of explanation.

First, as shown in Fig. 15A, in a state where a detection target object is located at one of the locations, STA 102 is installed at any two of the installation location candidates, and CSI is acquired in that state. In the example shown in Fig. 15A, when a detection target object is located at position 6, STA 102 is installed at positions 1 and 3, and CSI ₆ (1) and CSI ₆ (3) are acquired in that state, respectively. Then, CSI 6 ( _1,3 ) is calculated from CSI ₆ (1) and CSI ₆ (3) using the above formula 2.

15B, the installation positions of STA 102 are not changed from positions 1 and 3, and detection target objects are sequentially placed at positions 2, 5, 7, and 10 adjacent to position 6. In each state, CSI ₂ (1), CSI ₅ (1), CSI ₇ (1), and CSI ₁₀ (1) are obtained, and CSI ₂ (3), CSI ₅ (3), CSI ₇ (3), and CSI ₁₀ (3) are also obtained. Then, CSI ₂ (1,3), CSI ₅ (1,3), CSI ₇ (1,3), and CSI ₁₀ (1,3) are calculated using the above formula 2.

Using the above equation 1, the cross-correlation derivation section 207 derives the cross-correlation values _COR (6,2)(1,3), _{COR (6,5} )(1,3), COR _(6,7) (1,3), and COR( _{6,10 )} (1,3) between CSI6(1,3) and CSI2(1,3), _CSI5 (1,3), _CSI7 (1,3), and CSI10 ₍ 1,3), respectively. Next, the cross-correlation derivation unit 207 calculates a representative value of these cross-correlation values COR _(6,2) (1,3), COR _(6,5) (1,3), COR _(6,7) (1,3), and COR _(6,10) (1,3) as a representative cross-correlation value COR ₍₆₎ (1,3) corresponding to the detection target location No. 6. The representative value may be, for example, an average value or a maximum value. In the same manner, the cross-correlation derivation unit 207 calculates representative cross-correlation values COR ₍₁₎ (1,3) to COR ₍₂₀₎ (1,3) for all detection target locations No. 1 to No. 20.

Next, the cross-correlation derivation unit 207 calculates a representative value COR _rep (1, 3) of the obtained 20 representative cross-correlation values COR ₍₁₎ (1, 3) to COR ₍₂₀₎ (1, 3). Note that in this embodiment, the representative value COR _rep (i, 3) may be, for example, the average value COR _ave (i, 3) or the maximum value COR _max (i, 3).

The cross-correlation derivation unit 207 performs the above calculation for all combinations of candidate installation positions for the STAs 102, and calculates the representative value COR _rep (i, j) of the representative cross-correlation values for all combinations. The installation position selection unit 208 then selects the installation positions of the two STAs 102 that provide the smallest representative value COR _rep (i, j) of the representative cross-correlation values as the optimal installation positions, i.e., the installation positions that allow for the most accurate discrimination of objects in adjacent detection target locations.

The above-mentioned method for selecting the installation positions of the STAs 102 can also be applied when the number of STAs 102 is three or more. The cross-correlation derivation unit 207 calculates the representative value COR _rep (i, j, k, ...) of the representative cross-correlation values for all combinations of the number of installed STAs. Then, the installation position selection unit 208 selects the installation positions i, j, k, ... of the STAs 102 that have the smallest representative value COR _rep (i, j, k, ...) of the representative cross-correlation values as the optimal installation positions.

8. Method for selecting the installation position of STA according to the fifth embodiment In the first to fourth embodiments, when a plurality of STAs 102 are installed, the combinations of the installation positions of the STAs 102 are enormous. Therefore, a great deal of work is required to obtain CSI by experiments in an actual space, rather than by simulation using a computer model such as ray tracing. The fifth embodiment aims to reduce the time required to determine the installation positions of a plurality of STAs 102 by obtaining CSI individually on the premise that the time fluctuation of the propagation environment is small, and calculating cross-correlation using the individually obtained CSI.

The method for selecting the installation location of STA102 in this embodiment can be explained with reference to FIG. 16.

First, as shown in Fig. 16A, STA 102 is installed at position 1, and the positions of the detection target objects are moved in order to obtain CSI ₁ (1) to CSI ₂₀ (1) for all the positions of the detection target objects from No. 1 to No. 20. Next, as shown in Fig. 16B, STA 102 is installed at position 2, and the positions of the detection target objects are moved in order to obtain CSI ₁ (2) to CSI ₂₀ (2) for all the detection target objects from No. 1 to No. 20. The above process is performed for all the installation position candidates of STA 102 from No. 1 to No. 15, and CSI _n (i) (n = 1, 2, ..., 20) (i = 1, 2, ..., 15) is obtained for all the installation position candidates.

The cross-correlation deriving unit 207 derives a cross-correlation value when a plurality of STAs 102 are installed in the communication area based on CSI _n (i) acquired for each installation position candidate of the STA 102 and for each arrangement position of the detection target object. Specifically, for example, when three STAs 102 are installed, CSI _{n (i, j, k) is calculated from CSI n} (i), CSI _n (j), and CSI _n ( _k ) using the above formula 2. The cross-correlation deriving unit 207 derives a cross-correlation value by any of the methods of the first to fourth embodiments using the CSI _n (i, j, k) calculated in this way. Then, the installation position selecting unit 208 selects the installation position i, j, k of the STA 102 at which the cross-correlation value derived by the cross-correlation deriving unit 207 is the smallest as the optimal installation position.

9. Others The above embodiments can be modified in various ways without departing from the scope of the present disclosure. In other words, when the number, quantity, amount, range, etc. of each element is mentioned in the above embodiments, the technology according to the present disclosure is not limited to the mentioned number, unless otherwise specified or clearly specified in principle. In addition, the structures, etc. described in the above embodiments are not necessarily essential to the technology according to the present disclosure, unless otherwise specified or clearly specified in principle.

For example, the antenna 301 may be separated from the main body of the STA 102, and only the antenna 301 may be installed at the installation position shown by the black circle in FIG. 7. In this case, the antenna 301 corresponds to the "receiving antenna."

100 Object detection system 101 AP
102 STA (receiving antenna)
103 Object detection device 104 Antenna (transmitting antenna)
202 CSI extraction unit 203 Data preprocessing unit 204 Machine learning unit 205 Determination unit 206 CSI information storage unit 207 Cross-correlation derivation unit 208 Installation position selection unit

Claims (8)

An object detection system including a transmitting antenna having a fixed installation position, one or more receiving antennas having variable installation positions, and an object detection device that captures information included in wireless communication between the transmitting antenna and the one or more receiving antennas to detect an object within a communication area,
the transmitting antenna is configured to transmit a predetermined known signal;
The one or more receiving antennas are configured to transmit propagation path state information indicating a state of a propagation path to the transmitting antenna derived from the received known signal;
The object detection device includes:
An information acquisition process for acquiring propagation path state information transmitted by the one or more receiving antennas;
an information storage process for storing the acquired propagation path state information in association with the installation positions of the one or more receiving antennas and the arrangement state of the detection target object;
a derivation process for deriving, for each installation position of the one or more receiving antennas, a cross-correlation value between propagation path state information acquired when the detection target object is in a first arrangement state and propagation path state information acquired when the detection target object is in a second arrangement state;
a selection process for selecting an optimal installation position of the one or more receiving antennas based on the cross-correlation value;
and an output process for outputting a selection result of the optimum installation position. 2. The object detection system according to claim 1,
The object detection device includes:
In the derivation process, a cross-correlation value is derived between propagation path state information acquired in a state where the detection target object is not located within the communication area or is located at a first predetermined position, and propagation path state information acquired in a state where the detection target object is located at a second predetermined position within the communication area;
The object detection system is characterized in that, in the selection process, an installation position of the one or more receiving antennas at which the cross-correlation value is minimum is selected as the optimal installation position. 2. The object detection system according to claim 1,
The object detection device includes:
In the derivation process, a cross-correlation value between propagation path state information acquired in a state where the detection target object is not located within the communication area and propagation path state information acquired in a state where the detection target object is located within the communication area is derived for each of a plurality of locations of the detection target object within the communication area;
The object detection system is characterized in that, in the selection process, an installation position of the one or more receiving antennas at which a representative value of the cross-correlation values derived for each of the multiple placement positions of the detection target object is minimized is selected as the optimal installation position. 2. The object detection system according to claim 1,
The object detection device includes:
In the derivation process,
With respect to each of a plurality of positions of the detection target object within the communication area,
a process of deriving a cross-correlation value between propagation path state information acquired when the detection target object is placed at the placement position and propagation path state information acquired when the detection target object is placed at a position nearby the placement position, for a plurality of positions nearby the placement position;
deriving a representative value of the cross-correlation values derived for the plurality of neighboring positions of the arrangement position as a representative cross-correlation value for the arrangement position;
an object detection system configured to select, in the selection process, an installation position of the one or more receiving antennas at which a representative value of the representative cross-correlation values derived for each of the multiple placement positions of the detection target object is minimized as the optimal installation position. 2. The object detection system according to claim 1,
The object detection device includes:
In the information acquisition process, propagation path state information transmitted from one receiving antenna installed within the communication area is acquired,
In the information storage process, the acquired propagation path state information is stored in association with an installation position of the one receiving antenna and an arrangement position of a detection target object;
In the derivation process, a cross-correlation value is derived when a plurality of receiving antennas are installed within the communication area based on propagation path state information acquired for each installation position of the one receiving antenna and for each arrangement position of the detection target object;
The object detection system is characterized in that, in the selection process, installation positions of the plurality of receiving antennas that minimize the cross-correlation value are selected as the optimum installation positions. An object detection device that detects objects within a communication area by capturing information contained in wireless communication between a transmitting antenna whose installation position is fixed and one or more receiving antennas whose installation positions are movable,
an information acquisition process for acquiring propagation path state information indicating a state of a propagation path between the one or more receiving antennas and the transmitting antenna;
an information storage process for storing the acquired propagation path state information in association with the installation positions of the one or more receiving antennas and the arrangement state of the detection target object;
a derivation process for deriving, for each installation position of the one or more receiving antennas, a cross-correlation value between propagation path state information acquired when the detection target object is in a first arrangement state and propagation path state information acquired when the detection target object is in a second arrangement state;
a selection process for selecting an optimal installation position of the one or more receiving antennas based on the cross-correlation value;
and an output process for outputting a selection result of the optimum installation position. 1. An object detection method for detecting an object within a communication area by capturing information included in wireless communication between a transmitting antenna whose installation position is fixed and one or more receiving antennas whose installation positions are variable, comprising:
transmitting a predetermined known signal from the transmitting antenna;
transmitting propagation path state information indicating a state of a propagation path to the transmitting antenna derived from the received known signal to the one or more receiving antennas;
Acquire propagation path state information transmitted by the one or more receiving antennas;
storing the acquired propagation path state information in association with the installation positions of the one or more receiving antennas and the arrangement state of the detection target object;
deriving a cross-correlation value between propagation path state information acquired when the detection target object is in a first arrangement state and propagation path state information acquired when the detection target object is in a second arrangement state, for each installation position of the one or more receiving antennas;
A method for detecting an object, comprising the steps of: selecting an optimum installation position for said one or more receiving antennas based on said cross-correlation value; 7. An object detection program comprising a program for causing a computer to execute the process performed by the object detection device according to claim 6.

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