Communication device, information processing method, and program

Abstract

A communication apparatus, an information processing method, and a program are provided. The present invention improves the estimation accuracy of the distance and angle between a plurality of devices. A communication device is provided with: a wireless communication unit; and a control unit that acquires a correlation between a first signal and a second signal, which is a signal corresponding to the first signal received by the wireless communication unit, at each predetermined time when a signal including a pulse is transmitted as the first signal by another communication device. Conversion of correlation operation results, which are results obtained by taking correlation between second signals and first signals per predetermined time, into a bin pattern matrix, which is a matrix comprising a plurality of elements representing correlation operation results when signals are assumed to be received under respective conditions of a plurality of set times and set angles, and a bin pattern matrix comprising a plurality of elements representing correlation operation results when signals are assumed to be received under respective conditions of a plurality of set times and set angles. The reception time and the arrival angle of the second signal are estimated on the basis of the set time and the set angle corresponding to each of the plurality of elements of the extended signal vector in the form of a matrix product of the extended signal vector, which is a vector comprising the plurality of elements of the amplitude and the phase of the signal, and the presence or absence of the signal of the set time and the set angle. The interval of the set time is shorter than the predetermined time.

Description

Technical Field

[0001] The present invention relates to a communication device, an information processing method and a program. Background Art

[0002] In recent years, a location determination technology has been developed in which one device determines the location of another device based on the result of sending and receiving signals between the devices. As an example of the location determination technology, the following patent document 1 discloses a technology in which a UWB (Ultra-Wide Band) is used for wireless communication so that a UWB receiver determines the incident angle of a wireless signal from a UWB transmitter.

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

[0004] However, in the technology described in Patent Document 1, although the incident angle of the wireless signal is determined, there is room for further improvement in terms of improving the accuracy of measuring the distance and angle 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 desired to further improve the measurement accuracy of the distance and angle between these devices. Summary of the invention

[0006] Therefore, the present invention has been made in view of the above-mentioned problems, and an object of the present invention is to provide a structure capable of improving the estimation accuracy of the distance and angle between a plurality of devices.

[0007] In order to solve the above-mentioned problems, according to a certain aspect of the present invention, a communication device is provided, comprising: a wireless communication unit, which receives a signal from another communication device in a wireless manner; and a control unit, which takes the correlation between the second signal and the first signal, which is a signal corresponding to the first signal and received by the wireless communication unit when the other communication device sends a signal containing a pulse as the first signal, at each specified time, and the first signal, and converts the result obtained by taking the correlation between the second signal and the first signal at each specified time, that is, the correlation operation result, into a form of a matrix consisting of a plurality of elements representing the correlation operation result when the signal is assumed to be received under respective conditions of a plurality of set times and set angles, that is, a bin pattern matrix, and a vector consisting of a plurality of elements representing the presence or absence of a signal at each of the set times and the set angles and the amplitude and phase of the signal, that is, an extended signal vector, and estimates the reception time and arrival angle of the second signal based on the set times and the set angles corresponding to each of the plurality of elements of the extended signal vector, wherein the interval of the set times is shorter than the specified time.

[0008] In addition, in order to solve the above-mentioned problems, according to another aspect of the present invention, there is provided an information processing method, comprising the following steps: receiving a signal from another communication device in a wireless manner; and taking the correlation between the second signal and the first signal, which is a signal corresponding to the first signal received when the other communication device sends a signal containing a pulse as the first signal, at each specified time, and converting the result obtained by taking the correlation between the second signal and the first signal at each specified time, namely, the correlation operation result, into a form of a matrix including a plurality of elements representing the correlation operation results assumed to be received at each of a plurality of set times and set angles, namely, a bin pattern matrix, and a vector including a plurality of elements representing the presence or absence of a signal at each of the set times and the set angles and the amplitude and phase of the signal, namely, an extended signal vector, and estimating the reception time and arrival angle of the second signal based on the set times and the set angles corresponding to each of the plurality of elements of the extended signal vector, wherein the interval of the set times is shorter than the specified time.

[0009] In addition, in order to solve the above-mentioned problems, according to another aspect of the present invention, a program is provided to enable a computer to function as a control unit, and for the above-mentioned control unit, when the other communication device sends a signal containing a pulse as the first signal, the correlation between the second signal and the first signal is obtained at each specified time, and the result obtained by taking the correlation between the second signal and the first signal at each specified time, that is, the correlation operation result, is transformed into a matrix consisting of a plurality of elements representing the above-mentioned correlation operation results when the signal is assumed to be received at each of the plurality of set times and set angles, that is, a bin pattern matrix, and a vector consisting of a plurality of elements representing the presence or absence of a signal at each of the plurality of set times and the plurality of set angles, that is, an extended signal vector. The reception time and arrival angle of the above-mentioned second signal are estimated based on the set times and the set angles corresponding to each of the plurality of elements of the extended signal vector, and the interval of the set times is shorter than the above-mentioned specified time.

[0010] As described above, according to the present invention, a configuration is provided that can improve the accuracy of estimating the distances and angles between a plurality of devices. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0012] Figure 2This is a diagram showing an example of an arrangement of a plurality of antennas provided in a vehicle according to the present embodiment.

[0013] Figure 3 This is a diagram showing an example of position parameters of the portable device according to the present embodiment.

[0014] Figure 4 This is a diagram showing an example of position parameters of the portable device according to the present embodiment.

[0015] Figure 5 This is a diagram showing an example of a processing block device for signal processing in a communication unit according to the present embodiment.

[0016] Figure 6 This is a coordinate diagram showing an example of CIR involved in this embodiment.

[0017] Figure 7 This is a sequence diagram showing an example of the flow of the distance measurement process executed in the system according to the present embodiment.

[0018] Figure 8 This is a sequence diagram showing an example of the flow of the angle estimation process executed in the system involved in this embodiment.

[0019] Fig. 9 This is a coordinate diagram for explaining the technical issues of this embodiment.

[0020] Fig.10 This is a coordinate diagram for explaining the technical issues of this embodiment.

[0021] Fig.11 This is a coordinate diagram for explaining the technical issues of this embodiment.

[0022] Fig.12 This is a coordinate diagram for explaining the technical issues of this embodiment.

[0023] Fig.13 This is a diagram for explaining a situation where multipath separation based on time information is difficult.

[0024] Fig.14 This is a diagram used to illustrate multipath separation based on time information and angle information.

[0025] Fig.15 The diagram schematically shows the difference between the delay profiles, the bin mode matrix, and the estimated spread signal vector of 2D-FOCUSS and FOCUSS according to one embodiment of the present invention.

[0026] Fig.16This is a diagram for explaining in detail the difference in delay profile between 2D-FOCUSS and FOCUSS according to the present embodiment.

[0027] Fig.17 This is a diagram for explaining in detail the difference between the bin pattern matrices of 2D-FOCUSS and FOCUSS according to the present embodiment.

[0028] Fig.18 This is a diagram for explaining in detail the difference between the extended signal vectors of 2D-FOCUSS and FOCUSS according to the present embodiment.

[0029] Fig.19 This is a diagram for explaining an example of a directional pattern vector according to the present embodiment.

[0030] Fig. 20 This is a diagram for explaining an example of a directional pattern vector according to the present embodiment.

[0031] Fig.21 This is a diagram for explaining an example of a directional pattern vector according to the present embodiment.

[0032] Fig. 22 : is a flowchart showing an example of the flow of the position parameter estimation process executed by the communication unit 200 according to the present embodiment.

[0033] Fig.23 This is a diagram for explaining the outline of beam space processing according to the present embodiment.

[0034] Fig.24 This is a diagram for explaining the multi-beam forming according to the present embodiment.

[0035] Fig.25 This is a diagram for explaining a matrix of a signal after multi-beam forming according to the present embodiment.

[0036] Fig.26 This is a diagram for explaining a method of performing selection based on an angle region after selection based on a time region according to the present embodiment.

[0037] Fig. 27 This is a diagram for explaining a method of performing selection based on a time region after selection based on an angle region according to the present embodiment.

[0038] Fig.28 This is a diagram for explaining a method of simultaneously performing selection based on a time region and selection based on an angle region according to the present embodiment.

[0039] Fig.29 This is a diagram for explaining a method of simultaneously performing selection based on a time region and selection based on an angle region according to the present embodiment. DETAILED DESCRIPTION

[0040] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In addition, in this specification and the drawings, components having substantially the same functional structure are denoted by the same reference numerals to omit repeated description.

[0041] In addition, in this specification and the drawings, sometimes different letters are added after the same reference numeral to distinguish elements having substantially the same functional structure. For example, multiple elements having substantially the same functional structure are distinguished as needed, such as wireless communication units 210A, 210B, and 210C. However, when there is no need to specifically distinguish between multiple elements having substantially the same functional structure, only the same reference numeral is added. For example, when there is no need to specifically distinguish between wireless communication units 210A, 210B, and 210C, they are simply referred to as wireless communication units 210.

[0042] <<1. Example of structure>>

[0043] Figure 1 FIG. 1 is a diagram showing an example of a structure of a system 1 according to an embodiment of the present invention. Figure 1 As shown, the system 1 according to the present embodiment includes a portable device 100 and a communication unit 200. The communication unit 200 of the present embodiment is mounted on a vehicle 202. The vehicle 202 is an example of an object used by a user.

[0044] The present invention relates to a communication device on the authenticated party side and a communication device on the authenticator side. Figure 1 In the illustrated example, the portable device 100 is an example of a communication device on the authenticated party side, and the communication unit 200 is an example of a communication device on the authenticator side.

[0045] In the system 1, when a user (e.g., a driver of a vehicle 202) approaches the vehicle 202 with a portable device 100, wireless communication for authentication is performed between the portable device 100 and a communication unit 200 mounted on the vehicle 202. If the authentication is successful, the door locks of the vehicle 202 are unlocked or the engine is started, and the vehicle 202 becomes usable by the user. The system 1 is also called a smart key system. The components are described in order below.

[0046] (1) Portable device 100

[0047] The portable device 100 is configured as any device carried by a user. The any device includes an electronic key, a smart phone, a wearable terminal, etc. Figure 1 As shown, the portable device 100 includes a wireless communication unit 110 , a storage unit 120 , and a control unit 130 .

[0048] The wireless communication unit 110 has a function of wirelessly communicating with the communication unit 200 mounted on the vehicle 202. The wireless communication unit 110 receives a wireless signal from the communication unit 200 mounted on the vehicle 202, and transmits a wireless signal.

[0049] The wireless communication between the wireless communication unit 110 and the communication unit 200 is realized by using, for example, UWB (Ultra-Wide Band) signals. In wireless communication using UWB signals, if a pulse method is used, the propagation delay time of the radio wave can be measured with high accuracy by using radio waves with very short pulse widths of less than nanoseconds, and ranging based on the propagation delay time can be performed with high accuracy. In addition, the propagation delay time refers to the time taken from sending a radio wave to receiving it. The wireless communication unit 110 is configured as a communication interface capable of performing communication via UWB, for example.

[0050] In addition, signals using UWB can be transmitted and received as, for example, ranging signals, angle estimation signals, and data signals. The ranging signal is a signal that is transmitted and received in the ranging process described below. The ranging signal may also be composed of a frame format that does not have a payload portion for storing data, or may be composed of a frame format that has a payload portion. The angle estimation signal refers to a signal that is transmitted and received in the angle estimation process described below. The angle estimation signal may also have the same structure as the ranging signal. The data signal is preferably composed of a frame format that has a payload portion for storing data.

[0051] Here, the wireless communication unit 110 includes at least one antenna 111. Also, the wireless communication unit 110 transmits and receives wireless signals via the at least one antenna 111.

[0052] The storage unit 120 has a function of storing various information used for the operation of the portable device 100. For example, the storage unit 120 stores a program for the operation of the portable device 100, an ID (identifier), a password, an authentication algorithm, etc. The storage unit 120 is composed of a storage medium such as a flash memory and a processing device that performs recording and reproduction on the storage medium.

[0053] The control unit 130 has a function of executing processing at the portable device 100. As an example, the control unit 130 controls the wireless communication unit 110 to perform communication between itself and the communication unit 200 of the vehicle 202. The control unit 130 reads information from the storage unit 120 and writes information to the storage unit 120. The control unit 130 also functions as an authentication control unit that controls authentication processing performed between itself 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.

[0054] (2) Communication unit 200

[0055] The communication unit 200 is provided in correspondence with the vehicle 202. Here, the communication unit 200 is provided in the vehicle 202, or is built into the vehicle 202 as a communication module, etc., and is mounted on the vehicle 202. In addition, the communication unit 200 may be provided in a parking lot of the vehicle 202, and the vehicle 202 and the communication unit 200 may be configured separately. In this case, the communication unit 200 can wirelessly send a control signal to the vehicle 202 based on the communication result between itself and the portable device 100, and remotely control the vehicle 202. Figure 1 As shown, the communication unit 200 includes a plurality of wireless communication units 210 ( 210A to 210D), a storage unit 220 , and a control unit 230 .

[0056] The wireless communication unit 210 has a function of wirelessly communicating with the wireless communication unit 110 of the portable device 100. The wireless communication unit 210 receives wireless signals from the portable device 100 and transmits wireless signals to the portable device 100. The wireless communication unit 210 is configured as a communication interface capable of performing communication in UWB, for example.

[0057] Here, each wireless communication unit 210 includes an antenna 211. Each wireless communication unit 210 transmits and receives wireless signals via the antenna 211.

[0058] The storage unit 220 has a function of storing various information used for the operation of the communication unit 200. For example, the storage unit 220 stores programs and authentication algorithms used for the operation of the communication unit 200. The storage unit 220 is composed of a storage medium such as a flash memory and a processing device that performs recording and reproduction on the storage medium.

[0059] The control unit 230 has a function of controlling all operations of the communication unit 200 and the on-board devices mounted on 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 the storage unit 220 and writes information to the storage unit 220. The control unit 230 also functions as an authentication control unit that controls the authentication process performed between the control unit 230 and the portable device 100. In addition, the control unit 230 also functions as a door lock control unit that controls the door locks of the vehicle 202, and locks and unlocks the door locks. In addition, the control unit 230 also functions as an engine control unit that controls the engine of the vehicle 202, and starts / stops the engine. In addition, the power source of the vehicle 202 may be a motor or the like in addition to the engine. The control unit 230 is configured as an electronic circuit such as an ECU (Electronic Control Unit).

[0060] <<2. Technical Features>>

[0061] <2.1. Position parameters>

[0062] The communication unit 200 (more specifically, the control unit 230) according to the present embodiment performs a position parameter estimation process to estimate a position parameter indicating the position of the portable device 100. Figure 2 to Figure 4 Various definitions related to position parameters are explained.

[0063] Figure 2 2 is a diagram showing an example of the arrangement of a plurality of antennas 211 (wireless communication units 210) provided in a vehicle 202 according to the present embodiment. Figure 2As shown, four antennas 211 (211A-211D) are provided on the top of the vehicle 202. Antenna 211A is provided on the right front side of the vehicle 202. Antenna 211B is provided on the left front side of the vehicle 202. Antenna 211C is provided on the right rear side of the vehicle 202. Antenna 211D is provided on the left rear side of the vehicle 202. In addition, the distance between adjacent antennas 211 is set to be less than half of the wavelength λ of the angle estimation signal described below. As a coordinate system based on the communication unit 200, a local coordinate system of the communication unit 200 is set. An example of the local coordinate system of the communication unit 200 is a coordinate system with the center of the four antennas 211 as the origin, the front-rear direction of the vehicle 202 as the X-axis, the left-right direction of the vehicle 202 as the Y-axis, and the up-down direction of the vehicle 202 as the Z-axis. The X-axis is parallel to the axis connecting the antenna pairs in the front-to-back direction (e.g., antenna 211A and antenna 211C, and antenna 211B and antenna 211D). The Y-axis is parallel to the axis connecting the antenna pairs in the left-to-right direction (e.g., antenna 211A and antenna 211B, and antenna 211C and antenna 211D).

[0064] In addition, the configuration shape of the four antennas 211 is not limited to a square, and can be a parallelogram, a trapezoid, a rectangle, or any other shape. Of course, the number of antennas 211 is not limited to four.

[0065] Figure 3 2 is a diagram showing an example of the position parameter of the portable device 100 according to the present embodiment. The position parameter 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 result of transmission and reception of the distance measurement signal described below between one of the plurality of wireless communication units 210 and the portable device 100. The distance R may also be the distance from one of the wireless communication units 210 that transmits and receives the distance measurement signal described below to the portable device 100.

[0066] Additionally, positional parameters can contain Figure 3 The angle of the portable device 100 with the communication unit 200 as a reference is constituted by the angle α from the X-axis to the portable device 100 and the angle β from the Y-axis to the portable device 100. The angles α and β are angles formed between the straight line connecting the origin of the first predetermined coordinate system and the portable device 100 and the coordinate axis. For example, the first predetermined coordinate system is a local coordinate system of the communication unit 200. The angle α is the angle formed between the straight line connecting the origin and the portable device 100 and the X-axis. The angle β is the angle formed between the straight line connecting the origin and the portable device 100 and the Y-axis.

[0067] Figure 42 is a diagram showing an example of position parameters of the portable device 100 according to the present embodiment. The position parameters may include coordinates of the portable device 100 in the second predetermined 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 examples of such coordinates. That is, the second predetermined coordinate system may be a local coordinate system of the communication unit 200. In addition, the second predetermined coordinate system may be a global coordinate system.

[0068] <2.2.CIR>

[0069] (1) CIR calculation and processing

[0070] In the position parameter estimation process, the portable device 100 and the communication unit 200 perform communication for estimating the position parameter. At this time, the portable device 100 and the communication unit 200 calculate the CIR (Channel Impulse Response).

[0071] CIR refers to the response when a pulse is input to the system. When the wireless communication unit of one of the portable device 100 and the communication unit 200 (hereinafter, also referred to as the transmitting side) transmits a signal including a pulse as the first signal, the CIR of this embodiment is calculated based on the second signal, which is a signal corresponding to the first signal and received by the wireless communication unit of the other of the portable device 100 and the communication unit 200 (hereinafter, also referred to as the receiving side). 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 is also referred to as the transmitting signal, and the second signal is also referred to as the receiving signal.

[0072] As an example, CIR may also be the result obtained by taking the correlation between the transmitted signal and the received signal at each specified time, that is, the result of the correlation operation. The correlation here may also be a process of taking the correlation between the transmitted signal and the received signal while staggering the relative positions at each time sequence, that is, sliding correlation. CIR includes a correlation value representing the height of the correlation between the transmitted signal and the received signal as an element at each moment with a specified time as an interval. The specified time is, for example, the interval at which the receiving side samples the received signal. Therefore, the elements that constitute CIR are also called sampling points. The correlation value may also be a complex number with IQ components. In addition, the correlation value may also be the amplitude or phase of a complex number. In addition, the correlation value may also be the power of the sum of the squares of the I component and the Q component of the complex number (or the square of the amplitude).

[0073] CIR is also understood as a set of elements with values ​​at each time (hereinafter also referred to as CIR values). In this case, CIR is a time series change of CIR values. When CIR is a correlation operation result, CIR value is a correlation value.

[0074] As another example, the CIR may be the received signal (complex number having IQ components) itself at each predetermined time. In addition, the CIR may be the amplitude or phase of the received signal at each predetermined time. In addition, the CIR may be a power value that is the square sum of the I component and the Q component of the received signal at each predetermined time.

[0075] In addition, the portable device 100 and the communication unit 200 use a time counter to obtain the time. The time counter refers to a counter that counts (typically, increments) a value representing the elapsed time (hereinafter, also referred to as a count value) at a predetermined time interval (hereinafter, also referred to as a count cycle). The current time is calculated based on the count value, the count cycle, and the count start time obtained by counting the time counter. On the one hand, the case where the count cycle and the count start time are consistent between different devices is also called synchronization. On the other hand, the case where at least one of the count cycle and the count start time is different between different devices is also called asynchronous or non-synchronous. The portable device 100 and the communication unit 200 may be synchronized or asynchronous. In addition, the multiple wireless communication units 210 may be synchronized or asynchronous with each other. The above-mentioned specified time when calculating the CIR may also be an integer multiple of the count cycle of the time counter. In the following description, unless otherwise specified, the description is made based on the case where the portable device 100 and the multiple wireless communication units 210 are synchronized with each other.

[0076] Below, refer to Figure 5-6 The CIR calculation process in a case where the transmitting side is the portable device 100 and the receiving side is the communication unit 200 will be described in detail.

[0077] Figure 5 FIG. 2 is a diagram showing an example of a processing block device for signal processing of the communication unit 200 according to the present embodiment. 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 .

[0078] The oscillator 212 generates a signal having the same frequency as the frequency of a carrier wave that carries a transmission signal, and outputs the generated signal to the multiplier 213 and the 90-degree phase shifter 214 .

[0079] The multiplier 213 multiplies the reception signal received by the antenna 211 and the signal output from the oscillator 212, and outputs the multiplication result to the LPF 216. The LPF 216 outputs a signal having a frequency lower than the frequency of the carrier wave that transmits the transmission signal among the input signals to the correlator 218. The signal input from the LPF 216 to the correlator 218 is the I component (i.e., the real part) among the components corresponding to the envelope of the reception signal.

[0080] 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 by the signal output from the 90-degree phase shifter 214, and outputs the multiplication result to the LPF 217. The LPF 217 outputs a signal of a frequency lower than the frequency of the carrier that transmits the transmission signal in the input signal to the correlator 218. The signal input from the LPF 216 to the correlator 218 is the Q component (i.e., the imaginary part) of the component corresponding to the envelope of the received signal.

[0081] Correlator 218 calculates CIR by taking sliding correlation between the received signal consisting of the I component and the Q component output from LPF 216 and LPF 217 and the reference signal. Note that the reference signal here is the same signal as the transmitted signal before being multiplied by the carrier.

[0082] Accumulator 219 accumulates and outputs the CIR output from correlator 218.

[0083] Here, the transmitting side may transmit a signal including a preamble code including more than one preamble code symbols as a transmission signal. The preamble code is a sequence known between the transmitter and the receiver. Typically, the preamble code is configured at the beginning of the transmission signal. The preamble code symbol is a pulse array including more than one pulse. The pulse array is a collection of multiple pulses separated in time sequence. The preamble code symbol is an object to be accumulated by the accumulator 219. That is, the correlator 218 calculates the CIR of each preamble code symbol by taking the sliding correlation between the parts corresponding to the multiple preamble code symbols contained in the received signal and the preamble code symbols contained in the transmission signal (i.e., the reference signal). Moreover, the accumulator 219 accumulates the CIR of each preamble code symbol for the more than one preamble code contained in the preamble code, and outputs the accumulated CIR.

[0084] (2) Example of CIR

[0085] Figure 6 An example of the CIR output from the accumulator 219 is shown. Figure 6 This is a coordinate diagram showing an example of CIR involved in this embodiment. Figure 6The CIR shown is the CIR when the moment when the transmission side sends the transmission signal is assumed to be the counting start moment made by the time counter. Such a CIR is also called a delay profile. The horizontal axis of this coordinate graph refers to the delay time. The delay time refers to the elapsed time from the moment when the transmission side sends the transmission signal. The vertical axis of this coordinate graph is the absolute value of the CIR value (for example, the power value). In addition, the following describes the case where CIR refers to the delay profile.

[0086] One piece of information that constitutes information that changes along a time series, such as a CIR value at a certain delay time of the CIR, is also called a sampling point. Typically, in the CIR, a set of sampling points between zero-crossing points corresponds to one pulse. Figure 6 The CIR shown includes a set 21 of sampling points corresponding to a certain pulse and a set 22 of sampling points corresponding to other pulses.

[0087] Set 21 corresponds to, for example, signals (e.g., pulses) that arrive at the receiving side via a fast path. A fast path refers to the shortest path between a transmitter and a receiver. A fast path refers to a straight path between a transmitter and a receiver in an environment without any obstructions. Set 22 corresponds to, for example, signals (e.g., pulses) that arrive at the receiving side via a path other than a fast path. In this way, signals that arrive via multiple paths are also called multipath waves.

[0088] (3) Detection of the first incoming wave

[0089] The receiving side detects a signal satisfying a predetermined detection criterion among wireless signals received from the transmitting side as a signal that reaches the receiving side via a fast path, and estimates a position parameter based on the detected signal.

[0090] Hereinafter, the signal detected as a signal that arrives at the receiving side via a fast path will also be referred to as the first incoming wave. The first incoming wave may be any of a direct wave, a delayed wave, or a composite wave. A direct wave is a signal that is directly (i.e., not reflected, etc.) received by the receiving side via the shortest path between the transmitter and the receiver. In other words, a direct wave is a signal that arrives at the receiving side via a fast path. A delayed wave is a signal that is indirectly received by the receiving side via a path that is not the shortest between the transmitter and the receiver, i.e., due to factors such as reflection. The delayed wave is received by the receiving side with a delay compared to the direct wave. A composite wave is a signal that is received by the receiving side in a state where multiple signals via multiple different paths are composited.

[0091] The receiving side detects the signal that meets the predetermined detection criterion in the wireless signal received from the transmitting side as the first arrival wave. An example of a predetermined detection criterion is the case where the power value of the CIR exceeds the predetermined threshold for the first time. That is, the receiving side may also detect the pulse corresponding to the part of the CIR whose power value exceeds the predetermined threshold for the first time as the first arrival wave. Another example of a predetermined detection criterion is the case where the received power value of the received wireless signal (that is, the sum of the squares of the I component and the Q component of the received signal) exceeds the predetermined threshold for the first time. That is, the receiving side may also detect the signal whose received power value exceeds the predetermined threshold for the first time in the received signal as the first arrival wave.

[0092] It should be noted here that the signal detected as the first wave is not necessarily limited to the direct wave. For example, if the direct wave is received in a state where the delayed wave cancels each other, the power value of the CIR may be lower than a predetermined threshold value, and the direct wave is not detected as the first wave. In this case, the delayed wave or the synthesized wave that arrives later than the direct wave is detected as the first wave.

[0093] <2.3. Estimation of position parameters>

[0094] (1) Distance estimation

[0095] The communication unit 200 performs distance measurement processing. The distance measurement processing is processing for 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 shown. The distance measurement process includes: transmitting and receiving a distance measurement signal; and calculating the distance R based on the propagation delay time of the distance measurement signal. The propagation delay time is the time it takes for a signal to be received after it is transmitted.

[0096] Here, any one of the plurality of wireless communication units 210 included in the communication unit 200 transmits and receives a distance measurement signal. The wireless communication unit 210 that transmits and receives a distance measurement signal is hereinafter referred to as a host. The distance R is the distance between the wireless communication unit 210 (more precisely, the antenna 211) functioning as a host and the portable device 100.

[0097] In the ranging process, a plurality of ranging signals may be sent and received between the communication unit 200 and the portable device 100. A ranging signal sent from one device to another device among the plurality of ranging signals is also referred to as a first ranging signal. Next, a ranging signal sent from a device that receives the first ranging signal to a device that has sent the first ranging signal as a response to the first ranging signal is also referred to as a second ranging signal. Next, a ranging signal sent from a device that receives the second ranging signal to a device that has sent the second ranging signal as a response to the second ranging signal is also referred to as a third ranging signal.

[0098] Below, refer to Figure 7 An example of the flow of distance measurement processing will be described.

[0099] Figure 7 1 is a sequence diagram showing an example of the flow of the distance measurement process executed in the system 1 according to the present embodiment. This sequence involves the portable device 100 and the communication unit 200. In this sequence, the wireless communication unit 210A functions as a master.

[0100] like Figure 7 As shown, first, the portable device 100 transmits the first distance measurement signal (step S102). When the wireless communication unit 210A receives the first distance measurement signal, the control unit 230 calculates the CIR of the first distance measurement signal. Thereafter, the control unit 230 detects the first arrival of the first distance measurement signal of the wireless communication unit 210A based on the calculated CIR (step S104).

[0101] Next, the wireless communication unit 210A transmits the second distance measurement signal as a response to the first distance measurement signal (step S106). Upon receiving the second distance measurement signal, the portable device 100 calculates the CIR of the second distance measurement signal. Thereafter, the portable device 100 detects the first arrival of the second distance measurement signal based on the calculated CIR (step S108).

[0102] Next, the portable device 100 transmits the third ranging signal as a response to the second ranging signal (step S110). When the wireless communication unit 210A receives the third ranging signal, the control unit 230 calculates the CIR of the third ranging signal. Thereafter, the control unit 230 detects the first arrival of the third ranging signal of the wireless communication unit 210A based on the calculated CIR (step S112).

[0103] The portable device 100 measures the time INT1 from the time of sending the first ranging signal to the time of receiving the second ranging signal and the time INT2 from the time of receiving the second ranging signal to the time of sending the third ranging signal. Here, the time of receiving the second ranging signal is the time of receiving the first incoming wave of the second ranging signal detected in step S108. Then, the portable device 100 sends a signal including information indicating the time INT1 and INT2 (step S114). Such a signal is received by the wireless communication unit 210A, for example.

[0104] The control unit 230 measures the time INT3 from the reception time of the first ranging signal to the transmission time of the second ranging signal and the time INT4 from the transmission time of the second ranging signal to the reception time of the third ranging signal. Here, the reception time of the first ranging signal is the reception time of the first arrival wave of the first ranging signal detected in step S104. Similarly, the reception time of the third ranging signal is the reception time of the first arrival wave of the third ranging signal detected in step S112.

[0105] 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 by the following equation: m .

[0106] Mathematical formula 1

[0107]

[0108] Then, the control unit 230 calculates the estimated propagation delay time τ m The distance R is estimated by multiplying the speed of the signal.

[0109] - One reason for the reduction in estimation accuracy

[0110] The reception time of the distance measurement signal at the start or end of time INT1, INT2, INT3, and INT4 is the reception time of the first arrival wave of the distance measurement signal. As described above, the signal detected as the first arrival wave is not necessarily limited to a direct wave.

[0111] When a delayed wave or a composite wave that arrives later than a direct wave is detected as the first wave, the reception time of the first wave is delayed compared to the case where the direct wave is detected as the first wave. In this case, the propagation delay time τ m The estimated result of φ varies with respect to the true value (the estimated result in the case where the direct wave is detected as the first incoming wave). Furthermore, the ranging accuracy is reduced by the amount of the variation.

[0112] -Replenish

[0113] Alternatively, the receiving side may regard the moment when the predetermined detection criterion is satisfied as the reception moment of the first wave. That is, the receiving side may regard the moment when the power value of the CIR exceeds the predetermined threshold for the first time or the moment when the reception power value of the received wireless signal exceeds the predetermined threshold for the first time as the reception moment of the first wave. In addition, the receiving side may regard the moment of the peak value of the detected first wave (that is, the moment when the power value in the part corresponding to the first wave in the CIR is the highest or the moment when the reception power value in the first wave is the highest) as the reception moment of the first wave.

[0114] (2) Angle estimation

[0115] The communication unit 200 performs angle estimation processing. The angle estimation processing is to estimate Figure 3 The angle acquisition process includes: receiving an angle estimation signal; and calculating the angles α and β based on the reception result of the angle estimation signal. The angle estimation signal is a signal sent and received in the angle estimation process. Figure 8 An example of the flow of the angle estimation process will be described.

[0116] Figure 8 1 is a sequence diagram showing an example of the flow of the angle estimation process executed in the system 1 according to the present embodiment. This sequence involves the portable device 100 and the communication unit 200 .

[0117] like Figure 8 As shown, first, the portable device 100 transmits a signal for angle estimation (step S202). Next, if the wireless communication units 210A to 210D each receive a signal for angle estimation, the control unit 230 calculates the CIR of the signal for angle estimation received by each of the wireless communication units 210A to 210D. Thereafter, the control unit 230 detects the first incoming wave of the angle estimation signal for each of the wireless communication units 210A to 210D based on the calculated CIR (steps S204A to S204D). Next, the control unit 230 detects the phase of the detected first incoming wave for each of the wireless communication units 210A to 210D (steps S206A to S206D). Then, the control unit 230 estimates the angles α and β based on the phase of the first incoming wave detected for each of the wireless communication units 210A to 210D (step S208).

[0118] Here, the phase of the first arrival wave is the phase of the reception time of the first arrival wave in the CIR. Alternatively, the phase of the first arrival wave may be the phase of the reception time of the first arrival wave in the received wireless signal.

[0119] The details of the process of step S208 are described below. The phase of the first incoming wave detected by the wireless communication unit 210A is denoted by P A The phase of the first incoming wave detected by the wireless communication unit 210B is set to P B The phase of the first incoming wave detected by the wireless communication unit 210C is set to P C The phase of the first incoming 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 is AC and Pd BDAnd the antenna array phase difference Pd in ​​the Y-axis direction BA and Pd DC They are represented by the following formulae respectively.

[0120] Mathematical formula 2

[0121] Pd AC =(P A -P C )

[0122] Pd BD =(P B -P D )

[0123] Pd DC =(P D -P C )

[0124] Pd BA =(P B -P A ) (2)

[0125] The angles α and β are calculated by the following equations: Here, λ is the wavelength of the radio wave, and d is the distance between the antennas 211 .

[0126] Mathematical formula 3

[0127] αorβ=arccos(λ·Pd / (2·π·d)) (3)

[0128] Therefore, the angles calculated based on the phase differences of the respective antenna arrays are expressed by the following equations.

[0129] Mathematical formula 4

[0130] α AC =arccos(λ·Pd AC / (2·π·d))

[0131] α BD =arccos(λ·Pd BD / (2·π·d))

[0132] β DC =arccos(λ·Pd DC / (2·π·d))

[0133] β BA =arccos(λ·Pd BA / (2·π·d)) (4)

[0134] The control unit 230 calculates the angle α based on the above AC , α BD , β DC and βBA For example, the control unit 230 calculates the angles α and β by averaging the angles calculated for each of the two arrays in the X-axis and Y-axis directions as shown in the following equation.

[0135] Mathematical formula 5

[0136] α=(α AC +α BD ) / 2

[0137] β=(β DC +β BA ) / 2 (5)

[0138] - One reason for the reduction in estimation accuracy

[0139] As described above, the angles α and β are calculated based on the phase of the first incoming wave. As described above, the signal detected as the first incoming wave is not necessarily limited to the direct wave.

[0140] In other words, a delayed wave or a composite wave may be detected as the first incoming wave. Typically, the phases of the delayed wave and the composite wave differ from the phase of the direct wave, and therefore the angle estimation accuracy decreases by the difference.

[0141] -Replenish

[0142] In addition, the angle estimation signal and the distance measurement signal may be the same. Figure 7 The third distance measurement signal shown is Figure 8 The angle estimation signals shown may be the same. In this case, the communication unit 200 can calculate the distance R and the angles α and β by receiving a single wireless signal that serves as both the angle estimation signal and the second distance measurement signal.

[0143] (3) Coordinate estimation

[0144] The control unit 230 performs coordinate estimation processing. The coordinate estimation processing is Figure 4 The three-dimensional coordinates (x, y, z) of the portable device 100 shown in the figure are estimated. As the coordinate estimation process, the following first calculation method and second calculation method can be adopted.

[0145] - Calculation method 1

[0146] The first calculation method is a method of calculating the coordinates x, y, and z based on the results of the distance measurement process and the angle estimation process. In this case, first, the control unit 230 calculates the coordinates x and y using the following equations.

[0147] Mathematical formula 6

[0148] x=R·cosα

[0149] y=R·cosβ (6)

[0150] Here, the following relationship holds true for the distance R and the coordinates x, y, and z.

[0151] Mathematical formula 7

[0152]

[0153] The control unit 230 calculates the coordinate z using the following equation using the above relationship.

[0154] Mathematical formula 8

[0155]

[0156] - Second calculation method

[0157] The second calculation method is a method of calculating the coordinates x, y, and z without estimating the angles α and β. First, according to the above-mentioned mathematical formulas (4), (5), (6), and (7), the following relationship holds.

[0158] Mathematical formula 9

[0159] x / R=cosα (9)

[0160] Mathematical formula 10

[0161] y / R=cosβ (10)

[0162] Mathematical formula 11

[0163] x 2 +y 2 +z 2 =R 2 (11)

[0164] Mathematical formula 12

[0165] d·cosα=λ·(Pd AC / 2+Pd BD / 2) / (2·π) (12)

[0166] Mathematical formula 13

[0167] d·cosα=λ·(Pd DC / 2+Pd BA / 2) / (2·π) (12)

[0168] If equation (12) is sorted out with respect to cosα and substituted into equation (9), the coordinate x can be obtained by the following equation.

[0169] Mathematical formula 14

[0170] x=R·λ·(Pd AC / 2+Pd BD / 2) / (2·π·d) (14)

[0171] If cosβ is rearranged into equation (13) and substituted into equation (10), the coordinate y is obtained by the following equation.

[0172] Mathematical formula 15

[0173] y=R·λ·(Pd DC / 2+Pd BA / 2) / (2·π·d) (15)

[0174] Furthermore, when equations (14) and (15) are substituted into equation (11) and rearranged, the coordinate z is obtained by the following equation.

[0175] Mathematical formula 16

[0176]

[0177] The above describes the estimation process of the coordinates of the portable device 100 in the local coordinate system. The coordinates of the portable device 100 in the global coordinate system can be estimated 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.

[0178] - One reason for the reduction in estimation accuracy

[0179] As described above, the coordinates are calculated based on the propagation delay time and the phase. Moreover, these are estimated based on the first arrival wave. Therefore, for the same reason as in the distance measurement process and the angle estimation process, the accuracy of the coordinate estimation may be reduced.

[0180] (4) Estimation of the area

[0181] Alternatively, the location parameter may include an area where the portable device 100 is located among a plurality of predefined areas. As an example, when the area is defined based on the distance from the communication unit 200, the control unit 230 estimates the area where the portable device 100 is located based on the distance R estimated by the distance measurement process. As another example, when the area is defined based on the angle from the communication unit 200, the control unit 230 estimates the area where the portable device 100 is located based on the angles α and β estimated by the angle estimation process. As another example, when the area is defined based on three-dimensional coordinates, the control unit 230 estimates the area where the portable device 100 is located based on the coordinates (x, y, z) estimated by the coordinate estimation process.

[0182] In addition, as a process unique to the vehicle 202, the control unit 230 may estimate the area where the portable device 100 is located from a plurality of areas including the inside and outside of the vehicle 202. This makes it possible to provide detailed services, such as providing different services when the user is inside the vehicle and when the user is outside the vehicle. In addition, the control unit 230 may determine the area where the portable device 100 is located from an area within a predetermined distance from the vehicle 202, that is, a surrounding area, and an area greater than a predetermined distance from the vehicle 202, that is, a distant area.

[0183] (5) Use of estimated position parameter results

[0184] The estimated result of the position parameter can be used, for example, for authentication of the portable device 100. For example, when the portable device 100 is located on the driver's seat side and in a short distance from the communication unit 200, the control unit 230 determines that the authentication is successful and unlocks the vehicle door.

[0185] <<3. Technical issues>>

[0186] Reference Figures 9 to 12 , the technical issues of this embodiment are explained. Figures 9 to 12 This is a coordinate diagram for explaining the technical issues of this embodiment. The horizontal axis is the chip length representing the delay time, and the vertical axis is the absolute value of the CIR value (for example, the power value). The chip length is the time width of each pulse. For example, when a pulse is produced at a bandwidth of 500MHz, the pulse width of about 2ns is the chip length.

[0187] Fig. 9 The delay time 1T is shown in C The signal via the fast path arrives and at a delay time of 3T C The CIR in the case where the signal arrives via a path other than the fast path. Fig. 9 , then at the delay time 1T C and 3T C At each point, the CIR waveform is at its peak. Therefore, it can be seen that the delay time is separated by 2T. C The separation of the two multipath waves is fully realized in the CIR waveform.

[0188] Fig.10 The delay time 1T is shown in C The signal via the fast path arrives and at a delay time of 2T C The CIR in the case where the signal arrives via a path other than the fast path. C The first wave of signal arriving at the delay time 2T C The signal of the second wave arriving at the same phase. Fig.10, then on the one hand, during the delay time 1T C At the time of delay 2T, the CIR waveform is at its peak. C At the delay time 1T, the CIR waveform is not at its peak. C The incoming signal is delayed by 2T. C The incoming signals are synthesized in phase and appear as one waveform. Therefore, it can be seen that the delay time is separated by 1T. C The separation of the two multipath waves is not easy to achieve in the CIR waveform.

[0189] Fig.11 The delay time is shown in 1.2T C The signal arrives via the fast path and at a delay time of 1.7T C and 3.6T C The CIR in the case where the signal arrives via a path other than the fast path. In addition, at a delay time of 1.2T C The first wave of signal arriving at the delay time 1.7T C The signal of the second wave arriving at is inverted. Fig.11 , then on the one hand, at the delay time 1.2T C and 3.6T C At the delay time 2.2T, the CIR waveform reaches its peak. C Nearby is the second peak. This point is far from the actual delay time of 1.7T. C Therefore, it can be seen that the delay time is separated by 0.5T C The separation of the two multipath waves is not easy to achieve in the CIR waveform.

[0190] like Fig.10 and Fig.11 As shown in FIG. 1 , when the difference in the delay time between the two multipath waves arriving at the receiving side is short, the delay time that becomes the peak in the CIR waveform may vary relative to the original delay time. Therefore, the delay time at the reception time detected as the first incoming wave may vary relative to the original delay time. In this case, the ranging accuracy is reduced by the amount of the variation.

[0191] Fig.12 The delay time 1T is shown in C The signal arrives via the fast path and at a delay time of 1.5T C The CIR waveform 23 is a signal arriving via a path other than the fast path. The CIR waveform 21 is a signal at a delay time 1T. C The CIR waveform in this case is that the signal via the fast path is received as a single body. CIR waveform 22 is at the delay time 1.5T CThe CIR waveform in this case is that the signal via the path other than the fast path is received as a single body. In addition, at the delay time 1T C The first wave of signal arriving at the delay time 2T C The second wave of signal arriving at the same place is 90 degrees out of phase.

[0192] When the difference in delay time between two multipath waves arriving at the receiving end is short, a delayed wave or a composite wave may be detected as the first incoming wave. Fig.12 In the example shown, a composite wave is detected as the first incoming wave. Typically, the phases of the delayed wave and the composite wave differ from the phase of the direct wave, so the angle estimation accuracy is reduced by the difference.

[0193] like Fig.12 As shown in the example, when the composite wave of the direct wave and the delayed wave is detected as the first incoming wave, the delayed wave is synthesized at the sampling point 31 near the peak, thereby greatly changing the phase. Therefore, if the angle is estimated based on the phase of the sampling point 31, the estimation accuracy will be reduced.

[0194] On the other hand, at low power sampling points before the peak, such as sampling point 32, the influence of the delayed wave is reduced, so the phase change is reduced. However, the reduced influence of the delayed wave is exchanged for a lower power value, so the influence of noise increases, which leads to a corresponding decrease in estimation accuracy.

[0195] Therefore, it is desirable to be able to separate multipath waves with a higher resolution than CIR.

[0196] <<4. Technical Features>>

[0197] <4.1. Detection of the first incoming wave>

[0198] The portable device 100 and the communication unit 200 detect the first incoming wave by the processing described in detail below. Hereinafter, as an example, a case where the communication unit 200 is responsible for detecting the first incoming wave will be described. The processing described below may also be executed by the portable device 100.

[0199] (1) Formulation of delay distribution

[0200] First, the delay profile (i.e., CIR) of the PN (Pseudo-Noise) correlation method is formulated. The PN correlation method is a method of calculating the CIR by transmitting a signal composed of a random sequence such as a PN sequence signal shared by the transmitting and receiving sides and taking a sliding correlation between the transmitted signal and the received signal. In addition, the PN sequence signal is a signal in which 1s and 0s are arranged roughly randomly.

[0201] Hereinafter, a PN sequence signal u(t) of unit amplitude is transmitted as a transmission signal (for example, a preamble symbol of a distance measurement signal and an angle estimation signal). The unit amplitude is a predetermined amplitude known during transmission and reception.

[0202] In addition, in the following, the antenna on the receiving side receives the multipath wave of L waves as a signal corresponding to the transmission signal sent from the transmitting side. The multipath wave refers to a signal received by the receiving side via multiple paths. That is, when the transmitting side sends one signal, L signals via multiple paths are received by the receiving side.

[0203] In this case, the received signal x(t) is expressed by the following equation.

[0204] Mathematical formula 17

[0205]

[0206] Here, t is the time. i 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 frequency of the carrier wave of the transmitted signal. v(t) is the internal noise. Internal noise refers to the noise generated inside the circuit on the receiver side.

[0207] For example, in the PN correlation method, as shown in the following equation, the time of a known transmission signal u(t) is shifted on the receiver side, and the correlation with the reception signal x(t) is taken.

[0208] Mathematical formula 18

[0209]

[0210] In addition, ＊ () is the complex conjugate of u().

[0211] z(τ) is also called the delay profile. In addition, |z(τ)| 2 Also known as power delay profile. τ is the delay time.

[0212] The delay distribution of the L-wave multipath wave is expressed by the following equation.

[0213] Mathematical formula 19

[0214]

[0215] Here, r(τ) is the autocorrelation function of the PN sequence signal. The autocorrelation function is a function that takes the correlation between a signal and itself. r(τ) is given by the following formula.

[0216] Mathematical formula 20

[0217]

[0218] In addition, n(τ) is an internal noise component. n(τ) is given by the following formula.

[0219] Mathematical formula 21

[0220]

[0221] (2) Sparse reconstruction

[0222] The number of samples of the received signal is set to M (however, M>L). Furthermore, the received signal is sampled at M discrete delay times τ1, τ2, ..., τ M The discrete delay time is expressed as a discrete value of the delay time. z(τ) is a delay profile calculated based on the received signal sampled at the discrete delay time τ. The data vector z consisting of M delay profiles is expressed by the following equation. However, the following equation is an equation for the case where the receiving side receives only one preamble symbol.

[0223] Mathematical formula 22

[0224] z=[z(τ1),z(τ2),…,z(τ M )] T (twenty two)

[0225] When receiving a multipath wave of an L wave, a data vector z is expressed as follows.

[0226] Mathematical formula 23

[0227]

[0228] Mathematical formula 24

[0229] r(τ)=[r(τ1-τ), r(τ2-τ),…, r(τ M -τ)] T (twenty four)

[0230] Mathematical formula 25

[0231] n=[n(τ1),n(τ2),…,n(τ M )] T (25)

[0232] In addition, r(τ) is called the distance pattern vector.

[0233] Furthermore, when the data vector z is expressed in a matrix, it is expressed as follows.

[0234] Mathematical formula 26

[0235]

[0236] Mathematical formula 27

[0237]

[0238] Mathematical formula 28

[0239]

[0240] Here, A0 is also called a pattern matrix.

[0241] In addition, S0 is also called a signal vector.

[0242] In sparse reconstruction, the data vector z is transformed into a form consisting of the matrix product of A and s.

[0243] Mathematical formula 29

[0244]

[0245] Mathematical formula 30

[0246]

[0247] Mathematical formula 31

[0248]

[0249] T1, T2, …, T N Indicates the N delay times to be searched. T1, T2, ..., T N Also called a delay time bin. A delay time bin is an example of a set time. In addition, N>>L.

[0250] Here, A is also referred to as a delay time bin pattern matrix. The delay time bin pattern matrix is ​​a matrix composed of a plurality of elements representing the delay distribution when the signal is assumed to be received at each of a plurality of delay time bins. For example, r(T1), which is an element of the delay time bin pattern matrix A, is the delay distribution of the signal when the signal is assumed to be received at time T1.

[0251] In addition, s is also called a spread signal vector. The spread signal vector is a vector composed of a plurality of elements indicating the presence or absence of a signal in each delay time bin and the amplitude and phase of the signal.

[0252] (3) Estimation of propagation delay time based on the extended signal vector

[0253] According to sparse reconstruction, the delay profile z is modeled in the form of As+n. Therefore, by solving the underdetermined problem with N unknowns and M condition number (M<N), the extended signal vector s can be obtained. The control unit 230 estimates the reception time of the first incoming wave based on the delay time bins corresponding to the multiple elements in the extended signal vector s.

[0254] Here, on the one hand, a non-zero element in the extended signal vector indicates that a signal exists in the delay time bin corresponding to the 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 bin corresponding to the zero element. Therefore, the control unit 230 estimates the delay time bin corresponding to the non-zero element among the delay time bins corresponding to the multiple elements of the extended signal vector s as the reception time of the first incoming wave.

[0255] At this time, the control unit 230 estimates the sparse solution of the extended signal vector s, and estimates the delay time bin corresponding to the non-zero element in the estimated sparse solution as the reception time of the first incoming wave. The sparse solution is a vector in which only a predetermined number of elements are non-zero. The predetermined number is the number of pulses included in the received signal as pulses corresponding to the pulses included in the transmitted signal. That is, the sparse solution is a vector in which, ideally, only L elements are non-zero and the other elements are zero when a multipath wave of L waves is received. For example, in s=[s1, s2, ..., s N ] is non-zero, it is determined that a signal is received at the delay time T2. In addition, it is also conceivable that a situation in which an originally zero element becomes non-zero due to noise, but it is also possible that the control unit 230 implements an estimation that there is a pulse corresponding to a certain element when the noise is not considered and a certain element is non-zero. In this case, according to the estimation method described below, it is also possible to eliminate the influence of noise and achieve a high-precision estimation. On the other hand, it is also possible that, with respect to the non-zero element, the control unit 230 determines whether it is noise, and estimates the element determined to be noise as zero.

[0256] In particular, the control unit 230 estimates the earliest delay time bin among the delay time bins corresponding to the non-zero elements in the elements included in the extended signal vector s as the reception time of the first arrival wave. N ] When s2, s4 and s6 are non-zero, it is determined that the signal is received via the fast path at the delay time T2, and the signal is received via a path other than the fast path at the delay times T4 and T6.

[0257] The resolution of the signal obtained by the model after sparse reconstruction is determined by the size of N (i.e., the number of elements of the extended signal vector s) when modeling in the sparse reconstruction. Therefore, by increasing the number of N during sparse reconstruction, multipath waves can be separated with a resolution finer than CIR. Therefore, in this embodiment, the number of delay time bins N is made larger than the number of samples M of the received signal. In other words, in this embodiment, the N delay time bins T1, T2, ..., T N The time interval is longer than the M discrete delay times τ1, τ2, ..., τ M According to such a structure, the multipath waves can be separated with a resolution finer than the sampling interval of the received signal. As a result, the reception time of the first incoming wave can be obtained with a resolution finer than the CIR.

[0258] (3) Compressed sensing algorithm

[0259] The control unit 230 uses a compressed sensing algorithm to estimate the extended signal vector s that becomes a sparse solution. The compressed sensing algorithm refers to an algorithm that assumes that the unknown vector is a sparse vector and estimates the unknown vector based on linear observation of the unknown vector. In this embodiment, the extended signal vector s is an example of an unknown vector. Linear observation refers to the result obtained by multiplying the unknown vector by a coefficient. In this embodiment, the bin pattern matrix A is an example of a coefficient. The delay profile z is an example of a linear observation.

[0260] Examples of compressed sensing algorithms include FOCUSS (Focal Underdetermined System Solver), ISTA (Iterative Shrinkage Thresholding Algorithm), and FISTA (Fast ISTA). The control unit 230 may also adopt any of these compressed sensing algorithms. Below, as an example, an example of using FOCUSS to estimate the extended signal vector s is described. FOCUSS refers to an algorithm that assumes an initial value for an unknown vector and repeatedly estimates the unknown vector using a generalized inverse matrix and a weight matrix. FOCUSS can estimate unknown vectors with high accuracy using a small number of iterations by using a generalized inverse matrix and a weight matrix. The basic principle of FOCUSS is described in the non-patent literature. <Irina F.Gorodnitsky,Member,IEEE,and Bhaskar D.Rao,“Sparse Signal Reconstruction from Limited DataUsing FOCUSS:ARe-weighted Minimum Norm Algorithm”,IEEE TRANSACTIONS ON SIGNALPROCESSING,VOL.45,NO.3,MARCH 1997> There are detailed instructions in .

[0261] The problem of estimating the extended signal vector s that is a sparse solution based on the delay profile z is an underdetermined problem with N unknowns and M condition numbers (in addition, M < N). Therefore, other conditions are added to find a solution. Typically, the norm of the extended signal vector s is added to be the minimum condition, and the minimum norm solution is found. In addition, the norm refers to the length of the vector.

[0262] -Determination of the initial value s0 of FOCUSS

[0263] In the above mathematical formula (29), when the internal noise n is ignored, if the delay profile z is multiplied by the inverse matrix of the delay time bin pattern matrix A, the matrix A disappears (that is, becomes the unit matrix), and therefore the extended signal vector s can be extracted. However, there is no inverse matrix of the delay time bin pattern matrix A. Therefore, the minimum norm solution s mn As shown in the following equation, the delay profile z is obtained by multiplying the generalized inverse matrix of the delay time bin pattern matrix A. The generalized inverse matrix may be a Moore-Penrose generalized inverse matrix.

[0264] Mathematical formula 32

[0265] smn =A - z=A H (AA H ) -1 z (32)

[0266] Here, A – is the generalized inverse matrix of the delayed time bin pattern matrix A. The generalized inverse matrix of the delayed time bin pattern matrix A – It is expressed by the following formula.

[0267] Mathematical formula 33

[0268] A - =A H (AA H ) -1 (33)

[0269] Even if the delayed time bin pattern matrix A is multiplied by the generalized inverse matrix A- of the delayed time bin pattern matrix A, the delayed time bin pattern matrix A does not completely disappear, so as the minimum norm solution s mn And calculate a vector similar to the extended signal vector s as a sparse solution. In addition, the minimum norm solution s mn This becomes the initial value s0 of FOUCSS.

[0270] -Application of FOCUSS

[0271] Minimum norm solution s mn It is not a sparse solution. Therefore, the control unit 230 estimates the vector obtained by assigning weights to the extended signal vector s, which minimizes the norm, that is, the weighted minimum norm solution, as a sparse solution for estimating the extended signal vector s. By estimating the weighted minimum norm solution, a sparse solution can be estimated. The weighted minimum norm solution is expressed by the following formula.

[0272] Mathematical formula 34

[0273] s=W(AW) - z (34)

[0274] Here, W is a weight matrix. Typically, the weight matrix W is a diagonal matrix. That is, the problem of finding a weighted minimum norm solution for the extended signal vector s is described as follows.

[0275] Mathematical formula 35

[0276] Find s=Wq

[0277] Where q=argmin||q||subject to AWq=z (35)

[0278] Specifically, the control unit 230 estimates the weighted minimum norm solution of the extended signal vector s by repeatedly calculating the equations (36), (37), and (38) shown in the following STEP 1 to STEP 3.

[0279] Mathematical formula 36

[0280] STEP 1:

[0281] W k =diag(|s k-1 (1),…,s k-1 (N)|) (36)

[0282] Mathematical formula 37

[0283] STEP 2:

[0284] q k =(AW k ) - z (37)

[0285] Mathematical formula 38

[0286] STEP 3:

[0287] s k =W k q k (38)

[0288] Here, k is the number of repetitions. k is a candidate for the weighted minimum norm solution. (AW K ) - It is AW k As explained above, s k The initial value of is taken as the minimum norm solution s mn It is given by the following formula.

[0289] Mathematical formula 39

[0290] s| k＝0 =s mn (39)

[0291] The control unit 230 repeatedly executes the above-mentioned STEP1 to STEP3. As an example, STEP1 to STEP3 may be repeatedly executed. k As another example, STEP1 to STEP3 may be repeatedly performed a predetermined number of times. In this way, it is possible to estimate the extended signal vector s as a weighted minimum norm solution closer to the true value. This point will be described below.

[0292] The above-mentioned mathematical formula (37) is used to transform the mathematical formula (38) into the following formula.

[0293] Mathematical formula 40

[0294] s k =W k (AW k ) - z (40)

[0295] If the noise n in equation (29) is ignored, equation (40) is transformed into the following equation.

[0296] Mathematical formula 41

[0297] s k =W k (AW k ) - As (41)

[0298] Here, if W k (AW k ) - If A is a matrix that does not change s, like the unit matrix, then s k is equal to s. In FOCUSS, the weight matrix W is repeatedly updated k While using W k (AW k ) - A is a matrix that does not change s, such as a unit matrix, and thus it is possible to estimate the extended signal vector s as a weighted minimum norm solution that is closer to the true value.

[0299] (4) Singular value decomposition

[0300] Alternatively, the control unit 230 may perform singular value decomposition when estimating the extended signal vector s to obtain AW k The generalized inverse matrix (AW k ) - At this time, the control unit 230 may also use TSVD (Truncated singular value decomposition) to solve (AW k ) - .

[0301] In this case, the control unit 230 replaces AW in the equation (37) of STEP 2 above. k The singular value decomposition is converted into a diagonal matrix containing singular values ​​greater than a predetermined threshold value, and then (AW k ) - AW k It is decomposed by singular value as shown below.

[0302] Mathematical formula 42

[0303]

[0304] Here, S t is a diagonal matrix consisting of t non-zero singular values. t It is composed of t The corresponding matrix of t columns of left singular vectors. t It is composed of t The corresponding t columns of right singular vectors are composed of matrices. t is the dimension of the signal part space. The signal part space is a space composed of signals with power higher than the threshold. In addition, V t H Take the matrix V t The complex conjugate transpose of t The adjoint matrix of . At this time, (AW k ) - Solve it using the following formula.

[0305] Mathematical formula 43

[0306]

[0307] Here, S t The dimension of the signal part space contains t non-zero singular values. That is, S t It is a diagonal matrix composed of t singular values ​​with values ​​greater than a predetermined threshold. Moreover, t is equal to the number L of multipath waves. Therefore, by solving the generalized inverse matrix using only singular values ​​belonging to the signal part space (i.e., taking larger values) as described above, the influence of noise can be reduced. This is because the singular values ​​that do not belong to the signal part space (i.e., take smaller values) correspond to noise. By reducing the influence of noise, the generalized inverse matrix can be solved stably and with high accuracy even under the influence of noise.

[0308] (5) Regularization

[0309] On the one hand, in the above, the control unit 230 solves (AW k ) - On the other hand, the control unit 230 may also be configured to solve (AW k ) - Regularization using R-FOCUSS (Regularized-FOCUSS) or the like is performed. At this time, the control unit 230 may use the following mathematical formula (44) instead of the mathematical formula (37) in STEP 2. k H Take matrix A k The complex conjugate transpose of k The adjoint matrix of .

[0310] Mathematical formula 44

[0311]

[0312] However, in the above mathematical formula (44), k A k H If it is not regular, it is impossible to solve the inverse matrix (A k A k H ) -1 Therefore, the control unit 230 may use the following equation (45) instead of equation (44) in the above-mentioned STEP 2.

[0313] Mathematical formula 45

[0314]

[0315] Here, α in equation (45) is a small positive number. I is the unit matrix. α is also called a regularization parameter. As in equation (45), by using the regularization parameter, even in A k A k H If it is not regular, it can also be solved by making A k A k H +αI regularization to solve A k A k H The inverse matrix (A k A k H ) -1 In addition, by using the regularization parameter, it is easier to achieve S k In addition, the regularization parameters of FOCUSS are mentioned in the above non-patent literature.

[0316] Alternatively, the control unit 230 may solve A k A k H The inverse matrix (A k A k H ) -1 In this case, the control unit 230 replaces A in the above equation (44) with k A k H The singular value decomposition is formed into a diagonal matrix containing singular values ​​greater than the first threshold, and then (A k A k H ) -1 . A kA k H It is decomposed by singular value as shown below.

[0317] Mathematical formula 46

[0318]

[0319] At this time, (A k A k H ) -1 Calculate it using the following formula.

[0320] Mathematical formula 47

[0321]

[0322] In addition, A m A m H is a square matrix, so the singular value decomposition here is also called eigenvalue decomposition. Moreover, TSVD is also called TEVD (Truncated Eigen Value Decomposition).

[0323] Above, for (AW k ) - The calculation example of (AW k ) - When singular value decomposition is used in the calculation of , unnecessary singular values ​​can be removed, which can sometimes shorten the calculation time. k ) - When singular value decomposition is not used in the calculation of , the effect of improving the estimation accuracy can be expected by not removing singular values.

[0324] (6) Threshold processing

[0325] Alternatively, threshold processing may be performed in FOCUSS. Here, threshold processing is processing that makes elements below a predetermined threshold become 0. For example, the control unit 230 may make the weight matrix W in the mathematical formula (36) of STEP 1 above k Among the included elements, elements below a predetermined threshold value are set to 0. As an example, threshold processing shown in the following equation may be performed in the above-mentioned STEP 1.

[0326] Mathematical formula 48

[0327] STEP 1:

[0328]

[0329] Here, wk (i) is the weight matrix W k The i-th diagonal component of . k-1 (i) is the extended signal vector s k-1 The i-th component of |s k-1 (i)| max Yes k-1 (i) The maximum value of the size of the elements contained. -5 |s k-1 (i)| max is an example of a threshold.

[0330] According to the above threshold processing, when the weight matrix Wk is created, the elements of the extended signal vector s that take values ​​less than the threshold are considered not as signals but as noise, and are converted to zero. Thus, the extended signal vector s can be converged earlier. In addition, the number of non-zero elements is reduced, so a sparse solution can be easily obtained.

[0331] (7)2D-FOCUSS

[0332] In the above description, the case where the control unit 230 uses FOCUSS as the compressed sensing algorithm in estimating the extended signal vector s that becomes a sparse solution is exemplified.

[0333] According to the processing using FOCUSS as described above, the spread signal vector s can be estimated with high accuracy, and the estimation accuracy of the distance between devices can be improved.

[0334] However, FOCUSS is a process for performing multipath separation based on time information (distance information). Therefore, when the difference in propagation delay time (sometimes simply referred to as delay time) of multipath waves is extremely small, it is sometimes difficult to separate the multipath.

[0335] On the one hand, for example, Fig.13 A coordinate diagram showing the signal strength of the received signal along the time axis is shown. When the propagation delay time difference between the direct wave and the reflected wave is extremely small, as shown in FIG. Fig.13 As shown, the signal strengths of the two overlap, making it difficult to separate the two based on time information.

[0336] on the other hand, Fig.14 FIG. 2 shows a coordinate diagram that uses a time axis and an arrival angle axis to express the signal strength of a received signal. Fig.14 As shown, even when the propagation delay time difference between the direct wave and the reflected wave is extremely small, the two can be easily separated when the arrival angles of the two waves are different.

[0337] The technical concept according to the present embodiment is conceived with attention paid to the above-mentioned points, and improves the accuracy of estimating the distance and angle between devices.

[0338] Therefore, it is preferable that the control unit 230 uses 2D-FOCUSS instead of the FOCUSS described above to estimate the extended signal vector s. In addition, when the control unit 230 implements 2D-FOCUSS, it is basically desirable that the communication unit 200 has a plurality of antennas 211. However, when the antenna 211 is provided to be movable, it is also possible to receive signals while moving a single antenna 211, and virtually regard the signals as being received by a plurality of antennas 211 and perform signal processing as described below.

[0339] On the one hand, as described above, FOCUSS is a type of compressed sensing algorithm that estimates a spread signal vector s using a delayed time bin pattern matrix including a time information packet.

[0340] The delay time bin pattern matrix A of FOCUSS is expressed, for example, by the following mathematical formula (49).

[0341] Mathematical formula 49

[0342] A=[r(T1),…,r(T N )] (49)

[0343] On the other hand, 2D-FOCUSS is a type of compressed sensing algorithm that estimates a spread signal vector s using a bin pattern matrix including time information and angle information.

[0344] The bin pattern matrix A of 2D-FOCUSS is represented by, for example, the following mathematical formula (50).

[0345] Mathematical formula 50

[0346]

[0347] In the above equations (49) and (50), N represents the number of propagation delay time bins. In the above equation (50), P represents the number of arrival angle bins. In the above equation (50), a(T n ,θ p ) represents the mode vector. a(θ p ) represents the number of antennas 211 receiving the signals from the direction θ p The vector (also called the directional pattern vector) of the phase relationship of the received signals between the antennas 211 in this case. n ) is given by n A vector consisting of the autocorrelation functions of the transmitted signal having peaks (also called a time pattern vector).

[0348] Hereinafter, estimation of the extended signal vector s using 2D-FOCUSS by the control unit 230 will be described in detail.

[0349] On the other hand, the iterative calculations in 2D-FOCUSS are basically the same as those in FOCUSS. For example, in 2D-FOCUSS, the weighted minimum norm solution of the extended signal vector s is estimated by iteratively calculating the above equations (36) to (38), as in FOCUSS.

[0350] On the other hand, in 2D-FOCUSS and FOCUSS, the input delay profile and bin pattern matrix, and the output (estimated) spread signal vector are different from each other.

[0351] Fig.15 This is a diagram schematically showing the difference between the delay profiles, bin pattern matrix, and estimated spread signal vector between 2D-FOCUSS and FOCUSS.

[0352] like Fig.15 As shown, the delay distribution z of FOCUSS (k) is an M×1 column vector. In comparison, the delay distribution z of 2D-FOCUSS is an MK×1 column vector.

[0353] M represents the number of delay time samples, and K represents the number of antennas 211 (hereinafter, also referred to as elements) included in the communication unit 200. In addition, k represents an element (kth element) of an arbitrary number among a plurality of elements.

[0354] In addition, if Fig.15 As shown, the delayed time bin pattern matrix A of FOCUSS is an M×N matrix containing time information. In comparison, the bin pattern matrix A of 2D-FOCUSS is an MK×NP matrix containing time information and angle information.

[0355] In addition, N represents the number of propagation delay time bins, and P represents the number of arrival angle bins.

[0356] In addition, if Fig.15 As shown, the extended signal vector s estimated in FOCUSS (k) is an N×1 column vector. In comparison, the estimated extended signal vector s in 2D-FOCUSS is an NP×1 column vector.

[0357] The following describes in detail the differences between the delay profiles, bin pattern matrices, and extended signal vectors of 2D-FOCUSS and FOCUSS.

[0358] First, refer to Fig.16 The difference in delay profiles between 2D-FOCUSS and FOCUSS is described in detail.

[0359] On the one hand, the latency distribution z of FOCUSS (k)like Fig.16 As shown in the upper part of M M sampled delay profiles are used as elements.

[0360] On the other hand, the delay distribution of 2D-FOCUSS is as follows Fig.16 As shown in the lower part of , it includes the delay times τ1 to τ1 for the first to kth elements, respectively. M M×K delay profiles are sampled.

[0361] In this way, the delay profile z of 2D-FOCUSS, that is, the correlation calculation result, may be a result obtained by taking the correlation between the second signal and the first signal at every predetermined time and antenna 211.

[0362] Next, refer to Fig.17 The difference between the bin pattern matrices of 2D-FOCUSS and FOCUSS is described in detail.

[0363] like Fig.17 As shown in the upper part of , the delay time bin pattern matrix A of FOCUSS includes M×N elements, for the delay time τ1~τ M The sampled autocorrelation function is defined to simulate the delay time T1 to T N The autocorrelation function of the delay profile in this case of receiving the signal. In addition, a delay profile measured in advance may be used instead of the autocorrelation function simulating the delay profile.

[0364] On the other hand, Fig.17 As shown in the lower part of , the bin pattern matrix A of 2D-FOCUSS includes MK×NP elements, for the delay time τ1~τ in the column direction M The autocorrelation function of each of the K elements sampled is defined as a function of the arrival angles θ1 to θ P Delay time T1~T N The autocorrelation function of the delay distribution of the received signal in this case.

[0365] In this way, the bin pattern matrix A of 2D-FOCUSS may be a matrix composed of a plurality of elements representing correlation calculation results assumed when a plurality of antennas 211 each receive a signal at a plurality of set times and set angles.

[0366] Next, refer to Fig.18 The difference between the extended signal vectors of 2D-FOCUSS and FOCUSS is described in detail.

[0367] On the one hand, if Fig.18As shown in the upper part of the figure, the estimated extended signal vector s in FOCUSS (k) Contains delay time T1~T N The corresponding N warehouses.

[0368] In addition, as described above, the extended signal vector s estimated in FOCUSS (k) The portion outside the bin where the signal exists becomes zero.

[0369] On the other hand, Fig.18 As shown in the lower left part of , the extended signal vector s estimated in 2D-FOCUSS includes delay times T1 to T N ·Arrival angle θ1~θ P The corresponding N×P bins.

[0370] In addition, np Indicates the delay time T n Arrival angle θ p In addition, in the extended signal vector s estimated in 2D-FOCUSS, the part other than the bin where the signal exists also becomes zero.

[0371] Accordingly, the control unit 230 may regard the time and angle corresponding to the element with the earliest delay time among the non-zero elements (non-zero bins) of the extended signal vector s estimated using 2D-FOCUSS as the reception time and arrival angle of the signal, respectively.

[0372] That is, on the one hand, the control unit 230 may estimate the earliest setting time among the setting times corresponding to the non-zero elements in the sparse solution of the estimated extended signal vector s as the receiving time of the second signal, and estimate the setting angle corresponding to the non-zero element corresponding to the earliest setting time as the arrival angle of the second signal.

[0373] On the other hand, the control unit 230 may estimate the reception time and arrival angle of the second signal by making the estimated spread signal vector s a matrix based on a set time and a set angle and performing an amplitude peak search on the matrix.

[0374] Fig.18 The lower right side of shows an N×P matrix obtained by the control unit 230 transforming the estimated spread signal matrix s based on the delay time and the arrival angle. Alternatively, the control unit 230 searches for the peak value of the amplitude of the matrix and estimates the delay time and arrival angle corresponding to the element where the peak value is detected as the reception time and arrival angle of the signal.

[0375] Alternatively, when there are a plurality of peaks with the earliest delay times, the control unit 230 may estimate the delay time and arrival angle corresponding to the element in which the peak with the largest amplitude is detected as the reception time and arrival angle of the signal, respectively.

[0376] According to the peak search as described above, it is expected that the estimation accuracy can be improved compared with the case where the time and angle corresponding to the element with the earliest delay time are estimated as the reception time and arrival angle of the signal, respectively.

[0377] The differences between the delay profiles, bin pattern matrices, and estimated spread signal vectors of 2D-FOCUSS and FOCUSS have been described in detail above.

[0378] Next, a specific example of a directional pattern vector will be described.

[0379] For example, Fig.19 As shown, when three antennas 211A to 211C are arranged at equal intervals d, the pattern vector of the direction associated with the signal represented by the single-dot chain line is expressed as shown in the following mathematical formula (51).

[0380] Mathematical formula 51

[0381]

[0382] In the above equation (51), d represents the element spacing, and λ represents the wavelength. In addition, each element of the directional mode vector represented by the above equation (51) represents the phase difference with the reference element (for example, antenna 211A (first element).

[0383] However, the arrival angle of the signal estimated using 2D-FOCUSS is not limited to a one-dimensional angle (θ).

[0384] For example, Fig. 20 As shown, it is also possible that the angle of arrival of the signal becomes a two-dimensional angle In this case, when three or more antennas 211 are arranged in the plane, the bin pattern matrix A of 2D-FOCUSS is changed as shown in the following mathematical formula (52). In this case, each bin pattern matrix A becomes a matrix of ML×NPU.

[0385] Mathematical formula 52

[0386]

[0387] In addition, for example, Fig.21 As shown, when the three antennas 211A to 211C are arranged in an L shape at equal intervals d, the directional pattern vector is expressed as shown in the following mathematical formula (53).

[0388] Mathematical formula 53

[0389]

[0390] As exemplified above, the directional pattern vectors and bin pattern matrices according to the present embodiment can be flexibly transformed according to the arrangement of the elements.

[0391] Furthermore, the arrival angle of the signal estimated using 2D-FOCUSS may be a three-dimensional angle.

[0392] <4.2. Estimation of position parameters>

[0393] The control unit 230 estimates the position parameter based on the first incoming wave detected by the above-described process.

[0394] - Ranging Processing

[0395] 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 incoming wave estimated by the above-described processing. Figure 7 As described above.

[0396] However, the portable device 100 calculates CIR and performs sparse reconstruction and FOCUSS on the second ranging signal. Furthermore, the portable device 100 measures the time INT1 based on the estimated reception time of the first arrival of the second ranging signal.

[0397] On the other hand, the communication unit 200 calculates the CIR for the first ranging signal, performs sparse reconstruction and 2D-FOCUSS. Furthermore, the communication unit 200 measures the time INT3 based on the estimated reception time of the first arrival of the first ranging signal. Similarly, the communication unit 200 calculates the CIR for the third ranging signal, performs sparse reconstruction and 2D-FOCUSS. Furthermore, the communication unit 200 measures the time INT4 based on the estimated reception time of the first arrival of the third ranging signal.

[0398] Then, the control unit 230 estimates the propagation delay time based on the time T1 to T4, and estimates the distance R. As described above, the reception time of the first incoming wave can be searched with a resolution finer than the CIR, and thus the ranging accuracy can be improved accordingly.

[0399] - Arrival angle estimation processing

[0400] The communication unit 200 can estimate the arrival angle of the first incoming wave by performing 2D-FOCUSS as described above.

[0401] <4.3. Processing Flow>

[0402] Fig. 22 : is a flowchart showing an example of the flow of the position parameter estimation process executed by the communication unit 200 according to the present embodiment.

[0403] like Fig. 22 As shown, first, the control unit 230 calculates the delay profile by the PN correlation method (step S302). Next, the control unit 230 transforms the delay profile into a form including the matrix product of the bin pattern matrix and the extended signal vector by sparse reconstruction (step S304). Next, the control unit 230 estimates the extended signal vector that becomes the weighted minimum norm solution by 2D-FOCUSS (step S306). Moreover, the control unit 230 estimates the position parameter based on the estimated extended signal vector (step S308).

[0404] <4.4. Application targets of 2D-FOCUSS>

[0405] As described above, the transmitting side may transmit a signal including a plurality of preambles including one or more preamble symbols as a transmission signal. In this case, the receiving side may calculate the CIR of each preamble symbol by taking the correlation between each portion corresponding to the plurality of preamble symbols in the received signal and the preamble symbol at every predetermined time.

[0406] Alternatively, 2D-FOCUSS may be applied to the accumulated CIR obtained by accumulating the CIR of each preamble symbol. That is, the control unit 230 may transform the accumulated CIR obtained by accumulating the CIR of each preamble symbol into a form including the matrix product of the bin pattern matrix and the extended signal vector as a form for transforming the CIR into a form including the matrix product of the bin pattern matrix and the extended signal vector. Furthermore, the sparse solution of the extended signal vector s is estimated by 2D-FOCUSS to estimate the reception time of the first incoming wave.

[0407] On the other hand, 2D-FOCUSS may be applied to the CIR of each preamble symbol. In this case, the final extended signal vector s may be estimated by accumulating the extended signal vector s estimated for each preamble symbol. That is, the control unit 230 may estimate the reception time and arrival angle of the first incoming wave based on the result of accumulating the extended signal vector s for each CIR of a plurality of preambles, that is, the accumulated extended signal vector s, as the reception time and arrival angle of the first incoming wave estimated based on the extended signal vector s.

[0408] Alternatively, the CIR may be calculated for each pulse. In this case, 2D-FOCUSS may be applied to the CIR obtained by accumulating the CIR for each pulse, or may be applied to the CIR for each pulse.

[0409] Furthermore, the CIR may be calculated for the entire preamble. In this case, 2D-FOCUSS may be applied to the CIR calculated for the entire preamble.

[0410] The same results can be obtained in either method.

[0411] <4.5.2 Application scope of D-FOCUSS>

[0412] Alternatively, 2D-FOCUSS may be applied to the CIR as a whole.

[0413] On the other hand, FOCUSS can also be applied to a part of the CIR. In detail, 2D-FOCUSS can also be applied to a vector (hereinafter also referred to as a partial vector) composed of elements corresponding to a part of the set time and set angle among the elements of each of the multiple set times and set angles contained in the extended signal vector s. In this case, the control unit 230 estimates the sparse solution of the partial vector as the sparse solution of the estimated extended signal vector s. That is, the control unit 230 estimates the vector that minimizes the norm of the vector obtained by assigning weights to the partial vector, that is, the weighted minimum norm solution. As a result, compared with the case where 2D-FOCUSS is applied to the entire CIR, the computational burden can be reduced.

[0414] In particular, as long as the purpose is to detect the first wave, it is desirable to limit the application of 2D-FOCUSS to the portion of the CIR near the reception time and arrival angle of the first wave. In this case, the portion of the vector consisting of the elements corresponding to the set time near the reception time and the set angle near the arrival angle of the first wave in each of the multiple set times and set angles contained in the extended signal vector s is used as the object to apply 2D-FOCUSS. When the CIR is calculated based on the preamble symbol, a strong correlation is obtained only in the delay time and arrival angle where the pulse array of the transmitted signal and the pulse array of the received signal are completely consistent, and the correlation is low in other parts. Therefore, even if 2D-FOCUSS is limited to the portion of the CIR near the reception time and arrival angle of the first wave, the detection accuracy of the first wave can be maintained.

[0415] <4.6. Beam Space Processing>

[0416] Next, beam space processing according to this embodiment will be described.

[0417] As described above, in 2D-FOCUSS, two-dimensional estimation of distance and angle is performed. Compared with one-dimensional estimation, the calculation time of estimation of two or more dimensions such as 2D-FOCUSS increases.

[0418] Therefore, the control unit 230 according to the present embodiment can also reduce the calculation time by performing beam space (also referred to as sub-space) processing as pre-processing of an estimation algorithm such as 2D-FOCUSS.

[0419] The beam space processing is a process of forming a beam in the direction of arrival of a signal, and can be said to be a process of obtaining a signal in which the main beam direction is emphasized by applying a spatial filter whose strengths differ depending on the direction of arrival to the signal.

[0420] Fig.23 This is a diagram for explaining the outline of beam space processing according to the present embodiment.

[0421] Alternatively, the control unit 230 according to the present embodiment may generate delay profiles z1 to z2 obtained from K elements. K The beam space processing is performed and the selected signals y1 to y B As input to 2D-FOCUSS.

[0422] exist Fig.23 In the example shown, the control unit 230 uses, as inputs of 2D-FOCUSS, signals y2 and y3 whose magnitudes exceed a threshold value among signals y1 to y5 obtained by performing multi-beamforming on delay profiles z1 to z3.

[0423] According to the above-described processing, the number of inputs to the estimation algorithm can be reduced compared to a case where beam space processing is not performed, and the calculation time of the estimation algorithm can be significantly reduced.

[0424] Hereinafter, the beam space processing according to the present embodiment will be described in more detail.

[0425] First, multi-beam forming of beam space processing is described.

[0426] Fig.24 This is a diagram for explaining the multi-beam forming according to the present embodiment.

[0427] It can also be, for example, Fig.24 As shown, the control unit 230 according to the present embodiment performs multi-beam forming by using arbitrary weights w1 to w3 for the delay profiles z1 to z3.

[0428] The control unit 230 can flexibly set the direction of the beam by adjusting the phase of the weights w1 to w3 , and can flexibly set the shape of the beam (beam pattern) by adjusting the amplitude of the weights w1 to w3 .

[0429] The multi-beam forming methods are roughly divided into a fixed pattern type and an adaptive type.

[0430] On the one hand, if Fig.23 As shown, the fixed pattern type is a method of forming a plurality of beams directed in arbitrary directions by pre-given the amplitude and phase of the weight w.

[0431] When performing fixed pattern multi-beam forming, the control unit 230 may apply any one of uniform distribution, binomial distribution, Chebysh distribution, or Taylor distribution to the amplitude of the weight w.

[0432] For example, when binomial distribution is applied, although the signal separation performance decreases due to the thickening of the main lobe, a filter that completely reduces the side lobes to zero can be realized, thereby obtaining an advantage of being resistant to noise.

[0433] On the other hand, the adaptive type is based on the delay distribution z1~z K A method to determine the phase and amplitude of the weight w.

[0434] When using the adaptive type, it is theoretically possible to form an appropriate beam pattern in an appropriate direction according to the radio wave environment. Therefore, the adaptive type has the advantage of removing noise and interference waves and directing the beam toward the direction of arrival of the signal, and if beamforming is possible, directing the beam toward the direction of arrival of the real signal.

[0435] As an adaptive type, there is a method of using delay profiles z1 to z K The eigenvalue decomposition of eigenvalues ​​is used as the eigenvector of weight w in the beam space method or the DCMP (Directionally Constrained Minimization of Power) adaptive array method.

[0436] In the above, the multi-beam forming method according to the present embodiment has been described by taking specific examples.

[0437] Furthermore, the control unit 230 can also perform dynamic multi-beam forming by using a fixed pattern type and an adaptive type separately according to the radio wave environment and the like.

[0438] Next, beam selection according to this embodiment will be described in detail.

[0439] like Fig.23As shown, the control unit 230 involved in this embodiment may also control the signal y passing through the formed beam. b Medium size (e.g., y b In the case of a vector (norm), signals exceeding the threshold are selected.

[0440] Alternatively, the control unit 230 may select N signals y in order from the signal with the largest magnitude. b .

[0441] In addition, when the eigenvector beam space method is used in the multi-beam forming, the control unit 230 may convert the signal y b The size of is replaced by the eigenvalue obtained by eigenvalue decomposition.

[0442] In addition, the selected signal y b The number can be appropriately designed according to the number of components.

[0443] In addition, the control unit 230 according to the present embodiment can select the signal y based not only on the angle region but also on the time region. b , thus further reducing the calculation time.

[0444] Fig.25 This is a diagram for explaining a matrix of a signal after multi-beam forming according to the present embodiment.

[0445] Fig.25 In , the column vector is the signal vector of the b-th beam, and the row vector is the signal vector of the m-th time sample.

[0446] Beam selection includes: a method of performing a selection based on the angle region after a selection based on the time region, a method of performing a selection based on the time region after a selection based on the angle region, and a method of performing both a selection based on the time region and a selection based on the angle region.

[0447] First, a method of performing a selection based on an angle region after a selection based on a time region is described. This structure of performing a selection based on an angle region after a selection based on a time region is more preferred in a system with a high resolution of the time region. Specifically, when the resolution of the time region is high, the possibility that the noise in the time region can be separated from the real signal is high, so the noise can be removed with high precision by first selecting the time region. In other words, the data in a state where the noise has been removed is processed in the subsequent selection of the angle region, so the selection of the angle region can be performed with higher precision.

[0448] Fig.26This is a diagram for explaining a method of performing selection based on an angle region after selection based on a time region according to the present embodiment.

[0449] In this case, the control unit 230 first selects a row vector whose norm of the row vector in the time domain exceeds a threshold value.

[0450] Figure 26 to Figure 28 In the diagram, selected objects are represented by no color, and non-selected objects are represented by dots.

[0451] In addition, here, when there are multiple clusters of row vectors exceeding the threshold value and the clusters are separated by a constant time or more, the control unit 230 may distinguish the corresponding clusters as individual incoming waves. Thus, when there are two or more waves, the control unit 230 may not select the time-delayed signal. In this way, unnecessary calculations can be omitted.

[0452] Next, the control unit 230 selects a column vector whose norm of the column vector in the angle region exceeds a threshold value in the matrix composed of only the selected row vectors.

[0453] Alternatively, the control unit 230 may perform selection using different thresholds in the time domain and the angle domain.

[0454] Next, a method of performing a selection based on a time region after a selection based on an angle region is described. A structure in which a time region is selected after an angle region is selected is more preferred in a system in which the resolution of the angle region is high. Specifically, when the resolution of the angle region is high, the possibility that the noise in the angle region can be separated from the true signal is high. Therefore, by first performing an angle region selection, the noise can be removed with high accuracy. In other words, in the subsequent time region selection, the data in a state in which the noise has been removed is processed, so the time region selection can be performed with higher accuracy.

[0455] Fig. 27 This is a diagram for explaining a method of performing selection based on a time region after selection based on an angle region according to the present embodiment.

[0456] In this case, the control unit 230 first selects a column vector whose norm of the column vector of the angle region exceeds a threshold value.

[0457] Here, preferably, when the angle that does not need to be estimated can be predetermined, the control unit 230 does not select the column vector of the predetermined angle range. This can save unnecessary calculations. Such control is particularly useful when the position information of the obstruction around the communication unit is known.

[0458] Next, the control unit 230 selects row vectors whose norms of row vectors in the time domain exceed a threshold value in the matrix composed of only the selected column vectors.

[0459] Alternatively, the control unit 230 may perform selection using different thresholds in the time domain and the angle domain.

[0460] Next, refer to Fig.28 A method of simultaneously performing selection based on a time region and selection based on an angle region according to the present embodiment will be described.

[0461] When this method is adopted, the control unit 230 may select an element whose size of each element of the matrix of the signal exceeds a threshold value.

[0462] Alternatively, instead of using the threshold value, the control unit 230 may select N elements in order from the element with the largest size.

[0463] In addition, when this method is used, it is different from the above two methods. Fig.28 As shown, the control unit 230 can also select a non-rectangular range in the time-space region. This allows selection of elements with a higher degree of freedom compared to the case of sequentially selecting time or angle.

[0464] Furthermore, when the selection based on the time region and the selection based on the angle region are performed simultaneously, it is also possible to detect more than two incoming waves based on the two-dimensional distance between the time and angle regions of the clusters. Fig.29 As shown, when the two-dimensional distance d between cluster C1 and cluster C2 exceeds a threshold value, the control unit 230 may distinguish cluster C1 and cluster C2 as separate incoming waves. In addition, when there are two or more waves, the control unit 230 may not select the time-delayed signal. In this way, unnecessary calculations can be omitted.

[0465] The beam space processing according to the present embodiment has been described above. According to the beam space processing according to the present embodiment, the calculation time can be significantly reduced by limiting the information input to the estimation algorithm.

[0466] Here, a more detailed description will be given of signal conversion in the case where 2D-FOCUSS is adopted as the estimation algorithm.

[0467] When beam space processing is not performed, the input to 2D-FOCUSS is the delay profile z and the bin pattern matrix A as described above.

[0468] On the other hand, when beam space processing is performed, the input to 2D-FOCUSS is the delay profile zb and bin pattern matrix B.

[0469] Specifically, the control unit 230 performs multi-beam forming by using the weights (matrix) defined in the following mathematical formula (54) and using the signal transformation shown in the following mathematical formula (55), and then performs signal selection as shown in the following mathematical formula (56), thereby obtaining the delay profile z b and the bin pattern matrix B. In the following equations (54) to (56), F is the number of beams generated in multi-beam forming.

[0470] Mathematical formula 54

[0471]

[0472] Mathematical formula 55

[0473]

[0474] Mathematical formula 56

[0475] Z F =vec(Z F )

[0476]

[0477] The control unit 230 inputs the delay profile zb and the bin pattern matrix B obtained as described above into 2D-FOCUSS and estimates the extended signal vector s.

[0478] In addition, the bin pattern matrix B and the extended signal vector s are as shown in Fig.17 and Fig.18 As described, it has a delay time T1 to T N The corresponding elements N and the arrival angles θ1 to θ P The corresponding bin of element P.

[0479] The control unit 230 may determine the setting range of the bin pattern matrix B and the bin of the extended signal vector s based on the result of the beam space processing.

[0480] More specifically, the control unit 230 may determine the bin setting range based on the time and angle ranges selected in the beam space processing. This can further reduce the number of bins (N and P) and further reduce the amount of calculation.

[0481] Furthermore, the beam space processing according to the present embodiment can be applied not only to 2D-FOCUSS but also to various N-dimensional estimation algorithms (N≥2) for simultaneously estimating two or more parameters.

[0482] Examples of the above parameters include distance, speed, acceleration, and dielectric constant.

[0483] Furthermore, the input to the N-dimensional estimation algorithm is not limited to UWB signals, but may be any wave such as radio waves, ultrasonic waves, and light.

[0484] Furthermore, the input to the N-dimensional estimation algorithm may be a reflected wave from radar or the like.

[0485] <4.7. Modifications>

[0486] In the above embodiment, an example in which the norm is a so-called l0 norm is described. The l0 norm refers to a case in which the multiplier p of the lp norm is 0. The lp norm is defined by the following equation.

[0487] Mathematical formula 57

[0488] ||x|| p =|x1| p +|x2| p +…+|x n | p (57)

[0489] On the other hand, the l0 norm is defined by the following equation where p=0 in the above equation (57).

[0490] Mathematical formula 58

[0491] ||x||0=|x1| 0 +|x2| 0 +…+|x n | 0 (58) In the mathematical formula (58), consider making 0 0 =0.

[0492] That is, the l0 norm refers to the number of non-zero components of a vector.

[0493] The method of repeatedly executing equations (36) to (38) listed in the above embodiment is a method of minimizing the l0 norm as a weighted minimum norm solution of the extended vector s. In contrast, the control unit 230 may minimize the lp norm as a weighted minimum norm solution of the extended vector s. Specifically, the control unit 230 may use the following equation instead of the above STEP 1.

[0494] Mathematical formula 59

[0495] STEP 1:

[0496]

[0497] Here, p is a constant greater than or equal to 0 and less than or equal to 1. When p is 0, the above equation (59) is the same as the equation (36). That is, the control unit 230 may make p equal to 0 in the above equation (59) to estimate the weighted minimum norm solution.

[0498] Even when p takes a value other than 0, the reception time of the first incoming wave can be estimated with high accuracy, similarly to the above-described embodiment.

[0499] <<5.Supplement>>

[0500] The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to such examples. It is understood that if a person has common knowledge in the technical field to which the present invention belongs, it is self-evident that various variations or modifications can be thought of within the scope of the technical ideas recorded in the claims, and these, of course, also belong to the technical scope of the present invention.

[0501] For example, in the above-mentioned embodiment, the control unit 230 performs calculation of CIR, detection of the first incoming wave, and estimation of position parameters, but the present invention is not limited to such an example. At least any one of these processes may be performed by the wireless communication unit 210. For example, each of the plurality of wireless communication units 210 may perform calculation of CIR and detection of the first incoming wave based on the received signal received by each. In addition, the estimation of position parameters may be performed by the wireless communication unit 210 that functions as a host, for example.

[0502] For example, in the above embodiment, an example of calculating the angles α and β based on the antenna array phase difference of the antenna pair is described, but the present invention is not limited to such an example. As an example, the communication unit 200 may also calculate the angles α and β by performing beamforming using the plurality of antennas 211. In this case, the communication unit 200 scans the main lobes of the plurality of antennas 211 in all directions, determines that the portable device 100 exists in the direction with the maximum received power, and calculates the angles α and β based on such a direction.

[0503] For example, in the above embodiment, as shown in FIG. Figure 3 As described above, the local coordinate system is described as a coordinate system having coordinate axes parallel to the axis connecting the antenna pair, but the present invention is not limited to such an example. For example, the local coordinate system may also be a coordinate system having coordinate axes that are not parallel to the axis connecting the antenna pair. In addition, the origin is not limited to the center of the multiple antennas 211. Alternatively, the local coordinate system involved in this embodiment may be arbitrarily set based on the configuration method of the multiple antennas 211 possessed by the communication unit 200.

[0504] For example, in the above-mentioned embodiment, an example is described in which the authenticated party is the portable device 100 and the authenticator is the communication unit 200, but the present invention is not limited to such an example. The roles of the portable device 100 and the communication unit 200 may be opposite. For example, the portable device 100 may also determine the position parameter. In addition, the roles of the portable device 100 and the communication unit 200 may also be dynamically exchanged. In addition, the communication unit 200 may also mutually determine and authenticate the position parameter.

[0505] For example, in the above-mentioned embodiment, an example of applying the present invention to a smart key system is described, but the present invention is not limited to such an example. The present invention can be applied to any system that estimates location parameters and performs authentication by sending and receiving signals. For example, the present invention can be applied to a device pair of any two devices including objects such as portable machines, vehicles, smart phones, drones, home houses, and household appliances. In this case, one of the device pair acts as an authenticator and the other acts as an authenticated person. In addition, the device pair may include two devices of the same type or two devices of different types. In addition, the present invention can also be applied to the situation where a wireless LAN (Local Area Network) router determines the location of a smart phone.

[0506] For example, in the above-mentioned embodiment, the case where UWB is used as the wireless communication standard is cited, but the present invention is not limited to such an example. For example, a device using infrared rays may be used as the wireless communication standard.

[0507] In addition, a series of processes performed by each device described in this specification may be implemented using any form of software, hardware, or a combination of software and hardware. The programs constituting the software are, for example, pre-stored in a recording medium (non-transitory media) provided inside or outside each device. Moreover, each program is read into RAM when executed by a computer, and is executed by a processor such as a CPU. The above-mentioned recording medium is, for example, a magnetic disk, an optical disk, a magnetic optical disk, a flash memory, etc. In addition, the above-mentioned computer program may also be distributed, for example, via a network, instead of using a recording medium.

[0508] In addition, the processing described in the flowcharts in this specification does not necessarily have to be performed in the order shown in the diagrams. Several processing steps may be performed in parallel. In addition, additional processing steps may be used, or some processing steps may be omitted.

[0509] Description of Reference Numerals

[0510] 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.

Claims

1. A communication device, characterized in that: have: a wireless communication unit that receives signals from other communication devices wirelessly; and A control unit, which takes the correlation between the second signal, which is a signal corresponding to the first signal received by the wireless communication unit when the other communication device sends a signal containing a pulse as the first signal, and the first signal at each specified time, and converts the result obtained by taking the correlation between the second signal and the first signal at each specified time, that is, the correlation operation result, into a form of a matrix consisting of a plurality of elements representing the correlation operation result when the signal is assumed to be received under the conditions of a plurality of set times and set angles, that is, a bin pattern matrix, and a vector consisting of a plurality of elements representing the presence or absence of a signal at each of the set times and the set angles and the amplitude and phase of the signal, that is, an extended signal vector, and estimates the reception time and arrival angle of the second signal based on the set times and the set angles corresponding to each of the plurality of elements of the extended signal vector, The interval of the set time is shorter than the prescribed time.

2. The communication device according to claim 1, characterized in that The correlation calculation result is a result obtained by taking a correlation between the second signal and the first signal at the predetermined time and for each antenna included in the wireless communication unit.

3. The communication device according to claim 2, characterized in that: The bin pattern matrix is ​​a matrix composed of a plurality of elements representing the correlation calculation results when it is assumed that each of the plurality of antennas receives a signal at each of the plurality of set times and the plurality of set angles.

4. The communication device according to claim 3, characterized in that: The control unit estimates the set time and the set angle corresponding to the non-zero element among the plurality of elements of the extended signal vector as the reception time and arrival angle of the second signal, respectively.

5. The communication device according to claim 4, characterized in that: The control unit estimates a sparse solution of the extended signal vector, and estimates the set time and the set angle corresponding to the non-zero element in the estimated sparse solution of the extended signal vector as the reception time and arrival angle of the second signal, respectively. The sparse solution is a vector in which only a predetermined number of elements are non-zero, The predetermined number is the number of pulses included in the second signal as pulses corresponding to the pulses included in the first signal.

6. The communication device according to claim 5, characterized in that: The control unit estimates the reception time and arrival angle of the second signal by making the estimated spread signal vector a matrix obtained based on the set time and the set angle and performing an amplitude peak search on the matrix.

7. The communication device according to claim 5, characterized in that: The control unit estimates the earliest set time among the set times corresponding to the non-zero elements in the sparse solution of the estimated extended signal vector as the reception time of the second signal, and estimates the set angle corresponding to the non-zero element corresponding to the earliest set time as the arrival angle of the second signal.

8. The communication device according to claim 5, characterized in that: The control unit estimates a weighted minimum norm solution, which is a vector obtained by giving weights to the extended signal vector and minimizing the norm of the vector, as the sparse solution for estimating the extended signal vector.

9. The communication device according to claim 8, characterized in that: The control unit estimates the weighted minimum norm solution by repeatedly calculating equation (1), equation (2) and equation (3). Mathematical formula 1 Mathematical formula 2 q k =(AW k ) - from …(2) Mathematical formula 3 s k =W k q k …(3) Here, k is the number of repetitions, s is k is a candidate for the weighted minimum norm solution, p is a constant greater than 0 and less than 1, A is the bin pattern matrix, (AW K ) - It is AW k The generalized inverse matrix of k The initial value of is given by, Mathematical formula 4 S| k＝0 =s mn …(4) Here, s mn is the extended signal vector whose norm is minimized.

10. The communication device according to claim 9, characterized in that: The control unit estimates the weighted minimum norm solution by setting p to 0 in the mathematical formula (1).

11. The communication device according to claim 9, characterized in that: The control unit replaces AW in the mathematical formula (2) k The singular value decomposition is formed into a diagonal matrix containing singular values ​​greater than a predetermined threshold value, and then (AW k ) - .

12. The communication device according to claim 9, characterized in that: The control unit uses equation (5) instead of equation (2). Mathematical formula 5 Here, A k H Yes A k The adjoint matrix of .

13. The communication device according to claim 10, characterized in that: The control unit uses equation (6) instead of equation (5), Mathematical formula 6 Here, α is a small positive quantity and I is the identity matrix.

14. The communication device according to claim 9, characterized in that: The control unit makes W k Among the included elements, elements having a value less than or equal to a predetermined threshold value are set to zero.

15. The communication device according to claim 8, characterized in that: The control unit estimates the sparse solution of the extended signal vector as a vector that minimizes the norm of a vector obtained by assigning weights to the elements corresponding to a part of the set time or the set angle among the elements of each of the multiple set times and the set angles contained in the extended signal vector, that is, a weighted minimum norm solution.

16. The communication device according to claim 1, characterized in that: The control unit estimates the distance between the communication device and the other communication device based on the estimated reception time of the second signal.

17. The communication device according to claim 1, characterized in that: The other communication device transmits a signal including a plurality of preamble symbols which are pulse arrays including one or more pulses as the first signal, The control unit transforms the result obtained by accumulating the correlation between the part of the second signal corresponding to the multiple preamble code symbols and the preamble code symbols at each specified time, that is, the multiple correlation operation results, into a form including the matrix product of the bin pattern matrix and the extended signal vector, as a form for transforming the correlation operation result into a form including the matrix product of the bin pattern matrix and the extended signal vector.

18. The communication device according to claim 1, characterized in that: The other communication device transmits a signal including a plurality of preamble symbols which are pulse arrays including one or more pulses as the first signal, The control unit estimates the reception time and arrival angle of the second signal based on the result obtained by accumulating the extended signal vectors of each of the multiple correlation operation results, based on the result of correlation between each of the parts of the second signal corresponding to the multiple preamble code symbols taken at each specified time and the preamble code symbols.

19. The communication device according to claim 1, characterized in that: The control unit performs beam space processing on the correlation calculation result and transforms the selected signal into a format including the matrix product.

20. The communication device according to claim 19, characterized in that The control unit performs multi-beam forming by adjusting a phase and an amplitude of a weight in the beam space processing.

21. The communication device according to claim 20, characterized in that: The control unit applies any one of uniform distribution, binomial distribution, Chebyshev distribution, or Taylor distribution to the amplitude of the weight.

22. The communication device according to claim 20, characterized in that The control unit adjusts the phase and amplitude of the weight based on the correlation calculation result.

23. The communication device according to claim 22, characterized in that: The control unit adjusts the phase and amplitude of the weight based on an eigenvector obtained by eigenvalue decomposition of the correlation calculation result.

24. The communication device according to claim 22, characterized in that The control unit uses a DCMP adaptive array to adjust the phase and amplitude of the weight.

25. The communication device according to claim 19, characterized in that The control unit selects a signal whose size exceeds a threshold value from among the signals passing through the beam formed in the beam spatial processing.

26. The communication device according to claim 25, characterized in that The control unit further selects signals whose magnitudes exceed the threshold value in the time region after selecting the signals whose magnitudes exceed the threshold value in the angle region.

27. The communication device according to claim 25, characterized in that The control unit further selects signals whose magnitudes exceed the threshold value in the time region after selecting the signals whose magnitudes exceed the threshold value in the angle region.

28. The communication device according to claim 27, characterized in that The control unit does not select a signal in a predetermined angle range.

29. The communication device according to claim 25, characterized in that The control unit selects signals whose magnitudes exceed a threshold value in the time domain and the angle domain.

30. The communication device according to claim 26 or 29, characterized in that: The control unit does not select signals related to the second and subsequent waves.

31. The communication device according to claim 25, characterized in that The control section selects signals using different thresholds in the time domain and the angle domain.

32. The communication device according to claim 19, characterized in that The control unit determines setting ranges of bins of the bin pattern matrix and the extended signal vector based on a result of the beam space processing.

33. An information processing method, characterized in that: The steps include: wirelessly receiving signals from other communication devices; and The correlation between the second signal, which is a signal corresponding to the first signal received when the other communication device sends a signal including a pulse as the first signal, and the first signal is taken at each specified time, and the result obtained by taking the correlation between the second signal and the first signal at each specified time, that is, the correlation operation result, is transformed into a form of a matrix consisting of a plurality of elements representing the correlation operation result when the signal is assumed to be received under respective conditions of a plurality of set times and set angles, that is, a bin pattern matrix, and a vector consisting of a plurality of elements representing the presence or absence of a signal at each of the set times and the set angles and the amplitude and phase of the signal, that is, an extended signal vector, and the reception time and arrival angle of the second signal are estimated based on the set times and the set angles corresponding to the plurality of elements of the extended signal vector, The interval of the set time is shorter than the prescribed time.

34. A program, characterized in that Make the computer function as a control unit, For the control unit, when a signal including a pulse is sent as the first signal by the other communication device, the correlation between the second signal and the first signal is obtained at each specified time by the wireless communication unit that receives the signal wirelessly from the other communication device, and the result obtained by taking the correlation between the second signal and the first signal at each specified time, that is, the correlation operation result, is transformed into a form of a matrix consisting of a plurality of elements representing the correlation operation result when the signal is assumed to be received under the conditions of a plurality of set times and set angles, that is, a bin pattern matrix, and a vector consisting of a plurality of elements representing the presence or absence of a signal at each of the set times and the set angles and the amplitude and phase of the signal, that is, an extended signal vector, and the reception time and arrival angle of the second signal are estimated based on the set times and the set angles corresponding to the plurality of elements of the extended signal vector. The interval of the set time is shorter than the prescribed time.

Images