Signal processing device and method and program

Abstract

The present invention provides a signal processing device, method, and program that can simultaneously perform meteorological observations, object detection, and ground surface observations. [Solution] The system includes a receiving unit 20 that receives radio waves reflected from an object to be measured and generates a received signal, and a processing unit 30 that branches the received signal generated by the receiving unit and performs at least two or more processes in parallel from long-period SAR (Synthetic Aperture Radar) analysis, medium-period spectral analysis, and short-period meteorological analysis.

Description

[Technical Field]

[0001] The present invention relates to a signal processing device, method, and program for processing a received signal obtained by receiving radio waves reflected from an object to be measured. [Background technology]

[0002] Traditionally, satellite-mounted SAR (Synthetic Aperture Radar) and aircraft-mounted SAR have been widely used for observing the Earth's surface. On the other hand, weather radar, which measures rainfall, is widely used for meteorological observation purposes. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2021-47019 [Non-patent literature]

[0004] [Non-Patent Document 1] FS Marzano, et al., “Potential of high-resolution detection and retrieval of precipitation fields from X-band spaceborne synthetic aperture radar over land”, Hydrol. Earth Syst. Sci., 15, 859‐875, 2011. [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] While both SAR and weather radar transmit and receive high-frequency radio waves to generate images, their processing methods differ, and they are generally designed, manufactured, and used as separate radar systems. This is inefficient not only in terms of monetary cost, but also in terms of the amount of Earth's resources and radio frequency resources used.

[0006] Furthermore, while SAR observations are said to be able to observe the Earth's surface regardless of weather conditions, this is in comparison to optical observations. In principle, SAR observations become less accurate during rainy weather due to radio wave attenuation and phase delay.

[0007] SAR observations utilize both the amplitude and phase of radio waves and assume that conditions other than distance remain unchanged for the target while the satellite or aircraft is moving. However, during rainy weather, differences in the irradiation path to the target and the temporal changes in the rain clouds themselves cause differences in radio wave attenuation and phase delay even within the coherence time, which leads to a deterioration in observation accuracy. In SAR, radio wave attenuation and phase delay due to rainfall have not received much attention. However, in areas with heavy rainfall, radio wave attenuation of more than 10 dB round trip can occur depending on the frequency, and differences in radio wave attenuation due to differences in rainfall distribution cannot be ignored. Furthermore, KDP observations using dual-polarization weather radar, which have been introduced in Japan, are observation methods that utilize the phase delay of radio waves due to rainfall, and it is clear that differences in phase delay due to differences in rainfall distribution cannot be ignored.

[0008] This effect is more pronounced at higher frequencies. In principle, for the same antenna size, radar achieves higher resolution as the frequency of transmitted and received radio waves increases. However, a challenge exists in that higher frequencies are more susceptible to weather conditions.

[0009] Furthermore, SAR observes objects that do not change much over time, such as topography, and has difficulty observing objects that change over time. For example, it is not good at observing and identifying ships moving on rough seas.

[0010] In Non-Patent Document 1, it has been proposed to perform meteorological analysis (rainfall estimation) based on SAR images. However, in the analysis based on SAR images, it is impossible to observe meteorological parameters such as wind speed and to perform high-resolution observations in the altitude direction.

[0011] Also, in Patent Document 1, it has been proposed to perform object detection based on SAR images. However, for example, when coherence cannot be maintained for a long time due to the sway of a ship or the like and SAR analysis cannot be performed, object detection cannot be performed.

[0012] In view of the above circumstances, an object of the present invention is to provide a signal processing apparatus, a method, and a program capable of simultaneously performing observations of the ground surface, meteorological observations, and detection of objects and the like by SAR analysis.

Means for Solving the Problems

[0013] The signal processing apparatus of the present invention includes a receiving unit that receives radio waves reflected from a measurement object and generates a received signal, and a processing unit that branches the received signal generated by the receiving unit and executes at least two or more of long-period SAR (Synthetic Aperture Radar) analysis, medium-period spectrum analysis, and short-period meteorological analysis in parallel.

Effects of the Invention

[0014] According to the signal processing apparatus of the present invention, radio waves reflected from a measurement object are received to generate a received signal, and the received signal is branched, and at least two or more of long-period SAR (Synthetic Aperture Radar) analysis, medium-period spectrum analysis, and short-period meteorological analysis are executed in parallel. Therefore, it is possible to simultaneously perform observations of the ground surface, meteorological observations, and detection of objects and the like by SAR analysis.

Brief Description of the Drawings

[0015] [Figure 1]Block diagram showing the schematic configuration of a spectral analysis SAR device using one embodiment of the signal processing device of the present invention. [Figure 2] This figure shows the branching of the received signal in a spectral analysis SAR device according to the first embodiment. [Figure 3] This figure shows the image of the received signal and the processing unit of the pulse data in the processing of the first embodiment. [Figure 4] Block diagram showing the processing flow of the second embodiment [Figure 5] Image diagram of the pulse-by-pulse processing of the weather analysis process in the second embodiment. [Figure 6] Block diagram showing the processing flow of the third embodiment [Figure 7] Image diagram of the pulse-by-pulse processing of the weather analysis process in the third embodiment. [Figure 8] This diagram illustrates the pseudo-transmission pulses and corresponding reception intervals of an FMCW (Frequency Modulated Continuous Wave) transmission signal, which is divided into short segments. [Figure 9] Block diagram showing the processing flow of the fifth embodiment. [Figure 10] Block diagram showing a specific example of the processing in the fifth embodiment. [Figure 11] Block diagram showing the processing flow of the sixth embodiment [Figure 12] Block diagram showing the processing flow of the seventh embodiment. [Figure 13] A diagram illustrating a method for doubling the number of sectors (effectively doubling the azimuthal spacing between sectors). [Figure 14] Block diagram showing the processing flow of the eighth embodiment. [Figure 15] Block diagram showing the processing flow of the ninth embodiment. [Figure 16] Block diagram showing the processing flow of the 10th embodiment. [Figure 17] Block diagram showing the processing flow of the 11th embodiment. [Figure 18] Block diagram showing the processing flow of the 12th embodiment. [Figure 19] Block diagram showing the processing flow of the 13th embodiment. [Modes for carrying out the invention]

[0016] Hereinafter, an embodiment of a spectral analysis SAR device using one embodiment of the signal processing device of the present invention will be described in detail with reference to the drawings. Figure 1 is a block diagram showing the schematic configuration of the spectral analysis SAR device 1 of this embodiment.

[0017] The spectral analysis SAR device 1 of this embodiment receives radio waves reflected from an object to be measured, generates a received signal, and branches this received signal to perform at least two or more processes in parallel from long-period SAR analysis, medium-period spectral analysis, and short-period meteorological analysis. In other words, it is a system configured to perform three different analysis processes using a single received signal.

[0018] As shown in Figure 1, the spectral analysis SAR device 1 of this embodiment comprises a transmitting unit 10, a receiving unit 20, and a processing unit 30.

[0019] The transmitting unit 10 transmits a high-frequency transmission signal toward the object to be measured. The transmission frequency of the transmission signal varies depending on the purpose of observation, and for example, L-band (1-2 GHz), C-band (4-8 GHz), and X-band (8-12 GHz) are used.

[0020] The receiving unit 20 receives radio waves reflected from the object to be measured and generates a received signal (IQ data). The receiving unit 20 includes, for example, an antenna, a circulator, a frequency converter, and an AD converter. The sampling frequency in the AD converter is determined according to the bandwidth of the transmission signal transmitted from the transmitting unit 10.

[0021] The processing unit 30 branches the received signal generated by the receiving unit 20 and performs various processing on each branched received signal to execute at least two of the following in parallel: long-period SAR analysis, medium-period spectral analysis, and short-period meteorological analysis. The processing in the processing unit 30 will be described in detail later.

[0022] The processing unit 30 includes a GPU (Graphics Processing Unit), a CPU (Central Processing Unit), semiconductor memory, and storage such as a hard disk. A computer program including one embodiment of the signal processing program of the present invention is installed on the storage, and the processing in the processing unit 30 is executed by the GPU and CPU when this computer program is executed. The processing by the processing unit 30 may be implemented solely by the computer program, or some of the processing may be implemented by hardware such as electrical circuits.

[0023] The processing in the processing unit 30 will be described in detail below.

[0024] The processing in the first embodiment is a process that executes three processes in parallel: long-period SAR analysis, medium-period spectral analysis, and short-period meteorological analysis. Figure 2 shows the branching of the received signal when these three analysis processes are performed. Figure 3 shows the image of the received signal and the processing unit of the pulse data in the processing of the first embodiment. Figure 3 shows the pulse number of each pulse signal of the received signal and the structure of a single pulse signal. In the structure of a single pulse signal, the range (distance) grid point number is shown, and Nr is the furthest range grid point number.

[0025] In the example shown in Figure 3, short-period weather analysis divides the repeatedly transmitted and received pulse data into data blocks (sectors) of several tens of pulses (for example, 32 pulses), and calculates weather parameters such as the reflectance factor Z, Doppler velocity V, Doppler amplitude W, and rainfall intensity R for each sector. In the case of dual-polarization radar that transmits and receives both horizontal and vertical polarization, dual-polarization parameters such as the reflectance factor difference ZDR, inter-polarization correlation coefficient ρhv, inter-polarization phase difference φdp, and inter-polarization phase difference change rate KDP are also calculated.

[0026] In mid-period spectral analysis, objects are detected by dividing repeatedly transmitted and received pulse data into data clusters of tens to thousands of pulses (e.g., 1000 pulses), performing a fast Fourier transform, and analyzing the resulting frequency spectrum. Known methods can be used for object detection.

[0027] SAR analysis generates SAR images based on pulse data lasting several seconds to tens of seconds or more (for example, 300,000 pulses), which is a longer timeframe than meteorological analysis. Known methods can be used to generate SAR images.

[0028] The processing of the first embodiment makes it possible to simultaneously perform meteorological observation, object detection, and ground surface observation using SAR images. In addition, the processing of the first embodiment may be configured to perform two processes in parallel: long-period SAR analysis and short-period meteorological analysis.

[0029] Next, the processing of the second embodiment will be described in detail.

[0030] The processing in the second embodiment executes two of the three analysis processes of the processing in the first embodiment—long-period SAR analysis and short-period meteorological analysis—in parallel, and also performs processing in the short-period meteorological analysis process that degrades the distance resolution and improves the sensitivity of precipitation observations.

[0031] Specifically, in the process of the second embodiment, as a process of degrading the above-described range resolution and improving the sensitivity of precipitation observation, by coherently adding complex signals continuous in the range direction, instead of degrading the range resolution, the SNR is improved. FIG. 4 is a block diagram showing the flow of the process of the second embodiment, and FIG. 5 is an image diagram of the process for each pulse of the meteorological analysis process of the second embodiment.

[0032] As shown in FIG. 4, in the meteorological analysis process of the second embodiment, before calculating the meteorological parameters for each sector, the above-described coherent addition in the range direction is performed.

[0033] Specifically, for the IQ data (complex signal) x[k r = I[k r + iQ[k r (k r = 0, ···, N r-1 ) (Nr is the number of range observation grid points within a pulse), coherent addition is performed, and the IQ data x'[k r_low = I'[k r_low + iQ'[k r_low (k r_low =0, ···, N r_low-1 ) (N r_low is the number of range observation grid points after the coherent addition process) is generated. For example, taking the number of coherent additions as N r_coh , the following formula is calculated.

[0034]

Equation

[0035] Next, the processing of the third embodiment will be described. The processing of the third embodiment performs coherence addition in the distance direction, similar to the processing of the second embodiment. However, it improves the distance resolution of the heavy rain area by estimating the S / NR of each sector based on observation results such as calculated meteorological parameters, and reducing the number of coherence additions in the distance direction under conditions where the SNR is above a certain level. Figure 6 is a block diagram showing the flow of processing in the third embodiment, and Figure 7 is an image diagram of the processing for each pulse in the meteorological analysis processing of the third embodiment.

[0036] As shown in Figure 6, in the weather analysis process of the third embodiment, similar to the weather analysis process of the second embodiment, coherent addition in the distance direction is performed before calculating the weather parameters for each sector, and then the SNR for each sector is calculated. Then, the number of coherent additions is determined according to the SNR for each sector, and the weather parameters for each sector are calculated by performing coherent addition of that number.

[0037] Specifically, the SNR for each sector is calculated using the following procedure. First, the average power for each range grid point within the sector is calculated. For example, the received power [dBm] for each range is calculated using the following formula.

[0038]

number

[0039] Next, the SNR [dB] is calculated from the received power P [dBm] and noise level [dBm] using the following formula.

[0040]

number

[0041] A list of candidate coherent integral numbers for the second coherent integral is set in advance (e.g., 1, 5, 10, 25, 50, 100). Note that the candidate coherent integral numbers for the second coherent integral are divisors of the first distance coherent integral (because we want to divide each first distance coherent integral region into integers).

[0042] Next, the coherent integration number in the distance direction required to maintain a sufficient SNR at each range grid point is calculated using the following formula. ceil() is a function that rounds up decimals to integers.

[0043]

number

[0044] Next, the smallest second coherent cumulative number N that is equal to or greater than Na. r_coh2 Select the following. For example, if Na = 20 and the candidates for the second coherent cumulative number are (1, 5, 10, 25, 50, 100), then N r_coh2 Let = 25. Finally, coherent integration is performed based on the second coherent integral number to improve the distance resolution. As a result, as shown in Figure 7, observation sensitivity can be ensured with low distance resolution in areas with a small SNR, while observations can be performed with high distance resolution in areas with heavy rainfall and a large SNR.

[0045] Next, the processing of the fourth embodiment will be described. The processing of the fourth embodiment is a process for performing meteorological analysis using SAR with an FMCW (Frequency Modulated Continuous Wave) radar, in which coherence is maintained at the expense of distance resolution by dividing at least one of the transmitted signal, received signal, and beat signal into short segments during the frequency sweep in the analysis.

[0046] FMCW radar performs observations while continuously changing the frequency (up chirp or down chirp). Radio waves reflected from the observed object (measured object) experience a time delay depending on the distance, so the frequency difference between the reflected wave and the transmitted frequency increases the further away the observed object is.

[0047] Using this, a beat signal (a signal of the difference frequency) is generated by mixing the transmitted signal and the received signal in a mixer, and by performing an FFT on this, a distance spectrum containing signal intensity and phase information for each beat frequency (distance) is obtained.

[0048] Generally, distance spectra are calculated for each frequency sweep time, and the distance spectrum for each frequency sweep time corresponds to one pulse of data in a typical pulse radar. SAR analysis is usually performed using high-resolution distance spectra obtained with the full frequency sweep time. However, if the frequency sweep time is longer than the coherence time of the meteorological echo, using the full frequency sweep time in meteorological analysis, as in SAR analysis, will result in a loss of coherence in the meteorological echo, making it impossible to obtain meaningful observational data.

[0049] Therefore, in the processing of the fourth embodiment, when performing meteorological analysis, the transmitted signal is divided into time intervals equivalent to the coherent time of the meteorological echo and analyzed to generate short-time pseudo-transmitted pulse signals, and pulse compression is performed on the received signal (not the beat signal). Alternatively, equivalent processing is performed by dividing the beat signal. As a result, the distance resolution deteriorates, but coherence can be maintained for meteorological echoes.

[0050] Specifically, when performing meteorological analysis using the short-time pulse compression described above, the following processing is performed assuming that the receiving unit 20 is equipped with FMCW radar hardware capable of receiving the baseband (not the beat signal) of the received signal.

[0051] Figure 8 is an illustrative diagram of pseudo-transmission pulses and corresponding reception intervals, where the FMCW transmission signal is divided into short intervals. In the example in Figure 8, the pseudo-transmission pulse is 30 μs. First, the first 30 μs of the frequency sweep is taken as pseudo-transmission pulse 1, and the time from the end of pseudo-transmission pulse 1 until the transmission signal is reflected from the observation target at the maximum observation distance and received is taken as reception interval 1 (upper part of Figure 8). If the maximum observation distance is assumed to be 18 km, the reception interval will be 150 μs (pseudo-transmission pulse 30 μs + time for the radio wave to travel 18 km round trip 120 μs). However, if there is a large amount of feedback from the transmission signal into the received signal, the time overlapping with the transmission pulse is treated as a blind range (missing interval). Pulse compression is performed on the received signal in this reception interval using pseudo-transmission pulse 1. Here, since the length of pseudo-transmission pulse 1 and the length of the received signal in the reception interval are different, pseudo-transmission pulse 1 is zero-filled to make it the same length as the received signal. After this, general pulse compression processing is performed. For example, pulse compression is performed using the following procedure.

[0052] The pseudo-transmit pulse 1 s(t) and the received signal r(t) in the receiving section are converted to the frequency domain S(f) and R(f), respectively, using FFT.

[0053]

number

[0054]

number

[0055]

number

[0056] The pulse-compressed signals from each section that created these pseudo-transmission pulses are treated like a single pulse from a normal pulse radar for subsequent processing. For example, a high-quality range profile of range-received power is obtained by incoherently integrating the pulse-compressed signals from each of the 1st to Nth pseudo-transmission pulses. When observing Doppler velocity, it is also necessary to consider that the frequency of the transmitted signal differs for each of the 1st to Nth pseudo-transmission pulses (in Figure 8, due to up-chirp, the Nth pulse has a higher frequency than the first pulse).

[0057] For example, using 128 frequency sweep cycles as one set, the Doppler velocity for each distance is obtained by calculating the FFT or the average phase of the pulse pair for each interval of the same transmission frequency (pseudo-transmission pulses 1 to N). In the case of FFT, a high-quality Doppler velocity spectrum is obtained by further incoherent integration from 1 to N. In the pulse phase method for calculating the average phase of the pulse pair, the phase calculated for each pseudo-pulse is averaged from 1 to N. Here, as mentioned earlier, the incoherent integration or average value is calculated from the pulse-compressed signal from 1 to N, taking into account that the frequency of the transmitted signal differs for each pseudo-transmission pulse from 1 to N.

[0058] Next, the processing of the fifth embodiment will be described.

[0059] The fifth embodiment involves creating feature quantities related to radio wave attenuation, phase delay, or both based on the results of the meteorological analysis, compensating for the amplitude, phase, or both of the pulse data used for SAR analysis based on the created feature quantities, and then performing the SAR analysis. Figure 9 is a block diagram showing the flow of the fifth embodiment.

[0060] As shown in Figure 9, in the processing of the fifth embodiment, after the meteorological analysis, feature quantities related to radio wave attenuation and phase delay are calculated based on the results. Here, at least one of the following features related to radio wave attenuation and phase delay is calculated: the integrated value of the received power, the integrated value of the reflectivity factor Z, the integrated value of the rainfall intensity R, the interpolar phase difference φdp, and the integrated rainfall attenuation. Then, SAR analysis is performed using the calculated feature quantities to compensate for radio wave attenuation and phase delay. This makes it possible to improve the quality of the SAR image.

[0061] Figure 10 is a block diagram showing a specific example of a process for performing SAR analysis that compensates for radio wave attenuation and phase delay using features related to radio wave attenuation and phase delay. In the example shown in Figure 10, in the SAR analysis, after pulse compression, a process to compensate for radio wave attenuation and phase delay, described later, is performed, and then range migration compensation and azimuth compression are performed to generate a SAR image. Known methods can be used for pulse compression, range migration compensation, and azimuth compression.

[0062] The following explains how to compensate for radio wave attenuation.

[0063] The radio wave attenuation coefficient A [dB / km] (rain attenuation amount) due to rainfall at each sector range grid point is calculated from the reflection factor Z and the interpolar phase difference change rate KDP using the following formula. The following is an example, and the radio wave attenuation coefficient A may be calculated using other methods (e.g., the ZPHI method). Here, the reflection factor Z is the true number [mm 6 / m 3 The interpolar phase difference change rate (KDP) is [dB / km].

[0064]

number

[0065]

number

[0066] For each sector (e.g., 32 pulses), the received power P, reflection factor Z, rainfall intensity R, and interpolar phase difference φdp for each range grid point are calculated.

[0067] Next, as shown in the equation below, the received power P, the reflectance factor Z, and the rainfall intensity R are integrated in the range direction.

[0068]

number

[0069] Furthermore, as shown in the formula below, the change in φdp from the front to the back of the range for each sector is calculated as Δφdp.

[0070]

number

[0071] Next, the phase delay amount Δφ due to weather is calculated using the following formula: rainThe following is calculated. F is a feature related to the phase delay due to rainfall, and one of the PIP, PIZ, PIR, or Δφdp calculated above is substituted into it.

[0072]

number

[0073]

number

[0074]

number

[0075] While the above example shows a method that directly compensates for the received signal, a phase delay can also be incorporated into the matched filter used for azimuth compression.

[0076] Next, the processing of the sixth embodiment will be described.

[0077] The processing of the sixth embodiment involves compensating for the phase of pulse data by autofocus processing based on the feature quantities calculated in the processing of the fifth embodiment. Figure 11 is a block diagram showing the processing flow of the sixth embodiment. In the example shown in Figure 11, in SAR analysis, after pulse compression and range migration compensation processing are performed, autofocus processing based on the above feature quantities is performed, and then azimuth compression is performed to generate a SAR image. Known methods can be used for pulse compression, range migration compensation processing and azimuth compression.

[0078] The autofocus processing based on the above features will be explained.

[0079] It can be difficult to accurately estimate the absolute value of phase delay directly from features related to phase delay caused by rainfall. This is because the coefficients in the equation representing the relationship between features related to phase delay caused by rainfall and the phase delay itself change depending on the particle size distribution, average particle size, and particle type of the precipitation particles. On the other hand, there is a strong correlation between features related to phase delay caused by rainfall and the phase delay caused by rainfall. Therefore, in this embodiment, by combining it with an existing autofocus method, we achieve highly accurate compensation for phase delay caused by rainfall.

[0080] Specifically, regarding known autofocus methods, the low-frequency phase error Δφ low This is estimated. For example, Map Drift Autofocus is used to estimate the average phase error of each sub-aperture (each pulse cluster).

[0081] Next, assuming the following relationship between the feature quantity F related to phase delay due to rainfall and the phase delay, k1 and k2 are calculated based on Δφ using existing autofocus methods. low It is calculated based on the least squares method, Δφ low and Δφ rain We calculate k1 and k2 such that the square of the difference between them is minimized. The coefficients may also be estimated based on other equations, other features, or other optimization methods. (The absolute value of the phase lag is approximately Δφ) low (Estimate a coefficient that fits this.)

[0082]

number

[0083]

number

[0084] Next, the processing of the seventh embodiment will be described.

[0085] The processing in the seventh embodiment involves creating high-resolution feature quantities in the radar propagation direction with respect to radio wave attenuation and phase delay using super-resolution processing, and then compensating for the amplitude, phase, or both of the pulse data used for SAR analysis based on the feature quantities for each pulse. Figure 12 is a block diagram showing the processing flow of the seventh embodiment.

[0086] In the example shown in Figure 12, after performing meteorological analysis using super-resolution technology, high-resolution features are created in the radar propagation direction for radio wave attenuation and phase delay. Then, based on the features for each pulse, a process is performed to compensate for the amplitude, phase, or both of the pulse data used in SAR analysis, thereby generating a SAR image.

[0087] In typical meteorological analysis, as mentioned above, rainfall intensity R and interpolar phase difference change rate KDP are calculated for each sector data, which consists of several dozen pulses (for example, 32 pulses). Therefore, the estimated rainfall attenuation A and phase delay Δφ are also calculated for each sector. Consequently, the azimuthal resolution of the estimated rainfall attenuation A and phase delay Δφ is worse than the azimuthal resolution of each individual pulse.

[0088] Therefore, in the seventh embodiment, high-resolution feature quantities related to rainfall attenuation and phase delay in the azimuthal direction (pulse direction, sector direction) are created using super-resolution technology, and based on these, rainfall attenuation and phase delay are compensated for to generate a SAR image.

[0089] The following describes an example of super-resolution processing.

[0090] First, sector data (a collection of pulses) is extracted. Sector oversampling is then performed so that the same pulse spans multiple sectors. Here, we will explain sector oversampling in detail.

[0091] Normally, the pulses contained in each sector are independent. For example, if each sector contains 32 pulses, pulses 0 to 31 are designated as sector 1, and pulses 32 to 63 as sector 2. In contrast, sector oversampling creates sectors by overlapping some pulses. For example, when doubling the number of sectors (apparently doubling the azimuthal spacing between sectors), as shown in Figure 13, pulses 0 to 31 are designated as sector 1, pulses 16 to 47 as sector 2, and pulses 32 to 63 as sector 3.

[0092] Next, after determining the sectors by performing sector oversampling as described above, the weighted received power P is calculated using the following formula based on the pulse information for each sector and the total antenna gain G[n] for transmission and reception at each azimuth angle.

[0093]

number

[0094] Then, similar to the fifth and sixth embodiments, the reflection factor Z and rainfall intensity R are calculated based on the received power P calculated as described above. These are then integrated in the range direction (calculating PIP, PIZ, and PIR as described above). Finally, rainfall attenuation and phase lag are compensated based on the calculated feature quantities.

[0095] Next, the processing of the eighth embodiment will be described.

[0096] The processing in the eighth embodiment involves identifying precipitation particles based on the results of meteorological analysis, and creating feature quantities related to radio wave attenuation and phase delay due to rainfall based on the identified particle type. Figure 14 is a block diagram showing the processing flow of the eighth embodiment.

[0097] In the processing of the eighth embodiment, the types of precipitation particles such as rain, snow, and hail are specifically determined for each sector and range grid point based on the results of meteorological analysis. For example, particle discrimination is performed using methods such as fuzzy logic based on the reflectance factor Z, the rate of change of interpolar phase difference KDP, the interpolar correlation coefficient ρhv, the reflectance factor difference ZDR, and the temperature distribution of numerical weather prediction data. Based on the particle discrimination results, the coefficients of the formulas that calculate rainfall attenuation and phase delay from each feature shown in the processing of the fifth embodiment are changed. This makes it possible to calculate rainfall attenuation and phase delay according to the particle type, enabling more accurate amplitude and phase compensation.

[0098] Next, the processing of the ninth embodiment will be described. In the second to eighth embodiments, two of the three analysis processes of the first embodiment—long-period SAR analysis and short-period meteorological analysis—are executed in parallel. However, in the ninth to thirteenth embodiments, which will be described below, two of the three analysis processes—long-period SAR analysis and medium-period spectral analysis—are executed in parallel, and object detection analysis is performed as part of the medium-period spectral analysis. Figure 15 is a block diagram showing the processing flow of the ninth embodiment.

[0099] In the object detection analysis shown in Figure 15, as described above, the repeatedly transmitted and received pulse data is divided into data sets of tens to thousands of pulses (for example, 1000 pulses), and the frequency spectrum obtained by performing a fast Fourier transform or similar is analyzed to detect objects. Known methods can be used for object detection.

[0100] Next, the processing of the tenth embodiment will be described.

[0101] The processing of the tenth embodiment performs object detection analysis using coherent integration, FFT, and incoherent integration. Figure 16 is a block diagram showing the processing flow of the tenth embodiment. As shown in Figure 16, in the object detection analysis of the processing of the tenth embodiment, coherent integration, FFT, and incoherent integration are performed on the branched received signal in that order, and then object detection is performed.

[0102] Specifically, a frequency spectrum with high SNR and frequency resolution is created by coherent integration in the pulse direction, FFT using multiple pulses after coherent integration, and finally incoherent integration of the Doppler frequency spectrum after FFT, thereby enabling object detection.

[0103] In coherent integration, coherent integration is performed on IQ data x[n] = I[n] + iQ[n] (n=0,1,···,N-1) (where N is the number of pulses in the sector) within the same range of a sector, generating IQ data x'[m] = I'[m] + iQ'[m] (m=0,1,···,M-1) (where M is the number of IQ data after coherent integration). Specifically, the following equation is calculated. Note that N coh This represents the coherent integral number.

[0104]

number

[0105] And then, power spectral data S c For [n,p], perform incoherent integration and obtain the power data spectrum S v [p] (p=0,1,···,N fp -1; N fp This generates the number of FFT points. Specifically, calculate the following:

[0106]

number

[0107] Next, the processing of the 11th embodiment will be described.

[0108] The processing in the 11th embodiment determines the range in which the SAR analysis process will be performed based on the results of the object detection analysis. Figure 17 is a block diagram showing the processing flow of the 11th embodiment. The SAR analysis space determination process shown in Figure 17 determines the surrounding space as the SAR analysis space when some object is detected in the object detection analysis. For example, the SAR analysis space (the space in which the SAR image is generated) is set to a 10km square area centered on the point where the object was detected. The type of object is assumed to be predetermined.

[0109] As described above, by determining the execution space for SAR analysis based on the object detection results and performing SAR processing only on the areas where objects are detected, the processing load can be reduced and the processing speed can be increased.

[0110] Next, the processing of the twelfth embodiment will be described.

[0111] The processing in the 12th embodiment involves determining the SAR analysis method based on the results of the object detection analysis described above, and then executing the SAR analysis process. Figure 18 is a block diagram showing the processing flow of the 12th embodiment.

[0112] The SAR analysis method determination shown in Figure 18 involves deciding on the autofocus method and whether or not to perform inverse synthetic aperture radar (ISAR). Various autofocus methods have been proposed for SAR analysis. For example, PGA assumes the presence of strong scatters (objects that return strong reflected waves) in the observation space. Therefore, if an object is detected by object detection analysis, PGA is selected; if no object is detected, methods such as Map Drift, which are useful even when strong scatters are not present, can be selected, allowing for the selection of an appropriate autofocus method depending on the situation. In addition, if a moving object is detected by object detection analysis, it is also possible to perform inverse synthetic aperture radar (ISAR) on that object. This allows for obtaining a clearer SAR image of the moving object.

[0113] Next, the processing of the 13th embodiment will be described.

[0114] The processing of the 13th embodiment involves generating a trained model by learning the relationship between the output of the object detection process (frequency spectrum) and the type of object using machine learning, based on reliable information such as a SAR image or visible image taken in clear weather. When visible images are unavailable, such as at night or during rainfall, the type of object is estimated by inputting only the frequency spectrum into the trained model. Figure 19 is a block diagram showing the processing flow of the 13th embodiment.

[0115] More specifically, the trained model used for discrimination is machine-learned using frequency spectra as explanatory variables and the object type corresponding to each frequency spectrum created based on SAR images or visible images as the target variable. This enables object detection and object type discrimination even when it is difficult to generate clear SAR images in radar equipped with SAR functionality. The SAR images used when generating the trained model may be those generated by the SAR analysis of the spectral analysis SAR device 1 of this embodiment.

[0116] It should be noted that the present invention is not limited to the embodiments described above, and the components can be modified and implemented in practice without departing from the spirit of the invention. Furthermore, various inventions can be formed by appropriately combining the multiple components disclosed in the embodiments described above. For example, all the components shown in the embodiments may be combined as appropriate. It goes without saying that various modifications and applications are possible without departing from the spirit of the invention.

[0117] The following further notes are disclosed regarding the present invention.

[0118] (Note 1) The signal processing device of the present invention comprises a receiving unit that receives radio waves reflected from an object to be measured and generates a received signal, and a processing unit that branches the received signal generated by the receiving unit and performs at least two or more processes in parallel from long-period SAR (Synthetic Aperture Radar) analysis, medium-period spectral analysis, and short-period meteorological analysis.

[0119] (Note 2) In the signal processing device described in Appendix 1, the processing unit can perform long-period SAR analysis and short-period meteorological analysis in parallel.

[0120] (Note 3) In the signal processing device described in Appendix 1 or 2, the processing unit can perform processing in meteorological analysis that degrades the distance resolution while improving the sensitivity of precipitation observations.

[0121] (Note 4) In the signal processing device described in Appendix 3, the processing unit can coherently add sector by sector the distance-direction continuous complex signals generated from the received signal in meteorological analysis.

[0122] (Note 5) In the signal processing device described in Appendix 4, the processing unit can estimate the SNR of each sector based on the result of coherent summation, and reduce the number of additions in the distance direction based on the SNR.

[0123] (Note 6) In the signal processing device described in Appendix 3, the processing unit can analyze at least one of the following in the weather analysis of an FMCW (Frequency Modulated Continuous Wave) radar: the transmitted signal to the object to be measured, the received signal, and the beat signal based on the transmitted and received signals to the object to be measured, in intervals of a predetermined time.

[0124] (Note 7) In the signal processing device described in Appendix 2, the processing unit can calculate a feature quantity relating to at least one of radio wave attenuation and phase delay based on the results of meteorological analysis, and perform SAR analysis by compensating for at least one of radio wave attenuation and phase delay based on the calculated feature quantity.

[0125] (Note 8) In the signal processing device described in Appendix 7, the processing unit can calculate at least one of the following feature quantities: the integrated value of the received power, the integrated value of the reflectance factor, the integrated value of the rainfall intensity, and the interpolar phase difference.

[0126] (Note 9) In the signal processing device described in Appendix 7 or 8, the processing unit can estimate at least one of radio wave attenuation and phase delay based on a feature quantity, and compensate at least one of the amplitude and phase of the received signal based on the estimated at least one of radio wave attenuation and phase delay.

[0127] (Note 10) In the signal processing device described in Appendix 7, the processing unit can perform SAR analysis by compensating for the phase delay by performing autofocus processing based on feature quantities.

[0128] (Note 11) In the signal processing device described in Appendix 7, the processing unit can calculate at least one feature quantity of radio wave attenuation and phase delay using super-resolution processing based on the results of meteorological analysis.

[0129] (Note 12) In the signal processing device described in Appendix 7, the processing unit can determine precipitation particles based on the results of meteorological analysis and calculate at least one feature quantity of radio wave attenuation and phase delay due to rainfall based on the determined particle type.

[0130] (Note 13) In the signal processing device described in Appendix 1, the processing unit can branch the received signal and perform long-period SAR analysis and medium-period spectral analysis in parallel.

[0131] (Note 14) In the signal processing device described in Appendix 13, the processing unit can perform object detection analysis as a medium-period spectral analysis.

[0132] (Note 15) In the signal processing device described in Appendix 14, the processing unit can perform object detection analysis by performing coherent integration, FFT, and incoherent integration in that order.

[0133] (Note 16) In the signal processing device described in Appendix 14 or 15, the processing unit can determine the space in which to perform SAR analysis based on the results of object detection analysis.

[0134] (Note 17) In the signal processing device described in any of Appendix 14 to 16, the processing unit can determine the SAR analysis method based on the results of object detection analysis.

[0135] (Note 18) In the signal processing device described in any of Appendix 14 to 17, the processing unit can perform object discrimination based on the results of object detection analysis. (Note 19) In the signal processing device described in any of Appendix 14 to 18, the processing unit can estimate the type of object by inputting only the frequency spectrum based on the results of object detection analysis into a trained model that has been machine-learned to obtain the relationship between the frequency spectrum related to the object and the type of object that has been acquired in advance.

[0136] (Note 20) The signal processing method of the present invention generates a received signal by receiving radio waves reflected from an object to be measured. The received signal generated by the receiver is split, and at least two of the following processes are performed in parallel: long-period SAR (Synthetic Aperture Radar) analysis, medium-period spectral analysis, and short-period meteorological analysis.

[0137] (Note 21) The signal processing program of the present invention causes a computer to perform the following steps: receiving radio waves reflected from an object to be measured and generating a received signal; and branching the received signal generated by the receiving unit and performing at least two or more processes in parallel from long-period SAR (Synthetic Aperture Radar) analysis, medium-period spectral analysis, and short-period meteorological analysis. [Explanation of symbols]

[0138] 1. Spectral analysis SAR device 10 Transmitter 20 Receiver 30 Processing Unit

Claims

1. A receiving unit that receives radio waves reflected from the object to be measured and generates a received signal, A signal processing device comprising a processing unit that branches the received signal generated by the receiving unit and performs in parallel at least two of the following processes: SAR (Synthetic Aperture Radar) analysis using long-period received signals received at multiple different locations; object detection analysis of objects contained in the object being measured by spectral analysis using medium-period received signals; and meteorological analysis using short-period received signals based on radio waves reflected from precipitation particles.

2. The signal processing apparatus according to claim 1, wherein the processing unit performs the processing of the long-period SAR analysis and the short-period weather analysis in parallel.

3. The signal processing device according to claim 2, wherein the processing unit performs a process in the meteorological analysis that degrades the distance resolution and improves the sensitivity of precipitation observation.

4. The signal processing device according to claim 3, wherein the processing unit coherently adds a distance-continuous complex signal generated from the received signal sector by sector in the meteorological analysis.

5. The signal processing apparatus according to claim 4, wherein the processing unit estimates the SNR of each sector based on the result of the coherent sum, and reduces the number of additions in the distance direction based on the SNR.

6. The signal processing device according to claim 3, wherein the processing unit analyzes at least one of the following in the weather analysis of the FMCW (Frequency Modulated Continuous Wave) radar, a transmission signal to the object to be measured, the reception signal, and a beat signal based on the transmission signal to the object to be measured, in intervals of a predetermined time.

7. The signal processing apparatus according to claim 2, wherein the processing unit calculates feature quantities relating to at least one of radio wave attenuation and phase delay based on the results of the meteorological analysis, and performs the SAR analysis by compensating for at least one of the radio wave attenuation and phase delay based on the calculated feature quantities.

8. The signal processing apparatus according to claim 7, wherein the processing unit calculates at least one of the integrated value of received power, the integrated value of the reflectance factor, the integrated value of rainfall intensity, and the interpolar phase difference as the feature quantities.

9. The signal processing apparatus according to claim 7, wherein the processing unit estimates at least one of the radio wave attenuation and phase delay based on the feature quantity, and compensates at least one of the amplitude and phase of the received signal based on the estimated at least one of the radio wave attenuation and phase delay.

10. The signal processing apparatus according to claim 7, wherein the processing unit performs autofocus processing based on the feature quantity to compensate for the phase delay and perform the SAR analysis.

11. The signal processing apparatus according to claim 7, wherein the processing unit calculates at least one feature quantity of radio wave attenuation and phase delay using super-resolution processing based on the results of the meteorological analysis.

12. The signal processing apparatus according to claim 7, wherein the processing unit determines the type of precipitation particle based on the results of the meteorological analysis, and calculates at least one feature quantity of radio wave attenuation and phase delay due to rainfall based on the determined particle type.

13. The signal processing apparatus according to claim 1, wherein the processing unit branches the received signal and performs the processing of long-period SAR analysis and medium-period spectral analysis in parallel.

14. The signal processing device according to claim 1, wherein the processing unit performs coherent integration, FFT, and incoherent integration in that order to perform the object detection analysis.

15. The signal processing apparatus according to claim 1, wherein the processing unit determines the space in which the SAR analysis is performed based on the results of the object detection analysis.

16. The signal processing apparatus according to claim 1, wherein the processing unit determines the SAR analysis method based on the results of the object detection analysis.

17. The signal processing device according to claim 1, wherein the processing unit performs object discrimination based on the results of the object detection analysis.

18. The signal processing device according to claim 17, wherein the processing unit estimates the type of object by inputting only the frequency spectrum based on the results of the object detection analysis to a trained model that has been trained by machine learning the relationship between the frequency spectrum related to the object obtained in advance and the type of the object.

19. It receives radio waves reflected from the object being measured and generates a received signal. A signal processing method that branches the generated received signal and performs at least two or more processes in parallel from among SAR (Synthetic Aperture Radar) analysis using long-period received signals received at multiple different locations, object detection analysis of objects included in the object being measured by spectral analysis using medium-period received signals, and meteorological analysis using short-period received signals based on radio waves reflected from precipitation particles.

20. The steps include receiving radio waves reflected from the object to be measured and generating a received signal, A signal processing program that causes a computer to perform the following steps in parallel: branching the generated received signal and performing SAR (Synthetic Aperture Radar) analysis using long-period received signals received at multiple different locations; object detection analysis of objects contained in the object being measured by spectral analysis using medium-period received signals; and meteorological analysis using short-period received signals based on radio waves reflected from precipitation particles.

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