Monitoring device and monitoring method

Abstract

To enable monitoring of the presence or absence of moving objects at least one of indoors or outdoors with higher accuracy than before.SOLUTION: A monitoring device includes: a moving object reflection component extraction unit 1201 which extracts reflection components caused by a moving object from a signal received by a sensor 11; a profile distribution generation unit 1202 which generates a profile distribution, which is a waveform indicating the relation between the reception intensity of the reflection components caused by the moving object and the propagation distance or the propagation delay time, based on the reflection components caused by the moving object extracted by the moving object reflection component extraction unit 1201; a profile feature extraction unit 1203 which extracts a feature amount related to the waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit 1202; and a calculation unit 1204 which performs at least one of detection of the presence or absence of a moving object at least one of indoors or outdoors, or calculation of the reliability of the detection of the presence or absence of a moving object, based on the feature amount extracted by the profile feature extraction unit 1203 or a time-series change in the feature amount.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a monitoring device and a monitoring method for monitoring the presence or absence of a moving object at least either indoors or outdoors. [Background technology]

[0002] In recent years, there has been a trend to introduce radio wave sensors such as millimeter wave radar into indoor spaces such as vehicle interiors to monitor elderly people or detect abandoned infants, etc. Compared to cameras, millimeter wave radar has the advantage of being superior in terms of privacy and less susceptible to the effects of lighting or weather.

[0003] On the other hand, the radio waves used by millimeter-wave radar have properties such as transmission, reflection, and diffraction. Therefore, when sensing indoors with a millimeter-wave radar, the radio waves may pass through windows or walls and be reflected by people outside, resulting in multipath detection that can lead to false positives. Due to these phenomena, it can be difficult to determine whether an object detected by the millimeter-wave radar is inside or outside the room. This can lead to false positives in vehicle interior sensing using millimeter-wave radar. For example, a false alarm may be issued if a child is found abandoned in the vehicle even when no one is present.

[0004] In response to this, a technology is known that uses a simple configuration to determine whether a detected object is inside or outside the vehicle (see, for example, Patent Document 1). This conventional technology is equipped with two antennas, a narrow directional antenna and a wide directional antenna. In addition, the conventional technology compares the reception strength of a signal received by outputting radio waves from the narrow directional antenna with the reception strength of a signal received by outputting radio waves from the wide directional antenna, and determines whether the detected object is inside or outside the vehicle based on the comparison result. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2010-188855 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, in conventional technology, the accuracy of the determination is not high because the reception strength of the signals received by each of the narrow directional antenna and the wide directional antenna is compared to determine whether the detected object is inside or outside the vehicle. Furthermore, the conventional technology requires two antennas, a narrow directional antenna and a wide directional antenna, which complicates the hardware configuration and increases the amount of calculation required to process signals received by these antennas.

[0007] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a monitoring device that is capable of monitoring the presence or absence of moving objects at least indoors or outdoors with greater accuracy than conventional methods. [Means for solving the problem]

[0008] The monitoring device according to the present disclosure is characterized in that it includes a moving object reflection component extraction unit that extracts reflection components caused by a moving object from a signal received by a sensor that transmits a signal and receives a signal reflected by the moving object; a profile distribution generation unit that generates a profile distribution, which is a waveform indicating the relationship between the reception intensity of the reflection component caused by the moving object and the propagation distance or the propagation delay time, based on the reflection components caused by the moving object extracted by the moving object reflection component extraction unit; a profile feature extraction unit that extracts features related to the waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit; and a calculation unit that performs at least one of the processes of detecting the presence or absence of a moving object at least either indoors or outdoors, or calculating the reliability of the detection of the presence or absence of a moving object, based on the features extracted by the profile feature extraction unit or changes in the features over time. Effect of the Invention

[0009] According to the present disclosure, since it is configured as described above, it is possible to monitor the presence or absence of a moving object at least either indoors or outdoors with higher accuracy than in the past. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an example of the configuration of a monitoring system including a monitoring device according to a first embodiment. [Diagram 2] 4 is a flowchart illustrating an example of the operation of the monitoring device according to the first embodiment. [Diagram 3] Figures 3A and 3B are diagrams for explaining an example of the operation of the monitoring device of embodiment 1, where Figure 3A is a diagram showing an example of the transmission and reception of radio waves and the distance profile distribution when a moving object is present indoors, and Figure 3B is a diagram showing an example of the transmission and reception of radio waves and the distance profile distribution when a moving object is present outside the room. [Figure 4] 4A and 4B are diagrams for explaining an example of the operation of the monitoring device of embodiment 1, in which FIG. 4A is a diagram showing an example of a distance profile distribution when a moving object is present indoors, and FIG. 4B is a diagram showing an example of a distance profile distribution when a moving object is present outside the room. [Diagram 5] 5A and 5B are diagrams for explaining multipath. [Figure 6] 6A and 6B are diagrams for explaining an example of the operation of the monitoring device of embodiment 1, in which FIG. 6A shows an example of a distance profile distribution when a moving object is present in a room, and FIG. 6B shows an example of a threshold value used when the feature is at a peak position. [Figure 7] 7A and 7B are diagrams for explaining an example of the operation of the monitoring device of embodiment 1, in which FIG. 7A is a diagram showing an example of a distance profile distribution, and FIG. 7B is a diagram showing an example of peak position extraction when a distance profile distribution for each moving object speed is used. [Figure 8]4 is a diagram for explaining an example of the operation of a calculation unit in the first embodiment. FIG. [Figure 9] FIG. 11 is a block diagram showing an example of the configuration of a monitoring system including a monitoring device according to a second embodiment. [Figure 10] 11 is a diagram for explaining an example of operation in a case where a plurality of radio waves are input to the monitoring device according to the second embodiment. FIG. [Figure 11] 11A and 11B are diagrams for explaining an example of the operation of a monitoring device according to embodiment 2, where FIG. 11A is a diagram showing an example of a distance profile distribution obtained by the monitoring device according to embodiment 1, and FIG. 11B is a diagram showing an example of a distance profile distribution obtained by the monitoring device according to embodiment 2. [Figure 12] FIG. 11 is a block diagram showing a configuration example of a monitoring system including a monitoring device according to a third embodiment. [Figure 13] 13A and 13B are diagrams illustrating an example of a hardware configuration of the monitoring device according to the first to third embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, the embodiments will be described in detail with reference to the drawings. Embodiment 1 FIG. 1 is a diagram showing an example of the configuration of a monitoring system 1 including a monitoring device 12 according to the first embodiment. The monitoring system 1 monitors the presence or absence of a moving object at least either indoors or outdoors. The monitoring system 1 includes a sensor 11, a monitoring device 12, and a control device 13, as shown in FIG. The monitoring system 1 can be used, for example, as a monitoring system that monitors the presence or absence of a moving object at least either inside or outside a vehicle.

[0012] The sensor 11 transmits a signal to a room targeted by the monitoring device 12, and receives the signal reflected by an object present in or outside the room. The object includes a moving object such as a person. This sensor 11 may be any sensor capable of transmitting a signal and receiving a signal reflected by a moving object, such as a radio wave sensor capable of transmitting and receiving radio waves or an ultrasonic sensor capable of transmitting and receiving ultrasonic waves. In the first embodiment, there is one sensor 11. The signal received by this sensor 11 is output to a monitoring device 12 .

[0013] The monitoring device 12 monitors the presence or absence of a moving object at least either inside or outside the room, based on the signal received by the sensor 11. The monitoring device 12 according to the first embodiment detects the presence or absence of a moving object at least either inside or outside the room. A signal indicating the monitoring result by this monitoring device 12 is output to a control device 13. An example of the configuration of the monitoring device 12 will be described later.

[0014] The control device 13 performs various controls based on the monitoring results by the monitoring device 12. For example, when the monitoring device 12 detects the presence of a moving object inside or outside the room, the control device 13 may issue a notification to the outside indicating this. This enables the monitoring system 1 to take security measures such as theft detection or suspicious person detection.

[0015] The monitoring device 12 includes, for example, a moving object reflection component extracting section 1201, a profile distribution generating section 1202, a profile feature extracting section 1203, and a computing section 1204, as shown in FIG. Note that the monitoring device 12 may perform part of the processing on a cloud server or in another ECU (Electronic Control Unit).

[0016] The moving object reflection component extraction unit 1201 extracts reflection components due to a moving object from the signal received by the sensor 11. That is, the moving object reflection component extraction unit 1201 extracts reflection components due to a moving object from the signal received by the sensor 11 by suppressing reflection components due to objects that exist around the sensor 11 and have a speed of 0. At this time, for example, the moving object reflection component extraction unit 1201 extracts reflection components due to a moving object by using a known process such as MTI (Moving Target Indicator) processing. A signal indicating the extraction result by the moving object reflection component extraction unit 1201 is output to a profile distribution generation unit 1202 .

[0017] The profile distribution generating unit 1202 generates a profile distribution based on the reflection components due to the moving object extracted by the moving object reflection component extracting unit 1201 . Here, for example, the profile distribution generating unit 1202 generates a distance profile distribution as the profile distribution. The distance profile distribution is a waveform that indicates the relationship between the reception intensity of the reflected component from the moving object and the propagation distance. In this case, for example, the profile distribution generating unit 1202 generates the distance profile distribution by applying a Fourier transform process in the fast time direction to the reflected component from the moving object. Note that the distance profile distribution shown below indicates the relationship between the reception intensity of the reflected component from the moving object and a distance that is 1 / 2 the propagation distance, and the distance indicated by the peak position coincides (including the meaning of approximately coinciding) with the distance obtained by drawing a straight line from the sensor 11 to the moving object. Furthermore, the profile distribution generating unit 1202 may generate a profile distribution for each velocity of a moving object as the profile distribution. A signal indicating the result of generation by the profile distribution generation unit 1202 is output to the profile feature extraction unit 1203 .

[0018] Based on the profile distribution generated by the profile distribution generating unit 1202, the profile feature extracting unit 1203 extracts a feature amount relating to the waveform shape of the profile distribution. At this time, for example, the profile feature extraction unit 1203 may perform a frequency analysis on the profile distribution generated by the profile distribution generation unit 1202, and extract the frequency analysis result as a feature amount. Also, for example, the profile feature extraction unit 1203 may extract, as a feature amount, a gradient in a falling section in the profile distribution generated by the profile distribution generation unit 1202. Note that the falling section is a section from the maximum value to a predetermined value or less in the distance profile distribution. Furthermore, for example, the profile feature extraction unit 1203 may extract a peak position in the profile distribution generated by the profile distribution generation unit 1202 as a feature amount. The profile feature extraction unit 1203 may extract, as the feature amount, a combination of the frequency analysis result, the gradient, or the peak position described above. A signal indicating the extraction result by this profile feature extraction section 1203 is output to a calculation section 1204 .

[0019] The calculation unit 1204 detects the presence or absence of a moving object at least either inside or outside the room, based on the feature amount extracted by the profile feature extraction unit 1203 or a time-series change in the feature amount. A signal indicating the result of detection by the calculation unit 1204 is output to the external control device 13.

[0020] Next, an example of the operation of the monitoring device 12 according to the first embodiment shown in Fig. 1 will be described with reference to Fig. 2. In the following, it is assumed that the sensor 11 is a radio wave sensor that transmits and receives radio waves, and the profile distribution generating unit 1202 generates a distance profile distribution as a profile distribution. In the monitoring device 12 according to the first embodiment shown in Fig. 1, first, as shown in Fig. 2, the moving object reflection component extraction unit 1201 extracts reflection components due to a moving object from the radio waves received by the sensor 11 (step ST101). That is, the moving object reflection component extraction unit 1201 extracts reflection components due to a moving object by suppressing reflection components due to objects existing around the sensor 11 and having a speed of 0 from the radio waves received by the sensor 11. At this time, for example, the moving object reflection component extraction unit 1201 extracts reflection components due to a moving object by using a known process such as MTI processing. A signal indicating the extraction result by the moving object reflection component extraction unit 1201 is output to a profile distribution generation unit 1202 .

[0021] Next, the profile distribution generating unit 1202 generates a distance profile distribution based on the reflection components due to the moving object extracted by the moving object reflection component extracting unit 1201 (step ST102). At this time, for example, the profile distribution generating unit 1202 generates the distance profile distribution by applying a Fourier transform process in the fast time direction to the reflection components due to the moving object. Furthermore, the profile distribution generating unit 1202 may generate, as the profile distribution, a distance profile distribution for each velocity of a moving object. A signal indicating the result of generation by the profile distribution generation unit 1202 is output to the profile feature extraction unit 1203 .

[0022] Next, the profile feature extraction unit 1203 extracts a feature amount relating to the waveform shape of the distance profile distribution based on the distance profile distribution generated by the profile distribution generation unit 1202 (step ST103). At this time, for example, the profile feature extraction unit 1203 may perform a frequency analysis on the distance profile distribution generated by the profile distribution generation unit 1202, and extract the frequency analysis result as a feature amount. Also, for example, the profile feature extraction unit 1203 may extract, as a feature amount, a gradient in a falling section in the distance profile distribution generated by the profile distribution generation unit 1202. Also, for example, the profile feature extraction unit 1203 may extract a peak position in the distance profile distribution generated by the profile distribution generation unit 1202 as a feature amount. The profile feature extraction unit 1203 may extract, as the feature amount, a combination of the frequency analysis result, the gradient, or the peak position described above. A signal indicating the extraction result by this profile feature extraction section 1203 is output to a calculation section 1204 .

[0023] Next, the calculation unit 1204 detects the presence or absence of a moving object at least either inside or outside the room, based on the feature amount extracted by the profile feature extraction unit 1203 or a time-series change in the feature amount (step ST104). A signal indicating the result of detection by the calculation unit 1204 is output to the external control device 13.

[0024] Here, a case will be considered in which the profile feature extracting unit 1203 extracts, as a feature amount, the result of frequency analysis of the distance profile distribution generated by the profile distribution generating unit 1202 in order to determine the difference in the waveform shape of the distance profile distribution. In this case, the profile feature extraction unit 1203 can use a Fourier transform-based process or a wavelet transform process as the frequency analysis.

[0025] For example, the upper part of FIG. 3A shows a case where radio waves transmitted by the sensor 11 are reflected by a moving object present in the room. In this case, the radio waves transmitted by the sensor 11 are repeatedly reflected by various objects in the room including the moving object, becoming multipath (see FIG. 5), which makes them easier to receive by the sensor 11. Note that, as shown in FIG. 5, for example, a direct wave reflected by the moving object and returning directly to the sensor 11 corresponds to a peak position, and the longer the propagation distance of the multipath, the more rearward the position of the peak position becomes, and the lower the reception intensity becomes. Also, the more times a multipath is reflected, the longer the propagation distance becomes. In this way, when a moving object is present in the room, many multipaths passing through the moving object are received. Therefore, as shown in the lower part of Fig. 3A and Fig. 4A, the distance profile distribution has a peak at a position corresponding to the linear distance from the sensor 11 to the moving object, and the reception strength gradually attenuates in the falling section thereafter. In this case, the frequency analysis result of the distance profile distribution shows that the low frequency components are large.

[0026] On the other hand, for example, the upper part of Fig. 3B shows a case where the radio wave transmitted by the sensor 11 is reflected by a moving object present outside the room. In this case, the reflected radio wave is unlikely to return to the sensor 11, so there are fewer multipaths passing through the moving object compared to when the moving object is present indoors. Note that reference numeral 301 in the upper part of Fig. 3B shows a radio wave that does not return to the sensor 11. Therefore, as shown in the lower part of Fig. 3B and Fig. 4B, the distance profile distribution has a peak at a position corresponding to the linear distance from the sensor 11 to the moving object, and the reflection intensity attenuates sharply in the subsequent falling section. In this case, the frequency analysis result of the distance profile distribution shows a large amount of high frequency components.

[0027] Therefore, in this case, the calculation unit 1204 can determine that there is a moving object inside the room when the low frequency components are large, and can determine that there is a moving object outside the room when the high frequency components are large. For example, the calculation unit 1204 can determine that a moving object exists indoors when there is a frequency component of 1 cycle / m in the frequency analysis result, which is the feature extracted by the profile feature extraction unit 1203, and can determine that a moving object exists outdoors when there is a frequency component of 5 cycles / m. Note that the threshold value for determining whether the low frequency component or the high frequency component is large can be set appropriately.

[0028] Also, consider a case where the profile feature extracting unit 1203 extracts the gradient in the falling section of the distance profile distribution as a feature amount in order to determine the difference in the waveform shape of the distance profile distribution.

[0029] For example, as shown in the upper part of Fig. 3A, when the radio wave transmitted by the sensor 11 is reflected by a moving object present in the room, the distance profile distribution has a peak at a position corresponding to the linear distance from the sensor 11 to the moving object, and the reflection intensity gradually attenuates in the subsequent falling section, as shown in the lower part of Fig. 3A and Fig. 4A. That is, in this case, the gradient in the falling section becomes smaller.

[0030] On the other hand, for example, in the case where the radio wave transmitted by the sensor 11 is reflected by a moving object present outside the room as shown in the upper part of Fig. 3B, the distance profile distribution has a peak at a position corresponding to the linear distance from the sensor 11 to the moving object, and the reflection intensity attenuates sharply in the falling section thereafter as shown in the lower part of Fig. 3B and Fig. 4B. That is, in this case, the gradient in the falling section becomes large.

[0031] Therefore, in this case, the calculation unit 1204 can determine that there is a moving object inside the room when the gradient is smaller than a predetermined value, and can determine that there is a moving object outside the room when the gradient is larger than the predetermined value. For example, the calculation unit 1204 can determine that a moving object is present indoors when the gradient, which is the feature amount extracted by the profile feature extraction unit 1203, is -1 or more, and can determine that a moving object is present outdoors when the gradient is less than -2. Note that the threshold value for determining whether the gradient is small or large can be set appropriately.

[0032] Also, consider a case where the profile feature extracting unit 1203 extracts the peak position in the distance profile distribution as a feature amount in order to determine the difference in the waveform shape of the distance profile distribution.

[0033] For example, as shown in the upper part of Figure 3A, when the radio waves transmitted by sensor 11 are reflected by a moving object present in the room, the peak in the distance profile distribution will be located in a section close to sensor 11, that is, within a distance range equivalent to the room, as shown in Figure 6A.

[0034] On the other hand, for example, as shown in the upper part of Figure 3B, when the radio waves transmitted by sensor 11 are reflected by a moving object outside the room, the peak in the distance profile distribution will be located in a section far from sensor 11, that is, within a distance range corresponding to the outside of the room.

[0035] Therefore, the calculation unit 1204 can determine that there is a moving object inside the room when the peak is located within a distance range corresponding to the indoors, and can determine that there is a moving object outside the room when the peak is located within a distance range corresponding to the outdoors. For example, as shown in FIG. 6, the calculation unit 1204 calculates a peak value from 0 to R in If the object is within the range of R, it is determined that a moving object is present in the room. out If the moving object is located within the above range, it is possible to determine that a moving object is present outside the room. in The range of R can clearly be considered as an indoor space. out The above range can clearly be considered as an outdoor space, and can be set appropriately.

[0036] In addition, the profile feature extraction unit 1203 may extract features by combining the frequency analysis results, gradients, or peak positions shown above, and the calculation unit 1204 can comprehensively detect the presence or absence of a moving object based on these features. For example, when the profile feature extraction unit 1203 extracts the frequency analysis result and the peak position as the feature amount, the calculation unit 1204 may determine that there is a moving object inside the room as a final determination if it can be determined from the frequency analysis result that there is a moving object inside the room and from the peak position that there is a moving object inside the room, and may determine that there is a moving object outside the room as a final determination if it can be determined from the frequency analysis result that there is a moving object outside the room and from the peak position that there is a moving object outside the room. This is thought to improve the detection accuracy of the monitoring device 12 according to the first embodiment.

[0037] Also, consider a case where the profile distribution generating unit 1202 generates, as the profile distribution, a distance profile distribution for each velocity of a moving object. In this case, for example, the profile feature extraction unit 1203 can extract a feature amount for each velocity of the moving object based on the distance profile distribution for each velocity of the moving object, where the feature amount is at least one of a frequency analysis result, a gradient, and a peak position.

[0038] For example, Fig. 7 shows a case where the profile feature extraction unit 1203 extracts the peak position for each velocity of a moving object from the distance profile distribution for each velocity of the moving object. The circles in the two-dimensional distribution showing the relationship between the velocity and distance of the moving object in Fig. 7B indicate the peak for each velocity of the moving object detected by CFAR (Constant False Alarm Rate) processing.

[0039] In this case, the calculation unit 1204 can determine that there is a moving object inside the room if a certain number or more of peaks are located within a distance range corresponding to the indoors, and can determine that there is a moving object outside the room if a certain number or more of peaks are located within a distance range corresponding to the outdoors.

[0040] In addition, the calculation unit 1204 may process only those feature values ​​for each speed of the moving object extracted by the profile feature extraction unit 1203 that are within a predetermined range from the position where the target moving object is assumed to be located, and may exclude other feature values ​​from processing. For example, in Figure 7, R t From R t -R wid Only peaks within the range of R t is the position where the target moving object is assumed to exist. Also, R wid is a predetermined range and can be set appropriately.

[0041] In addition, the calculation unit 1204 may process only those feature amounts that are within a predetermined range from the speed assumed to be the target speed of the moving object, among the peak positions for each speed of the moving object extracted by the profile feature extraction unit 1203, and may exclude other feature amounts from processing. For example, if the target moving object is a sleeping person, the speed range of the sleeping person can be set based on the expected breathing volume during sleep, etc. Therefore, by processing only feature amounts included in the above speed range, the calculation unit 1204 can exclude feature amounts of unexpected movement speeds from the processing target, thereby improving accuracy and reducing the processing load.

[0042] Consider a case where the calculation unit 1204 detects the presence or absence of a moving object at least either indoors or outdoors based on a time series change in the feature amount extracted by the profile feature extraction unit 1203. Here, the feature amount is at least one of the frequency analysis result, the gradient, and the peak position.

[0043] For example, if the feature amount is a gradient, the calculation unit 1204 can determine that a moving object is present inside the room if the gradient is -1 or greater for a certain period of time. An example of the certain period of time is 3 seconds. Furthermore, if the number of times the gradient is less than -2 is greater than the number of times it is -1 or greater during the certain period of time, the calculation unit 1204 can determine that a moving object is present outside the room, assuming that the reaction is due to a moving object outside rather than a moving object inside the room.

[0044] Furthermore, the calculation unit 1204 can determine, as a detection result, one of four patterns as shown in FIG. In FIG. 8, the first pattern shows a pattern where there is no moving object inside the room and no moving object outside the room. The second pattern shows a pattern where there is a moving object inside the room and no moving object outside the room. The third pattern shows a pattern where there is no moving object inside the room and a moving object outside the room. The fourth pattern shows a pattern where there is a moving object inside the room and a moving object outside the room. For example, the calculation unit 1204 may detect all of the above four patterns, or may detect only some of the patterns, such as detecting only the second and third patterns.

[0045] In this way, the monitoring device 12 according to the first embodiment extracts the reflected components due to a moving object from the wave signal received by the sensor 11 to generate a profile distribution, and detects the presence or absence of a moving object at least either indoors or outdoors based on a feature amount related to the waveform shape of this profile distribution or a time series change in the feature amount. This enables the monitoring device 12 according to the first embodiment to accurately detect the presence or absence of a moving object at least either indoors or outdoors. Furthermore, the sensor 11 may be a single sensor, and the monitoring device 12 according to the first embodiment can detect the presence or absence of a moving object at least either indoors or outdoors with a minimum sensor configuration.

[0046] In the above, the profile distribution generating unit 1202 generates a distance profile distribution as the profile distribution. On the other hand, the distance in the distance profile distribution can be replaced with time by the relational expression: time = distance ÷ (2 × speed of light). Therefore, the profile distribution generating unit 1202 may generate, as the profile distribution, a delay profile distribution, which is a waveform indicating the relationship between the reception intensity of the reflected component by a moving object and the propagation delay time, instead of a distance profile distribution.

[0047] In the above, a monitoring system that monitors the presence or absence of a moving object inside or outside a vehicle has been given as an example of an application of the monitoring system 1 including the monitoring device 12. However, the application of the monitoring system 1 including the monitoring device 12 is not limited to this. For example, the monitoring system 1 may be a monitoring system that monitors the presence or absence of moving objects at least in either the interior or exterior of a vehicle other than an automobile, such as a bus, a train, a ship, or an airplane. Furthermore, for example, the monitoring system 1 may be a monitoring system that monitors the presence or absence of moving objects at least in or outside a room, toilet, bathroom, or elevator car in a house, factory, office, or the like. In other words, the monitoring system 1 may be any monitoring system that monitors the presence or absence of a moving object at least either inside or outside an indoor space partitioned by a window or a wall.

[0048] As described above, according to the first embodiment, the monitoring device 12 includes a moving object reflection component extracting unit 1201 that extracts a reflection component caused by a moving object from a signal received by the sensor 11 that transmits a signal and receives a signal reflected by the moving object, a profile distribution generating unit 1202 that generates a profile distribution, which is a waveform indicating the relationship between the reception intensity of the reflection component caused by the moving object and the propagation distance or the propagation delay time, based on the reflection component caused by the moving object extracted by the moving object reflection component extracting unit 1201, a profile feature extracting unit 1203 that extracts a feature amount related to the waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generating unit 1202, and a calculation unit 1204 that detects the presence or absence of a moving object at least either indoors or outdoors, based on the feature amount extracted by the profile feature extracting unit 1203 or a time series change in the feature amount. As a result, the monitoring device 12 according to the first embodiment can monitor the presence or absence of a moving object at least either indoors or outdoors with higher accuracy than the conventional method. Moreover, in the monitoring device 12 according to the first embodiment, it is possible to detect the presence or absence of a moving object using a signal from a single sensor 11, and the amount of calculation can be reduced compared to the conventional case. Also, in the monitoring system 1 according to the first embodiment, since only a single sensor 11 is required, it is possible to simplify the hardware configuration compared to the conventional case.

[0049] Furthermore, according to the first embodiment, the profile feature extraction unit 1203 extracts the result of frequency analysis of the profile distribution generated by the profile distribution generation unit 1202 as a feature amount. Furthermore, according to the first embodiment, the profile feature extraction unit 1203 extracts, as a feature amount, the gradient in a falling section in the profile distribution generated by the profile distribution generation unit 1202. Furthermore, according to embodiment 1, if the gradient extracted by the profile feature extraction unit 1203 is smaller than a predetermined value, the calculation unit 1204 determines that a moving object is present inside the room, and if the gradient is larger than the predetermined value, it determines that a moving object is present outside the room. Furthermore, according to the first embodiment, the profile feature extraction unit 1203 extracts, as a feature amount, a peak position in the profile distribution generated by the profile distribution generation unit 1202. As a result, the monitoring device 12 according to the first embodiment is capable of monitoring the presence or absence of a moving object at least either indoors or outdoors with higher accuracy than conventional techniques.

[0050] Furthermore, according to the first embodiment, the profile distribution generating unit 1202 generates a profile distribution, which is a waveform indicating the relationship between the reception intensity of the reflected component from a moving object and the propagation distance or the propagation delay time for each speed of the moving object. This makes it easier to exclude from the processing targets the feature amounts based on the reflected components from moving objects other than the target moving object, thereby further improving the accuracy.

[0051] According to the first embodiment, the monitoring method includes a step in which the moving object reflection component extracting unit 1201 extracts a reflection component caused by a moving object from a signal received by the sensor 11, which transmits a signal and receives a signal reflected by the moving object, a step in which the profile distribution generating unit 1202 generates a profile distribution, which is a waveform indicating the relationship between the reception intensity of the reflection component caused by the moving object and the propagation distance or the propagation delay time, based on the reflection component caused by the moving object extracted by the moving object reflection component extracting unit 1201, a step in which the profile feature extracting unit 1203 extracts a feature amount related to the waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generating unit 1202, and a step in which the computing unit 1204 detects the presence or absence of a moving object at least either indoors or outdoors, based on the feature amount extracted by the profile feature extracting unit 1203 or a time series change in the feature amount. As a result, the monitoring method according to the first embodiment enables monitoring the presence or absence of a moving object at least either indoors or outdoors with higher accuracy than the conventional method.

[0052] Embodiment 2 The monitoring system 1 according to the first embodiment has one sensor 11. In contrast, the monitoring system 1 according to the second embodiment has a plurality of sensors 11.

[0053] FIG. 9 is a block diagram showing a configuration example of a monitoring system 1 including a monitoring device 12 according to a second embodiment. In the monitoring system 1 according to the second embodiment shown in FIG. 9, the number of sensors 11 is changed from one to a plurality of sensors, and the moving body reflection component extracting unit 1201 of the monitoring device 12 is changed to a moving body reflection component extracting unit 1201b, in comparison with the monitoring system 1 according to the first embodiment shown in FIG. 1. Note that FIG. 9 shows a case where two sensors 11 are provided in the monitoring system 1, but this is not limiting, and three or more sensors 11 may be provided. The other configuration examples of the monitoring system 1 according to the second embodiment shown in FIG. 9 are the same as the configuration examples of the monitoring system 1 according to the first embodiment, and only different parts will be described with the same reference numerals.

[0054] The sensor 11 in the second embodiment has a plurality of receiving antennas or a plurality of receiving sections that are installed so as to be able to receive reflected waves from different arrival directions.

[0055] The moving object reflection component extraction unit 1201b extracts reflection components due to moving objects for each arrival angle of radio waves received by multiple sensors 11. That is, the moving object reflection component extraction unit 1201b extracts reflection components due to moving objects for each arrival angle of radio waves received by the sensors 11 by suppressing reflection components due to objects with a speed of 0 that exist around the sensors 11. At this time, for example, the moving object reflection component extraction unit 1201b extracts reflection components due to moving objects for each arrival angle of radio waves by using a known process such as MTI processing. Note that the arrival angle here means a predetermined angle range. A signal indicating the extraction result for each radio wave arrival angle by the moving object reflection component extraction unit 1201 b is output to the profile distribution generation unit 1202 .

[0056] The profile distribution generating unit 1202 in the second embodiment generates a profile distribution for each radio wave arrival angle based on the reflection components caused by a moving object for each radio wave arrival angle extracted by the moving object reflection component extracting unit 1201b.

[0057] In this way, when there are multiple sensors 11, as shown in FIG. 10, for example, the moving object reflection component extraction unit 1201b of the monitoring device 12 according to embodiment 2 extracts reflection components from moving objects in each arrival direction.

[0058] Here, for example, as shown in FIG. 11A, a case is considered in which there is one sensor 11 and there are moving objects both inside and outside the room. In this case, the distance profile distribution may be in a state in which a waveform due to a moving object present inside the room and a waveform due to a moving object present outside the room are superimposed. That is, when the distance from the sensor 11 of the moving object present inside the room and the distance from the sensor 11 of the moving object present outside the room are equal to each other, the waveform due to the moving object present inside the room and the waveform due to the moving object present outside the room are superimposed. In FIG. 11A, reference numeral 1101 indicates a waveform when a moving object is present inside the room, and reference numeral 1102 indicates a waveform when a moving object is present outside the room. In the example of FIG. 11, the waveform indicated by reference numeral 1101 is weaker than the waveform indicated by reference numeral 1102, and is cancelled out by the waveform indicated by reference numeral 1102, so that there is a possibility that the moving object present inside the room cannot be detected. On the other hand, when there are multiple sensors 11, radio waves can be directed in each direction, indoors and outdoors, by using an existing beam control method such as Digital Beam Forming. Therefore, in this case, even if there are moving objects both indoors and outdoors, as shown in FIG. 11B, the waveforms due to the moving object present indoors and the moving object present outdoors can be separated by dividing the distance profile distribution by the arrival angle. In FIG. 11B, the distance profile distribution on the left side shows the waveform when there is a moving object present indoors, and the distance profile distribution on the right side shows the waveform when there is a moving object present outdoors. In this case, even if the waveforms on the left side and the waveforms on the right side of FIG. 11B are in a superimposed relationship, it is possible to detect the moving object present indoors and the moving object present outdoors, respectively, from the respective waveforms.

[0059] As shown in FIG. 7, a case will be considered in which the profile distribution generating unit 1202 extracts a profile distribution for each velocity of a moving object as the profile distribution, and there are a plurality of sensors 11. In this case, for example, the calculation unit 1204 can detect the direction of the peak for each speed of the moving object. Therefore, the calculation unit 1204 can calculate the three-dimensional position where the moving object is assumed to exist from the peak position for each speed of the moving object, can detect the presence or absence of a moving object indoors from the number of three-dimensional positions within a distance range equivalent to the indoors, and can detect the presence or absence of a moving object outdoors from the number of three-dimensional positions within a distance range equivalent to the outdoors.

[0060] As described above, according to the second embodiment, the moving object reflection component extraction unit 1201b extracts reflection components caused by a moving object for each arrival angle of the signal from the signals received by the multiple sensors 11. This allows the monitoring device 12 according to the second embodiment to have improved accuracy compared to the first embodiment.

[0061] Embodiment 3 The monitoring device 12 according to the first embodiment has been described as detecting the presence or absence of a moving object at least either inside or outside a room. In contrast, the monitoring device 12 according to the third embodiment will be described as determining the reliability of the detection of the presence or absence of a moving object.

[0062] Fig. 12 is a block diagram showing a configuration example of a monitoring system 1 including a monitoring device 12 according to embodiment 3. In the monitoring system 1 according to embodiment 3 shown in Fig. 12, the calculation unit 1204 of the monitoring device 12 is changed to a calculation unit 1204b, compared to the monitoring system 1 according to embodiment 1 shown in Fig. 1. The other configuration example of the monitoring system 1 according to embodiment 3 shown in Fig. 12 is similar to the configuration example of the monitoring system 1 according to embodiment 1, and the same reference numerals are used and only the different parts will be described.

[0063] The calculation unit 1204b has the same functions as the calculation unit 1204 in the first embodiment, and also has a function of calculating the reliability of the detection of the presence or absence of a moving object. At this time, for example, the calculation unit 1204b may calculate the reliability of detection of the presence or absence of a moving object based on the feature amount extracted by the profile feature extraction unit 1203. That is, the calculation unit 1204b may calculate the reliability of detection of the presence or absence of a moving object based on one or more of the frequency analysis result, the gradient, or the peak position, which are the feature amounts extracted by the profile feature extraction unit 1203. The calculation unit 1204b may also calculate the time average value of the calculated reliability as the final reliability. Furthermore, for example, the calculation unit 1204b may calculate the reliability of detection of the presence or absence of a moving object based on a time-series change in the feature amount extracted by the profile feature extraction unit 1203. Also, for example, the calculation unit 1204b may calculate the reliability of the detection of the presence or absence of a moving object based on a time-series change in the detection of the presence or absence of a moving object.

[0064] Here, consider a case where the calculation unit 1204b calculates the reliability of detection of the presence or absence of a moving object based on the feature amount extracted by the profile feature extraction unit 1203. In the following, a case where the feature amount is the gradient in the falling section of the distance profile distribution is shown, but the same applies to the case where the feature amount is the frequency analysis result or the peak position.

[0065] In this case, for example, when the gradient in the falling section of the profile distribution is −1 or more, the calculation unit 1204b determines that the reliability of the detection of the presence of a moving object in the room is high, for example, the reliability is 80% or more. Also, for example, when the gradient in the falling section of the profile distribution is greater than or equal to -2 and less than -1, the calculation unit 1204b determines that the reliability of the detection of the presence of a moving object in the room is medium, for example, the reliability is 50% or more. Furthermore, for example, when the gradient in the falling section of the profile distribution is less than −2, the calculator 1204b determines that the reliability of the detection of the presence of a moving object in the room is low, for example, the reliability is 20% or more.

[0066] In this way, the calculation unit 1204b calculates the reliability according to the degree of deviation of the gradient in the profile distribution from the threshold value used for the determination in the calculation unit 1204.

[0067] The method of determining the reliability by the calculation unit 1204b may be, for example, a method based on predefined rules, a statistical method based on observed data, or machine learning.

[0068] Next, a case will be considered in which the calculation unit 1204b calculates the reliability of the detection of the presence or absence of a moving object based on a time-series change in the detection of the presence or absence of a moving object.

[0069] In this case, for example, if the detection of the presence or absence of a moving object within a predetermined period fluctuates in the manner of "moving object in the room", "moving object in the room", "moving object outside the room", and "moving object in the room", the calculation unit 1204b determines that the detection of the presence of a moving object in the room is highly reliable, for example, 75% or more, since the detection of the presence of a moving object in the room is more frequent than the detection of the presence of a moving object outside the room, and determines that the detection result of the presence of a moving object outside the room is reliable, for example, 25%. The above-mentioned certain period can be, for example, 5 seconds. In addition, if the calculation unit 1204b determines that the detection of the presence or absence of a moving object fluctuates significantly and the detection is not stable, it may temporarily suspend notification of the detection result to the external control device 13 until the detection stabilizes.

[0070] In the above description, the calculation unit 1204b has both a function for detecting the presence or absence of a moving object and a function for calculating the reliability of the presence or absence of a moving object. However, the calculation unit 1204b may not have a function for detecting the presence or absence of a moving object, but may only have a function for calculating the reliability of the presence or absence of a moving object. That is, the function for detecting the presence or absence of a moving object may be provided in an external configuration of the monitoring device 12, for example, in the control device 13. In this case, the control device 13 may detect the presence or absence of a moving object at least either indoors or outdoors by combining and processing the reliability calculated by the monitoring device 12 and information for detecting the presence or absence of a moving object from another sensor.

[0071] FIG. 12 shows a case where the calculation unit 1204 in the monitoring system 1 according to the first embodiment is changed to a calculation unit 1204b. However, the present invention is not limited to this, and the calculation unit 1204 in the monitoring system 1 according to the second embodiment may be changed to a calculation unit 1204b, and the same effects as those described above can be obtained.

[0072] As described above, according to the third embodiment, the monitoring device 12 includes moving object reflection component extraction units 1201, 1201b which extract reflection components caused by a moving object from a signal received by the sensor 11 which transmits a signal and receives a signal reflected by the moving object, a profile distribution generation unit 1202 which generates a profile distribution which is a waveform showing the relationship between the reception intensity of the reflection component caused by the moving object and the propagation distance or the propagation delay time based on the reflection components caused by the moving object extracted by the moving object reflection component extraction units 1201, 1201b, a profile feature extraction unit 1203 which extracts features related to the waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit 1202, and a calculation unit 1204b which calculates the reliability of detection of the presence or absence of a moving object based on the features extracted by the profile feature extraction unit 1203 or changes in the features over time, or detects the presence or absence of a moving object at least either indoors or outdoors and calculates the reliability of the detection of the presence or absence of the moving object. As a result, the monitoring device 12 according to the third embodiment can monitor the presence or absence of a moving object at least either inside or outside a room with higher accuracy than in the past. Moreover, in the monitoring device 12 according to the third embodiment, it is possible to detect the presence or absence of a moving object using a signal from a single sensor 11, and the amount of calculation can be reduced compared to the conventional case. Also, in the monitoring system 1 according to the third embodiment, since only a single sensor 11 is required, it is possible to simplify the hardware configuration compared to the conventional case.

[0073] Moreover, according to the third embodiment, the calculation unit 1204b calculates the reliability of detection of the presence or absence of a moving object, based on the feature amount extracted by the profile feature extraction unit 1203. That is, the calculation unit 1204b calculates the reliability of detection of the presence or absence of a moving object, based on one or more of the feature amounts extracted by the profile feature extraction unit 1203, that is, the frequency analysis result for the profile distribution, the gradient in the falling section in the profile distribution, or the peak position in the profile distribution. Furthermore, according to the third embodiment, the calculation unit 1204b calculates the reliability of detection of the presence or absence of a moving object based on the time-series change in the feature amount extracted by the profile feature extraction unit 1203. Furthermore, according to the third embodiment, the calculation unit 1204b calculates the reliability of the detection of the presence or absence of a moving object based on a time-series change in the detection of the presence or absence of a moving object. As a result, the monitoring device 12 according to the third embodiment is capable of monitoring the presence or absence of a moving object at least either indoors or outdoors with higher accuracy than conventional techniques.

[0074] Furthermore, according to this embodiment 3, the monitoring method includes the steps of: the moving object reflection component extraction units 1201, 1201b extracting reflection components caused by a moving object from a signal received by the sensor 11, which transmits a signal and receives a signal reflected by the moving object; the profile distribution generation unit 1202 generating a profile distribution, which is a waveform indicating the relationship between the reception intensity of the reflection component caused by the moving object and the propagation distance or the propagation delay time, based on the reflection components caused by the moving object extracted by the moving object reflection component extraction units 1201, 1201b; the profile feature extraction unit 1203 extracting features related to the waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit 1202; and the calculation unit 1204b calculating the reliability of detection of the presence or absence of a moving object, or detecting the presence or absence of a moving object at least either indoors or outdoors and calculating the reliability of the detection of the presence or absence of the moving object, based on the features extracted by the profile feature extraction unit 1203 or changes in the features over time. As a result, the monitoring method according to the third embodiment makes it possible to monitor the presence or absence of a moving object at least either indoors or outdoors with higher accuracy than in the past.

[0075] Finally, a hardware configuration example of the monitoring device 12 according to the first to third embodiments will be described with reference to Fig. 13. Note that although a hardware configuration example of the monitoring device 12 according to the first embodiment will be described here, the same applies to the hardware configuration examples of the monitoring device 12 according to the second and third embodiments. The functions of the moving body reflection component extractor 1201, profile distribution generator 1202, profile feature extractor 1203 and calculator 1204 in the monitoring device 12 are realized by a processing circuit 51. The processing circuit 51 may be dedicated hardware as shown in Fig. 13A, or may be a CPU (also called a Central Processing Unit, central processing unit, processing unit, calculator, microprocessor, microcomputer, processor, or DSP (Digital Signal Processor)) 52 that executes a program stored in a memory 53 as shown in Fig. 13B.

[0076] When the processing circuit 51 is a dedicated hardware, the processing circuit 51 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination of these. The functions of each of the moving body reflection component extraction unit 1201, the profile distribution generation unit 1202, the profile feature extraction unit 1203, and the calculation unit 1204 may be realized by the processing circuit 51 individually, or the functions of each unit may be realized by the processing circuit 51 collectively.

[0077] When the processing circuit 51 is a CPU 52, the functions of the moving body reflection component extraction unit 1201, the profile distribution generation unit 1202, the profile feature extraction unit 1203, and the calculation unit 1204 are realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in the memory 53. The processing circuit 51 realizes the functions of each unit by reading and executing the programs stored in the memory 53. That is, the monitoring device 12 includes a memory 53 for storing a program that, when executed by the processing circuit 51, results in the execution of each step shown in FIG. It can also be said that these programs cause a computer to execute the procedures and methods of the moving body reflection component extraction unit 1201, the profile distribution generation unit 1202, the profile feature extraction unit 1203, and the calculation unit 1204. Here, examples of memory 53 include non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable ROM), EEPROM (Electrically EPROM), magnetic disk, flexible disk, optical disk, compact disk, mini disk, or DVD (Digital Versatile Disc).

[0078] It should be noted that the functions of the moving body reflection component extraction unit 1201, the profile distribution generation unit 1202, the profile feature extraction unit 1203 and the calculation unit 1204 may be partly realized by dedicated hardware and partly realized by software or firmware. For example, the function of the moving body reflection component extraction unit 1201 can be realized by the processing circuit 51 as dedicated hardware, and the functions of the profile distribution generation unit 1202, the profile feature extraction unit 1203 and the calculation unit 1204 can be realized by the processing circuit 51 reading out and executing a program stored in the memory 53.

[0079] Thus, the processing circuitry 51 can realize each of the above-mentioned functions by hardware, software, firmware, or a combination of these.

[0080] It should be noted that the embodiments may be freely combined, or any of the components in each embodiment may be modified, or any of the components in each embodiment may be omitted.

[0081] Various aspects of the present disclosure are summarized below as appendices.

[0082] (Appendix 1) a moving object reflection component extraction unit that extracts a reflection component caused by a moving object from a signal received by a sensor that transmits a signal and receives a signal reflected by the moving object; a profile distribution generating unit that generates a profile distribution, which is a waveform indicating a relationship between a reception intensity of the reflected component from the moving object and a propagation distance or a propagation delay time, based on the reflected component from the moving object extracted by the moving object reflected component extracting unit; a profile feature extraction unit that extracts a feature amount related to a waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit; a calculation unit that performs at least one of detecting the presence or absence of a moving object at least either indoors or outdoors, or calculating a reliability of the detection of the presence or absence of a moving object, based on the feature amount extracted by the profile feature extraction unit or a time-series change in the feature amount; A monitoring device comprising: (Appendix 2) The moving object reflection component extraction unit extracts reflection components caused by a moving object from the signals received by the plurality of sensors for each arrival angle of the signal. 2. The monitoring device according to claim 1. (Appendix 3) The profile feature extraction unit extracts, as a feature, a result of frequency analysis of the profile distribution generated by the profile distribution generation unit. 3. The monitoring device according to claim 1 or 2. (Appendix 4) The profile feature extraction unit extracts, as a feature amount, a gradient in a falling section of the profile distribution generated by the profile distribution generation unit. 4. A monitoring device according to any one of claims 1 to 3. (Appendix 5) The calculation unit determines that a moving object is present inside the room when the gradient extracted by the profile feature extraction unit is smaller than a predetermined value, and determines that a moving object is present outside the room when the gradient is larger than the predetermined value. 5. The monitoring device according to claim 4. (Appendix 6) The profile feature extraction unit extracts, as a feature, a peak position in the profile distribution generated by the profile distribution generation unit. 6. A monitoring device according to any one of claims 1 to 5, (Appendix 7) The profile distribution generating unit generates, as the profile distribution, a profile distribution which is a waveform indicating the relationship between the reception intensity of the reflected component from the moving object and the propagation distance or the propagation delay time for each speed of the moving object. 7. A monitoring device according to any one of claims 1 to 6, (Appendix 8) The calculation unit calculates a reliability of detection of the presence or absence of a moving object based on the feature amount extracted by the profile feature extraction unit. 8. A monitoring device according to any one of claims 1 to 7, (Appendix 9) The calculation unit calculates a reliability of detection of the presence or absence of a moving object based on one or more of a frequency analysis result for the profile distribution, a gradient in a falling section in the profile distribution, or a peak position in the profile distribution, which are the feature amounts extracted by the profile feature extraction unit. 9. The monitoring device according to claim 8. (Appendix 10) The calculation unit calculates a reliability of detection of the presence or absence of a moving object based on a time-series change in the feature amount extracted by the profile feature extraction unit. 8. A monitoring device according to any one of claims 1 to 7, (Appendix 11) The calculation unit calculates a reliability of the detection of the presence or absence of a moving object based on a time series change in the detection of the presence or absence of a moving object. 8. A monitoring device according to any one of claims 1 to 7, (Appendix 12) A moving object reflection component extraction unit extracts a reflection component due to a moving object from a signal received by a sensor that transmits a signal and receives a signal reflected by the moving object; a profile distribution generating unit generating a profile distribution, which is a waveform indicating a relationship between a reception intensity of the reflection component from the moving object and a propagation distance or a propagation delay time, based on the reflection component from the moving object extracted by the moving object reflection component extracting unit; a profile feature extraction unit extracting a feature amount related to a waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit; a calculation unit performing at least one of a process of detecting the presence or absence of a moving object at least either indoors or outdoors, or a process of calculating a reliability of the detection of the presence or absence of a moving object, based on the feature amount extracted by the profile feature extraction unit or a time-series change in the feature amount; A monitoring method comprising: [Explanation of symbols]

[0083] 1 monitoring system, 11 sensor, 12 monitoring device, 13 control device, 51 processing circuit, 52 CPU, 53 memory, 1201, 1201b moving object reflection component extraction unit, 1202 profile distribution generation unit, 1203 profile feature extraction unit, 1204, 1204b calculation unit.

Claims

1. a moving object reflection component extraction unit that extracts a reflection component caused by a moving object from a signal received by a sensor that transmits a signal and receives a signal reflected by the moving object; a profile distribution generating unit that generates a profile distribution, which is a waveform indicating the relationship between the reception intensity of the reflection component from the moving object and a propagation distance or a propagation delay time, based on the reflection component from the moving object extracted by the moving object reflection component extracting unit; a profile feature extraction unit that extracts a feature amount related to a waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit; a calculation unit that performs at least one of detecting the presence or absence of a moving object at least either indoors or outdoors, or calculating a reliability of the detection of the presence or absence of a moving object, based on the feature amount extracted by the profile feature extraction unit or a time-series change in the feature amount; A monitoring device comprising:

2. The moving object reflection component extraction unit extracts reflection components caused by a moving object from the signals received by the plurality of sensors for each arrival angle of the signal.

2. The monitoring device according to claim 1.

3. The profile feature extraction unit extracts, as a feature, a result of frequency analysis of the profile distribution generated by the profile distribution generation unit.

3. The monitoring device according to claim 1 or 2.

4. The profile feature extraction unit extracts, as a feature amount, a gradient in a falling section of the profile distribution generated by the profile distribution generation unit.

3. The monitoring device according to claim 1 or 2.

5. The calculation unit determines that a moving object is present inside the room when the gradient extracted by the profile feature extraction unit is smaller than a predetermined value, and determines that a moving object is present outside the room when the gradient is larger than the predetermined value.

5. The monitoring device according to claim 4.

6. The profile feature extraction unit extracts, as a feature, a peak position in the profile distribution generated by the profile distribution generation unit.

3. The monitoring device according to claim 1 or 2.

7. The profile distribution generating unit generates, as the profile distribution, a profile distribution which is a waveform indicating the relationship between the reception intensity of the reflected component from the moving object and the propagation distance or the propagation delay time for each speed of the moving object.

3. The monitoring device according to claim 1 or 2.

8. The calculation unit calculates a reliability of detection of the presence or absence of a moving object based on the feature amount extracted by the profile feature extraction unit.

3. The monitoring device according to claim 1 or 2.

9. The calculation unit calculates a reliability of detection of the presence or absence of a moving object based on one or more of a frequency analysis result for the profile distribution, a gradient in a falling section in the profile distribution, or a peak position in the profile distribution, which are the feature amounts extracted by the profile feature extraction unit.

9. The monitoring device according to claim 8.

10. The calculation unit calculates a reliability of detection of the presence or absence of a moving object based on a time-series change in the feature amount extracted by the profile feature extraction unit.

3. The monitoring device according to claim 1 or 2.

11. The calculation unit calculates a reliability of the detection of the presence or absence of a moving object based on a time series change in the detection of the presence or absence of a moving object.

3. The monitoring device according to claim 1 or 2.

12. A moving object reflection component extraction unit extracts a reflection component due to a moving object from a signal received by a sensor that transmits a signal and receives a signal reflected by the moving object; a profile distribution generating unit generating a profile distribution, which is a waveform indicating a relationship between a reception intensity of a reflected component from a moving object and a propagation distance or a propagation delay time, based on the reflected component from the moving object extracted by the moving object reflection component extracting unit; a profile feature extraction unit extracting a feature amount related to a waveform shape of the profile distribution based on the profile distribution generated by the profile distribution generation unit; a calculation unit performing at least one of a process of detecting the presence or absence of a moving object at least either indoors or outdoors, or a process of calculating a reliability of the detection of the presence or absence of a moving object, based on the feature amount extracted by the profile feature extraction unit or a time-series change in the feature amount; The monitoring method includes:

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