Communication devices, information processing methods and computer-readable storage media

Abstract

This invention relates to a communication device, an information processing method, and a computer-readable storage medium, which improves the accuracy of distance measurement between multiple devices. The communication device includes: a wireless communication unit that wirelessly receives signals from other communication devices; and a control unit that estimates an extended signal matrix that minimizes a predetermined norm by using iterative calculations of a regularization parameter as a positive, minute amount, and estimates the reception time of a signal received by the wireless communication unit based on the extended signal matrix that minimizes the predetermined norm. The control unit performs the iterative calculations in multiple stages, sets the value of the regularization parameter used in iterative calculations after the second stage to a value greater than or equal to the value of the regularization parameter used in the iterative calculation of the previous stage, and changes the value of the regularization parameter in iterative calculations after the second stage based on the reception status of a second signal.

Description

Technical Field

[0001] This invention relates to communication devices, information processing methods, and computer-readable storage media. Background Technology

[0002] In recent years, techniques have been developed for one device to determine the location of another device based on the results of signal transmission and reception between devices. As an example of location determination technology, Patent Document 1 below discloses a technique for determining the angle of incidence of a wireless signal from a UWB transmitter by utilizing UWB (Ultra-Wide Band) for wireless communication.

[0003] Patent Document 1: International Publication No. 2015 / 176776

[0004] However, in the technology described in the aforementioned Patent Document 1, although the incident angle of the wireless signal is determined, there is still room for further improvement in terms of the accuracy of measuring the distance between the UWB receiver and the UWB transmitter.

[0005] That is, in the technology of measuring the distance between one device and another device, it is desirable to further improve the accuracy of the distance measurement between these devices. Summary of the Invention

[0006] Therefore, the present invention was made in view of the above-mentioned problems, and the object of the present invention is to provide a structure that can improve the accuracy of distance measurement between multiple devices.

[0007] To address the aforementioned issues, according to one aspect of the present invention, a communication device is provided, comprising: a wireless communication unit for wirelessly receiving signals from other communication devices; and a control unit that, at predetermined intervals, acquires the correlation between a second signal received by the wireless communication unit as a signal corresponding to the first signal when the other communication device transmits a signal containing a pulse as a first signal, and the first signal; converts a data matrix into a matrix product comprising an extended mode matrix and an extended signal matrix; the data matrix is ​​a matrix arranging one or more matrices representing the correlation operation results obtained at the predetermined intervals between the second signal and the first signal in the wireless communication unit; the extended mode matrix is ​​a matrix composed of multiple elements representing the correlation operation results assumed to be received at each of a plurality of set times; and the extended signal matrix... The matrix is ​​a matrix that arranges extended signal vectors for one or more of the aforementioned related calculation results. The extended signal vector is a vector composed of multiple elements representing the presence or absence of a signal at each of the aforementioned set times in the wireless communication unit, as well as the amplitude and phase of the signal. The control unit estimates the extended signal matrix that minimizes a predetermined norm by using iterative calculations of a regularization parameter that is a positive small amount. Based on the extended signal matrix that minimizes the predetermined norm, the control unit estimates the reception time of the second signal. The control unit performs the iterative calculations in multiple stages, sets the value of the regularization parameter used in the iterative calculations after the second stage to be greater than or equal to the value of the regularization parameter used in the iterative calculations of the previous stage, and changes the value of the regularization parameter in the iterative calculations after the second stage based on the reception status of the second signal.

[0008] Furthermore, to address the aforementioned issues, according to another aspect of the present invention, an information processing method is provided, comprising: acquiring, at predetermined time intervals, the correlation between a second signal received by a wireless communication unit as a signal corresponding to the first signal when another communication device transmits a signal containing a pulse as a first signal, and the first signal; converting a data matrix into a matrix product comprising an extended mode matrix and an extended signal matrix, wherein the data matrix is ​​a matrix arranging one or more matrices representing the correlation operation results obtained at the predetermined time intervals between the second signal and the first signal in the wireless communication unit, the extended mode matrix being a matrix composed of multiple elements representing the correlation operation results assumed at each of a plurality of set time intervals when a signal is received, and the extended signal matrix being a matrix for one or more of the aforementioned phases. The result of the operation is arranged into a matrix of extended signal vectors, which are vectors composed of multiple elements representing the presence or absence of a signal at each of the aforementioned set times in the aforementioned wireless communication unit, as well as the amplitude and phase of the signal; the extended signal matrix that minimizes a predetermined norm is estimated by using iterative calculations with a regularization parameter as a positive small amount; based on the extended signal matrix that minimizes the aforementioned predetermined norm, the reception time of the aforementioned second signal is estimated, and the estimation further includes: dividing the iterative calculation into multiple stages for execution, setting the value of the regularization parameter used in the iterative calculations after the second stage of the multiple stages to be greater than or equal to the value of the regularization parameter used in the iterative calculations of the previous stage, and changing the value of the regularization parameter in the iterative calculations after the second stage based on the reception status of the aforementioned second signal.

[0009] Furthermore, to address the aforementioned issues, according to another aspect of the present invention, a computer-readable storage medium is provided, which stores a program that enables a computer to function as a control unit. This control unit, at predetermined intervals, acquires the correlation between a second signal received by a wireless communication unit (which corresponds to a first signal) and the first signal when another communication device transmits a signal containing a pulse as a first signal. The control unit converts a data matrix into a matrix product comprising an extended mode matrix and an extended signal matrix. The data matrix is ​​a matrix arranging one or more matrices representing correlation operation results obtained at predetermined intervals from the second signal received by the wireless communication unit. The extended mode matrix is ​​a matrix composed of multiple elements representing the correlation operation results assumed at each of a plurality of set times when a signal is received. The extended signal matrix... The matrix is ​​a matrix that arranges extended signal vectors for one or more of the aforementioned related calculation results. The extended signal vector is a vector composed of multiple elements representing the presence or absence of a signal at each of the aforementioned set times in the wireless communication unit, as well as the amplitude and phase of the signal. The control unit estimates the extended signal matrix that minimizes a predetermined norm by using iterative calculations of a regularization parameter as a positive small amount. Based on the extended signal matrix that minimizes the predetermined norm, the receiving time of the second signal is estimated. The procedure causes the control unit to perform the iterative calculation in multiple stages. The value of the regularization parameter used in the iterative calculations after the second stage in the multiple stages is set to be greater than or equal to the value of the regularization parameter used in the iterative calculations of the previous stage. Furthermore, in the iterative calculations after the second stage, the value of the regularization parameter is changed based on the receiving status of the second signal.

[0010] As described above, according to the present invention, a structure is provided that can improve the accuracy of distance measurement between multiple devices. Attached Figure Description

[0011] Figure 1 This is a diagram illustrating an example of the structure of a system according to one embodiment of the present invention.

[0012] Figure 2 This is a diagram illustrating an example of the configuration of multiple antennas installed on a vehicle according to this embodiment.

[0013] Figure 3 This is a diagram showing an example of the position parameters of the portable device involved in this embodiment.

[0014] Figure 4 This is a diagram showing an example of the position parameters of the portable device involved in this embodiment.

[0015] Figure 5 This diagram illustrates an example of a signal processing module in a communication unit according to this embodiment.

[0016] Figure 6 This is a diagram illustrating an example of a CIR as described in this embodiment.

[0017] Figure 7 This is a timing diagram illustrating an example of the ranging process executed in the system described in this embodiment.

[0018] Figure 8 This is a timing diagram illustrating an example of the angle estimation process performed in the system described in this embodiment.

[0019] Figure 9 These are diagrams used to illustrate the technical issues addressed in this embodiment.

[0020] Figure 10 These are diagrams used to illustrate the technical issues addressed in this embodiment.

[0021] Figure 11 These are diagrams used to illustrate the technical issues addressed in this embodiment.

[0022] Figure 12 These are diagrams used to illustrate the technical issues addressed in this embodiment.

[0023] Figure 13 This diagram illustrates the case of four antennas forming a 2×2 planar array.

[0024] Figure 14 It is used to explain y (k) A graph showing the relationship between y[i] and y[i].

[0025] Figure 15 This is a flowchart illustrating a control example of the regularization parameters involved in this embodiment.

[0026] Figure 16 This is an explanatory diagram illustrating the general outline of the extended signal matrix Y involved in this embodiment.

[0027] Figure 17 This is a flowchart illustrating an example of the iterative calculation process using regularization parameters and convergence determination values ​​involved in this embodiment.

[0028] Figure 18 This is a flowchart illustrating a control example of the regularization parameters involved in the second embodiment.

[0029] Figure 19 This is a flowchart illustrating an example of the position parameter estimation process performed by the communication unit involved in this embodiment.

[0030] Figure 20 This diagram illustrates the case where four antennas form a linear array.

[0031] Explanation of reference numerals in the attached figures

[0032] 1...System; 100...Portable device; 110...Wireless communication unit; 111...Antenna; 120...Storage unit; 130...Control unit; 200...Communication unit; 202...Vehicle; 210...Wireless communication unit; 211...Antenna; 220...Storage unit; 230...Control unit. Detailed Implementation

[0033] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Furthermore, in this specification and the drawings, constituent elements having substantially the same functional structure are labeled with the same reference numerals, thereby omitting repeated descriptions.

[0034] Furthermore, in this specification and accompanying drawings, there are instances where elements with substantially the same functional structure are distinguished by using different letters after the same reference numerals. For example, multiple elements with substantially the same functional structure may be distinguished as wireless communication units 210A, 210B, and 210C as needed. However, when it is not necessary to specifically distinguish each of the multiple elements with substantially the same functional structure, only the same reference numerals are used. For example, when it is not necessary to specifically distinguish wireless communication units 210A, 210B, and 210C, they may be simply referred to as wireless communication unit 210.

[0035] <<1. Example of Composition>>

[0036] Figure 1 This is a diagram illustrating an example of the structure of system 1 according to one embodiment of the present invention. For example... Figure 1 As shown, the system 1 involved in this embodiment includes a portable device 100 and a communication unit 200. In this embodiment, the communication unit 200 is mounted on a vehicle 202. The vehicle 202 is an example of an object used by a user.

[0037] This invention relates to a communication device on the authenticator's side and a communication device on the authenticator's side. Figure 1 In the example shown, the portable device 100 is an example of a communication device on the authenticator side, and the communication unit 200 is an example of a communication device on the authenticator side.

[0038] In System 1, when a user (e.g., the driver of vehicle 202) approaches vehicle 202 carrying a portable device 100, wireless communication for authentication occurs between the portable device 100 and the communication unit 200 mounted on vehicle 202. If authentication is successful, the door locks of vehicle 202 are unlocked or the engine is started, making vehicle 202 usable by the user. System 1 is also referred to as a smart key system. The constituent elements will be described in turn below.

[0039] (1) Portable 100

[0040] The portable device 100 can be any device carried by the user. Such devices include electronic keys, smartphones, and wearable terminals, etc. Figure 1 As shown, the portable device 100 includes a wireless communication unit 110, a storage unit 120, and a control unit 130.

[0041] The wireless communication unit 110 has the function of performing wireless communication with the communication unit 200 mounted on the vehicle 202. The wireless communication unit 110 receives wireless signals from the communication unit 200 mounted on the vehicle 202 and transmits wireless signals.

[0042] Wireless communication between the wireless communication unit 110 and the communication unit 200 is achieved, for example, using UWB (Ultra-Wide Band) signals. In wireless communication using UWB signals, if a pulse mode is used, the propagation delay time of the radio wave can be measured with high precision by using radio waves with a very short pulse width of less than nanoseconds, enabling high-precision ranging based on the propagation delay time. Furthermore, the propagation delay time refers to the time required from transmitting the radio wave to receiving it. The wireless communication unit 110 is configured, for example, as a communication interface capable of UWB communication.

[0043] Furthermore, UWB signals can be transmitted and received, for example, as ranging signals, angle estimation signals, and data signals. Ranging signals refer to the signals transmitted and received in the ranging process described later. Ranging signals can be constructed using a frame format without a payload portion for storing data, or using a frame format with a payload portion. Angle estimation signals refer to the signals transmitted and received in the angle estimation process described later. Angle estimation signals can also have the same structure as ranging signals. Data signals are preferably constructed using a frame format with a payload portion for storing data.

[0044] Here, the wireless communication unit 110 has at least one antenna 111. Moreover, the wireless communication unit 110 transmits and receives wireless signals via at least one antenna 111.

[0045] The storage unit 120 has the function of storing various information for the operation of the portable device 100. For example, the storage unit 120 stores programs for the operation of the portable device 100, as well as IDs (identifiers), passwords, and authentication algorithms used for authentication. The storage unit 120 is composed, for example, of a storage medium such as flash memory and a processing device that performs recording and reproducing to the storage medium.

[0046] The control unit 130 has the function of performing the processing of the portable device 100. As an example, the control unit 130 controls the wireless communication unit 110 to communicate with the communication unit 200 of the vehicle 202. The control unit 130 reads information from and writes information to the storage unit 120. The control unit 130 also functions as an authentication control unit that controls the authentication process performed between the control unit 130 and the communication unit 200 of the vehicle 202. The control unit 130 is composed of electronic circuits such as a CPU (Central Processing Unit) and a microprocessor.

[0047] (2) Communication Unit 200

[0048] The communication unit 200 is configured to correspond with the vehicle 202. Here, the communication unit 200 is located inside the vehicle 202, or it may be built into the vehicle 202 as a communication module, or the communication unit 200 may be mounted on the vehicle 202. Alternatively, the communication unit 200 may be installed in the parking lot of the vehicle 202, or the vehicle 202 and the communication unit 200 may be configured separately. In this case, the communication unit 200 wirelessly transmits control signals to the vehicle 202 based on the communication results with the portable device 100, enabling remote control of the vehicle 202. Figure 1 As shown, the communication unit 200 includes multiple wireless communication units 210 (210A to 210D), a storage unit 220, and a control unit 230.

[0049] The wireless communication unit 210 has the function of performing wireless communication with the wireless communication unit 110 of the portable computer 100. The wireless communication unit 210 receives wireless signals from the portable computer 100 and transmits wireless signals to the portable computer 100. The wireless communication unit 210 is configured, for example, as a communication interface capable of UWB communication.

[0050] Here, each wireless communication unit 210 has an antenna 211. Moreover, each wireless communication unit 210 transmits and receives wireless signals via the antenna 211.

[0051] The storage unit 220 has the function of storing various information for the operation of the communication unit 200. For example, the storage unit 220 stores programs for the operation of the communication unit 200, as well as authentication algorithms, etc. The storage unit 220 is composed, for example, of a storage medium such as flash memory and a processing device that performs recording and reproducing to the storage medium.

[0052] The control unit 230 has the function of controlling all operations of the communication unit 200 and the vehicle-mounted equipment installed in the vehicle 202. As an example, the control unit 230 controls the wireless communication unit 210 to communicate with the portable device 100. The control unit 230 reads information from and writes information to the storage unit 220. The control unit 230 also functions as an authentication control unit to control the authentication process performed with the portable device 100. Furthermore, the control unit 230 also functions as a door lock control unit to control the door locks of the vehicle 202, locking and unlocking the door locks. Additionally, the control unit 230 also functions as an engine control unit to control the engine of the vehicle 202, starting and stopping the engine. Moreover, the power source equipped in the vehicle 202 can be a motor or other type of motor besides an engine. The control unit 230 may be configured as an electronic circuit such as an ECU (Electronic Control Unit).

[0053] <<2. Characteristics of Technology>>

[0054] <2.1. Position Parameters>

[0055] In this embodiment, the communication unit 200 (more specifically, the control unit 230) performs position parameter estimation processing, which indicates the location where the portable device 100 exists. Hereinafter, refer to... Figures 2-4 This section explains the various definitions related to position parameters.

[0056] Figure 2 This diagram illustrates an example of the configuration of the plurality of antennas 211 (wireless communication units 210) provided on the vehicle 202 according to this embodiment. Figure 2As shown, four antennas 211 (211A-211D) are installed on the roof of vehicle 202. Antenna 211A is located on the front right side of vehicle 202. Antenna 211B is located on the front left side of vehicle 202. Antenna 211C is located on the rear right side of vehicle 202. Antenna 211D is located on the rear left side of vehicle 202. Furthermore, the distance between adjacent antennas 211 is set to be less than half the wavelength λ of the signal used for angle estimation (described later). A local coordinate system for communication unit 200 is established, with the center of the four antennas 211 as the origin, the front-rear direction of vehicle 202 as the X-axis, the left-right direction of vehicle 202 as the Y-axis, and the up-down direction of vehicle 202 as the Z-axis. Furthermore, the X-axis is parallel to the axis connecting the antenna pairs in the front-to-back direction (e.g., antennas 211A and 211C, and 211B and 211D). Additionally, the Y-axis is parallel to the axis connecting the antenna pairs in the left-to-right direction (e.g., antennas 211A and 211B, and 211C and 211D).

[0057] Furthermore, the configuration shape of the four antennas 211 is not limited to a square; they can be parallelograms, trapezoids, rectangles, or any other arbitrary shape. Of course, the number of antennas 211 is not limited to four.

[0058] Figure 3 This is a diagram illustrating an example of the position parameters of the portable device 100 according to this embodiment. The position parameters may include the distance R between the portable device 100 and the communication unit 200. Figure 3 The distance R shown is the distance from the origin of the local coordinate system of the communication unit 200 to the portable device 100. The distance R is estimated based on the results of the transmission and reception of the ranging signal (described later) between one of the multiple wireless communication units 210 and the portable device 100. The distance R can be the distance from the wireless communication unit 210 that transmits and receives the ranging signal (described later) to the portable device 100.

[0059] Additionally, position parameters may include Figure 3 The angles shown are those of the portable device 100, defined by angle α from the X-axis and angle β from the Y-axis, with the communication unit 200 as the reference. Angles α and β are the angles formed by the line connecting the origin and the portable device 100 in the first defined coordinate system and the coordinate axes. For example, the first defined coordinate system is the local coordinate system of the communication unit 200. Angle α is the angle formed by the line connecting the origin and the portable device 100 and the X-axis. Angle β is the angle formed by the line connecting the origin and the portable device 100 and the Y-axis.

[0060] Figure 4This diagram illustrates an example of the position parameters of the portable device 100 according to this embodiment. The position parameters may include the coordinates of the portable device 100 in a second defined coordinate system. Figure 4 The coordinates x on the X-axis, y on the Y-axis, and z on the Z-axis of the portable device 100 shown are an example of such coordinates. That is, the second defined coordinate system can be the local coordinate system of the communication unit 200. In addition, the second defined coordinate system can also be a global coordinate system.

[0061] <2.2.CIR>

[0062] (1) CIR calculation and processing

[0063] The portable device 100 and the communication unit 200 communicate to estimate the position parameters during the position parameter estimation process. At this time, the portable device 100 and the communication unit 200 calculate the CIR (Channel Impulse Response).

[0064] CIR refers to the response when a pulse is input to the system. In this embodiment, CIR is calculated based on a second signal received by the wireless communication unit of the other party (hereinafter, also called the receiving side) as a signal corresponding to the first signal, when the wireless communication unit of one of the portable device 100 and the communication unit 200 (hereinafter also called the transmitting side) transmits a signal containing a pulse as a first signal. CIR can also be said to represent the characteristics of the wireless communication path between the portable device 100 and the communication unit 200. Hereinafter, the first signal will also be referred to as the transmitting signal, and the second signal will also be referred to as the receiving signal.

[0065] As an example, CIR can be the result of a correlation operation, which obtains the correlation between the transmitted and received signals at predetermined time intervals. This correlation can be a sliding correlation, a process that obtains the correlation between the transmitted and received signals by shifting the relative positions of the signals in different time directions. CIR contains correlation values ​​representing the degree of correlation between the transmitted and received signals as elements at predetermined time intervals. The predetermined time interval is, for example, the interval at which the receiving side samples the received signal. Therefore, the elements constituting CIR are also called sampling points. The correlation value can be a complex number with I and Q components. Alternatively, the correlation value can be the amplitude or phase of a complex number. Furthermore, the correlation value can also be the sum of the squares of the I and Q components of a complex number (or the square of the amplitude), i.e., power.

[0066] CIR can also be understood as a set of values ​​at each time point (hereinafter referred to as CIR values). In this case, CIR is the time series variation of CIR values. When CIR is the result of a correlation operation, the CIR values ​​are the correlation values.

[0067] Furthermore, the portable device 100 and the communication unit 200 use a time counter to obtain the time. A time counter is a counter that counts (typically increments) a value representing elapsed time (hereinafter also called a count value) at predetermined time intervals (hereinafter also called a counting period). The current time is calculated based on the count value counted by the time counter, the counting period, and the counting start time. Between different devices, the same counting period and counting start time are also called synchronization. On the other hand, between different devices, at least one of the counting period and counting start time is different, also called asynchrony or non-synchronization. The portable device 100 and the communication unit 200 can be synchronized or asynchronous. Additionally, each of the plurality of wireless communication units 210 can be synchronized or asynchronous with each other. The predetermined time used to calculate the CIR can also be an integer multiple of the counting period of the time counter. In the following description, unless otherwise specified, the case where the portable device 100 and each of the plurality of wireless communication units 210 are synchronized with each other will be described.

[0068] The following is for reference Figures 5-6 The CIR calculation process is explained in detail when the transmitting side is a portable device 100 and the receiving side is a communication unit 200.

[0069] Figure 5 This diagram illustrates an example of a signal processing module in the communication unit 200 according to this embodiment. For example... Figure 5 As shown, the communication unit 200 includes an oscillator 212, a multiplier 213, a 90-degree phase shifter 214, a multiplier 215, an LPF (Low Pass Filter) 216, an LPF 217, a correlator 218, and an accumulator 219.

[0070] Oscillator 212 generates a signal with the same frequency as the carrier frequency of the transmitted signal and outputs the generated signal to multiplier 213 and 90-degree phase shifter 214.

[0071] Multiplier 213 multiplies the received signal received by antenna 211 with the signal output from oscillator 212, and outputs the result to LPF 216. LPF 216 outputs the signal from the input signal whose frequency is below the carrier frequency of the transmitted signal to correlator 218. The signal input to correlator 218 is the I component (i.e., the real part) of the component corresponding to the envelope of the received signal.

[0072] The 90-degree phase shifter 214 delays the phase of the input signal by 90 degrees and outputs the delayed signal to the multiplier 215. The multiplier 215 multiplies the received signal received by the antenna 211 with the signal output from the 90-degree phase shifter 214 and outputs the result to the LPF 217. The LPF 217 outputs the signal from the input signal whose frequency is below the carrier frequency of the transmitted signal to the correlator 218. The signal input to the correlator 218 is the Q component (i.e., the imaginary part) of the component corresponding to the envelope of the received signal.

[0073] The correlator 218 calculates the CIR by obtaining the received signal, consisting of the I and Q components output from LPF216 and LPF217, and performing a sliding correlation with a reference signal. Furthermore, the reference signal here refers to the same signal as the transmitted signal before being multiplied by the carrier.

[0074] Accumulator 219 accumulates the CIR output from correlator 218 and outputs it.

[0075] Here, the transmitting side can transmit a signal containing a preamble as a transmit signal, which includes multiple preamble symbols. A preamble is a sequence known between the transmitter and receiver. The preamble is typically placed at the beginning of the transmit signal. A preamble symbol is a pulse arrangement containing one or more pulses. A pulse arrangement is a collection of multiple pulses separated in the time direction. The preamble symbols are the objects accumulated by accumulator 219. That is, correlator 218 calculates the CIR of each preamble symbol by obtaining the sliding correlation between each portion corresponding to the multiple preamble symbols contained in the received signal and the preamble symbols contained in the transmit signal (i.e., the reference signal). Then, accumulator 219 accumulates the CIR of each preamble symbol for the multiple preambles contained in the preamble and outputs the accumulated CIR.

[0076] (2) Examples of CIR

[0077] Figure 6 This represents an example of the CIR output from accumulator 219. Figure 6 This is a diagram illustrating an example of a CIR as described in this embodiment. Figure 6 The CIR shown is the CIR when the transmitting side sends the signal, assuming the start time of the count based on a time counter. This type of CIR is also known as a delay distribution. The horizontal axis of this graph represents the delay time, which is the elapsed time since the transmitting side sent the signal. The vertical axis of this graph represents the absolute value of the CIR (e.g., power value). Furthermore, the following explains the case where the CIR refers to a delay distribution.

[0078] The shape of the CIR, more specifically the shape of the time series variation of the CIR values, is also referred to as the CIR waveform. Typically, in the CIR, the set of elements between zero-crossings corresponds to a pulse. A zero-crossing is an element with a value of zero. However, in noisy environments, this is not the case. For example, the set of elements between the intersection of a reference level and the time series variation of the CIR values ​​can also be understood as corresponding to a pulse. Figure 6 The CIR shown includes a set 21 of elements corresponding to a certain pulse and a set 22 of elements corresponding to other pulses.

[0079] Set 21 corresponds, for example, to signals arriving at the receiving side via a fast path (e.g., a pulse). A fast path refers to the shortest path between the transmitter and receiver. In an unobstructed environment, a fast path is a straight path between the transmitter and receiver. Set 22 corresponds, for example, to signals arriving at the receiving side via paths other than the fast path (e.g., a pulse). Thus, signals arriving via multiple paths are also referred to as multipath waves.

[0080] (3) Detection of the first wave of arrival

[0081] The receiving side detects a signal that meets a predetermined detection criterion from the wireless signals received from the transmitting side, and identifies it as the signal that has arrived at the receiving side via a fast path. The receiving side then estimates position parameters based on the detected signal. Hereinafter, the signal detected as the signal that has arrived at the receiving side via a fast path will also be referred to as the first wave of arrival.

[0082] The receiving side detects the signal in the received wireless signal that meets a predetermined detection criterion as the first wave of arrival. An example of a predetermined detection criterion is that the CIR value (e.g., amplitude or power) begins to exceed a predetermined threshold. That is, the receiving side can also detect the signal corresponding to the portion of the CIR value that begins to exceed the predetermined threshold as the first wave of arrival. Hereinafter, the predetermined threshold used to detect the first wave of arrival will also be referred to as the fast path threshold.

[0083] The signal received at the receiving end can be any of three types: direct wave, delayed wave, or composite wave. A direct wave is a signal received by the receiving end via the shortest path between the transmitter and receiver. In other words, a direct wave is a signal that arrives at the receiving end via a fast path. A delayed wave is a signal that arrives at the receiving end via a path other than the shortest path between the transmitter and receiver. Delayed waves are received by the receiving end with a delay compared to direct waves. A composite wave is a signal received by the receiving end in a combined state from multiple signals that have traveled through multiple different paths.

[0084] It should be noted that the signal detected as the first wave of arrival is not necessarily a direct wave. For example, when a direct wave is received in a state where it cancels out a delayed wave, if the CIR value corresponding to the element of the direct wave is below a predetermined threshold, the direct wave is not detected as the first wave of arrival. In this case, a delayed wave or a composite wave that arrives later than the direct wave is detected as the first wave of arrival.

[0085] <2.3. Estimation of Position Parameters>

[0086] (1) Distance estimation

[0087] The communication unit 200 performs ranging processing. Ranging processing refers to the process of estimating the distance between the communication unit 200 and the portable device 100. The distance between the communication unit 200 and the portable device 100 is, for example,... Figure 3 The distance R is shown. Ranging processing includes transmitting and receiving ranging signals, and calculating the distance R based on the propagation delay time of the ranging signals. The propagation delay time refers to the time required from signal transmission to reception.

[0088] Here, any one of the multiple wireless communication units 210 of the communication unit 200 transmits and receives ranging signals. Hereinafter, the wireless communication unit 210 that transmits and receives ranging signals is also referred to as the host. The distance R is the distance between the wireless communication unit 210 (more precisely, the antenna 211) that functions as the host and the portable device 100.

[0089] In the ranging process, multiple ranging signals can be transmitted and received between the communication unit 200 and the portable device 100. The ranging signal transmitted from one device to another among the multiple ranging signals is also referred to as the first ranging signal. Next, the ranging signal transmitted from the device receiving the first ranging signal to the device that transmitted the first ranging signal in response to the first ranging signal is also referred to as the second ranging signal. Finally, the ranging signal transmitted from the device receiving the second ranging signal to the device that transmitted the second ranging signal in response to the second ranging signal is also referred to as the third ranging signal.

[0090] The following is for reference Figure 7 Here is an example illustrating the ranging process.

[0091] Figure 7 This is a timing diagram illustrating an example of the ranging process executed in system 1 according to this embodiment. In this sequence, it relates to the portable device 100 and the communication unit 200. In this sequence, the wireless communication unit 210A functions as a host.

[0092] like Figure 7As shown, firstly, the portable device 100 transmits a first ranging signal (step S102). When the first ranging signal is received by the wireless communication unit 210A, the control unit 230 calculates the CIR of the first ranging signal. Then, based on the calculated CIR, the control unit 230 detects the first arrival wave of the first ranging signal in the wireless communication unit 210A (step S104).

[0093] Next, the wireless communication unit 210A sends a second ranging signal as a response to the first ranging signal (step S106). When the portable device 100 receives the second ranging signal, it calculates the CIR of the second ranging signal. Then, based on the calculated CIR, the portable device 100 detects the first arrival wave of the second ranging signal (step S108).

[0094] Next, the portable device 100 sends a third ranging signal as a response to the second ranging signal (step S110). When the third ranging signal is received by the wireless communication unit 210A, the control unit 230 calculates the CIR of the third ranging signal. Then, based on the calculated CIR, the control unit 230 detects the first arrival wave of the third ranging signal in the wireless communication unit 210A (step S112).

[0095] The portable device 100 measures the time INT1 from the transmission time of the first ranging signal to the reception time of the second ranging signal, and the time INT2 from the reception time of the second ranging signal to the transmission time of the third ranging signal. Here, the reception time of the second ranging signal refers to the reception time of the first arrival wave of the second ranging signal detected in step S108. Then, the portable device 100 transmits a signal containing information representing the times INT1 and INT2 (step S114). This signal is received, for example, by the wireless communication unit 210A.

[0096] The control unit 230 measures the time INT3 from the time of receiving the first ranging signal to the time of transmitting the second ranging signal, and the time INT4 from the time of transmitting the second ranging signal to the time of receiving the third ranging signal. Here, the time of receiving the first ranging signal refers to the time of receiving the first arrival wave of the first ranging signal detected in step S104. Similarly, the time of receiving the third ranging signal refers to the time of receiving the first arrival wave of the third ranging signal detected in step S112.

[0097] Then, the control unit 230 estimates the distance R based on the times INT1, INT2, INT3, and INT4 (step S116). For example, the control unit 230 estimates the propagation delay time τ using the following formula. m .

[0098]

Mathematical Formula 1

[0099]

[0100] Then, the control unit 230 multiplies the signal speed by the estimated propagation delay time τ. m To estimate the distance R.

[0101] - One reason for the reduced accuracy of the estimation

[0102] The reception time of the ranging signal that becomes the start or end period of times INT1, INT2, INT3, and INT4 is the reception time of the first arrival wave of the ranging signal. As mentioned above, the signal detected as the first arrival wave is not necessarily a direct wave.

[0103] When a delayed wave or composite wave, arriving later than the direct wave, is detected as the first wave of arrival, the reception time of the first wave of arrival is delayed compared to the case where the direct wave is detected as the first wave of arrival. In this case, the propagation delay time τ m The estimated result varies from the true value (the estimated result when the direct wave is detected as the first arriving wave). As a result, the ranging accuracy decreases by the amount of variation.

[0104] -Replenish

[0105] Furthermore, the receiving side may also use the moment when a specified detection benchmark is met as the reception moment of the first wave of arrival. That is, the receiving side may also use the moment when the power value of the CIR begins to exceed a specified threshold, or the moment when the received power value of the received wireless signal begins to exceed a specified threshold, as the reception moment of the first wave of arrival. In addition, the receiving side may also use the moment when the peak value of the detected first wave of arrival is reached (i.e., the moment when the power value is highest in the portion of the CIR corresponding to the first wave of arrival, or the moment when the received power value of the first wave of arrival is highest) as the reception moment of the first wave of arrival.

[0106] (2) Angle estimation

[0107] Communication unit 200 performs angle estimation processing. Angle estimation processing refers to estimating... Figure 3 The processing of angles α and β is shown below. Angle estimation processing includes receiving angle estimation signals and calculating angles α and β based on the received angle estimation signals. Angle estimation signals refer to the signals transmitted and received during angle estimation processing. The following refers to... Figure 8 An example illustrating the angle estimation process is provided.

[0108] Figure 8This is a timing diagram illustrating an example of the angle estimation process performed in system 1 according to this embodiment. In this sequence, it relates to the portable device 100 and the communication unit 200.

[0109] like Figure 8 As shown, firstly, the portable device 100 transmits an angle estimation signal (step S202). Next, when each of the wireless communication units 210A to 210D receives the angle estimation signal, the control unit 230 calculates the CIR of the angle estimation signal received by each of the wireless communication units 210A to 210D. Then, for each of the wireless communication units 210A to 210D, the control unit 230 detects the first arrival wave of the angle estimation signal based on the calculated CIR (steps S204A to S204D). Next, for each of the wireless communication units 210A to 210D, the control unit 230 detects the phase of the detected first arrival wave (steps S206A to S206D). Then, based on the phase of the first arrival wave detected for each of the wireless communication units 210A to 210D, the control unit 230 estimates angles α and β (step S208).

[0110] Here, the phase of the first wave of arrival is the phase of the first wave of arrival received in the CIR. Alternatively, the phase of the first wave of arrival can also be the phase of the first wave of arrival received in the received radio signal.

[0111] The following is a detailed explanation of the processing in step S208. The phase of the first arriving wave detected by the wireless communication unit 210A is set to P. A The phase of the first arriving wave detected by the wireless communication unit 210B is set to P. B The phase of the first arriving wave detected by the wireless communication unit 210C is set to P. C The phase of the first arriving wave detected by the wireless communication unit 210D is set to P. D In this case, the antenna array phase difference Pd in ​​the X-axis direction AC and Pd BD and the antenna array phase difference Pd in ​​the Y-axis direction. BA and Pd DC They can be expressed using the following formulas.

[0112]

Mathematical Formula 2

[0113] Pd AC =(P A -P C )

[0114] Pd BD =(P B -P D )

[0115] Pd DC =(P D -P C )

[0116] Pd BA =(P B -P A (2) Calculate angles α and β using the following formula. Here, λ is the wavelength of the radio wave, and d is the distance between antennas 211.

[0117]

Mathematical Expression 3

[0118] αorβ=arccos(λ·Pd / (2·π·d)) (3)

[0119] Therefore, the angles calculated based on the phase differences of each antenna array are expressed by the following formulas.

[0120]

Mathematical Expression 4

[0121] α AC =arccos(λ·Pd AC / (2·π·d))

[0122] α BD =arccos(λ·Pd BD / (2·π·d))

[0123] β DC =arccos(λ·Pd DC / (2·π·d))

[0124] β BA =arccos(λ·Pd BA / (2·π·d)) (4)

[0125] Based on the angle α calculated above, the control unit 230 AC α BD β DC and β BA The angles α and β are calculated by averaging the angles calculated for each of the two arrays in the X-axis and Y-axis directions, as shown in the following formula.

[0126]

Mathematical Expression 5

[0127] α=(α AC +α BD ) / 2

[0128] β=(β DC +β BA ) / 2 (5)

[0129] - One reason for the reduced accuracy of estimation

[0130] As explained above, angles α and β are calculated based on the phase of the first wave of arrival. As mentioned above, the signal detected as the first wave of arrival is not necessarily a direct wave.

[0131] That is, delayed waves or composite waves are sometimes detected as the first arriving wave. Typically, the phase of delayed waves and composite waves differs from that of the direct wave, thus reducing the accuracy of angle estimation by a certain amount.

[0132] -Replenish

[0133] Furthermore, the signals used for angle estimation and distance measurement can be the same. For example, Figure 7 The third ranging signal shown is Figure 8 The angle estimation signal shown can also be the same. In this case, the communication unit 200 can calculate the distance R and the angles α and β by receiving a wireless signal that serves as both the angle estimation signal and the second ranging signal.

[0134] (3) Coordinate estimation

[0135] The control unit 230 performs coordinate estimation processing. Coordinate estimation processing refers to the estimation... Figure 4 The processing of the three-dimensional coordinates (x, y, z) of the portable device 100 shown can be performed as a coordinate estimation process using the following first calculation method and second calculation method.

[0136] - First Calculation Method

[0137] The first calculation method is based on the results of distance measurement processing and angle estimation processing to calculate the coordinates x, y, and z. In this case, the control unit 230 first calculates the coordinates x and y using the following formula.

[0138]

Mathematical Expression 6

[0139] x=R·cosα

[0140] y=R·cosβ (6)

[0141] Here, for distance R and coordinates x, y and z, the following relationship holds.

[0142]

Mathematical Expression 7

[0143]

[0144] The control unit 230 uses the above relationship to calculate the coordinate z using the following formula.

[0145]

Mathematical Expression 8

[0146]

[0147] - Second calculation method

[0148] The second calculation method omits the estimation of angles α and β and calculates the coordinates x, y, and z. First, through the above mathematical formulas (4)(5)(6)(7), the following relationship holds.

[0149]

Mathematical Expression 9

[0150] x / R=cosα (9)

[0151]

Mathematical Formula 10

[0152] y / R=cosβ (10)

[0153]

Mathematical Expression 11

[0154] x 2 +y 2 +z 2 =R 2 (11)

[0155]

Mathematical Expression 12

[0156] d·cosα=λ·(Pd AC / 2+Pd BD / 2) / (2·π) (12)

[0157]

Mathematical Expression 13

[0158] d.cosβ=λ·(Pd DC / 2+Pd BA / 2) / (2·π) (13)

[0159] If we rearrange mathematical expression (12) with respect to cosα and substitute it into mathematical expression (9), we can obtain the coordinate x through the following formula.

[0160]

Mathematical Expression 14

[0161] x=R·λ·(Pd AC / 2+Pd BD / 2) / (2·H·d) (14)

[0162] If we rearrange mathematical expression (13) with respect to cosβ and substitute it into mathematical expression (10), we can obtain the coordinate y through the following formula.

[0163]

Mathematical Expression 15

[0164] y=R·λ·(Pd DC / 2+Pd BA / 2) / (2·π·d)(15)

[0165] Then, if we substitute mathematical expressions (14) and (15) into mathematical expression (11) and rearrange them, we can obtain the coordinate z by the following formula.

[0166]

Mathematical Expression 16

[0167]

[0168] The above explains the process of estimating the coordinates of the portable device 100 in the local coordinate system. By combining the coordinates of the portable device 100 in the local coordinate system with the coordinates of the origin of the local coordinate system in the global coordinate system, the coordinates of the portable device 100 in the global coordinate system can also be estimated.

[0169] - One reason for the reduced accuracy of estimation

[0170] As explained above, coordinates are calculated based on propagation delay time and phase. Furthermore, these are all estimated based on the first wave of arrival. Therefore, for the same reasons as ranging and angle estimation processing, the accuracy of coordinate estimation may be reduced.

[0171] (4) Presumption of the existence of the region

[0172] The location parameters may also include the area where the portable device 100 exists among multiple predefined areas. As an example, when the area is defined by the distance from the communication unit 200, the control unit 230 estimates the area where the portable device 100 exists based on the distance R estimated through ranging processing. As another example, when the area is defined by the angle from the communication unit 200, the control unit 230 estimates the area where the portable device 100 exists based on the angles α and β estimated through angle estimation processing. As yet another example, when the area is defined by three-dimensional coordinates, the control unit 230 estimates the area where the portable device 100 exists based on the coordinates (x, y, z) estimated through coordinate estimation processing.

[0173] In addition, as a process specific to vehicle 202, control unit 230 can also estimate the area where the portable device 100 is located from multiple areas, including the interior and exterior of vehicle 202. This allows for detailed services, such as providing different services depending on whether the user is inside or outside the vehicle. Furthermore, control unit 230 can also determine the area where the portable device 100 is located from the surrounding area (within a predetermined distance from vehicle 202) and from the distant area (a distance beyond a predetermined distance from vehicle 202).

[0174] (5) Application of the estimated position parameters

[0175] The estimated location parameters can be used, for example, for the authentication of the portable device 100. For instance, if the portable device 100 is located in an area near the driver's seat and close to the communication unit 200, the control unit 230 determines that the authentication is successful and unlocks the car door.

[0176] <<3. Technical Issues>>

[0177] Reference Figures 9-12 The technical issues of this embodiment will be explained. Figures 9-12 This is a diagram used to illustrate the technical issues of this embodiment. The horizontal axis represents the chip length, which indicates the delay time, and the vertical axis represents the absolute value of the CIR value (e.g., the power value). Chip length refers to the time width of each pulse. For example, when creating pulses with a bandwidth of 500MHz, the pulse width of approximately 2ns is called the chip length.

[0178] exist Figure 9 The figure shows the time delay of 1T. C Signals arriving via the fast path arrive with a delay of 3T. C CIR in cases where signals arrive via paths other than the fast path. (See reference) Figure 9 At each delay time 1T C and 3T C The CIR waveform shows a peak. Therefore, it can be concluded that the delay time is 2T. C The separation of the two multipath waves is fully realized in the CIR waveform.

[0179] exist Figure 10 The figure shows the time delay of 1T. C Signals arriving via the fast path arrive with a delay of 2T. C CIR in cases where signals arrive via paths other than the fast path. Furthermore, during a delay time of 1T... C The first wave of signal arrived and at a delay of 2T C The arriving second wave signal is in phase. (Refer to...) Figure 10 During the delay time of 1T C The CIR waveform shows a peak, and at a delay time of 2T C The CIR waveform did not show a peak. Furthermore, at a delay time of 1T... C The arriving signal and the delay time 2T C The arriving signals are synthesized in phase and appear as a single waveform. Therefore, it is evident that achieving a time delay of 1T in a CIR waveform is difficult. C Separation of two multipath waves.

[0180] exist Figure 11 The figure shows a delay time of 1.2T. CSignals arriving via the fast path arrive with a delay of 1.7T. C and 3.6T C CIR in cases where signals arrive via paths other than the fast path. Furthermore, at a delay time of 1.2T... C The first wave of signal arrived with a delay of 1.7T. C The second wave of signal that arrives is out of phase. (Refer to...) Figure 11 With a delay time of 1.2T C and 3.6T C The CIR waveform exhibits a peak. On the other hand, at a delay time of 2.2T... C A second peak appeared nearby. This is significantly different from the actual latency of 1.7T. C Therefore, it is difficult to achieve a delay time interval of 0.5T in a CIR waveform. C Separation of two multipath waves.

[0181] like Figure 10 and Figure 11 As shown, when the difference in arrival delay times of two multipath waves at the receiving end is short, the delay time at which a peak appears in the CIR waveform may vary from the original delay time. Therefore, the delay time detected as the reception time of the first arriving wave may also vary from the original delay time. In this case, the ranging accuracy decreases by the amount corresponding to the variation.

[0182] exist Figure 12 The figure shows the time delay of 1T. C Signals arriving via the fast path arrive with a delay of 1.5T. C CIR waveform 23 is the CIR waveform when the signal arrives via a path other than the fast path. CIR waveform 21 is the CIR waveform with a delay time of 1T. C The CIR waveform when the signal via the fast path is received as a single unit. CIR waveform 22 is at a delay time of 1.5T. C The CIR waveform when a signal received via a path other than the fast path is received individually. Furthermore, at a delay time of 1T... C The first wave of signal arrived with a delay of 2T C The second wave of signal that arrived was 90 degrees out of phase.

[0183] When the difference in arrival time between two multipath waves at the receiving end is short, there is a possibility that the delayed wave or the composite wave may be detected as the first arriving wave. Figure 12 In the example shown, the composite wave is detected as the first arriving wave. Typically, the phase of the delayed wave and the composite wave differs from the phase of the direct wave, thus reducing the accuracy of the angle estimation by a different amount.

[0184] like Figure 12As illustrated in the example, when the composite wave of the direct wave and the delayed wave is detected as the first arriving wave, the delayed wave is synthesized at sampling point 31 near the peak, resulting in a significant phase shift. Therefore, if the angle is estimated based on the phase at sampling point 31, the estimation accuracy is reduced.

[0185] On the other hand, as with sampling point 32, at lower power sampling points earlier than the peak value, the influence of the delayed wave decreases, and therefore the phase variation decreases. However, while the influence of the delayed wave decreases, the power value decreases, thus increasing the influence of noise and correspondingly reducing the estimation accuracy.

[0186] Therefore, it is preferable to be able to separate multipath waves with a higher resolution than CIR.

[0187] <<4. Characteristics of Technology>>

[0188] <4.1. Detection of the first wave of arrival>

[0189] The portable device 100 and the communication unit 200 detect the first wave of arrival through the process described in detail below. Hereinafter, as an example, the case where the main body for detecting the first wave of arrival is the communication unit 200 will be described. The process described below can also be performed by the portable device 100.

[0190] (1) Formulation of delay distribution

[0191] First, the delay distribution (i.e., CIR) in the PN (Pseudo-Noise) correlation method is formulated. The PN correlation method refers to a method of calculating the CIR by transmitting a signal consisting of a random sequence of PN sequences shared by both the transmitting and receiving sides, and obtaining the sliding correlation between the transmitted and received signals. Furthermore, the PN sequence signal refers to a signal in which 1s and 0s are arranged almost randomly.

[0192] Hereinafter, it is assumed that a PN sequence signal u(t) with unit amplitude is transmitted as a transmission signal (e.g., a preamble symbol for a ranging signal and an angle estimation signal). Unit amplitude refers to a defined amplitude known between the transmitter and receiver.

[0193] Furthermore, the receiving antenna will receive the multipath wave of the L-wave as the signal corresponding to the transmitted signal from the transmitting side. A multipath wave refers to a signal received by the receiving side via multiple paths. That is, when the transmitting side transmits a signal, L signals via multiple paths are received by the receiving side.

[0194] In this case, the received signal x(t) is represented by the following formula.

[0195]

Mathematical Expression 17

[0196]

[0197] Here, t represents time. h i T is the complex response value of the i-th multipath wave. 0i is the propagation delay time of the i-th multipath wave. f is the carrier frequency of the transmitted signal. v(t) is the internal noise. Internal noise refers to the noise generated inside the circuitry on the receiver side.

[0198] For example, in the PN correlation method, the correlation with the received signal x(t) is obtained by staggering the timing of the transmitted signal u(t) known at the receiver side, as shown in the following equation.

[0199]

Mathematical Expression 18

[0200]

[0201] In addition, u * () is the complex conjugate of u().

[0202] z(τ) is also known as the delayed distribution. Additionally, |z(τ)| 2 Also known as power delay distribution. τ is the delay time.

[0203] The delay distribution in the case of receiving L-wave multipath waves is represented by the following formula.

[0204]

Mathematical Expression 19

[0205]

[0206] Here, r(τ) is the autocorrelation function of the PN sequence signal. The autocorrelation function is a function that obtains the correlation between the signal and itself. r(τ) is given by the following equation.

[0207]

Mathematical Expression 20

[0208]

[0209] Additionally, n(τ) is the internal noise component. n(τ) is given by the following equation.

[0210]

Mathematical Expression 21

[0211]

[0212] (2) Sparse Reconstruction

[0213] Let the number of samples of the received signal be M (where M > L). Then, the received signal is sampled over M discrete delay times τ1, τ2, ..., τ... mThe signal is sampled. Furthermore, discrete delay time refers to representing the delay time as a discrete value. z(τ) is the delay distribution calculated based on the received signal sampled at discrete delay time τ. The data vector z consisting of M delay distributions is represented by the following equation. However, this equation applies when the receiver receives only one preamble symbol.

[0214]

Mathematical Expression 22

[0215] z=[z(τ1),z(τ2),...,z(τ M )] T (twenty two)

[0216] In the case of receiving a multipath wave of an L-wave, the data vector z is as shown in the following equation.

[0217]

Mathematical Expression 23

[0218]

[0219]

Mathematical Expression 24

[0220] r(τ)=[r(τ1-τ), r(τ2-τ),..., r(τ M -τ)] T (twenty four)

[0221]

Mathematical Expression 25

[0222] n=[n(τ1),n(τ2),...,n(τ M )] T (25)

[0223] Furthermore, r(τ) is called the pattern vector.

[0224] In addition, when the data vector z is represented as a matrix, it is as shown in the following equation.

[0225]

Mathematical Expression 26

[0226]

[0227]

Mathematical Expression 27

[0228]

[0229]

Mathematical Expression 28

[0230]

[0231] Here, A0 is also called the pattern matrix.

[0232] In addition, S0 is also known as the signal vector.

[0233] In sparse reconstruction, the data vector z is transformed into a matrix product containing A and s.

[0234]

Mathematical Expression 29

[0235]

[0236]

Mathematical Expression 30

[0237]

[0238]

Mathematical Expression 31

[0239]

[0240] T1, T2, ..., T N Let T1, T2, ..., T represent the N delay times for the search. N Also known as a delay time interval. A delay time interval is an example of a time setting. Furthermore, N >> L.

[0241] Here, A is also called the extended mode matrix. The extended mode matrix is ​​a matrix composed of multiple elements representing the delay distribution of the signal at each of the multiple delay time intervals. For example, r(T1), which is an element of the extended mode matrix A, is the delay distribution of the signal at the time T1 when it is received.

[0242] Additionally, s is also known as the extended signal vector. The extended signal vector is a vector composed of multiple elements representing the presence or absence of a signal in each time delay interval, as well as the amplitude and phase of that signal.

[0243] (3) Estimation of propagation delay time based on extended signal vector

[0244] According to sparse reconstruction, the delay distribution z is modeled in the form of As+n. Therefore, by solving the underdetermined problem with N unknowns and M (M<N) condition number, the extended signal vector s can be obtained. The control unit 230 estimates the reception time of the first arriving wave based on the delay time intervals corresponding to multiple elements in the extended signal vector s.

[0245] Here, a non-zero element in the extended signal vector indicates that a signal exists in the delay time interval corresponding to that non-zero element. On the other hand, a zero element in the extended signal vector indicates that a signal does not exist in the delay time interval corresponding to that zero element. Therefore, the control unit 230 estimates the delay time interval corresponding to the non-zero element among the delay time intervals corresponding to the multiple elements in the extended signal vector s as the reception time of the first arriving wave.

[0246] At this time, the control unit 230 estimates the sparse solution of the extended signal vector s, and estimates the delay time interval corresponding to the non-zero elements in the estimated sparse solution as the reception time of the first arriving wave. A sparse solution is a vector in which only a specified number of elements are non-zero. The specified number is the number of pulses in the received signal corresponding to a pulse in the transmitted signal. That is, a sparse solution is a vector in which only L elements are non-zero, and the other elements are zero, in the case of receiving an L-wave multipath wave. For example, in s = [s1, s2, ..., s...] N If s2 in the equation is non-zero, it is determined that a signal was received during the delay time T2.

[0247] In particular, the control unit 230 estimates the earliest delay time interval among the delay time intervals corresponding to the non-zero elements contained in the extended signal vector s as the reception time of the first arriving wave. For example, in s = [s1, s2, ..., s...] N If s2, s4 and s6 in the equation are non-zero, it is determined that a signal received via the fast path was received at delay time T2, and a signal received via a path other than the fast path was received at delay times T4 and T6.

[0248] The resolution of the signal obtained by the sparse reconstruction model is determined by the size of N (i.e., the number of elements in the expanded signal vector s) during modeling in sparse reconstruction. Therefore, by increasing the amount of N during sparse reconstruction, multipath wave separation can be achieved with a finer resolution than CIR. Thus, in this embodiment, the number of delay time intervals N is made greater than the number of samples M of the received signal. In other words, in this embodiment, N delay time intervals T1, T2, ..., T... N The time interval is greater than the M discrete delay times τ1, τ2, ..., τ m The time interval is short. With the above structure, multipath wave separation can be achieved with a resolution finer than the sampling interval of the received signal. As a result, the reception time of the first arriving wave can be determined with a resolution finer than that of CIR.

[0249] (4) Compressed Sensing Algorithm

[0250] The control unit 230 uses a compressed sensing algorithm to estimate the extended signal vector s, which is a sparse solution. The compressed sensing algorithm is an algorithm that estimates the unknown vector based on linear observations of the unknown vector, assuming the unknown vector is a sparse vector. In this embodiment, the extended signal vector s is an example of an unknown vector. Linear observation refers to obtaining the result of multiplying the coefficients by the unknown vector. In this embodiment, the extended mode matrix A is an example of coefficients. The delay distribution z is an example of a linear observation.

[0251] Examples of compressed sensing algorithms include FOCUSS (Focal Underdetermined System Solver), ISTA (Iterative Shrinkage Thresholding Algorithm), and FISTA (Fast ISTA). In particular, FOCUSS is an algorithm that assumes initial values ​​for an unknown vector and repeatedly estimates the unknown vector using a generalized inverse matrix and a weight matrix. FOCUSS, by utilizing the generalized inverse matrix and the weight matrix, can estimate the unknown vector with high accuracy in a relatively small number of iterations. The basic principles of FOCUSS are explained in detail in the first non-patented document, "Irina F. Gorodnitsky, Member, IEEE, and Bhaskar D. Rao, 'Sparse Signal Reconstruction from Limited Data Using FOCUSS: A Re-weighted Minimum Norm Algorithm', IEEE TRANSACTIONS ON SIGNALPROCESSING, VOL.45, NO.3, MARCH 1997".

[0252] Another example of compressed sensing algorithms is M-FOCUSS (FOCUSS with multiple measurement vectors), which extends the aforementioned FOCUSS. M-FOCUSS refers to an algorithm that applies FOCUSS in parallel to multiple unknown vectors. The basic principles of M-FOCUSS are explained in detail in the second non-patent document "Shane F. Cotter, et al.; 'Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors', IEEE Transactions on Signal Processing, vol. 53, No. 7, Jul. 2005, pp. 2477-2488."

[0253] The control unit 230 in this embodiment uses M-FOCUSS to estimate the reception time of the first wave of arrival. Therefore, firstly, the control unit 230 performs sparse reconstruction to enable the use of M-FOCUSS. Specifically, the control unit 230 converts the data matrix obtained by extending the data vector z for multiple wireless communication units 210 into a matrix product of an extended mode matrix and an extended signal matrix obtained by extending the extended signal vector s for multiple wireless communication units 210. Then, the control unit 230 uses M-FOCUSS to estimate the extended signal matrix that satisfies predetermined conditions, and estimates the reception time of the first wave of arrival based on the estimation result.

[0254] - A redefinition of the mathematical formula for sparse reconstruction

[0255] The above describes the formula for calculating the CIR and performing sparse reconstruction for a received signal received by one wireless communication unit 210. Hereinafter, a formula will be provided for multiple received signals received by multiple wireless communication units 210.

[0256] The control unit 230 calculates the CIR of each of the plurality of wireless communication units 210 by acquiring, at predetermined intervals, the correlation between the received signal and the transmitted signal received by each of the plurality of wireless communication units 210 when the portable device 100 transmits a transmit signal, starting from a time point set in each of the plurality of wireless communication units 210. The time point set in each of the plurality of wireless communication units 210 refers to the count start time of the time counter of each of the plurality of wireless communication units 210. The following describes the case where the count start times of the plurality of wireless communication units 210 are the same. That is, each of the plurality of wireless communication units 210 is synchronized with each other. Of course, each of the plurality of wireless communication units 210 can also be asynchronous.

[0257] The number of wireless communication units 210 (i.e., the number of antennas 211) is set to K, and the index representing an individual antenna 211 is set to k. The z-axis represents the correlation between the received signal and the transmitted signal obtained by the k-th antenna. k (τ) is expressed by the following formula.

[0258]

Mathematical Expression 32

[0259]

[0260] Here, x k (t) is the received signal received by the k-th antenna.

[0261] The data vector z is discretized from the CIR of the k-th antenna with M samples. (k) It can be expressed as follows.

[0262]

Mathematical Expression 33

[0263]

[0264] Here, A s It is a mode matrix composed of all the mode vectors of the L-wave array. A s It can be expressed as follows.

[0265]

Mathematical Expression 34

[0266]

[0267]

Mathematical Expression 35

[0268]

[0269] In addition, s s s is the signal vector of the reference antenna (hereinafter also referred to as the reference antenna) among the K antennas. s It can be expressed as follows.

[0270]

Mathematical Expression 36

[0271]

[0272] In addition, B k B is a diagonal matrix representing the phase difference between the k-th antenna and the reference antenna. k It can be expressed as follows.

[0273]

Mathematical Expression 37

[0274]

[0275] Here, r kL This is the phase difference generated based on the angle of arrival when the k-th antenna receives the L-th pulse. Here, the phase difference refers to the phase delay relative to the reference antenna. As an example, refer to... Figure 13 For K=4, with four antennas forming a 2×2 planar array, B k Please provide an explanation.

[0276] Figure 13 This diagram illustrates the case where four antennas 211 form a 2×2 planar array. (See diagram for example.) Figure 13As shown, antennas 211A to 211D form a 2×2 planar array. The angle between the direction of arrival of the received signal (i.e., the straight line connecting the origin and the portable device 100) and the X-axis is set as α, and the angle between the direction of arrival of the received signal and the Y-axis is set as β. Furthermore, antenna 211A is designated as the first antenna (i.e., k = 1), antenna 211B as the second antenna (i.e., k = 2), antenna 211C as the third antenna (i.e., k = 3), and antenna 211D as the fourth antenna (i.e., k = 4). If k = 1 is used as the reference antenna, then B... k They can be represented by the following mathematical expressions.

[0277]

Mathematical Expression 38

[0278] B1 = diag[e] -j0 ,...,e -j0 ]

[0279] =diag[1,...,1]=I (38)

[0280]

Mathematical Expression 39

[0281]

[0282]

Mathematical Expression 40

[0283]

[0284]

Mathematical Expression 41

[0285]

[0286] Furthermore, I is the identity matrix.

[0287] Additionally, n (k) It is the internal noise vector of the k-th antenna.

[0288] In addition, y s (k) y is the signal vector of the k-th antenna. s (k) By B k and s s Expressed as follows.

[0289]

Mathematical Expression 42

[0290]

[0291] In sparse reconstruction, the data vector z (k) It is transformed into a matrix containing extended mode matrices A and y. (k) It takes the form of a matrix product.

[0292]

Mathematical Expression 43

[0293] z (k) =Ay (k) +n (k) (43)

[0294] Here, A is the extended mode matrix mentioned above. Additionally, y (k) This is equivalent to the extended signal vector of the k-th antenna.

[0295] Applications of M-FOCUSS

[0296] If we ignore internal noise and extend the above mathematical formula (43) for multiple wireless communication units 210, then as shown in the following formula, Z is converted into a matrix product containing A and Y.

[0297]

Mathematical Expression 44

[0298] Z = AY (44)

[0299] Z is a sequence of K data vectors z. (k) Z is a matrix. That is, Z is a vector that arranges the CIRs obtained in each of the plurality of wireless communication units 210. Z is also called a data matrix. Z is represented by the following formula.

[0300]

Mathematical Expression 45

[0301]

[0302] Y is a matrix that arranges the extended signal vectors of the multiple wireless communication units 210. Y is also called the extended signal matrix. Y is represented by the following formula.

[0303]

Mathematical Expression 46

[0304]

[0305] Here, y (k) y[i] is the k-th column vector of the extended signal matrix Y. On the other hand, y[i] is the i-th row vector of the extended signal matrix Y. (See reference...) Figure 14 For y (k) The relationship between y[i] and y[i] will be explained in detail.

[0306] Figure 14 It is used to explain y (k) A graph showing the relationship between y[i] and y[i]. For example... Figure 14 As shown, y (k) It is the extended signal vector corresponding to the CIR of the k-th antenna. Specifically, y (1) It is the extended signal vector corresponding to the CIR of the first antenna (i.e., k = 1). (2)This is the extended signal vector corresponding to the CIR of the second antenna (i.e., k=2). (3) This is the extended signal vector corresponding to the CIR of the third antenna (i.e., k = 3). (4) It is the extended signal vector corresponding to the CIR of the fourth antenna (i.e., k = 4). On the other hand, y[i] is a vector of elements corresponding to the i-th delay time in the CIRs of all antennas. For example, y[1] is a vector of elements corresponding to the delay time interval T1 in the four CIRs. y[N] is a vector of elements corresponding to the delay time interval T in the four CIRs. N The vector of the corresponding element.

[0307] The control unit 230 estimates the extended signal matrix Y that minimizes the specified norm. At this time, the control unit 230 estimates the extended signal matrix Y that minimizes the specified norm and is a sparse solution by taking the above mathematical formula (44) as a condition.

[0308] The defined norm is the norm of a vector that arranges the values ​​obtained by performing a defined operation on multiple elements corresponding to the same delay time in the elements constituting the extended signal matrix Y with respect to multiple delay times. That is, the defined norm can also be the norm of a vector that arranges N values ​​obtained by performing a defined operation on multiple elements constituting y[i].

[0309] As an example, the prescribed operation can also obtain the square root of the value obtained by summing the squares of multiple elements corresponding to the same delay time. In this case, the prescribed norm can also be the norm of an N-dimensional vector as shown in the following formula.

[0310]

Mathematical Expression 47

[0311] [||y T [1]||2,...,||y T [N]||2] T (47)

[0312] As another example, the prescribed operation can also be an average.

[0313] Here, the norm refers to the length of the vector. The norm can be the lp norm. The lp norm is expressed by the following formula.

[0314]

Mathematical Expression 48

[0315] ||x|| p =|x1| p +|x2| p +...+|x n | p (48)

[0316] Here, p is a constant greater than 0 and less than 1. However, in mathematical formula (48), 0 is considered to be... 0 =0.

[0317] Hereinafter, the control unit 230 estimates the extended signal matrix Y that minimizes the 1p norm, which is a predetermined norm. The 1p norm is the 1p norm of a vector whose square root is the sum of the squares of the elements that constitute the extended signal matrix Y corresponding to the same delay time, arranged with respect to multiple delay times. Specifically, the control unit 230 estimates the extended signal matrix Y that minimizes the predetermined norm by repeatedly calculating STEP1 to STEP3 as described below.

[0318]

Mathematical Expression 49

[0319] STEP 1:

[0320]

[0321]

Mathematical Expression 50

[0322] STEP2:

[0323]

[0324]

Mathematical Expression 51

[0325] STEP 3:

[0326] Y m =W m Q m (51)

[0327] Here, Y m It is a candidate extended signal matrix Y that minimizes the specified norm. m is the iteration number. y m-1 [N] is a component of Y m-1 A vector consisting of elements in the extended signal matrix corresponding to the i-th delay time. I is the index of the delay time. N is the maximum value of the index i of the delay time.

[0328] Y m The initial value Y0 is given by the following formula.

[0329]

Mathematical Expression 52

[0330] Y0 = A - Z (52)

[0331] Here, A - Y0 is the generalized inverse of the extended mode matrix A. The generalized inverse matrix can be a Moore-Penrose generalized inverse matrix. Therefore, the initial value Y0 is a minimum norm solution of Y. However, the initial value Y0 is not a sparse solution.

[0332] Control unit 230 repeatedly executes STEP1 to STEP3 as described above. As an example, STEP1 to STEP3 can also be repeatedly executed until Y... m The process continues until convergence is achieved. As another example, STEP1 through STEP3 can be executed repeatedly a specified number of times. This allows for the estimation of an extended signal matrix Y that is closer to the true value.

[0333] - Estimation of the reception time of the first arriving wave

[0334] The control unit 230 estimates the reception time of the first wave of arrival based on the extended signal matrix Y, which minimizes a predetermined norm, estimated by M-FOCUSS. In M-FOCUSS, the extended signal matrix Y is estimated under the condition of CIR matching with respect to multiple wireless communication units 210. Therefore, compared with the case of estimating the extended signal vector s under the condition of one CIR matching, the estimation accuracy of the reception time of the first wave of arrival can be improved.

[0335] The estimation method is related to the estimation of the propagation delay time based on the extended signal vector, as explained above. That is, the control unit 230 estimates the delay time corresponding to the non-zero elements in the extended signal matrix that minimizes the predetermined norm as the reception time of the first arriving wave. In particular, the control unit 230 estimates the earliest delay time among the delay times corresponding to the non-zero elements in the extended signal matrix that minimizes the predetermined norm as the reception time of the first arriving wave. Furthermore, the multiple y's constituting the extended signal matrix Y... (k) The common delay time has a non-zero element.

[0336] (5) Regularization parameters

[0337] The above describes an example of a method for estimating the extended signal matrix Y that minimizes the specified norm using M-FOCUSS, and for estimating the reception time of the first arriving wave based on the estimated extended signal matrix Y.

[0338] Here, in order to further improve the estimation accuracy of the extended signal matrix Y, one of the features of the control unit 230 involved in this embodiment is that, in the estimation of the extended signal matrix Y that minimizes the specified norm, iterative calculations using a regularization parameter as a positive small quantity are performed.

[0339] For example, in the case of M-FOCUSS, the control unit 230 may use the following mathematical formula (53) instead of mathematical formula (50) in the above STEP2.

[0340]

Mathematical Expression 53

[0341]

[0342] Here, α is the regularization parameter mentioned above. I is the identity matrix. Furthermore, iterative computation using the regularization parameter can be performed not only in M-FOCUSS but also in FOCUSS. The regularization parameters in FOCUSS and M-FOCUSS are mentioned in the first and second non-patent documents.

[0343] By using a regularization parameter in the iterative computation, Y m Convergence becomes easier, and the expected effect of improving the estimation accuracy of the extended signal matrix Y that minimizes the specified norm is anticipated.

[0344] On the other hand, if the value of the regularization parameter used in the iterative calculation is inappropriate, there is also a possibility that the estimation accuracy of the extended signal matrix Y, which minimizes the specified norm, may be degraded.

[0345] To avoid the situation described above, the control unit 230 in this embodiment can also control the regularization parameter to change to an appropriate value based on the reception status of the received signal during iterative calculation.

[0346] More specifically, one of the features of the control unit 230 in this embodiment is that it divides the iterative calculation into multiple stages for execution, sets the value of the regularization parameter used in the iterative calculation after the second stage in the multiple stages to be greater than the value of the regularization parameter used in the iterative calculation of the previous stage, and changes the value of the regularization parameter in the iterative calculation after the second stage based on the reception status of the second signal.

[0347] The control of the regularization parameters involved in this embodiment will be explained in detail below. Figure 15 This is a flowchart illustrating a control example of the regularization parameters involved in this embodiment.

[0348] In addition, Figure 15 The example illustrates a scenario where the control unit 230 divides the iterative calculation into two stages. Additionally, in... Figure 15 In the following explanations, the iterative calculation of the Nth stage will sometimes be referred to as the Nth estimate. For example, the iterative calculation of the first stage will be referred to as the 1st estimate, and the iterative calculation of the second stage will be referred to as the 2nd estimate.

[0349] exist Figure 15 In one example shown, the control unit 230 sets α_1 as the regularization parameter and γ_1 as the convergence determination value, and performs one estimation (S402).

[0350] The convergence criterion value mentioned above refers to Y used in each of the N estimations. mThe convergence determination benchmark value is set as γ. When the convergence determination value is set as γ, the control unit 230 can also perform convergence determination based on the following mathematical formula (54). However, the following mathematical formula (54) is just an example.

[0351]

Mathematical Expression 54

[0352]

[0353] Next, the control unit 230 estimates the arrival time of the received signal based on the extended signal matrix Y estimated in step S402 (S404). Here, refer to... Figure 16 An example of a method for estimating the arrival time of a received signal is illustrated.

[0354] Figure 16 This is an explanatory diagram illustrating the general outline of the extended signal matrix Y involved in this embodiment. (See diagram below.) Figure 16 As shown, the horizontal (row direction) of the extended signal matrix Y represents the element direction, and the vertical (column direction) represents the time direction. Figure 16 In the diagram, we have an example of an extended signal matrix Y with four elements.

[0355] First, the control unit 230 estimates the extended signal power vector p based on the extended signal matrix Y. Y Extended signal power vector p Y It is a vector obtained by calculating the square of the absolute value of each component of the extended signal matrix Y and averaging the component directions.

[0356] For example, if the element in row i and column j of the extended signal matrix Y based on the above mathematical formula (46) is set as Y(i,j), then the control unit 230 can also estimate the extended signal power vector p based on the following mathematical formula (55). Y .

[0357]

Mathematical Expression 55

[0358]

[0359]

[0360] Furthermore, the control unit 230 can also extend the signal power vector p Y The components exceeding a specified value are presumed to be the arrival time of the received signal. Furthermore, the specified value can also be set via the "extended signal power vector p". Y The value is obtained by multiplying the maximum value by 0.5, but it is not limited to this example.

[0361] The above illustrates one example involved in estimating the arrival time of a received signal. However, the method for estimating the arrival time of a received signal is not limited to the example described above. Furthermore, refer to... Figure 15 The following is a follow-up example of the control of regularization parameters involved in this embodiment.

[0362] After estimating the arrival time of the received signal (S404), the control unit 230 determines whether the arrival interval of the received signal is below the threshold θ (S406).

[0363] The arrival interval of the aforementioned received signals can be, for example, the interval between the first arriving wave and the second arriving wave. In this case, the threshold θ can be set to 0.5 ns or 2 ns, etc.

[0364] However, the above is just one example. The arrival interval of the received signal can also be the widest interval between arrival waves, the narrowest interval, the average of multiple intervals, or the median value, etc. Furthermore, this can be achieved simply by appropriately designing the threshold θ.

[0365] For example, the control unit 230 may also extend the signal power vector p in the estimated arrival time of the received signal. Y The arrival interval of the received signal is the difference between the arrival time of the largest received signal and the arrival time of the other received signal closest to that arrival time.

[0366] exist Figure 15 In one example shown, if the arrival interval of the received signal is greater than the threshold θ (S406: No), the control unit 230 sets α_2 as the regularization parameter and γ_2 as the convergence determination value, and performs two estimations (S408).

[0367] On the other hand, when the arrival interval of the received signal is less than or equal to the threshold θ (S406: Yes), the control unit 230 sets α_3 as the regularization parameter and γ_3 as the convergence determination value, and performs two estimations (S4010).

[0368] Here, one of the features of the control unit 230 in this embodiment is that the value of the regularization parameter used in the Nth estimation is changed to a value greater than or equal to the value of the regularization parameter used in the N-1th estimation as the previous stage.

[0369] Furthermore, one feature of the control unit 230 in this embodiment is that, after two estimations, the wider the arrival interval of the received signal, the smaller the value of the regularization parameter becomes.

[0370] exist Figure 15 In one example shown, the control unit 230 can also perform control such that the value of the regularization parameter satisfies α_3>α_2≧α_1.

[0371] For example, the value of α_1 in the first estimation is 10. －4 In this case, the control unit 230 can set the value of α_2 to 10. －3 Set the value of α_3 to 10. －2 .

[0372] In addition, Figure 15 In one example shown, a single threshold θ is set in the determination of step S406, but the number of thresholds used in this determination can be more than two. That is, the branches based on the arrival interval of the received signal can also be more than three. Even in this case, it is sufficient to satisfy the two features listed above related to the control of the regularization parameter.

[0373] Furthermore, one feature of the control unit 230 in this embodiment is that, regarding the convergence determination value used in the convergence determination of the iterative calculation, the convergence determination value used after the second estimation is smaller than the convergence determination value used in the N-1 estimations that serve as the previous stage.

[0374] exist Figure 15 In one example shown, the control unit 230 can control the convergence determination value to satisfy γ_1 > γ_2.

[0375] For example, in the first estimation, γ_1 is 10. －1 In this case, the control unit 230 can set γ_2 to 10. －6 .

[0376] Furthermore, the convergence determination value can be the same value in the same stage of calculation and is independent of the branch at step S406, or it can be a different value depending on the branch.

[0377] After two estimations (S408 or S410) using the regularization parameters and convergence determination values ​​set as described above, the control unit 230 estimates the arrival time and arrival angle of the first arriving wave based on the extended signal matrix Y' obtained in the two estimations (S412).

[0378] In addition, Figure 15 The example shown illustrates a case where the control unit 230 divides the iterative calculation into two stages (i.e., a first estimation and a second estimation), but the number of stages related to the iterative calculation can be three or more. Even in this case, it is sufficient to set the value of the regularization parameter used in the Nth estimation to be greater than the value of the regularization parameter used in the N-1th estimation, which is the previous stage, and to set the convergence determination value used in the Nth estimation to be less than the convergence determination value used in the N-1th estimation, which is the previous stage.

[0379] Next, the iterative calculation process using regularization parameters and convergence determination values ​​involved in this embodiment will be explained in more detail.

[0380] Figure 17 This is a flowchart illustrating an example of the iterative calculation process using regularization parameters and convergence criteria involved in this embodiment. Furthermore, Figure 17 An example is shown where the control unit 230 divides the iterative calculation into two stages (i.e., a first estimation and a second estimation).

[0381] exist Figure 17 In one example shown, the wireless communication unit 210 first receives the second signal (S500).

[0382] Next, the control unit 230 calculates the initial value in FOCUSS or M-FOCUSS (S501). In step S501, 0 is substituted into the variable m, which represents the iteration number.

[0383] Next, the control unit 230 performs a estimation consisting of steps S510 to S514.

[0384] In the first estimation, the control unit 230 sequentially increments m (S510) and performs calculations equivalent to STEP1 to STEP3 (S511 to S513) as described above. In the calculation equivalent to STEP2 as described above in S512, a regularization parameter is used.

[0385] Next, the control unit 230 uses the convergence determination value to perform a convergence determination related to the first estimation (S514).

[0386] If the control unit 230 determines that the convergence condition is not met (S514: No), it returns to step S510.

[0387] On the other hand, if the control unit 230 determines that the convergence condition is met (S514: Yes), it changes the regularization parameter and the convergence determination value (S515).

[0388] Next, the control unit 230 performs two estimations consisting of steps S520 to S524.

[0389] In the two estimations, the control unit 230 sequentially increments m (S520) and performs calculations equivalent to STEP1 to 3 described above (S521 to S523). In the calculation equivalent to STEP2 described above in S522, the regularization parameter modified in step S515 is used.

[0390] Next, the control unit 230 uses the convergence determination value changed in step S515 to perform a convergence determination related to the second estimation (S524).

[0391] If the control unit 230 determines that the convergence condition is not met (S524: No), it returns to step S520.

[0392] On the other hand, if the control unit 230 determines that the convergence condition is met (S524: Yes), it estimates the arrival time and arrival angle of the first arriving wave based on the extended signal matrix Y' obtained in the two estimations (S525).

[0393] The above example illustrates the iterative calculation process using regularization parameters and convergence criteria involved in this implementation.

[0394] In addition, Figure 17 The example illustrates the case where the number of iterations increases continuously between the first and second assumptions, but the number of iterations can also be reset to 0 between the first and second assumptions.

[0395] In addition, Figure 17 The example illustrates the case where iterative calculations are performed until the convergence condition is met, but iterative calculations can also end after a specified number of executions.

[0396] Furthermore, in the above example, the signal reception status used in the setting of the regularization parameter value after two estimations is used as an example of the arrival interval of the received signal. However, the signal reception status used in the setting of the regularization parameter value is not limited to the arrival interval of the received signal.

[0397] For example, the control unit 230 according to this embodiment may also change the value of the regularization parameter based on the signal power of the received signal after two estimations. More specifically, the control unit 230 may also be characterized in that the greater the signal power of the received signal after two estimations, the smaller the value of the regularization parameter.

[0398] The received power here could also be, for example, the extended signal power vector p. Y The maximum value. Alternatively, the received signal can also be the extended signal power vector p. Y The sum or average of multiple components with larger values. For example, the extended signal power vector p can also be used. Y The sum of the components above the specified value is taken as the received power.

[0399] Furthermore, the control unit 230 of this embodiment may also change the value of the regularization parameter based on the noise power of the received signal after two estimations. More specifically, the control unit 230 may also be characterized in that, after two estimations, the lower the noise power of the received signal, the smaller the value of the regularization parameter.

[0400] Furthermore, in setting the regularization parameter, when using the noise power of the received signal, the control unit 230 can estimate the noise power of the received signal after STEP3 in the iterative calculation by including STEP4. That is, the control unit 230 can also... Figure 17 After step S513 and step S523, STEP4 is added based on the following mathematical formula (56) to estimate the noise power of the received signal.

[0401]

Mathematical Expression 56

[0402] STEP4:

[0403]

[0404] In this mathematical expression (56), σ 2 It is noise power, ||·|| F It is the Frobenius norm, and Tr[·] is the trace of the matrix (the sum of its diagonal components).

[0405] Furthermore, the control unit 230 of this embodiment can also change the value of the regularization parameter based on the SNR (Signal to Noise Ratio) of the received signal after two estimations. More specifically, one feature of the control unit 230 is that after two estimations, the larger the SNR value of the received signal, the smaller the value of the regularization parameter. This allows setting a regularization parameter suitable for the SNR condition. Furthermore, SNR is an example of signal to noise ratio.

[0406] For example, when the received power is set to SP, the control unit 230 can also calculate the SNR according to the following mathematical formula (57).

[0407]

Mathematical Expression 57

[0408]

[0409] Furthermore, the control unit 230 can also set the value of the regularization parameter by combining the reception conditions of the various signals mentioned above after two estimations. As an example, the control of the regularization parameter based on the SNR of the received signal and the arrival interval of the received signal will be explained in detail.

[0410] Figure 18 This is a flowchart illustrating a control example of the regularization parameters involved in the second embodiment. Furthermore, in Figure 18 In, with Figure 15 Similarly, an example is shown where the control unit 230 divides the iterative calculation into two stages. Furthermore, in Figure 18 In the text, there exists omission and Figure 15Cases of repeated explanations.

[0411] First of all, Figure 18 In one example shown, the control unit 230 sets the regularization parameter to α_1 and the convergence determination value to γ_1, based on the input extended signal matrix Y0 and the noise power σ. 2(0) A first estimation is performed to estimate the extended signal matrix Y (S602). Hereinafter, α_1 will be set to 10. -4 Set γ_1 to 10 -1 , will σ 2(0) The case of setting it to 0 is used as the main example, but it is not limited to this case.

[0412] Next, the control unit 230 estimates the arrival time of the received signal based on the extended signal matrix Y estimated in step S602 (S604).

[0413] Next, the control unit 230, based on the extended signal matrix Y and noise power σ estimated in step S602, 2(a) This is used to estimate the SNR of the received signal (S606).

[0414] Furthermore, the control unit 230 determines whether the SNR of the received signal is below the specified value Φ (S608). Figure 18 In one example shown, if the SNR of the received signal is greater than the specified value Φ (S608: No), the control unit 230 sets the regularization parameter to α_2 and the convergence determination value to γ_2, and performs two estimations (S610). Alternatively, the specified value Φ can be set to, for example, 20 dB.

[0415] If the SNR of the received signal is below a specified value Φ (S608: Yes), the control unit 230 determines whether the arrival interval of the received signal is below a threshold θ (S612).

[0416] exist Figure 18 In one example shown, if the arrival interval of the received signal is greater than the threshold θ (S612: No), the control unit 230 sets the regularization parameter to α_3 and the convergence determination value to γ_2, and performs two estimations (S614).

[0417] On the other hand, when the arrival interval of the received signal is less than or equal to the threshold θ (S612: Yes), the control unit 230 sets the regularization parameter to α_4 and the convergence determination value to γ_2, and performs two estimations (S616).

[0418] Here, as described above, one feature of the control unit 230 according to this embodiment is that, after two estimations, the larger the SNR value of the received signal, the smaller the value of the regularization parameter. Furthermore, another feature of the control unit 230 is that, after two estimations, the wider the arrival interval of the received signal, the smaller the value of the regularization parameter.

[0419] By employing these two features, in Figure 18 In the example shown, the control unit 230 can also perform control such that the value of the regularization parameter satisfies α_4 > α_3 > α_2 ≥ α_1. Furthermore, α_2 here is an example of the first value.

[0420] For example, the value of α_1 in the first estimation is 10. -4 In this case, the control unit 230 can also set the value of α_2 to 10. -4 Set the value of α_3 to 10. -3 Set the value of α_4 to 10. -2 .

[0421] In addition, Figure 18 In one example, steps S608 and S610 each set a single predetermined value Φ and a threshold θ, but the number of predetermined values ​​and thresholds used in this determination can be two or more. That is, in the above process, the regularization parameters are branched into three modes (α_2, α_3, α_4) based on the SNR of the received signal and the arrival interval of the received signal, but they can also be branched into four modes or more.

[0422] In addition, Figure 18 In one example shown, in step S608, the SNR based on the received signal is determined, and in the subsequent step S610, the arrival interval based on the received signal is determined. However, it is also possible to determine the arrival interval based on the received signal first, and then determine the SNR based on the received signal.

[0423] Furthermore, one feature of the control unit 230 in this embodiment is that the convergence determination value used in the convergence determination of the iterative calculation is such that the convergence determination value used after the second estimation is smaller than the convergence determination value used in the previous stage, i.e., the N-1th estimation.

[0424] exist Figure 15 In one example shown, the control unit 230 can also control the convergence determination value to satisfy γ_1 > γ_2. For example, γ_1 in the first estimation is 10. -1 In this case, the control unit 230 can also set γ_2 to 10. -6 .

[0425] Furthermore, the convergence determination value can be the same value in the same stage of calculation and is independent of the branches at steps S608 and S612, or it can be different values ​​depending on the branch.

[0426] After two estimations (S610, S614 or S616) using the regularization parameters and convergence determination values ​​set as described above, the control unit 230 estimates the arrival time and arrival angle of the first arriving wave based on the extended signal matrix Y' obtained in the two estimations (S618).

[0427] The iterative calculation using regularization parameters described above has been explained in this embodiment. Based on this iterative calculation, the estimation accuracy of the extended signal matrix that minimizes the specified norm can be improved, thereby improving the accuracy of ranging value calculation and position estimation.

[0428] (6) Threshold processing

[0429] Thresholding can also be performed in M-FOCUSS. Thresholding here refers to setting elements below a second threshold to 0. For example, the control unit 230 can also set the weight matrix W in the mathematical formula (49) of STEP1 above. m Elements below the second threshold in the diagonal components are set to zero. The second threshold can also be based on the weight matrix W. m The value of the diagonal component is used to set the weight matrix W. As an example, the control unit 230 can also set the weight matrix W based on the maximum value of the diagonal component. m The diagonal components are set to zero if their ratio to the maximum value is below the second threshold.

[0430] Based on the threshold processing described above, the weight matrix W is constructed. m At that time, the extended signal matrix Y will be... m Elements in the matrix that have values ​​less than the second threshold are considered noise rather than signals and are converted to zero. This allows the extended signal matrix Y to... m It converges faster. In addition, since non-zero elements are reduced, sparse solutions can be easily obtained.

[0431] <4.2. Estimation of Position Parameters>

[0432] The control unit 230 estimates the position parameters based on the first arrival wave detected by the processing described above.

[0433] - Distance measurement processing

[0434] The control unit 230 estimates the distance R between the portable device 100 and the communication unit 200 based on the reception time of the first arriving wave estimated through the processing described above. Regarding the method for estimating the distance R, please refer to... Figure 7 As explained above.

[0435] In detail, the communication unit 200 calculates the CIR for the first ranging signal and performs sparse reconstruction and M-FOCUSS. Then, the communication unit 200 takes the earliest delay time interval corresponding to the delay time interval of the non-zero element contained in the extended signal matrix Y estimated by M-FOCUSS as the reception time of the first arrival wave of the first ranging signal, and the measurement time INT3.

[0436] Similarly, the communication unit 200 calculates the CIR for the third ranging signal and performs sparse reconstruction and M-FOCUSS. Then, the communication unit 200 takes the earliest delay time interval corresponding to the delay time interval of the non-zero element contained in the extended signal matrix Y estimated by M-FOCUSS as the reception time of the first arrival wave of the third ranging signal, and the measurement time INT4.

[0437] Then, the control unit 230 estimates the propagation delay time and the distance R based on times T1 to T4. As explained above, the reception time of the first arriving wave can be estimated with high accuracy using M-FOCUSS. Therefore, ranging accuracy can be improved.

[0438] - Angle estimation processing

[0439] The communication unit 200 estimates angles α and β based on the phase of the reception time of the first arriving wave, which is estimated through the processing described above. For the method of estimating angles α and β, please refer to... Figure 8 As explained above.

[0440] More specifically, the control unit 230 estimates angles α and β based on the phases of the non-zero elements contained in the extended signal matrix Y, which are estimated through the processing described above. In particular, the control unit 230 estimates angles α and β based on the phase of the element with the earliest corresponding delay time among one or more non-zero elements contained in the extended signal matrix Y. For example, when processing the extended signal matrix Y... Figure 13 In the extended signal matrix Y estimated by applying M-FOCUSS to the CIR obtained from the antenna structure shown, the earliest non-zero element is at the delay time T. i This occurs. In this case, the antenna array phase difference Pd AC The following formula can be used to calculate it.

[0441]

Mathematical Expression 58

[0442] Pd AC =Pd A -Pd C

[0443] =angle(Y(i,1))-angle(Y(i,3))...(58)

[0444] Alternatively, the phase difference Pd of the antenna array AC It can also be calculated using the following formula.

[0445]

Mathematical Expression 59

[0446] Pd AC =Pd A -Pd C

[0447] = angle(Y(i, 1) × Y(i, 3) * ...(59)

[0448] Furthermore, angle() is a function that calculates the phase angle of a complex number. Y(i,k) is the element in the i-th row and k-th column of the extended signal matrix Y.

[0449] The phase difference for other antenna arrays is calculated in the same way as above, including angles α and β.

[0450] As explained above, the reception time of the first wave of arrival can be estimated with high accuracy using M-FOCUSS. Angle estimation accuracy can also be improved by estimating the angle based on the phase of the elements constituting the extended signal matrix Y that correspond to the accurately estimated reception time of the first wave of arrival.

[0451] <4.3. Processing Flow>

[0452] Figure 19 This is a flowchart illustrating an example of the position parameter estimation process performed by the communication unit 200 according to this embodiment.

[0453] like Figure 19 As shown, firstly, the control unit 230 calculates the CIR of each antenna (step S302). Next, the control unit 230 converts the data matrix composed of the CIRs of each antenna into a matrix product containing the extended mode matrix and the extended signal matrix through sparse reconstruction (step S304). Then, the control unit 230 estimates the extended signal matrix that minimizes a specified norm using M-FOCUSS (step S306). Finally, the control unit 230 estimates the position parameters based on the estimated extended signal matrix (step S308).

[0454] <4.4. Application Targets of M-FOCUSS>

[0455] As explained above, the transmitting side can transmit a signal comprising multiple preambles containing one or more preamble symbols as a transmit signal. In this case, the receiving side can calculate the CIR of each preamble symbol by obtaining the correlation between each portion of the received signal corresponding to the multiple preamble symbols and the preamble symbol at predetermined time intervals after the transmitting side transmits the transmit signal.

[0456] M-FOCUSS can also be applied to the accumulated CIR of each preamble symbol. Alternatively, M-FOCUSS can also be applied to the CIR of each preamble symbol.

[0457] Alternatively, the CIR can be calculated for each pulse. In this case, M-FOCUSS can be applied to the accumulated CIR obtained by accumulating the CIR for each pulse, or it can be applied to the CIR for each pulse.

[0458] Alternatively, the CIR can be calculated for the entire preamble. In this case, M-FOCUSS can also be applied to the CIR calculated for the entire preamble.

[0459] The same result can be obtained in either method.

[0460] <4.5. Application Scope of M-FOCUSS>

[0461] M-FOCUSS can be applied to the entire CIR system.

[0462] On the other hand, M-FOCUSS can also be applied to a portion of the time axis in a CIR. Therefore, compared to applying M-FOCUSS to the entire CIR, the computational load can be reduced.

[0463] In particular, if the purpose is to detect the first wave of arrival, it is preferable to apply M-FOCUSS within a portion of the time frame near the reception time of the first wave of arrival in the CIR. Strong correlation is obtained only during the delay time when the pulse arrangement of the transmitted signal and the pulse arrangement of the received signal are perfectly aligned; correlation is low in other parts. Therefore, even when applying M-FOCUSS within a portion of the time frame near the reception time of the first wave of arrival in the CIR, the detection accuracy of the first wave of arrival can be maintained.

[0464] In this way, by applying M-FOCUSS to a portion of the time of receipt of the first arrival wave in the CIR, the detection accuracy can be maintained and the computational load can be reduced compared to applying M-FOCUSS to the entire CIR.

[0465] <<5. Supplement>>

[0466] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to these examples. Those skilled in the art will be able to conceive of various modifications or alterations within the scope of the technical concept described in the claims, and these should also be understood to fall within the technical scope of the present invention.

[0467] For example, in the above embodiments, the example of CIR being the result of correlation calculation has been described, but the present invention is not limited to the above example. As an example, CIR can also be the received signal (a complex number having IQ components) itself. The CIR value can be the received signal itself as a complex number having IQ components, or it can be the amplitude or phase of the received signal, or it can be the power of the sum of the squares of the I and Q components of the received signal (or the square of the amplitude). In this case, the receiving side detects the first wave of arrival based on the received signal. For example, the receiving side can also use the case where the amplitude or power of the received wireless signal begins to exceed a predetermined threshold as a predetermined detection reference for detecting the first wave of arrival. In this case, the receiving side can also detect the signal (more specifically, the sampling point) in the received signal whose amplitude or received power begins to exceed the predetermined threshold as the first wave of arrival.

[0468] For example, in the above embodiment, examples of CIR calculation, first wave of arrival detection, and position parameter estimation performed by the control unit 230 have been described, but the present invention is not limited to the above examples. At least one of these processes can also be performed by the wireless communication unit 210. For example, CIR calculation and first wave of arrival detection can be performed by each of the plurality of wireless communication units 210 based on the received signal received by each of them. In addition, the position parameter estimation can also be performed by, for example, the wireless communication unit 210 which functions as a host.

[0469] For example, in the above embodiment, an example of calculating angles α and β based on the phase difference of the antenna array in the antenna pair has been described, but the present invention is not limited to the above example. As an example, the communication unit 200 may also calculate angles α and β by using beamforming with multiple antennas 211. In this case, the communication unit 200 scans the main lobes of the multiple antennas 211 in all directions, determines that the portable device 100 is located in the direction with the highest received power, and calculates angles α and β based on that direction.

[0470] For example, in the above embodiments, as referred to Figure 3As explained, an example of a local coordinate system having coordinate axes parallel to the axis connecting the antenna pairs has been described, but the present invention is not limited to the above example. For example, the local coordinate system may also have coordinate axes that are not parallel to the axis connecting the antenna pairs. In addition, the origin is not limited to the center of the plurality of antennas 211. The local coordinate system involved in this embodiment may also be arbitrarily set based on the configuration of the plurality of antennas 211 of the communication unit 200.

[0471] For example, in the above embodiment, an example of four antennas 211 forming a 2×2 planar array was described, but the present invention is not limited to the above example. The number of antennas 211 is not limited to four, and their configuration shape is not limited to a planar array. For example, multiple antennas 211 can also be configured as a linear array. A linear array refers to multiple antennas 211 arranged on the same line. As an example, refer to... Figure 20 An example of a linear array consisting of four antennas 211 is given.

[0472] Figure 20 This diagram illustrates the case where four antennas 211 form a linear array. (Example) Figure 20 As shown, antennas 211A to 211D form a linear array. The axes aligning antennas 211A to 211D are defined as coordinate axes, and the angle between the coordinate axes and the direction of arrival of the received signal is defined as θ. Furthermore, antenna 211A is designated as the first antenna (i.e., k = 1), antenna 211B as the second antenna (i.e., k = 2), antenna 211C as the third antenna (i.e., k = 3), and antenna 211D as the fourth antenna (i.e., k = 4). If k = 1 is used as the reference antenna, then B... k They can be represented by the following mathematical expressions.

[0473]

Mathematical Expression 60

[0474]

[0475] For example, in the above embodiment, an example of applying M-FOCUSS to multiple CIRs in multiple wireless communication units 210 was described, but the present invention is not limited to the above example. M-FOCUSS can also be applied to multiple CIRs obtained from a single wireless communication unit 210. In this case, the control unit 230 takes a matrix arranging the multiple CIRs obtained from the single wireless communication unit 210 as a data matrix, and converts the data matrix into a matrix product containing an extended mode matrix and an extended signal matrix in which extended signal vectors are arranged for the multiple CIRs. Then, the control unit 230 estimates the reception time of the first wave of arrival by applying M-FOCUSS to the conversion result. In this example, as in the above embodiment, the estimation accuracy of the reception time of the first wave of arrival can be improved.

[0476] As an example, a wireless communication unit 210 may also receive a signal containing multiple preambles from the portable device 100. In this case, the control unit 230 calculates a CIR for a preamble received by the wireless communication unit 210. Then, the control unit 230 converts the multiple CIRs calculated based on the multiple preambles into a form containing the matrix product described above, and applies M-FOCUSS.

[0477] As another example, a wireless communication unit 210 can also receive signals from the portable device more than 100 times. Here, a signal refers to a signal containing more than one preamble. In this case, the control unit 230 calculates a CIR for a signal received by the wireless communication unit 210. Then, the control unit 230 converts the multiple CIRs calculated based on the multiple received signals into a form containing the above matrix product and applies M-FOCUSS.

[0478] Furthermore, in the case of multiple CIR applications M-FOCUSS obtained from a single wireless communication unit 210, B k It can be expressed as follows.

[0479]

Mathematical Expression 61

[0480]

[0481] On the other hand, the control unit 230 can also apply a CIR application FOCUSS obtained from a wireless communication unit 210.

[0482] For example, in the above embodiment, the example described is of the portable device 100 being the authenticator and the communication unit 200 being the authenticator, but the present invention is not limited to the above example. The functions of the portable device 100 and the communication unit 200 can also be reversed. For example, the portable device 100 can also determine position parameters. In addition, the functions of the portable device 100 and the communication unit 200 can be dynamically exchanged. Furthermore, the determination and authentication of position parameters can be performed between the communication units 200.

[0483] For example, the above embodiments illustrate the application of the present invention to a smart key system, but the present invention is not limited to these examples. The present invention can be applied to any system that estimates location parameters and performs authentication by transmitting and receiving signals. For example, the present invention can be applied to paired devices comprising any two devices, such as laptops, vehicles, smartphones, drones, homes, and home appliances. In this case, one of the paired devices acts as the authenticator, and the other acts as the authenticated party. Furthermore, the paired device can comprise two devices of the same type or two devices of different types. Additionally, the present invention can also be applied to wireless LAN (Local Area Network) routers to determine the location of smartphones.

[0484] For example, the above embodiments illustrate the use of UWB as a wireless communication standard, but the present invention is not limited to these examples. For instance, infrared standards can also be used as wireless communication standards.

[0485] Furthermore, the series of processes performed by the various devices described in this specification can also be implemented by programs stored on non-transitory computer-readable storage media. Each program is loaded into RAM, for example, when executed by a computer, and then executed by a processor such as a CPU. The aforementioned storage media include, for example, magnetic disks, optical disks, magneto-optical disks, and flash memory. Alternatively, the aforementioned programs may be distributed without using a storage medium, for example, via a network.

[0486] Furthermore, the processes illustrated in the flowcharts in this specification may not necessarily be executed in the order shown. Several processing steps may also be executed in parallel. Additionally, additional processing steps may be used, or some processing steps may be omitted.

Claims

1. A communication device, characterized in that, have: The wireless communication unit receives signals wirelessly from other communication devices; and Control Department The control unit performs the following processing: The correlation between the signal received by the wireless communication unit at predetermined intervals and the signal corresponding to the first signal (i.e., the second signal) and the first signal is obtained when the other communication device sends a signal containing a pulse as the first signal. The data matrix is ​​transformed into a matrix product containing the extended mode matrix and the extended signal matrix. The data matrix is ​​a matrix that arranges one or more correlation calculation results obtained at predetermined time intervals between the second signal and the first signal in the wireless communication unit. The extended mode matrix is ​​a matrix composed of multiple elements representing the results of the correlation calculations assumed to occur at each of a plurality of set times when a signal is received. The extended signal matrix is ​​a matrix that arranges extended signal vectors for one or more of the related operation results. The extended signal vector is a vector composed of multiple elements representing the presence or absence of a signal at each set time in the wireless communication unit, as well as the amplitude and phase of the signal. The extended signal matrix that minimizes the specified norm is estimated by using iterative calculation of a regularization parameter that is a small positive quantity. The reception time of the second signal is estimated based on the extended signal matrix that minimizes the specified norm. The control unit divides the iterative calculation into multiple stages for execution, sets the value of the regularization parameter used in the iterative calculation after the second stage to be greater than the value of the regularization parameter used in the iterative calculation of the previous stage, and changes the value of the regularization parameter in the iterative calculation after the second stage based on the reception status of the second signal.

2. The communication device according to claim 1, characterized in that, In the iterative calculations following the second stage, the control unit changes the value of the regularization parameter based on the interval of the arriving waves involved in the second signal.

3. The communication device according to claim 2, characterized in that, In the iterative calculations following the second stage, the wider the interval of the arriving waves involved in the second signal, the smaller the value of the regularization parameter becomes.

4. The communication device according to claim 1, characterized in that, In the iterative calculations following the second stage, the control unit sets the value of the regularization parameter based on the signal power involved in the second signal.

5. The communication device according to claim 1, characterized in that, In the iterative calculations following the second stage, the control unit sets the value of the regularization parameter based on the noise power involved in the second signal.

6. The communication device according to claim 5, characterized in that, In the iterative calculations following the second stage, the control unit sets the value of the regularization parameter based on the signal-to-noise ratio involved in the second signal.

7. The communication device according to claim 6, characterized in that, In the iterative calculations after the second stage, the larger the signal-to-noise ratio of the second signal, the smaller the value of the regularization parameter.

8. The communication device according to claim 6, characterized in that, When the signal-to-noise ratio (SNR) of the second signal is greater than a predetermined value, the control unit sets the value of the regularization parameter to a first value; when the SNR of the second signal is less than the predetermined value, the control unit sets the value of the regularization parameter to a value greater than the first value.

9. The communication device according to claim 1, characterized in that, Regarding the convergence determination value used for the iterative calculation, the control unit makes the convergence determination value used in the iterative calculation after the second stage smaller than the convergence determination value used in the iterative calculation of the previous stage.

10. The communication device according to claim 1, characterized in that, The control unit presumes the extended signal matrix that minimizes the norm of the prescribed norm, which is the norm of a vector formed by arranging the values ​​of multiple elements constituting the extended signal matrix corresponding to the same set time for a plurality of set times.

11. The communication device according to claim 10, characterized in that, The control unit presumes the extended signal matrix that minimizes the norm of the predetermined norm, which is the norm of a vector formed by arranging the square roots of the values ​​obtained by summing the squares of the elements that constitute the extended signal matrix corresponding to the same set time for a plurality of set times.

12. The communication device according to claim 6, characterized in that, The control unit estimates the extended signal matrix that minimizes the specified norm by repeatedly calculating mathematical formulas (1), (2), and (3) in the iterative calculation. 【Mathematical Formula 1】 【Mathematical Formula 2】 【Mathematical Expression 3】 Y m =W m Q m … (3) Here, Y m It is a candidate for the extended signal matrix that minimizes the specified norm, where m is the iteration number and y is the maximum number of iterations. m－1 [i] is a component of Y m－1 The vector is composed of the elements in the extended signal matrix corresponding to the i-th set time, N is the maximum value of the set time index i, P is a constant between 0 and 1, A is the extended mode matrix, Z is the data matrix, α is the regularization parameter, I is the identity matrix, and Y... m The initial value Y0 is given by the following formula, 【Mathematical Expression 4】 Y0=A － With…(4) Here, A － It is the generalized inverse matrix of A.

13. The communication device according to any one of claims 5 to 8, characterized in that, The control unit calculates the noise power involved in the second signal using mathematical formula (5) in the iterative calculation. 【Mathematical Expression 5】 Here, σ 2(m) The noise power is M, the number of time samples is K, and the number of elements is ||·||. F It is the Frobenius norm, and Tr[·] is the trace of the matrix, that is, the sum of the diagonal components.

14. An information processing method, characterized in that, include: The correlation between the signal received by the wireless communication unit at regular intervals and the signal corresponding to the first signal (i.e., the second signal) and the first signal is obtained when other communication devices send a signal containing pulses as the first signal. The data matrix is ​​transformed into a matrix product containing the extended mode matrix and the extended signal matrix. The data matrix is ​​a matrix that arranges one or more correlation calculation results obtained at predetermined time intervals between the second signal and the first signal in the wireless communication unit. The extended mode matrix is ​​a matrix composed of multiple elements representing the results of the correlation calculations assumed to occur at each of a plurality of set times when a signal is received. The extended signal matrix is ​​a matrix that arranges extended signal vectors for one or more of the related operation results. The extended signal vector is a vector composed of multiple elements representing the presence or absence of a signal at each set time in the wireless communication unit, as well as the amplitude and phase of the signal. The extended signal matrix that minimizes the specified norm is estimated by using iterative calculation of a regularization parameter that is a small positive quantity. The reception time of the second signal is estimated based on the extended signal matrix that minimizes the specified norm. The estimation further includes: dividing the iterative calculation into multiple stages for execution, setting the value of the regularization parameter used in the iterative calculation after the second stage of the multiple stages to be greater than the value of the regularization parameter used in the iterative calculation of the previous stage, and changing the value of the regularization parameter in the iterative calculation after the second stage based on the reception status of the second signal.

15. A computer-readable storage medium storing a program, characterized in that, The program enables the computer to function as a control unit. The control unit performs the following processing: The correlation between the signal received by the wireless communication unit at regular intervals and the signal corresponding to the first signal (i.e., the second signal) and the first signal is obtained when other communication devices send a signal containing pulses as the first signal. The data matrix is ​​transformed into a matrix product containing the extended mode matrix and the extended signal matrix. The data matrix is ​​a matrix that arranges one or more correlation calculation results obtained at predetermined time intervals between the second signal and the first signal in the wireless communication unit. The extended mode matrix is ​​a matrix composed of multiple elements representing the results of the correlation calculations assumed to occur at each of a plurality of set times when a signal is received. The extended signal matrix is ​​a matrix that arranges extended signal vectors for one or more of the related operation results. The extended signal vector is a vector composed of multiple elements representing the presence or absence of a signal at each set time in the wireless communication unit, as well as the amplitude and phase of the signal. The extended signal matrix that minimizes the specified norm is estimated by using iterative calculation of a regularization parameter that is a small positive quantity. The reception time of the second signal is estimated based on the extended signal matrix that minimizes the specified norm. The program causes the control unit to divide the iterative calculation into multiple stages for execution, sets the value of the regularization parameter used in the iterative calculation after the second stage to be greater than the value of the regularization parameter used in the iterative calculation of the previous stage, and changes the value of the regularization parameter in the iterative calculation after the second stage based on the reception status of the second signal.

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