Detection device and method of operating detection device

Abstract

A detection apparatus and a method of operating the detection apparatus are provided. The detection apparatus includes: a Fourier transform processing circuit configured to output first Fourier transform data by distance Fourier transform processing on a plurality of pulse signals, and output second Fourier transform data by Doppler Fourier transform processing on the first Fourier transform data; a filter characteristic estimation circuit configured to select a plurality of samples corresponding to noise in the second Fourier transform data by a constant false alarm rate (CFAR), obtain noise power data from the plurality of samples, and estimate frequency dependent filter compensation data based on the noise power data; and a detection circuit configured to compensate the first Fourier transform data based on the filter compensation data, and detect a target based on performing CFAR on the compensated first Fourier transform data.

Description

[0001] This application claims priority to Korean Patent Application No. 10-2024-0005016, filed on January 11, 2024, and Korean Patent Application No. 10-2024-0045546, filed on April 3, 2024, in the Korean Intellectual Property Office, the disclosures of which are hereby incorporated by reference in their entirety. Technical Field

[0002] Example embodiments relate to a detection device for detecting an object using radar and a method of operating the detection device, and more particularly to a detection device that processes using Fourier transform. Background Art

[0003] Generally, according to a desired frequency passband, a filter for filtering a signal may be implemented as a low-pass filter (LPF), a high-pass filter (HPF), a band-pass filter (BPF), a notch filter, etc. Filter characteristics (such as gain and cutoff frequency) may change due to changes in the surrounding environment, which may include temperature, humidity, pressure, etc. In the case where a detection device operates in a variable frequency band or multiple frequency bands, when the filter characteristics change, the detection performance may deteriorate. Summary of the Invention

[0004] Example embodiments provide a detection device and a method of operating the detection device that are robust to changes in filter characteristics.

[0005] According to an example embodiment, a detection device includes: a Fourier transform processing circuit configured to output first Fourier transform data by performing range Fourier transform processing on a plurality of pulse signals and output second Fourier transform data by performing Doppler Fourier transform processing on the first Fourier transform data; a filter characteristic estimation circuit configured to select a plurality of samples corresponding to noise in the second Fourier transform data by constant false alarm rate (CFAR), obtain noise power data from the plurality of samples, and estimate frequency-related filter compensation data based on the noise power data; and a detection circuit configured to compensate the first Fourier transform data based on the filter compensation data and detect a target based on performing CFAR on the compensated first Fourier transform data.

[0006] According to an exemplary embodiment, a method of operating a detection device includes: outputting first Fourier transform data by performing a distance Fourier transform process on a plurality of pulse signals and outputting second Fourier transform data by performing a Doppler Fourier transform process on the first Fourier transform data, selecting a plurality of samples corresponding to noise in the second Fourier transform data by performing a constant false alarm rate (CFAR) on the second Fourier transform data, obtaining noise power data from the plurality of samples, estimating frequency-related filter compensation data based on the noise power data, compensating the first Fourier transform data based on the filter compensation data, and detecting a target based on performing CFAR on the compensated first Fourier transform data.

[0007] According to an exemplary embodiment, a detection device includes: a transceiver configured to transmit and receive a plurality of pulse signals; and a processor electrically connected to the transceiver. The processor may be configured to select a plurality of samples corresponding to noise in the Doppler Fourier transform data of the plurality of pulse signals by performing a constant false alarm rate (CFAR) on the Doppler Fourier transform data, obtain noise power data from the plurality of samples, estimate frequency-related filter compensation data based on the noise power data, compensate the distance Fourier transform data of the plurality of pulse signals based on the filter compensation data, and detect a target based on performing CFAR on the compensated distance Fourier transform data. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a diagram illustrating a detection device according to an exemplary embodiment.

[0009] Figure 2 is a block diagram of a detection device according to an exemplary embodiment.

[0010] Figure 3 is a graph illustrating a compensation operation based on fixed filter characteristics.

[0011] Figure 4 depicts a graph illustrating a compensation operation based on an adaptively estimated filter characteristic according to an exemplary embodiment.

[0012] Figure 5 is a block diagram of a filter characteristic estimation circuit according to an exemplary embodiment.

[0013] Figure 6 is a diagram illustrating an operation of obtaining noise power data of a burst unit of a detection device according to an exemplary embodiment.

[0014] Figure 7 is a 3D diagram illustrating a spectrum of Doppler Fourier transform data according to an exemplary embodiment.

[0015] Figure 8is a 3D view showing Doppler Fourier transform data after constant false alarm rate (CFAR) according to an exemplary embodiment.

[0016] Figure 9 is a block diagram of a filter compensation data estimation circuit according to an exemplary embodiment.

[0017] Figure 10 is a view showing a frequency-based filter response according to an exemplary embodiment.

[0018] Figure 11 is a flowchart showing a method of operating a detection device according to an exemplary embodiment.

[0019] Figure 12 is a flowchart showing a method of estimating filter compensation data of a detection device according to an exemplary embodiment.

[0020] Figure 13 is a flowchart showing a detection method of a detection device according to an exemplary embodiment.

[0021] Figure 14 is a view showing a range Fourier transform result when there is no compensation for filter characteristics.

[0022] Figure 15 is a view showing a CFAR result when there is no compensation for filter characteristics.

[0023] Figure 16 is a view showing a range Fourier transform result when there is compensation for filter characteristics according to an exemplary embodiment.

[0024] Figure 17 is a view showing a CFAR result when there is compensation for filter characteristics according to an exemplary embodiment. DETAILED DESCRIPTION

[0025] Hereinafter, exemplary embodiments will be described with reference to the accompanying drawings.

[0026] Figure 1 is a view showing a detection device 100 according to an exemplary embodiment. The detection device 100 may include a plurality of antennas 110a and 110b, a transceiver 115, and a processor 140.

[0027] Multiple antennas 110a and 110b may include a transmit antenna 110a and a receive antenna 110b. The transmit antenna 110a may transmit a transmit signal generated from a transmit (TX) path 120 into free space. The receive antenna 110b may provide a received signal received from an external source to a receive path 130. In other examples, the transmit antenna 110a and / or the receive antenna 110b may each be provided as multiple, or a single transceiver antenna may be provided. According to an example embodiment, any of the antennas may be or include an array antenna.

[0028] The transceiver 115 may be configured to transmit and receive multiple pulse signals and may include a transmit path 120 and a receive path 130.

[0029] The transmit path 120 may convert a digital signal generated from a processor 140 into a transmit signal in the analog domain and process the converted transmit signal. The transmission path 120 may include filters, mixers, and amplifiers and / or various other components for converting, generating, and processing the transmit signal.

[0030] According to an example embodiment, the transmit path 120 may generate multiple pulse signals as transmit signals through an oscillator (not shown). For example, each pulse signal may include a continuous wave (CW) signal (within each pulse) or a frequency-modulated continuous wave (FMCW) signal. A signal frequency-modulated by FMCW within the time period of a pulse may be referred to as a chirp. For example, a chirp may have a frequency that linearly varies through linear frequency modulation (LFM). A chirp may have a sawtooth waveform. Optionally, a chirp may have a stepped frequency (SF) waveform.

[0031] According to an example embodiment, the transmit path 120 may continuously transmit multiple frequency-modulated chirps. Each of the chirps may be transmitted within a pulse repetition interval (PRI) period.

[0032] The receive path 130 may process an incoming received signal and convert the processed signal into a received signal in the digital domain. Similar to the transmit signal, the received signal may include multiple pulse signals. A single received pulse signal for a single transmitted pulse signal may be received through the receive path 130 after a round-trip time (the time it takes for the transmit signal to be reflected by an object and received by the receiver as a received signal).

[0033] According to an exemplary embodiment, the receiving path 130 may include a low noise amplifier (LNA) 131, a mixer 132, a filter 133, and an analog-to-digital converter (ADC) 134. The low noise amplifier 131 may perform low noise amplification on the received signal and provide the low noise amplified signal to the mixer 132. The mixer 132 may mix the transmitted signal and the received signal received from the receiving path 130 and output a mixed signal MS. The mixed signal MS may also be referred to as an IF signal and may have a frequency corresponding to the difference between the frequency of the transmitted signal and the frequency of the received signal. The frequency of the mixed signal MS may be referred to as the difference frequency.

[0034] The filter 133 may be configured to filter the mixed signal MS provided from the mixer 132. For example, the filter 133 may be implemented as a low pass filter (LPF), a high pass filter (HPF), etc. for filtering a specific frequency band of the mixed signal MS. For example, when the detection device 100 is configured to detect a short-range target or an ultra-short-range target, the filter 133 may be implemented as an HPF to reduce the influence of low frequency leakage signals.

[0035] The filter 133 may be substantially designed to have filter characteristics (such as gain and cut-off frequency), and the filter characteristics may vary according to changes in the surrounding environment (including temperature, humidity, pressure, etc.). The noise level of the filtered signal may also vary over time and frequency and as a function of the surrounding environment. The filtered signal may be transformed from the analog domain to the digital domain by the ADC 134 and then routed to the processor 140.

[0036] The processor 140 may process digital signals including information to be transmitted or process received signals.

[0037] According to an example embodiment, the processor 140 may perform various operations to detect a target from a received signal. For example, the processor 140 may detect a target by performing constant false alarm rate (CFAR) processing on the received signal. CFAR is an algorithm for constantly setting false alarms, and false alarms are determined as if there were reflection signals for the target even when there are no reflection signals. False alarms may be due to parasitic noise exceeding a threshold. The threshold may be allowed to adaptively change corresponding to changes in the average noise level, which may be caused by changes in the surrounding environment. Thus, CFAR may allow detecting a target from the received signal with a desired or required probability of false alarms. For example, the processor 140 may set the threshold higher when the noise level is high and lower when the noise level is low. Thus, the threshold may be raised or lowered to maintain a constant probability of false alarms. For example, if the threshold is set too low, more real objects will be detected, but at the cost of more false alarms. Conversely, if the threshold is set too high, there will be fewer false alarms, but fewer objects will be detected (too many objects will remain undetected). Thus, a constant probability of false alarms (which may be set according to the application) is desired.

[0038] In CFAR, when the noise level becomes more constant over the operating frequency band, false alarms may occur less frequently. Thus, the processor 140 may estimate filter characteristics and compensate for the noise level based on the inverse characteristics of the estimated filter characteristics.

[0039] According to an example embodiment, when there are changes in the surrounding environment, the processor 140 may estimate changed filter characteristics related to the changes in the surrounding environment. The estimation of filter characteristics may be performed using a plurality of pulse signals corresponding to the received signal. For example, the processor 140 may use the signals also used for detecting the target to estimate filter characteristics. Unless otherwise specifically mentioned below, the pulse signals processed by the processor 140 will refer to the signals transformed into the digital domain via the ADC 134 after mixing the received signal by the mixer 132.

[0040] The processor 140 may obtain range Fourier transform data and Doppler Fourier transform data by performing range Doppler Fourier transform on a plurality of pulse signals. In the Doppler Fourier transform data, peaks exist at the range and Doppler values corresponding to the position of the target. In addition, there are values based on the noise level in the regions where there is no target.

[0041] The processor 140 may perform CFAR processing on the Doppler Fourier transform data. The processor 140 may select a plurality of samples corresponding to the noise from the Doppler Fourier transform data based on the result of the CFAR processing.

[0042] The processor 140 may obtain noise power data from a plurality of samples and estimate filter compensation data as a function of frequency based on the obtained noise power data. According to an example embodiment, the processor 140 may calculate a variance value of the plurality of samples. When the noise level is assumed to be zero mean, the variance value may be regarded as the noise power, such that the processor 140 may obtain the calculated variance value as the noise power data.

[0043] The processor 140 may obtain the reciprocal of the obtained noise power data and estimate the reciprocal as the filter compensation data. The noise power data may be defined for each frequency. Thus, the noise power data may be regarded as data related to the frequency-dependent response characteristics of the replica filter. Therefore, the filter compensation data estimated as the reciprocal of the noise power data may inversely compensate the response characteristics of the filter.

[0044] According to an example embodiment, the processor 140 may estimate filter compensation data for a plurality of bursts to increase the reliability of the filter compensation data. Each of the plurality of bursts may include a plurality of pulse signals. For example, if the number of pulse signals is M and the number of bursts is L (where M and L are positive integers), the receiving circuit may estimate the filter compensation data for L bursts.

[0045] The processor 140 may detect a target by compensating the range Fourier transform data based on the estimated filter compensation data and perform CFAR processing on the compensated data. When there is no compensation for the changed response characteristics of the filter, a noise level may exist on the range bins of the range Fourier transform data. When there is a cut-off frequency of the filter within the frequency range to be detected, there may be different levels of noise for each range bin. Therefore, it may be difficult to ensure a constant noise level when not performing inverse compensation of the filter characteristics according to the changed filter characteristics. Thus, it may be at least difficult to perform CFAR on the range axis.

[0046] When the filter compensation data according to the changed response characteristics of the filter 133 is used for compensation, the range Fourier transform data may also have a constant noise level for each range bin. Therefore, CFAR may also be performed on the range Fourier transform data instead of the Doppler Fourier transform data.

[0047] Therefore, the detection device 100 according to the above embodiments may estimate the changed response characteristics of the filter 133 and perform inverse compensation by using the estimated filter characteristics to have detection performance that is robust to changes in the filter characteristics. In addition, CFAR may be performed on the range Fourier transform data instead of the Doppler Fourier transform data during the detection process, such that the amount of calculation may be reduced.

[0048] Figure 2is a block diagram of a detection device 200 according to an exemplary embodiment. The detection device 200 can be implemented as a processing circuit that executes instructions within a processor 140 that performs Figure 1 . The detection device 200 can include a Fourier transform processing circuit 210, a filter characteristic (FC) estimation circuit 220, and a detection circuit 230. At least a part of the Fourier transform processing circuit 210, the filter characteristic estimation circuit 220, and the detection circuit 230 can be configured to perform a part of the functions or operations executed by the processor 140 described above in Figure 1 .

[0049] The Fourier transform processing circuit 210 can be configured to perform a "range Fourier transform" process and a "Doppler Fourier transform" process. For example, the Fourier transform processing circuit 210 can perform a Fourier transform process (such as running a fast Fourier transform (FFT) or a discrete Fourier transform (DFT)).

[0050] According to an exemplary embodiment, the Fourier transform processing circuit 210 can obtain range Fourier transform data RFD, which is represented as a range-related spectrum, by performing a range Fourier transform on a plurality of pulse signals. In the range Fourier transform data RFD, peaks appear at ranges where there are targets. The Fourier transform processing circuit 210 can perform a Doppler Fourier transform on the range Fourier transform data RFD to obtain Doppler Fourier transform data DFD (also referred to as range-Doppler Fourier transform data), which is represented as a range-Doppler spectrum. For example, the Fourier transform processing circuit 210 can perform a Fourier transform on each range bin of the range Fourier transform data RFD obtained for each pulse along the pulse axis to perform a Doppler Fourier transform. This is sometimes referred to as two-dimensional (2D) FFT processing.

[0051] Therefore, the Doppler Fourier transform data DFD is represented as a spectrum on the range axis and the Doppler axis.

[0052] The filter characteristic estimation circuit 220 can estimate the response characteristics of a filter (e.g., a filter of Figure 1 ). For example, the filter characteristic estimation circuit 220 can perform a CFAR process on the Doppler Fourier transform data DFD. Through CFAR, a determination can be made as to whether there are targets for a plurality of pulse signals. Since it may be difficult to accurately determine the filter characteristics of each range bin, the filter characteristic estimation circuit 220 can perform CFAR on the Doppler Fourier transform data DFD rather than the range Fourier transform data RFD.

[0053] The filter characteristic estimation circuit 220 can perform CFAR on multiple Doppler bins for each range bin of the Doppler Fourier transform data DFD.

[0054] The filter characteristic estimation circuit 220 can select a plurality of samples corresponding to noise from the Doppler Fourier transform data DFD by CFAR.

[0055] The following example is provided: In this example, there are M pulse signals (pulses), the number of Fourier samples for each pulse is N, the index of the range bin is n (where N and n are positive integers), and the maximum number of targets present in the n-th range bin is Na (where Na is a positive integer). In the Doppler Fourier transform data DFD, the index of the pulse signal can be regarded as the Doppler bin, and the Fourier samples can be regarded as the range bins. Therefore, it is considered that up to Na Doppler bins represent the targets in the M Doppler bins.

[0056] If an example is provided in which M is significantly greater than Na in probability and there is only the noise level in the (M - Na) Doppler bins, then K Doppler bins (where K is a positive integer and has a size smaller than M - Na) are extracted for each range bin, and there can be K noise levels for each range bin.

[0057] Therefore, the filter characteristic estimation circuit 220 can select K Doppler bins (e.g., a plurality of samples) from the Doppler Fourier transform data DFD as the noise level, and obtain the noise power data using the selected samples.

[0058] According to an example embodiment, the filter characteristic estimation circuit 220 can exclude the samples in which the detection flag indicates a specific value from the Doppler Fourier transform data DFD by CFAR. The filter characteristic estimation circuit 220 can select a plurality of samples among the remaining samples except for the excluded samples.

[0059] According to an example embodiment, the filter characteristic estimation circuit 220 can calculate the variance value of the plurality of samples, and obtain the calculated variance value as the noise power data. For example, the filter characteristic estimation circuit 220 can calculate the square of the absolute value of each sample, and sum the calculated squares. Then, the filter characteristic estimation circuit 220 can calculate the average value of the sum value based on the sum to obtain the variance value of the noise level based on the plurality of samples. As described above, when the noise level is zero-mean, the variance value is regarded as the noise power, such that the variance value can serve as the noise power data.

[0060] Considering that the samples are Doppler bins, the noise power data can be defined for each range bin. The range bin can be replaced with the frequency of the mixed signal (IF signal), such that the noise power data can be defined as the data representing the noise power for each frequency.

[0061] Finally, the filter characteristic estimation circuit 220 may estimate filter compensation data FCD according to frequency based on the obtained noise power data. The filter characteristic estimation circuit 220 may calculate the reciprocal of the noise power data and estimate the calculated reciprocal as the filter compensation data FCD. For example, the filter compensation data FCD may be data for compensating, in reverse, a filter response having changed filter characteristics.

[0062] According to an example embodiment, the filter characteristic estimation circuit 220 may update the filter compensation data FCD by applying weights to each of the reference filter compensation data and the filter compensation data FCD. The reference filter compensation data may be data estimated before a plurality of pulse signals whose transmission or reception filter estimation target is.

[0063] The detection circuit 230 may compensate the range Fourier transform data RFD based on the filter compensation data FCD estimated by the filter characteristic estimation circuit 220. For example, an element-by-element product of the range Fourier transform data RFD and the filter compensation data FCD may be performed to compensate the noise level. For example, the detection circuit 230 may perform compensation using the range Fourier transform data RFD instead of the Doppler Fourier transform data DFD to perform CFAR on the range Fourier transform data RFD. This is because through the compensation, the noise level also appears uniformly in the range Fourier transform data RFD. According to an example embodiment, the detection circuit 230 may compensate the range Fourier transform data RFD based on the filter compensation data FCD updated by the filter characteristic estimation circuit 220.

[0064] Thereafter, the detection circuit 230 may use CFAR processing on the data compensated according to the compensation to detect a target. For example, when a detection flag output as a result of CFAR indicates a specific value (e.g., logic high), the detection circuit 230 may determine that a target has been detected.

[0065] The detection device 200 according to the above embodiment may detect a target by adaptively estimating the filter compensation data FCD, taking into account the changed filter characteristics. For example, the detection device 200 may use the estimated filter compensation data FCD to compensate the noise level to allow CFAR to be performed even on the range Fourier transform data RFD. Therefore, the amount of calculation in the detection operation can be reduced.

[0066] Figure 3 is a graph showing a compensation operation based on fixed filter characteristics (passband power (gain) versus frequency). Figure 4 Depicts a graph showing a compensation operation based on adaptively estimated filter characteristics according to an example embodiment.

[0067] Refer to Figure 3, when performing noise level compensation based on fixed filter characteristics, it may be difficult to perform compensation based on fixed filter characteristic FC1 or fixed filter characteristic FC2 to reduce the noise level to a predetermined level. Therefore, as shown in the figure, the compensation data CD may have a predetermined noise power in a specific frequency band and fluctuating noise power in other frequency bands.

[0068] Therefore, as Figure 3 shown, it may be difficult to perform CFAR on the compensation data CD having noise power fluctuating with frequency.

[0069] Referring to Figure 4 , when adaptively estimating filter characteristics according to the exemplary embodiment, the compensation data CD1 to the compensation data CD3 may all have a noise power at a predetermined level.

[0070] When there are filter characteristics FC1 to filter characteristics FC3 changed from previous filter characteristics (not shown), the detection device 200 may estimate each of the changed filter characteristics FC1 to filter characteristics FC3. The change in filter characteristics may be assumed to be caused by a detected change in the noise level, and the change in the noise level may in turn be caused by a change in environmental conditions. The detection device 200 may estimate the reciprocals of the estimated filter characteristics as filter compensation data FCD1 to filter compensation data FCD3, and use the filter compensation data FCD1 to filter compensation data FCD3 corresponding to the changed filter characteristics to compensate the noise power. Therefore, even when the changed filter characteristics FC1 to filter characteristics FC3 have different noise powers for each frequency, all the noise powers may be compensated to reach a predetermined noise level within the frequency range of interest. Note that the phrase "the filter characteristics FC1 to filter characteristics FC3 have different noise powers for each frequency" may mean that when the same level of noise is input to filters having each of the filter characteristics FC1 to filter characteristics FC3 at different times due to different environmental conditions, etc., among the cases of the filter characteristics FC1 to filter characteristics FC3, the output noise power at any frequency is different.

[0071] Therefore, the detection device 200 may detect a target by applying CFAR to the range Fourier transform data having a noise power at a predetermined level.

[0072] Figure 5 is a block diagram of a filter characteristic estimation circuit according to the exemplary embodiment.

[0073] Referring to Figure 5 , the filter characteristic estimation circuit 300 ( Figure 2An example of the FC estimation circuit 220) may include a CFAR circuit 310, a sample selection circuit 320, a noise power (NP) data calculation circuit 330, and a filter compensation data estimation circuit 340.

[0074] The CFAR circuit 310 may perform CFAR on the Doppler Fourier transform data DFD. In the Doppler Fourier transform data DFD, regions corresponding to noise and regions corresponding to targets may be identified through CFAR. The result of the identification may be a detection flag indicating whether a target is detected. The detection flag may indicate a specific value (e.g., logic high) in a region having a peak level in the Doppler Fourier transform data DFD, and may indicate 0 (e.g., logic low) in the remaining regions. A corresponding region may have a target.

[0075] The sample selection circuit 320 may select K samples (where K is a positive integer) from M Doppler bins for each range bin in the Doppler Fourier transform data DFD. For example, K samples may be selected from M Doppler bins for the nth range bin. The selected K samples may correspond to the noise level.

[0076] According to an example embodiment, the sample selection circuit 320 may exclude samples in the Doppler Fourier transform data DFD where the detection flag output by the CFAR circuit 310 indicates a specific value. For example, the sample selection circuit 320 may select a plurality of samples from the remaining samples other than the excluded samples. Thus, regions where a target is likely to exist are excluded from the samples, enabling more accurate estimation of the filter compensation data FCD.

[0077] The noise power data calculation circuit 330 may calculate noise power data NPD for each frequency from the selected plurality of samples.

[0078] The noise power data calculation circuit 330 may calculate the square of the absolute value of each sample and sum the calculated squares. The noise power data calculation circuit 330 may calculate the average of the sum values as the variance value of the plurality of samples, and obtain the calculated variance value as the noise power data NPD. For example, when the noise power data calculation circuit 330 calculates the variance value of M pulse signals, the noise power data calculation circuit 330 may calculate the value obtained by dividing the sum value by K as the variance value.

[0079] The filter compensation data estimation circuit 340 may calculate the reciprocal of the calculated noise power data NPD, and estimate the calculated reciprocal as the filter compensation data FCD. For example, when calculating NS elements (where NS is a positive integer) of the noise power data NPD for the nth range bin, the filter compensation data estimation circuit 340 may obtain the element-wise reciprocal for each element, and estimate the obtained reciprocal as the filter compensation data FCD.

[0080] The filter characteristic estimation circuit 300 according to the above embodiment can calculate noise power data NPD for the Doppler Fourier transform data DFD of M pulse signals, and estimate filter compensation data FCD from the calculated noise power data NPD. Therefore, instead of predetermined filter compensation data, adaptively estimated filter compensation data can be used for detection.

[0081] Hereinafter, with reference to Figures 6 to 8 the operation of the detection device according to an exemplary embodiment will be described.

[0082] Figure 6 is a diagram showing an operation of obtaining noise power data of a burst unit of the detection device 100 according to an exemplary embodiment. As Figure 6 shown in, the detection device 100 can transmit and receive M pulse signals through Figure 1 the transmission path and the reception path to perform detection. The M pulse signals can correspond to a single burst unit. Transmitting and receiving L bursts by the detection device 100 can refer to repeating the transmission and reception of the M pulse signals L times. Similarly, obtaining noise power data of L bursts can refer to repeating the operation of obtaining noise power data of the M pulse signals L times.

[0083] Among the L bursts, the I-th burst (where I is a positive integer less than or equal to L) and the (I + 1)-th burst will now be described. As described above, the Doppler Fourier transform data DFD can be represented by Doppler bins and range bins. Similarly, M Doppler bins are provided for the M pulse signals. In addition, there are N range bins (or Fourier samples) for each Doppler bin.

[0084] The detection device 100 can select K samples from the M Doppler bins for each range bin. For example, K samples can be selected from the M Doppler bins for the n-th range bin. The detection device can calculate the square of each value in the K samples selected for each range bin for the I-th burst, and sum the squared values. If the sum value of the I-th burst is defined as the first sum value (Sn,I) and the sum value of the (I + 1)-th burst is defined as the second sum value (Sn,I+1), then the detection device 100 can update the value obtained by adding the first sum value (Sn,I) to the second sum value (Sn,I+1) as a new second sum value (S'n,I+1).

[0085] The detection device 100 can obtain the final sum value of the L bursts by repeating the update of the sum value of each burst. The detection device 100 can obtain the value obtained by dividing the sum value for which the update has been completed by K×L as the noise power data. For example, the variance value of the noise power value corresponding to the K samples can be the noise power data.

[0086] Figure 7 is a three-dimensional (3D) graph showing the spectrum of Doppler Fourier transform data according to an exemplary embodiment.

[0087] Referring to Figure 7 , it can be seen that the noise power spectrum (dB scale) can have different values relative to the frequency. For example, when the filter has a cut-off frequency, it may be difficult for the noise power to exhibit a predetermined level before the cut-off frequency. Therefore, the detection device 100 can estimate frequency-dependent filter characteristics for compensating the noise power to reach a predetermined level in the frequency band of interest.

[0088] However, as can be seen from the noise power spectrum shown in linear scale, there are peaks in some regions. Peaks may exist due to noise and targets. Therefore, the peaks should be removed from the Doppler Fourier transform data.

[0089] Figure 8 is a 3D graph showing the Doppler Fourier transform data after CFAR according to an exemplary embodiment.

[0090] Referring to Figure 8 , the detection device 100 can perform CFAR on the Doppler Fourier transform data to select a plurality of samples. After CFAR, a detection flag DP indicating whether a target has been detected is output. Then, the detection device can select a plurality of samples from the remaining samples of the Doppler Fourier transform data except for the samples SAMP in which the detection flag DP indicates a specific value. The detection device can calculate the noise power data for each frequency from the plurality of selected samples. Finally, by removing the peaks that may correspond to the target according to the above embodiment, a more accurate calculation of the noise power data can be achieved.

[0091] The signal reflected on the target can be included in the K samples selected by the sample selection circuit 320, but the samples corresponding to the reflected signal are values that have been determined to be the noise level, and thus can be sufficiently regarded as the noise level. Therefore, even when the reflected signal is included in the samples, it may not be determined that the detection performance deteriorates.

[0092] Figure 9 is a block diagram of a filter compensation data estimation circuit according to an exemplary embodiment.

[0093] Referring to Figure 9 , the filter compensation data estimation circuit 400 is Figure 5 an example of the FCD estimation circuit 340 of

[0094] The inverse transformation circuit 410 can obtain the reciprocal of the noise power data and can obtain the reciprocal as the filter compensation data. For example, when the noise power data is defined as (where each element is the noise power value of the nth range bin), the filter compensation data output by the inverse transformation circuit 410 can be defined as . For ease of description, the data output by the inverse transformation circuit 410 through Figure 9 is referred to as the first filter compensation data FCD1.

[0095] The modeling circuit 420 can model a function that reflects the first filter compensation data FCD1 obtained through the inverse transformation circuit 410. The modeling circuit 420 can model the function based on a fitting algorithm (such as least squares, linear regression, or polynomial fitting) and output a second filter compensation data FCD2 according to the modeling. The modeled function can have the same slope as the filter characteristic function before the filter characteristics are changed. The modeled function can be a combination of multiple linear equations.

[0096] When the filter characteristics that change with the surrounding environment are changed, the cut-off frequency can move in the frequency domain or the filter gain can change. However, the slope of the filter response can remain almost unchanged. Therefore, the modeling circuit 420 according to the exemplary embodiment can estimate the variables A and / or f0 defined in the expression based on the first filter compensation data FCD1. Here, filter(f) is the filter response function before the change, and filter'(f) is the filter response function after the change. For example, the real numbers A and f0 can be the amplitude change coefficient and the frequency change coefficient of the filter response function after the change, respectively.

[0097] The update circuit 430 can perform an update of the filter compensation data based on the first filter compensation data FCD1 or the second filter compensation data FCD2. This update can cause a third filter compensation data FCD3 to be output from the update circuit 430.

[0098] The update circuit 430 can perform the update by applying weights to the first filter compensation data FCD1 or the second filter compensation data FCD2. For example, when the filter compensation data before the update (which can also be referred to as the reference filter compensation data) is defined as F prev and the filter compensation data after the update (e.g., the third filter compensation data FCD3) is defined as F new , the update circuit 430 can perform the update based on , where α is the weight and 0 < α ≤ 1, and is based on F prev 、F newThe updated and corrected filter compensation data of the weights. The weights can be set to a selected value among various values.

[0099] According to an exemplary embodiment, when the weight of the filter compensation data before update is intended to increase, the weight can be set to a relatively small value. In addition, when the weight of the filter compensation data before update is intended to decrease, the weight can be set to a relatively high value.

[0100] According to an exemplary embodiment, the update circuit 430 can also be based on to update the above f0, where is based on f 0,prev (f0 before update), f 0,new (f0 after update) and the updated and corrected f0 of the weights.

[0101] When performing the update by applying weights via the update circuit 430 according to the above exemplary embodiment, the existing frequency response characteristics can be appropriately considered, so that the influence of the error that may occur in the calculation of the frequency response characteristics after the change can be reduced.

[0102] Figure 10 is a graph showing the filter response as a function of frequency according to an exemplary embodiment.

[0103] Figure 10 Shows a first filter response RES1, a second filter response RES2, and a third filter response RES3. The first filter response RES1 is the filter response before changing the filter characteristics, the second filter response RES2 is the filter response after changing the filter characteristics, and the third filter response RES3 is the filter compensation data estimated according to the above exemplary embodiment.

[0104] As shown in the figure, the slopes of the linear functions forming the first filter response RES1 and the second filter response RES2 are the same. Therefore, the filter compensation data estimation circuit according to an exemplary embodiment (e.g., Figure 9 ) can estimate the frequency change coefficient f0 and output the second filter compensation data based on the estimated f0. In addition, the filter compensation data estimation circuit can update the estimated f0 based on the weights. As Figure 10 shown, the frequency change coefficient f0 can represent the frequency offset between the frequencies at which the first filter response RES1 and the second filter response RES2 start to slope downward after the flat passband region.

[0105] The filter compensation data estimation circuit according to the above exemplary embodiment can more robustly estimate the filter compensation data by updating based on the weights.

[0106] Figure 11is a flowchart showing a method of an operation detection device (e.g., Figure 1 detection device 100) according to an exemplary embodiment.

[0107] Referring to Figure 11 , in operation S110, the detection device may perform a range Fourier transform on a plurality of pulse signals to output range Fourier transform data RFD, and perform a Doppler Fourier transform on the range Fourier transform data RFD to output Doppler Fourier transform data DFD. For example, each pulse signal may correspond to a mixed signal (or IF signal) of a transmitted signal and a received signal.

[0108] The detection device may perform a range Fourier transform on each mixed signal to obtain range Fourier transform data RFD defined in the range bin domain. Thereafter, the detection device may perform a Doppler Fourier transform on each range bin to obtain Doppler Fourier transform data defined in the range bin domain and the Doppler bin domain.

[0109] In operation S120, the detection device may select a plurality of samples corresponding to noise from the Doppler Fourier transform data DFD through CFAR processing of the Doppler Fourier transform data DFD. For example, the detection device may perform CFAR on the Doppler Fourier transform data DFD for each range bin. According to an exemplary embodiment, in operation S120, a plurality of samples may be selected from the Doppler Fourier transform data DFD other than the samples detected as targets (e.g., samples in which a detection flag indicates a specific value).

[0110] In operation S130, the detection device may obtain noise power data from the plurality of samples. According to an exemplary embodiment, the detection device may calculate a variance value of the noise power from the plurality of samples and obtain the calculated variance value as the noise power data.

[0111] In operation S140, the detection device may estimate frequency-dependent filter compensation data based on the noise power data. For example, the detection device may estimate the reciprocal of the noise power data as the filter compensation data. The noise power data may have a plurality of elements for each range bin, and the reciprocal of each element may be estimated as an element of the filter compensation data.

[0112] In operation S150, the detection device may compensate the range Fourier transform data RFD based on the filter compensation data. The range Fourier transform data RFD to be compensated may be obtained by performing a range Fourier transform on a specific pulse. The specific pulse may be a signal that the detection device aims to detect a target for, and may be a chirp pulse signal transmitted to and received from the target.

[0113] According to an exemplary embodiment, compensation may be performed by element-wise multiplication of filter compensation data and range Fourier transform data RFD.

[0114] In operation S160, the detection device may detect a target by performing CFAR on the compensated data based on the compensation. Operation S160 may be performed on the range Fourier transform data RFD compensated by operation S150 instead of on the Doppler Fourier transform data DFD. When the detection flag indicates a specific value through CFAR, it may be determined that a target has been detected.

[0115] Finally, the operation method according to the above exemplary embodiment may adaptively estimate and compensate filter characteristics before the operation of detecting a target (e.g., operation S150 and operation S160) to achieve robust detection even in a changed environment. For example, CFAR for target detection may be directly applied to the range Fourier transform data RFD, thereby improving the processing speed.

[0116] In the above exemplary embodiment, the detection device may increase the number of multiple pulse signals to increase the number of selected samples. The accuracy of the obtained noise power data may be improved.

[0117] Figure 12 is a flowchart showing a method for estimating filter compensation data of a detection device according to an exemplary embodiment.

[0118] Referring to Figure 12 , an example is provided in which the index i of all pulse signals, the pulse train index I, and the index m of M pulse signals of a single pulse train are set to 1. In addition, M, K as the number of multiple samples, L as the number of pulse trains, and Ncal as the number of all pulse signals may be set, and may be defined as follows: Ncal > M > K. In addition, the reference filter compensation data may be set to Fref, and Pn as the noise power data of the nth range bin may be set to 0.

[0119] According to an exemplary embodiment, the above variables may be preset.

[0120] In operation S205, the detection device may determine whether the modulo operation mod of i and Ncal is 0. For example, the detection device may collect pulse signals until the number of pulse signals reaches Ncal. According to the above exemplary embodiment, the collection of pulse signals may include transmitting and receiving pulse signals, obtaining a mixed signal through mixing, and transforming the mixed signal into the digital domain.

[0121] In operation S210, the detection device may set I to 1. In operation S215, the detection device may set m to 1. Operations S210 and S215 are each for the case when estimating filter compensation data FCD for a new pulse train and new M pulse signals.

[0122] In operation S220, the detection device can determine whether m is greater than M - 1. When m is small, the detection device can increase m and i by 1 respectively. For example, the detection device can collect M pulse signals. The collection of M pulse signals can include selecting M pulse signals from among Ncal pulse signals.

[0123] When m is large, the process proceeds to operation S225, in which the detection device can perform a range-Doppler Fourier transform on the M pulse signals. Through operation S225, the detection device can obtain Doppler Fourier transform data.

[0124] In operation S230, the detection device can perform CFAR on the Doppler Fourier transform data for multiple Doppler bins of each range bin. The number of Doppler bins can be M.

[0125] In operation S235, the detection device can select multiple samples from the remaining samples in the Doppler Fourier transform data through CFAR, excluding the samples for which the detection flag indicates a specific value. The number of selected samples can be K.

[0126] In operation S240, the detection device can calculate the sum value of the K samples. For example, for each range bin, the detection device can sum the squares of the values of multiple samples of the I-th pulse train among multiple pulse trains.

[0127] In operation S245, the detection device can update the first sum value based on the sum of each pulse train. For example, the first sum value of the I-th pulse train can be updated by adding the first sum value calculated in operation S240 to the total sum values of the previous pulse trains including the (I - 1)-th pulse train.

[0128] In operation S250, the detection device can determine whether I is greater than L - 1. When I is less than L - 1, the detection device can increase I and i by 1 respectively. Then, the detection device can perform operations S215 to S250 again on the M pulse signals defined as the (I + 1)-th pulse train. By repeatedly performing operations S215 to S250 until I becomes greater than L - 1, the detection device can obtain the updated second sum value for all L pulse trains.

[0129] In operation S255, the detection device can obtain the value obtained by dividing the obtained second sum value by K×L as the noise power data. For example, the detection device can obtain the variance value of the samples of L pulse trains as the noise power data.

[0130] In the above exemplary embodiment, the detection device can obtain the noise power data based on L pulse trains instead of a single pulse train to improve reliability, and thus can improve the accuracy of the noise power data.

[0131] In operation S260, the detection device may estimate the reciprocal of the noise power data obtained through operation S255 as the filter compensation data FCD. For example, when the noise power data is the detection device may estimate calculated on a per-element reciprocal basis as the filter compensation data FCD.

[0132] In operation S265, the detection device may update the filter compensation data FCD by using weights applied to the reference filter compensation data FCD and the filter compensation data FCD, respectively. The reference filter compensation data FCD may be a value estimated before the estimated filter compensation data FCD. The weights may be set to various values. The filter compensation data FCD may be updated considering both the newly calculated filter compensation data FCD and the reference filter compensation data FCD to achieve a more robust estimation of the filter compensation data FCD.

[0133] In operation S270, the detection device may determine whether to re-estimate the filter compensation data FCD. When re-estimating the filter compensation data FCD, operations S205 to S265 may be repeatedly executed. The re-estimation may be performed on a new Ncal pulse signal.

[0134] Figure 13 is a flowchart showing a detection method of a detection device according to an exemplary embodiment.

[0135] Referring to Figure 13 , in operation S310, the detection device may perform a range Fourier transform on the j-th pulse signal (where j is a positive integer) based on the filter compensation data. Range Fourier transform data of the j-th pulse signal may be obtained through range Fourier transform processing.

[0136] In operation S320, the detection device may perform a per-element root operation on the filter compensation data. For example, when the filter compensation data including Ns elements is defined as then may be obtained as the result of the per-element root operation.

[0137] In operation S330, the detection device may perform a per-element product on the result of the root operation obtained in operation S320 and the range Fourier transform data obtained in operation S310.

[0138] In operation S340, the detection device may perform CFAR on the result of the per-element product to detect a target.

[0139] An operation S340 is performed on the range Fourier transform data, such that the operation efficiency can be improved compared to performing CFAR on the Doppler Fourier transform data.

[0140] Figure 14 is a diagram showing the range Fourier transform result when there is no compensation for the filter characteristics, and Figure 15 is a diagram showing the CFAR result when there is no compensation for the filter characteristics.

[0141] Referring to Figure 14 , the range Fourier transform results of the first target OBJ1 to the third target OBJ3 when there is no compensation can show different noise powers for each target. A peak level appears for the second target OBJ2, while only noise power appears for the first target OBJ1 and the third target OBJ3. For example, even when all three targets OBJ1 to OBJ3 are present, a peak level appears only for the second target OBJ2.

[0142] Referring to Figure 15 , Figure 14 , the results of the CFAR of

[0143] Figure 16 can show that the detection flags of only the second target OBJ2 indicate a specific value, and the detection flags of the first target OBJ1 and the third target OBJ3 indicate a logic low level. Therefore, the detection device can successfully detect only the second target OBJ2 and cannot detect the remaining targets. Figure 17 is a diagram showing the range Fourier transform result when there is compensation for the filter characteristics according to an exemplary embodiment, and

[0144] Referring to Figure 16 , the range Fourier transform results of the first target OBJ1 to the third target OBJ3 when there is compensation can show the peak levels of the targets, and the remaining noise power levels can be similar to each other.

[0145] Referring to Figure 17 , for Figure 16 , the results of the CFAR can show that the detection flags of the first target OBJ1 to the third target OBJ3 all indicate a specific value. Therefore, the detection device can successfully detect all the targets.

[0146] As described above, according to the exemplary embodiment, a detection device robust to changes in filter characteristics and a method of operating the detection device can be provided.

[0147] Although example embodiments have been shown and described above, it will be clear to those skilled in the art that modifications and changes can be made without departing from the scope of the inventive concept defined by the appended claims.

Claims

1. A detection device, comprising: A Fourier transform processing circuit configured to output first Fourier transform data by performing range Fourier transform processing on a plurality of pulse signals, and output second Fourier transform data by performing Doppler Fourier transform processing on the first Fourier transform data; A filter characteristic estimation circuit configured to select a plurality of samples corresponding to noise in the second Fourier transform data by using a constant false alarm rate process, obtain noise power data from the plurality of samples, and estimate frequency-dependent filter compensation data based on the noise power data; And A detection circuit configured to compensate the first Fourier transform data based on the filter compensation data, and detect a target based on performing a constant false alarm rate process on the compensated first Fourier transform data.

2. The detection device according to claim 1, wherein, The filter characteristic estimation circuit is configured to perform a constant false alarm rate process on a plurality of Doppler bins of each range bin in the second Fourier transform data.

3. The detection device according to claim 1, wherein The filter characteristic estimation circuit is configured to select the plurality of samples from the second Fourier transform data by a constant false alarm rate process except for samples in which a detection flag indicates a specific value.

4. The detection device according to claim 1, wherein The filter characteristic estimation circuit is configured to obtain a variance value of the plurality of samples as the noise power data.

5. The detection device according to claim 1, wherein, The noise power data is defined for each frequency.

6. The detection device according to claim 1, wherein, The filter characteristic estimation circuit is configured to estimate the reciprocal of the noise power data as the filter compensation data.

7. The detection device according to claim 1, wherein The filter characteristic estimation circuit is configured to estimate filter compensation data for a plurality of pulse trains, each of the plurality of pulse trains including the plurality of pulse signals.

8. The detection device according to claim 7, wherein, The filter characteristic estimation circuit is configured to: For the Ith pulse train among the plurality of pulse trains, sum the squares of the values of the plurality of samples for each range bin, where I is a positive integer; Update a first sum value based on the sum for each pulse train; Obtain a value obtained by dividing a second sum value by K×L as the noise power data, where in the second sum value, the update of the first sum value has been completed for the plurality of pulse trains, where K is the number of the plurality of samples, and L is the number of the plurality of pulse trains.

9. The detection device according to claim 1, wherein, The filter characteristic estimation circuit is further configured to update the filter compensation data by using weights respectively applied to reference filter compensation data and the filter compensation data.

10. The detection device according to claim 9, wherein, The detection circuit is configured to compensate the first Fourier transform data based on the updated filter compensation data.

11. A method of operating a detection device, the method comprising: Outputting first Fourier transform data by performing range Fourier transform processing on a plurality of pulse signals, and outputting second Fourier transform data by performing Doppler Fourier transform processing on the first Fourier transform data; Selecting a plurality of samples corresponding to noise in the second Fourier transform data by performing a constant false alarm rate process on the second Fourier transform data; Obtaining noise power data from the plurality of samples; Estimating frequency-dependent filter compensation data based on the noise power data; Compensating the first Fourier transform data based on the filter compensation data; And Detecting a target based on performing a constant false alarm rate process on the compensated first Fourier transform data.

12. The method according to claim 11, wherein, The step of selecting the plurality of samples includes: Perform constant false alarm rate (CFAR) processing on multiple Doppler bins for each range bin in the second Fourier transform data; and Select, through CFAR processing, the multiple samples from the second Fourier transform data except for the samples in which the detection flag indicates a specific value.

13. The method according to claim 11, wherein, The noise power data is defined for each frequency.

14. The method according to claim 11, wherein The step of estimating the filter compensation data includes estimating the reciprocal of the noise power data as the filter compensation data.

15. The method according to claim 11, further comprising estimating filter compensation data for a plurality of pulse trains, each of the plurality of pulse trains including the plurality of pulse signals.

16. The method according to claim 15, wherein, The step of obtaining noise power data from the multiple samples includes: For the Ith pulse train among the plurality of pulse trains, summing the squares of the values of the multiple samples for each range bin, where I is a positive integer; Updating a first sum value based on the sum for each pulse train; and Obtaining, as the noise power data, a value obtained by dividing a second sum value by K×L, where, in the second sum value, the update of the first sum value has been completed for the plurality of pulse trains, where K is the number of the multiple samples and L is the number of the plurality of pulse trains.

17. The method according to claim 11, further comprising updating the filter compensation data by using weights respectively applied to reference filter compensation data and the filter compensation data.

18. A detection device, comprising: A transceiver configured to transmit and receive a plurality of pulse signals; And A processor electrically connected to the transceiver, wherein the processor is configured to: Select multiple samples corresponding to noise in the Doppler Fourier transform data through CFAR processing of the Doppler Fourier transform data of the plurality of pulse signals; Obtain noise power data from the multiple samples; Estimate frequency-dependent filter compensation data based on the noise power data; Compensate the range Fourier transform data of the plurality of pulse signals based on the filter compensation data; and Detect a target based on performing CFAR processing on the compensated range Fourier transform data.

19. The detection device according to claim 18, wherein, The processor is configured to estimate the reciprocal of the noise power data as the filter compensation data.

20. The detection device according to claim 18, wherein, The processor is configured to update the filter compensation data by using weights respectively applied to reference filter compensation data and the filter compensation data.

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