Radar sensor with self-learning for motion detection

Abstract

A method for calibrating a radar motion sensor device (2) comprising the steps of obtaining (S1) a temporal sequence of samples of a radar data signal, each sample indicating a first power level (7) in a first frequency band of a radar signal (6) and identifying (S3) a plurality of valid trigger samples, wherein each valid trigger sample is identified as a sample having a first power level (7) that exceeds the first predetermined threshold (TLF) and is followed by a trailing event in a predetermined after the sample. The method further comprises analyzing the samples of a trigger context window associated with each of the plurality of valid trigger samples to determine a first updated threshold and using (S7) the first updated threshold (TLF, f) to perform motion detection with the radar motion sensor device (2).

Description

[0001] RADAR SENSOR WITH SELF-LEARNING FOR MOTION DETECTION

[0002] FIELD OF THE INVENTION

[0003] The present invention generally relates to a method for calibrating a radar sensor for motion detection and a radar sensor comprising a control module configured to perform the method.

[0004] BACKGROUND OF THE INVENTION

[0005] Motion detectors are used in many applications ranging from burglar alarms to automatic door openers or automatic control of illumination or HVAC systems. For example, motion detectors are often used in offices, e.g. in conference rooms, to detect when people are present to steer light modules and / or HVAC systems such that lights, ventilation, heating and air conditioning are turned on when people are present and automatically turned off when no people are present. Automatic control of these systems based on motion detection has the potential to save large amounts of energy since e.g. the risk of lights or air conditioning units in an office remaining on during the night, or over the weekend, when no people are present is reduced.

[0006] Typically, Passive Infrared Sensors, PIRs, are used to detect motion. PIRs operate by registering incident IR radiation and when e.g. people move in front of the PIR the intensity distribution on the PIR sensor changes which can be taken as an indication that people are present in front of the sensor. A drawback with PIRs is, however, that they are not very sensitive and small motions, e.g. small hand gestures or small head motions, made by people sitting around a conference table may not always be detected by the PIR whereby the lights, or others systems, are deactivated while people are still using the conference room. In other words, the PIRs are prone to high false negative rates meaning that PIRs often fail to sense motion when there in fact is motion to detect.

[0007] In view of the drawbacks of PIRs, radar sensors have been used instead. Radar sensors are active sensors which transmit a radar signal into the environment and measure the reflected radar signal. If an object (e.g. a human) moves this will alter the reflected radar signal and this alteration can be used as an indication of motion. Radar sensors tend to be much more sensitive compared to PIRs such that even small motions can be detected which reduced the false negative rate. At the same time, radar signals can penetrate non-metal materials such as glass, plastic or drywall meaning that it is possible to hide radar motion sensors in walls or integrate the radar motion sensor into devices without the radar sensor being visible.

[0008] On the other hand, radar sensors are in some scenarios too sensitive leading to an increase in false positive rates. That is, radar sensors are prone to detecting motion even when no people are present. For example, since radar signals can penetrate drywall and glass, it is possible that motion in a completely separate room is detected by the radar sensor or that a person passing by a room equipped with a radar sensor is registered by the radar sensor. To circumvent the issues with high false positive rates one common approach is to use a sensitivity setting that allows users to modify the sensitivity of the radar sensor. However, manually adjusting the sensitivity setting is a trial-and-error method and thus not user friendly. For example, the user may need to try a number of sensitivity levels before determining the right sensitivity setting, and sometimes users cannot identify one level to achieve satisfying false positive and false negative rates.

[0009] EP4162865 Al describes wireless sensing, monitoring and tracking, e.g., WiFi-based sleep monitoring system that can work on any low-cost Internet-of-things (loT) devices; heartbeat tracking and monitoring by processing wireless channel information and beamforming; detecting voice activity based on radio signals; map reconstruction based on wireless tracking; and wireless monitoring vital signs by processing, decomposing and enhancing wireless channel information.

[0010] In view of these shortcomings there is a need for an improved method for calibrating a radar motion sensor and an improved radar motion sensor.

[0011] SUMMARY OF THE INVENTION

[0012] It is an object of the present invention to provide a new and improved method for calibrating a radar motion sensor. This and other objects are achieved by the method and radar motion sensor of the independent claims. Embodiments of the present invention are defined in the dependent claims.

[0013] According to an aspect of the present invention, there is provided a method for calibrating a radar motion sensor device comprising obtaining a temporal sequence of samples of a radar data signal, each sample indicating a first power level in a first frequency band of a radar signal detected by the radar motion sensor device and identifying a plurality of valid trigger samples by comparing the first power level of each sample to a first predetermined threshold, wherein each valid trigger sample is identified as a sample having a first power level that exceeds the first predetermined threshold and is followed by a trailing event in a predetermined time duration after the sample. The method further comprises analyzing samples of a trigger context window associated with each of the plurality of valid trigger samples to determine a first updated threshold and using the first updated threshold to perform motion detection with the radar motion sensor device.

[0014] The present invention is at least based on the understanding that by detecting valid trigger samples (i.e. trigger samples associated with a trailing event indicating some level of activity after the trigger sample which exceeds the first predetermined threshold) motions that transpire over a very short time (e.g. due to someone walking by in the hall) are not considered when determining the updated threshold. In this way, the threshold utilized by the radar sensor device can be updated automatically, without user interaction, to decrease the false positives without increasing the false negatives.

[0015] The first updated threshold may be equal to or higher than the first predetermined threshold. In some implementations, the first updated threshold is strictly higher than the first predetermined threshold.

[0016] In embodiments, analyzing the samples of a trigger context window associated with each of the plurality of valid trigger samples comprises determining a first candidate threshold for each valid trigger sample based on the samples of the trigger context window; and clustering the first candidate thresholds into at least one group, each group being associated with a group center level. Wherein the first updated threshold is set to a value that separates the group associated with a highest group center level. In some cases, the value may exceed the first predetermined threshold. In some cases, the value may equal to the first predetermined threshold.

[0017] Preferably, the candidate threshold is set higher than the predetermined threshold so as to decrease false positives meaning that when the updated threshold is set it can be set higher than the predetermined first threshold but still at an appropriate level that separates the cluster of all collected candidate thresholds.

[0018] In embodiments, clustering comprises clustering the plurality of first candidate thresholds into at least two groups and setting the first updated threshold to separate the group associated with the highest group center level from the group associated with a second highest group center level.

[0019] By using two or more groups in the clustering it is possible to set accurate updated threshold levels even for radar motion sensors installed in places that are challenging for accurate motion detection by radar. For example, if the radar motion sensor is installed in a first conference room having a transparent wall or window into a neighboring second conference room the radar motion sensor may detect valid trigger samples also when people are moving the second conference room. However, by clustering the candidate thresholds into at least two groups an updated threshold can be identified to separate detected motions in the first room from detected motions in the second room. In general, the radar signals resulting from motion in the first room will be more powerful compared to the radar signals resulting from motion in the second room due to e.g. transmission losses through the transparent wall or window and increased propagation distance for the radar signals.

[0020] In embodiments, the first candidate threshold is greater than the first predetermined threshold and smaller than the maximum first power level in the trigger context window.

[0021] That is, the first candidate threshold is set such that at least one trigger sample can still be identified in the trigger context window.

[0022] In embodiments, the method further comprises identifying a plurality of trigger samples by comparing the first power level of each sample to a first predetermined threshold, wherein each trigger sample is identified as a sample having a first power level that exceeds the first predetermined threshold. For each identified trigger sample the method further comprises defining a trailing context window associated with the trigger sample comprising a predetermined number of samples temporally later than the trigger sample and determining, for each sample in the trailing context window associated with the trigger sample whether the first power level of the sample exceeds a predetermined trailing threshold. The method further comprises classifying the trigger sample as a valid trigger sample when the percentage of samples in the trailing context window exceed a percentage threshold.

[0023] Accordingly, trigger samples are first detected and, subsequently, it is determined if they are to be treated as valid trigger samples if the trailing event has occurred after the trigger sample.

[0024] In embodiments, the predetermined trailing threshold equals the first predetermined threshold.

[0025] It has been realized that the first predetermined threshold can be used as the trailing threshold. Accordingly, if the first power level remains higher (for at least a predetermined percentage of the samples) than the first predetermined threshold also during the trailing context window it can be established that a trailing event has occurred. The predetermined percentage may e.g. be 80% or 90%, although other percentages can also be used, as will be described in the below.

[0026] In embodiments, the trigger context window comprises a predetermined number of samples temporally earlier and / or later than the valid trigger sample. For example, the trigger context window comprises temporally earlier and / or later samples extending to a point in time at least 1 second earlier and / or later than the trigger sample, preferably at least 5 second earlier and / or later than the trigger sample, more preferably at least 10 seconds earlier and / or later than the trigger sample and most preferably at least 20 seconds earlier and / or later than the trigger sample.

[0027] That is, the trigger context window captures the temporal context surrounding the trigger sample and includes at least one of samples that are temporally earlier than the trigger sample and samples that are temporally later than the trigger sample. When determining the candidate threshold many samples can thus be taken into account whereby candidate thresholds can be found that, if used as replacement for the first predetermined threshold, would still enable a trigger sample to be identified in the trigger context window.

[0028] In embodiments, each sample further indicates a second power level in a second frequency band of the radar signal detected by the radar motion sensor device, the second frequency band being different from the first frequency band. Accordingly, two frequency bands, a first frequency band and a second frequency band may be considered at the same time for enhanced accuracy. Optionally, the second frequency band contains higher frequencies than the first frequency band whereby the first frequency band may be referred to as a low frequency band and the second frequency band may be referred to as a high frequency band.

[0029] In embodiments, the method further comprises identifying a plurality of trigger samples by comparing the first power level and the second power level of each sample to a first predetermined threshold and second predetermined threshold respectively, wherein each trigger sample is identified as a sample having a first power level and second power level that exceeds both the first predetermined threshold and the second predetermined threshold.

[0030] That is, the trigger sample may be detected with a stricter criteria requiring the power in both frequency bands to exceed a respective predetermined threshold. It has been realized that when people enter a room there are both low and high frequency components registered by the radar motion sensor. Accordingly, by using this stricter criteria the false positive rate can be reduced further compared to using only one frequency band and predetermined threshold for identifying the trigger samples.

[0031] In embodiments, the method further comprises, for each valid trigger sample, determining, a second candidate threshold based on the samples of the trigger context window, wherein the second candidate threshold is greater than the second predetermined threshold and smaller than the maximum second power level in the trigger context window. The method further comprising determining a second updated threshold by clustering a plurality of second candidate thresholds into at least one second group of second candidate thresholds, wherein each second group of second candidate thresholds is associated with a respective second group center level, and setting the second updated threshold to a value that separates the second group associated with a highest second group center level and using the first and second updated threshold to perform motion detection with the radar motion sensor device. In some cases, the value may exceed the second predetermined threshold. In some cases, the value may equal to the second predetermined threshold.

[0032] That is, the second candidate thresholds and the second updated threshold is determined analogously to the first candidate thresholds and the first updated threshold. In this way, an updated threshold which offers a reduced false positive rate, without increased false negatives, may be determined automatically for both frequency bands. Optionally, determining a second updated threshold comprises clustering the plurality of second candidate thresholds into at least two second groups and setting the second updated threshold to separate the second group associated with the highest second group center level from the second group associated with a second highest second group center level.

[0033] In embodiments, using the first updated threshold and second updated threshold to perform motion detection comprises obtaining a sample of a second radar data signal, the sample indicating the first power level and second power level, comparing the first power level to the first updated threshold, comparing the second power level to the second updated threshold, and outputting a signal indicating that motion has been detected in response to the first power level exceeding the first updated threshold and the second power level of the sample exceeding the second updated threshold. The second radar data signal may be a temporally later portion of an original radar data signal comprising both the radar data signal and the second radar data signal. It is understood that the radar motion sensor may continuously record a radar data signal and, once the updated threshold has been determined the updated threshold is used to detect that motion has occurred for future segments of the continuously recorded radar data signal. In embodiments where only one frequency band is used and using the first updated threshold to perform motion detection comprises: obtaining a sample of a second radar data signal, the sample indicating the first power level, comparing the first power level to the first updated threshold and outputting a signal indicating that motion has been detected in response to the first power level of the sample exceeding the first updated threshold.

[0034] According to a second aspect of the invention there is provided a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method according to the first aspect.

[0035] According to a third aspect of the invention, a controller for a radar motion sensor, the controller comprising a processor and a memory, wherein the processor is configured to perform the method according to the first aspect.

[0036] In embodiments, two consecutive trigger samples are separated by at least one trailing context window.

[0037] Accordingly, after one trigger sample has been identified, the method starts to look for the next trigger sample starting with samples that are at least the duration of the trailing context window temporally later than the previous trigger sample. As a consequence, the computational complexity is reduced since some samples may be ignored when looking for a next trigger sample after a previous trigger sample has been identified. For each trigger sample it is determined if it is a valid trigger sample by determining if a trailing event has occurred in the trailing context window.

[0038] In embodiments, when both frequency bands are used, the first and second candidate threshold is determined such that at least one sample in the trailing context window has a first and second power lever exceeding the first and second candidate threshold respectively.

[0039] The first and second candidate thresholds are hereby determined together such that at least one sample in the trigger context window fulfills the stricter criteria of being associated with a first and second power level that exceeds both the first and second candidate threshold. In this way, the first and second candidate thresholds can be set as high as possible while still ensuring that the trigger sample is identified in the trigger context window.

[0040] The invention according to the second and third aspect features the same or equivalent benefits as the invention according to the first aspect. Any functions described in relation to the method, may have corresponding features in a controller or a computer program product. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] This and other aspects of the present invention will now be described in more detail, with reference to the appended drawings showing embodiments of the present invention.

[0042] Fig. 1 shows a top-down view of an exemplary room-layout that could increase the likelihood for false positives for a radar motion sensor.

[0043] Fig. 2 is a flowchart illustrating the first learning phase, second learning phase and use phase for calibrating a radar motion sensor according to some implementations.

[0044] Fig. 3 is a graph showing an example of a raw radar signal from a radar motion sensor.

[0045] Fig. 4 is a graph showing the lower and high frequency spectral power of the raw radar signal.

[0046] Fig. 5 is a block diagram showing the processing performed in the second learning phase.

[0047] Fig. 6a-d are graphs comparing the candidate thresholds to the predetermined thresholds for the low and high frequency band respectively.

[0048] Fig. 7a and 7b are graphs illustrating the resulting updated thresholds levels obtained for the low and high frequency band respectively using clustering.

[0049] DETAILED DESCRIPTION

[0050] Fig. 1 shows a top down view of an exemplary indoor environment that introduces an extra challenging environment for radar motion sensors. Two rooms la, lb (e.g. conference rooms) are located next to each other and delimited by a transparent glass wall or window 3. In the first room la there is a radar motion sensor 2 installed with the purpose of detecting motion in the first room la. For example, upon detection of motion in the first room la the lights are turned on in the first room la and / or the HVAC system associated with the first room la is activated. On the other hand, if no motion has been detected in the first room la for a predetermined period of time, the lights and / or HVAC system associated with the first room la is deactivated.

[0051] However, since the radar motion sensor 2 is sensitive and utilizes electromagnetic signals capable of penetrating the transparent glass wall or window 3 the radar motion sensor 2 may detect motion associated with a first person 5a walking by in a hallway 4 or motion associated with a second person 5b moving the second room lb on the other side of the transparent glass wall or window 3. Detection of these motions may increase the number of false positives wherein the lights and / or HVAC system of the first room la is erroneously activated because a motion in the second room lb or hallway 4 is registered. With the present invention a method for automatically calibrating a radar motion sensor 2 is presented which will tune the sensitivity of the radar motion sensor 2 such that the false positive rate can be reduced. With reference to the flowchart in Fig. 2 a method according to some implementations will now be described in detail.

[0052] At step SI a radar data signal is obtained. The radar data signal may be extracted from a “raw” radar signal (sometimes referred to as simply the radar signal) being the signal recorded directly by the radar sensor. The radar sensor may use any type of Doppler radar, such as a pulsed Doppler radar or an unmodulated continuous wave (CW) Doppler radar. In the latter example, a constant frequency carrier wave in the GHz range is emitted and its reflection received. Transmitted and reflected waves are mixed to form an intermediate frequency (IF) signal in the time domain which is outputted as the “raw” radar signal. In available radar motion sensors, the carrier frequency may be for example 5.8 GHz, 24 GHz or 60 GHz. Accordingly, the radar signal comprises a plurality of successive samples, with each sample representing the radar signal at a respective point in time.

[0053] With further reference to Fig. 3, an exemplary (IF) radar signal 6 is shown with the horizontal axis denoting time and the vertical denoting received signal power. Around time to the radar signal 6 is relatively stable and comprises mainly noise. At around time ti a movement occurs in the vicinity of the radar motion sensor which causes the power level to fluctuate rapidly. The rapid fluctuation continues until time t2, and after time t2 some fluctuations are still present, although less rapid. After time t3 the fluctuations are reduced even further.

[0054] The frequency content of the radar signal 6 may be extracted in one or more frequency bands forming a radar data signal having a temporal sequence of samples with each sample representing the spectral power in the one or more frequency bands. For example, the radar data signal may be transformed from time domain (as shown in Fig. 3) to a time-frequency domain (e.g. using an FFT) so that the frequency content of the radar signal 6 can be analyzed.

[0055] With further reference to Fig. 4 the spectral power, over time, in a first frequency band of the radar signal 6 is indicated with line 7 and the spectral power, over time, in a second frequency band of the radar signal 6 is indicated with line 8. In this example, the first frequency band is a low frequency band with frequencies 0 - 4 Hz and the second frequency band is a high frequency with frequencies 6 - 10 Hz. However, it is understood that these frequency bands are merely exemplary and while these frequency bands are suitable for radar motion sensors using 5.8 GHz carrier signals, other frequency bands may be used if other carrier frequencies are used.

[0056] The respective power levels 7, 8 in different frequency bands are directly extracted from the radar signal 6 and, in general, a signal indicating for each time sample the power level in one or more frequency bands will be referred to as a radar data signal. In some implementations, two frequency bands are used and each sample in the radar data signal will represent the power level 7, 8 in each frequency band respectively. Accordingly, each sample in the radar data signal may be represented with a vector associated with an individual timestamp, wherein each vector element representing the spectral power in a respective frequency band. In a completely analogous alternative definition, each radar data signal indicates the spectral power level of only one frequency band whereby in embodiments where two or more frequency bands are used there are two or more radar data signals with samples that represent only one power level. The temporal sequence of samples in each radar signal will in such cases be corresponding in time, meaning that sample number A in one radar data signal will have the same timestamp as sample number A in another radar data signal.

[0057] In the below, it will be assumed that one radar data signal with samples representing one or more power levels will be used.

[0058] In Fig. 4, the low frequency spectral power level 7 increases rapidly from time to to time ti together with the high frequency spectral power level represented with line 8. Between times t2 and t3, the low frequency spectral power 7 is consistently higher compared to line 8 indicating that between t2 and t3 the low frequency band contains more spectral power than the high frequency band.

[0059] It has been realized that this general pattern, i.e. a rapid increase in both high and low frequency spectral power followed by a comparatively high level of low frequency spectral power and comparatively low level of high frequency spectral power is typical for when people enter a room (e.g. a conference room or office) and sit down (at e.g. a conference table or desk). First, when people enter the room and approach the radar sensor there are many types of motion registered by the radar motions sensor. Then, when the people sit down there are mostly small motions (e.g. hand gestures or head moving) which are mainly associated with low frequency components in the radar signal 6. At step S2 a trigger sample is determined. The trigger sample is determined by identifying the first sample which is associated with a first or second power level 6, 7 that exceeds one or more of the predetermined thresholds TLF, THF.

[0060] In some implementations, the trigger sample is determined as the first sample which is associated with a power level in the first frequency band that exceeds a first predetermined threshold TLF. In the example shown in Fig. 4 it is seen that the first power level (i.e. the spectral power in the first frequency band represented with line 7) reaches the first predetermined threshold TLF at time ti. Accordingly, the sample at ti may be identified as the trigger sample.

[0061] In some implementations, the trigger sample is determined as the first sample which is associated with a first and a second power level 7, 8 which both exceed a respective first and second predetermined threshold TLF, THF. AS seen in Fig. 4 the second power level 8 exceeded the second predetermined threshold level THF some time prior to time ti and remained above the second predetermined threshold level THF until time ti and beyond. Accordingly, the sample at ti is again identified as the trigger sample although it is understood that with this stricter criteria of requiring both power levels 7, 8 to exceed their respective predetermined thresholds TLF, THF which sample that is identified as trigger sample will generally be different from the sample identified with the more relaxed criteria (i.e. using only one power level and threshold).

[0062] The first and second predetermined threshold TLF, THF may be the same or different.

[0063] When the trigger sample (in this example the sample at time ti) has been identified the method continues to step S3 and determines if a trailing event has occurred. The trailing event will provide an indication whether people whose movement caused the criteria for identification of the trigger sample to be fulfilled continues to move and remains in proximity to the radar motion sensor.

[0064] When determining whether a trailing event in a predetermined time duration following the has occurred, a set of samples in a trailing window following the trigger sample may be analyzed. The trailing window may comprise e.g. all samples in a predetermined time duration following the trigger sample that was identified at time ti. However, this definition of the trailing window is merely exemplary, and many types of trailing windows can be used. For example, the trailing window or predetermined time duration is at least 20 seconds long, at least 40 seconds long or at least 60 seconds long and starts immediately after the trigger sample or after predetermined waiting time (e.g. 2, 5 or 10 seconds) after the trigger sample. In the depicted embodiment, the trailing window comprises the first sample after the trigger sample (at time ti) and all samples until time t3. To determine if a trailing event has occurred it is determined whether (or at least to what extent) at least one of the power levels continues to exceed their respective predetermined threshold TLF, THF.

[0065] If the first and / or second power level exceeds the first and / or second predetermined threshold level for a predetermined percentage of the samples in the trailing context window a trailing event has been identified. Preferably, in the trailing window people are often expected to sit down and exhibit small movements whereby at least the low frequency power level with its respective predetermined threshold TLF is used (optionally in combination with the high frequency power level) to determine if the trailing event has occurred. The predetermined percentage may be between 50% and 95%, and preferably between 70% and 95% such as 95%, 90% or 80%. Preferably, the percentage is adjusted based on the length of the trailing context window with higher percentages being used for shorter trailing context windows and vice versa. For example, if a 30 second trailing context window is used the predetermined percentage may be 90% whereas if a 60 second trailing context window is used the predetermined percentage may be 80%.

[0066] If no trailing event has been identified the method goes back to step S2 and monitors the radar data signal until the next trigger sample can be identified. Preferably, the method resumes its analysis of the radar data signal at a sample which occurs later than the trigger sample at time ti, such as at sample which is 10 seconds later than ti or at least the length of one trailing window later than ti. In some implementations, the method resumes its analysis to find the next trigger sample after the trigger context window and trailing context window has passed. However, it is also envisaged that the method resumes its analysis of the radar data signal at the very next sample after ti.

[0067] If a trailing event has been identified the trigger sample is classified as a valid trigger sample. The method continues to step S4 and stores a record of the samples in a trigger context window surrounding the valid trigger sample. The trigger context window comprises the valid trigger sample (at ti) and a plurality of samples temporally earlier and / or temporally later than the valid trigger sample. For example, the trigger context window extends ± 1 second, ± 5 seconds, ± 10 seconds, or ± 20 seconds from the valid trigger sample at time ti. The trigger context window is not necessarily symmetrical and may for example contain a longer duration of samples after ti compared to before ti or vice versa. In one example implementation, the trigger context window includes ti as well as 10 seconds of samples temporally earlier than ti and 20 seconds of samples temporally later than ti. In Fig. 4, the trigger context window extends from to to t2.

[0068] The record of the trigger context window includes a record of the first and / or the second power level for each sample in the trigger context window.

[0069] Steps S2, S3 and S4 are repeated a plurality of times until a plurality of valid trigger samples have been detected and a database of records with trigger context windows has been collected. These trigger context windows indicate portions of the radar data signal that are followed by a trailing event and therefore represent power level sequences associated with potentially valid movements associated with people actually entering the room of the radar sensor. The fact that a valid trailing event has been registered for each record indicates that these trigger context windows likely indicate people entering the room of the radar motion sensor and not e.g. merely walking by in the hallway.

[0070] Once a sufficient number of (e.g. a predetermined number of) records with trigger context windows have been collected in the database the first training phase El is completed, and the method continues to step S5 and S6 of the second training phase E2.

[0071] Step S5 involves, for each trigger context window in the database, determining a candidate threshold TLF.U, THF.U that in general is greater than or equal to the static predetermined threshold TLF, THF used in step S2 to determine the trigger sample. The process of determining candidate threshold levels TLF.U, THF.U, will now be described with further reference to Fig. 5 where eight records, labeled record 1-8, have been collected during the first training phase El. The number of records is merely exemplary, and it is envisaged that as few as two records may be used but often more than ten or even hundreds of records will be available after the first training phase El.

[0072] Each record comprises at least the first power level of each sample in one trigger context window. Optionally, each record comprises the first and second power level of each sample of the same trigger context window. The records are obtained by a threshold updating module 9 which is configured to determine, for each record and frequency band, a candidate threshold TLF.U, THF.U. The threshold updating module 9 accomplishes this by finding a candidate threshold TLF.U, THF.U which is greater than or equal to the predetermined threshold TLF.U, THF.U but still allows a trigger sample to be identified in the trigger context window of the respective record.

[0073] With further reference to Fig. 6a and 6b an example of the updated threshold TLF, ui for the first frequency band with respect to the predetermined threshold TLF is shown. In Fig. 6a and 6b, the power level 7 of the first frequency band of record 1 is plotted with respect to time along the horizontal axis with an arbitrary power unit a.u. on the vertical axis. In Fig. 6a it is seen that the power level 7 of the first frequency band is above the first predetermined threshold TLF set to 45 a.u. at some time prior to tawhereby a trigger sample is identified.

[0074] The threshold updating module 9 finds a first candidate threshold TLF, ui (see Fig. 6b) that is higher (equal to about 50 a.u.) compared to the first predetermined threshold TLF wherein at least one sample of the first power level 7 is above the first candidate threshold TLF, ui. Accordingly, this candidate threshold TLF, ui can be used to replace the first threshold TLF while still enabling a trigger sample to be identified in the same trigger context window.

[0075] The threshold updating module 9 performs the same processing for the remaining records and determines one first candidate threshold TLF, ui, . . . ., TLF,U8 for each record 1, 2, . . ., 8 wherein each first candidate threshold is greater than the first predetermined threshold TLF.

[0076] An updated threshold TLFJ is then determined at step S6 based on one or more of the candidate thresholds TLF, ui, ... ., TLF, U8.

[0077] In some implementations, a plurality of candidate thresholds TLF, ui, ... ., TLF,U8 are available and provided to a clustering module 10 which performs clustering (K-mean clustering) into K groups wherein K > 1 wherein the updated threshold TLFJ separates the group with the highest center level (group centroid) from the other groups (if present). An example of clustering with K = 2 is shown in Fig. 7a where the first and second candidate thresholds TLF, U1, TLF, u2 have been assigned to different groups and the updated threshold TLF,F has been determined to separate the two groups.

[0078] The processing for the first frequency band may be repeated also for the second frequency band. With reference to Fig. 6c and 6d an example of a second updated threshold THF, ui for the second frequency band with respect to the predetermined second threshold THF is shown. In Fig. 6c and 6d, the power level 8 of the second frequency band of record 1 is plotted with respect to time along the horizontal axis and with respect to the arbitrary power unit a.u. on the vertical axis. In Fig. 6c it is seen that the power level 8 of the second frequency band is above the second predetermined threshold THF set to 45 a.u. at ta. The threshold updating module 9 finds a second candidate threshold THF, ui (see Fig. 6d) that is higher (equal to about 50 a.u.) compared to the second predetermined threshold THF wherein at least one sample of the second power level 8 is above the second candidate threshold THF. UI .

[0079] The threshold updating module 9 also processes the other records and determines one second candidate threshold THF, ui, ...., THF, u8 for each record 1, 2, . . . , 8 wherein each second candidate threshold THF, ui, ...., THF, u8 is greater than the second predetermined threshold THF.

[0080] An updated second threshold THFJ is then determined at step S6 based on one or more of the candidate thresholds THF, ui, ... ., THF, U8. In some implementations, a plurality of candidate thresholds THF, ui, ... ., THF, U8 are provided to the clustering module 10 which performs clustering (K-mean clustering) into K groups wherein K > 1 wherein the updated threshold THF, f so as to separate the group with the highest center level (group centroid) from the other groups. An example of clustering with K = 2 is shown in Fig. 7b where the seventh and eight second candidate thresholds THF, U7, THF, U8 have been assigned to different groups and the updated threshold THF, f has been determined to separate the two groups.

[0081] Accordingly, in some implementations the processing at step S5 and S6 is separate for the two frequency bands. However it is also envisaged that the method is performed with only one frequency band, such as only for the first frequency band or only for the second frequency band.

[0082] In some implementations, the processing at step S5 (performed by the threshold updating module 9) for determining first and second candidate thresholds TLF, ui, THF, ui is linked between both frequency bands such that at least one sample which exceeds both the first and second candidate threshold TLF, ui, THF, ui is present in the trigger context window of each record.

[0083] As an example, the threshold updating module 9 determines the first candidate thresholds TLF, U1, THF, U1 using an iterative process. The iterative process is initiated with the threshold updating module 9 setting a respective attempt threshold level to equal the first and second predetermined threshold level. At iteration 1 the threshold updating module 9 then incrementally increases the first attempt threshold level and checks if a trigger sample having power levels above both attempt thresholds levels can be detected in the trigger context window. If this is the case, the threshold updating module 9 continues to iteration 2 and incrementally increases the second attempt threshold level and checks if a trigger sample having power levels above both attempt thresholds level can be detected in the trigger context window. If this is the case, the threshold updating module 9 continues to iteration 3 and again incrementally increases the first attempt threshold and so on. The process repeated by alternating incremental increase of the first and second attempt thresholds until after N iterations it is found that no sample in the trigger context window has a first and second power level above the first and second attempt threshold level. The threshold updating module 9 then assigns the attempt threshold levels of iteration N - 1 to the candidate threshold levels TLF. UI, THF. UI respectively.

[0084] Accordingly, using this iterative process candidate threshold levels TLF, U1, THF, ui can be maximized while still allowing at least one trigger sample to be identified in the trigger context window even with the stricter criteria for trigger sample identification requiring that both power levels should be above the respective candidate threshold TLF, ui, THF, ui.

[0085] It is further envisaged that as an alternative to using clustering for determining the updated threshold levels TLF, F, THF, f respectively different operations can be used. For example, the updated threshold levels TLF, F, THF, fare set as the average or median of the respective candidate threshold levels TLF, U, THF, U.

[0086] After step S6 has been completed a first updated threshold (and optionally also a second updated threshold) is available and the second learning phase E2 has been completed.

[0087] The method then goes to the use phase E3 and performs motion detection at step S7 for a second, future, radar data signal having samples using the first updated threshold and optionally also the second updated threshold. A motion is detected if the first, and optionally second, updated threshold is exceeded in a single sample by the first, and optionally the second, power level. Preferably, during phase E3, the processing related to determining a trailing event is skipped which makes the implantation less computationally intensive. Optionally, the radar motion sensor device can be periodically or manually (e.g. when the radar motion sensor is moved to a new room) reset whereby learning phase El and E2 are repeated with the default predetermined threshold values TLF, THF.

[0088] The radar motion detector may still be used during the first and second learning phase El, E2 e.g. while records are collected in learning phase El. For example, the first and second predetermined thresholds are used to identify a trigger sample which is used as an indication that a movement has been detected. While the first and second predetermined threshold may exhibit a relatively high false positive rate this is only a temporary solution until enough records have been collected and the updated threshold level(s) have been determined. Turning back to the exemplary room layout of Fig. 1 it is understood how the calibration process for determining updated thresholds described above will improve motion detection accuracy.

[0089] Firstly, since only trigger context windows for which a trailing event is detected after the trigger sample it is likely that no, or very few, trigger context windows associated with a person 5a walking by in the hallway are stored among the records. This is because the duration of the movement of the person 5a walking by is likely too short to fulfill the requirements of the trailing event. This means that the trigger context windows associated with e.g. a person 5a walking by in the hallway will not be stored as a record in the first learning phase El and therefore also ignored in the second learning phase E2 so as to not prevent setting higher updated threshold levels.

[0090] Secondly, if clustering is performed with at least two groups, it is possible to identify updated thresholds which separate motion by a person 5b in an adjacent room lb from motion by a person in the room la where the radar motion sensor 2 is located. The radar motion sensor 2 may detect both a trigger sample and a trailing event when a person 5b enters the adjacent room lb, however by finding candidate thresholds and performing clustering of these candidate thresholds into at least two groups it will in many applications be possible to distinguish between a person 5b entering the adjacent second room lb and a person entering the first room la where the radar motion sensor 2 is located. The clustering may optionally be performed so as to form more than two groups. For example, some conference rooms will have a transparent wall or window 3 facing two or more other conference rooms whereby clustering into three or more groups will be beneficial for accurate detection of motion in first room where the radar motion sensor 2 is located.

[0091] The person skilled In the art realizes that the present invention by no means is limited to the preferred embodiments described above. On the contrary, many modifications and variations are possible within the scope of the appended claims. For example, it is possible to use one the spectral power in one frequency band or two frequency bands. Additionally, it is envisaged that the spectral power in more than two frequency bands may be used to perform the calibration.

Claims

CLAIMS:

1. A method for calibrating a radar motion sensor device (2), comprising: obtaining (SI) a temporal sequence of samples of a radar data signal, extracted from a radar signal (6) detected by the radar motion sensor device (2), each sample indicating a first power level (7) in a first frequency band of the radar signal (6); identifying (S2) a plurality of trigger samples by comparing the first power level of each sample to a first predetermined threshold (TLF), wherein each trigger sample is identified as a sample having a first power level (7) that exceeds the first predetermined threshold (TLF); for each identified trigger sample: defining a trailing context window associated with the trigger sample comprising a predetermined number of samples temporally later than the trigger sample; determining (S3), for each sample in the trailing context window associated with the trigger sample whether the first power level (7) of the sample exceeds a predetermined trailing threshold; classifying the trigger sample as a valid trigger sample when the percentage of samples in the trailing context window exceed a percentage threshold; analyzing samples of a trigger context window associated with each of the plurality of valid trigger samples to determine a first updated threshold (TLF, F); and using (S7) the first updated threshold (TLF, F) to perform motion detection with the radar motion sensor device (2); wherein analyzing the samples of a trigger context window associated with each of the plurality of valid trigger samples comprises: determining a first candidate threshold (TLF, U) for each valid trigger sample based on the samples of the trigger context window; and clustering the first candidate thresholds (TLF, U) into at least one group, each group being associated with a group center level; wherein the first updated threshold (TLF, I) is set to a value that separates the group associated with a highest group center level;wherein the trigger context window comprises a predetermined number of samples temporally earlier and / or later than the valid trigger sample.

2. The method according to claim 1, wherein clustering comprises clustering the plurality of first candidate thresholds (TLF. U) into at least two groups and setting the first updated threshold (TLF, F) to separate the group associated with the highest group center level from the group associated with a second highest group center level.

3. The method according to claim 1 or claim 2, wherein the first candidate threshold (TLF. U) for each valid trigger sample is greater than the first predetermined threshold (TLF) and smaller than a maximum first power level (7) in the trigger context window.

4. The method according to claim any of the preceding claims, wherein the predetermined trailing threshold equals the first predetermined threshold (TLF).

5. The method according to any of the preceding claims, wherein each sample further indicates a second power level in a second frequency band of the radar signal (6) detected by the radar motion sensor device (2), the second frequency band being different from the first frequency band.

6. The method according to claim 5, wherein the second frequency band contains higher frequencies than the first frequency band.

7. The method according to any of claims 5-6, further comprising: identifying (S2) a plurality of trigger samples by comparing the first power level and the second power level of each sample to a first predetermined threshold (TLF) and second predetermined threshold (THF) respectively, wherein each trigger sample is identified as a sample having a first power level and second power level that exceeds both the first predetermined threshold (TLF) and the second predetermined threshold (THF).

8. The method according to any of claims 6-7, further comprising: for each valid trigger sample, determining, a second candidate threshold (THF,u) based on the second power level of the samples of the trigger context window, wherein thesecond candidate threshold (THF. U) is greater than the second predetermined threshold (THF) and smaller than the maximum second power level in the trigger context window; the method further comprising: determining a second updated threshold (TuF,f) by clustering a plurality of second candidate thresholds (THF. U) into at least one second group of second candidate thresholds, wherein each second group of second candidate thresholds is associated with a respective second group center level, and setting the second updated threshold (THF. I) to a value that separates the second group associated with a highest second group center level; and using (S7) the first and second updated threshold (TLF. F, THF. F) to perform motion detection with the radar motion sensor device (2).

9. The method according to claim 8, wherein using (S7) the first updated threshold (TLF. F) and second updated threshold (THF. F) to perform motion detection comprises: obtaining a sample of a second radar data signal, the sample indicating the first power level (7) and second power level (8), comparing the first power level (7) to the first updated threshold (TLF. F), comparing the second power level (8) to the second updated threshold (THF. F); and outputting a signal indicating that motion has been detected in response to the first power level (7) exceeding the first updated threshold (TLF. F) and the second power level (8) of the sample exceeding the second updated threshold (THF. F).

10. The method according to any of claims 1-4, wherein using the first updated threshold (TLF. F) to perform motion detection comprises: obtaining a sample of a second radar data signal, the sample indicating the first power level (7); comparing the first power level (7) to the first updated threshold (TLF. F); outputting a signal indicating that motion has been detected in response to the first power level (7) of the sample exceeding the first updated threshold (TLF. F).

11. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method according to any of the preceding claims.

12. A controller for a radar motion sensor (2), the controller comprising a processor and a memory, wherein the processor is configured to perform the method according to any of claims 1-10.

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