JP2009162689A - Electronic scanning radar device, and received wave direction estimation method and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an electronic scanning millimeter wave radar device and a received wave direction estimation program capable of reducing variable steps, and reducing an operation amount for angle detection, when detecting a received wave direction by using inherent development such as MUSIC method or a minimum norm method. <P>SOLUTION: This electronic scanning radar device loaded on a moving body has a transmitting unit for transmitting a transmission wave; a receiving unit constituted of a plurality of antennas, for receiving an incoming wave which is a reflected wave of the transmission wave from a target; a beat signal generating unit for generating a beat signal having a frequency of a difference between the transmission wave and the reflected wave; a frequency decomposing unit for calculating complex number data by performing frequency decomposition of the beat signal in time series into beat frequencies to the number of decomposition set beforehand; an angle range setting unit for calculating an angle range wherein the target exists from the complex number data; and an azimuth detection unit for calculating an angle spectrum in the angle range. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an on-vehicle electronic scanning radar apparatus, a reception wave direction estimation method, and an arrival wave direction estimation program used therefor, which use a reflected wave from a target with respect to a radiated transmission wave and detect the target. .

Conventionally, as an in-vehicle radar, an electronic scanning type radar using a method such as FMCW (Frequency Modulated Continuous Wave) radar, multi-frequency CW (Continuous Wave) radar, or pulse radar is used.
In each of the above radars, an arrival direction estimation method of an array antenna is used as a technique for detecting the direction of an incoming wave (or a received wave) that is a reflected wave from a target with respect to a transmission wave.
This arrival wave direction estimation method includes beam scanning method such as beamformer method and capon method, linear prediction method such as maximum entropy (MEM) method, minimum norm method, MUSIC (MUltiple SIgnal Classification) method, ESPRIT There is a null operation method called a super-resolution (high accuracy) algorithm such as (Estimation of Signal Parameters via Rotational Invariance Techniques) method (for example, see Non-Patent Documents 1 and 2).

In addition, the direction of arrival wave used for in-vehicle radar is estimated only by digital beam forming (DBF) of the beamformer method (for example, see Patent Document 1), or the detection accuracy of the direction of arrival of the received wave (or In recent years, a method combining DBF and the maximum entropy method (for example, see Patent Documents 2 and 3) has been performed in order to improve the target resolution.
In addition, a configuration in which a super-resolution algorithm such as MUSIC is applied to in-vehicle radar for the purpose of simplification of processing (for example, see Patent Documents 4 and 5), compared with a normal personal computer, arithmetic processing It has been developed to be applied to in-vehicle applications with low functionality.

In order to improve the accuracy of direction estimation, it is desirable that the super-resolution algorithm such as MUSIC estimates the direction of the incoming wave after estimating the number of the incoming waves.
In Non-Patent Documents 1 and 2, AIC (Akaike Information Criteria), MDL (Minimum Description Length), and the like are introduced as a method for estimating the number of incoming waves based on the maximum likelihood method in statistical processing.
However, in the estimation methods of Non-Patent Documents 1 and 2 described above, since it is necessary to collect and evaluate a large number of data, it is suitable as an in-vehicle radar application in which the relative distance from the target and the relative speed are fast. Not.

Patent Document 4 describes a method for estimating the number of incoming waves necessary for calculating a MUSIC spectrum with a light calculation load. That is, a technique is described that applies a threshold method in which a signal space and a noise space are distinguished and estimated after the eigenvalue calculation according to the size of the eigenvalue.
In this case, since the received intensity of the radar decreases as the measurement distance increases, a threshold value is stored and set for each relative distance to the target, and this threshold value is compared with the eigenvalue (proportional to the received intensity). Thus, the number of incoming waves is estimated.
Also, although not intended for in-vehicle use, there is one that normalizes eigenvalues with one of the diagonal component values of the original covariance matrix (ie, correlation matrix) and then distinguishes them with one threshold value. (For example, refer to Patent Document 6).
Kikuma Nobuyoshi, Adaptive Signal Processing with Array Antenna, Science and Technology Publishers, 1998 Nobuyoshi Kikuma, Adaptive Antenna Technology, Ohmsha, 2003 JP 2000-284044 A JP 2006-275840 A JP 2006-308542 A JP 2006-047282 A JP 2007-040806 A JP 2006-153579 A

However, in the MUSIC method, as shown below, the accuracy of direction-of-arrival estimation changes due to the variable step Δθ of θ in the arrival angle evaluation function.
That is, when the variable step Δθ is increased, the amount of calculation over the entire range in which θ is varied decreases, but the peak direction of the arrival direction evaluation function cannot be accurately detected, and the accuracy deteriorates.
On the other hand, by reducing the variable step Δθ, it is possible to accurately detect the peak direction of the arrival direction evaluation function, but there is a drawback that the amount of calculation over the entire range in which θ is varied increases.

  The present invention has been made in view of such circumstances, and when performing arrival wave direction detection using eigenexpansion such as the MUSIC method and the minimum norm method, the variable step is reduced, and the amount of calculation for angle detection is reduced. An object of the present invention is to provide an electronic scanning millimeter-wave radar device and a received wave direction estimation program that can be reduced.

  An electronic scanning radar apparatus according to the present invention is an electronic scanning radar apparatus mounted on a moving body, and includes a transmission unit that transmits a transmission wave and a plurality of incoming waves that are reflected waves from a target of the transmission wave. A beat signal generator configured to generate a beat signal having a frequency difference between the transmission wave and the reflected wave, and a beat frequency of a predetermined number of decompositions of the beat signal in time series. A frequency decomposition processing unit that calculates complex number data by frequency decomposition; an angle range setting unit that calculates an angle range in which a target exists from the complex number data; and an azimuth detection unit that calculates an angle spectrum within the angle range. It is characterized by that.

  In the electronic scanning radar apparatus according to the present invention, the angular range setting unit performs digital beam forming on the complex number data in the antenna arrangement direction, calculates the intensity of the spectrum for each angle channel, and detects the presence of the target. A DBF processing unit that obtains azimuth information, and a range detection unit that sets an angular range for calculating an angular spectrum based on frequency axis data and azimuth information in which the target exists are provided.

In the electronic scanning radar apparatus according to the present invention, the angle range setting unit divides into a plurality of groups according to the presence or absence of a target in the angle channel direction based on the spectrum intensity for each angle channel calculated by the DBF processing unit. A channel deleting unit for setting the spectrum intensity of an angle channel where no signal exists to “0”, and an IDBF processing unit for performing IDBF on the spectrum intensity for each angle channel, returning the complex intensity data to each antenna, and outputting it as reproduced complex data The range detection unit sets an angle range for calculating an angle spectrum based on the reproduction complex number data, direction information where the target exists, and a received wave number estimation value.
In the electronic scanning radar apparatus according to the present invention, the angle range setting unit divides into a plurality of groups according to the presence or absence of a target in the angle channel direction based on the spectrum intensity for each angle channel calculated by the DBF processing unit. A channel deleting unit for setting the spectrum intensity of an angle channel where no signal exists to “0”, and an IDBF processing unit for performing IDBF on the spectrum intensity for each angle channel, returning the complex intensity data to each antenna, and outputting it as reproduced complex data The azimuth detecting unit calculates a solution corresponding to an angle based on the reproduction complex number data and the received wave number estimation value.

In the electronic scanning radar apparatus according to the present invention, the angle setting unit divides the angle setting unit into a plurality of groups according to the presence / absence of the target in the angle channel direction based on the spectrum intensity for each angle channel calculated by the DBF processing unit. A channel deleting unit that sets the spectrum intensity of the non-existing angle channel to “0”, an IDBF processing unit that IDBFs the spectrum intensity of each angular channel, returns the complex number data to each antenna, and outputs the data as reproduced complex number data; Storage means for storing azimuth information of each target in the past azimuth detection cycle, wherein the range detection unit is adapted to store the reproduction complex number data and the azimuth of the past azimuth detection cycle stored in the storage means. Angle range for calculating angle spectrum based on information and received wave number estimate And setting.
The electronic scanning radar apparatus according to the present invention is characterized in that the received wave number estimation value is a fixed value.

  In the electronic scanning radar apparatus of the present invention, the angle range setting unit stores target detection means for detecting the presence of a target from the peak value of the intensity of the frequency axis, and azimuth information of each target in the past azimuth detection cycle. A storage unit; and a range detection unit that limits the angle range based on azimuth information of a past azimuth detection cycle stored in the storage unit and writes the obtained angle range to the storage unit. And

  The reception wave direction estimation method of the present invention is a reception wave direction estimation method by the electronic scanning radar apparatus according to any one of claims 1 to 7, which is mounted on a moving body, and a transmission unit transmits a transmission wave. A transmission process in which transmission is performed, a reception process in which a reception unit receives an incoming wave that is a reflected wave from a target of the transmission wave, and a beat signal generation unit is a difference between the transmission wave and the reflected wave A beat signal generating process for generating a beat signal having a frequency of a frequency, and a frequency resolving process for calculating complex number data by frequency-decomposing the beat signal into beat frequencies of a preset number of decompositions in time series An angle range setting process in which the angle range setting unit calculates an angle range in which the target exists from the complex number data, and an azimuth detection unit has an angle spectrum within the angle range. And having a direction detection step of calculating.

  The received wave direction estimation program of the present invention is a program for causing a computer to control the operation of the received wave direction estimation by the electronic scanning radar apparatus according to any one of claims 1 to 7 mounted on a moving body. A transmission process in which the transmission unit transmits a transmission wave; a reception process in which a reception unit receives an incoming wave that is a reflected wave from the target of the transmission wave through a plurality of antennas; and a beat signal generation unit in the transmission wave and the A beat signal generation process for generating a beat signal having a frequency difference between reflected waves, and a frequency resolution processing unit that frequency-decomposes the beat signal into a predetermined number of beat frequencies in advance to calculate complex number data Decomposition processing, an angle range setting process in which the angle range setting unit calculates an angle range in which the target exists from the complex number data, and an azimuth detection unit And having a direction detecting process of calculating the angular spectrum in the angular range.

  As described above, according to the present invention, since rough azimuth detection is possible by using the angle range output by the angle range setting unit, it is detected when calculating the angle spectrum by the azimuth detection unit. The angle spectrum is calculated with priority given to the specific angle range according to the target direction, so the variable step Δθ can be reduced, and the calculation resolution can be made finer. Can be improved. As a result, according to the present invention, in the spectrum calculation processing with the heavy processing load after the eigenvalue calculation, the angle range to be calculated is limited and narrowed to reduce the load and to improve the calculation resolution. It becomes possible.

  According to the present invention, since the target orientation at the past cycle can be confirmed by referring to the target orientation in the past cycle stored in the storage unit, the orientation detection unit calculates, for example, an angle spectrum. In this case, the angle spectrum can be calculated preferentially in a specific angle range that matches the direction of the past cycle, the variable step Δθ can be reduced, and the calculation resolution can be made finer. The calculation accuracy of the azimuth of the direction can be improved.

In addition, according to the present invention, DBF of the beat frequency where the target exists is performed, unnecessary angle channels are deleted (spectrum intensity is set to “0”), IDBF is performed after deletion, and reception is performed using the reproduced complex data. Since a correlation matrix is generated in the channel direction and eigenvalue calculation is performed, it is equivalent to receiving only the divided incoming waves, and even if more incoming waves are received than the number of receiving antennas, eigenvalue calculation is performed. The direction in which the incoming wave arrives can be calculated without error.
Further, according to the present invention, since the entire detectable angle range is divided into a plurality of angle ranges, the maximum value of the number of incoming waves (the number of targets) in actual use can be assumed in the divided narrow angle range. Therefore, spectrum estimation can be performed with a fixed number of incoming waves.

  Further, according to the present invention, when there are a plurality of target groups and each is located in a different direction, a plurality of angle ranges are set corresponding to a plurality of incoming waves, and each of the angle ranges is high. The arrival wave can be separated and the direction can be estimated with accuracy.

<First Embodiment>
Hereinafter, an electronic scanning radar apparatus (FMCW millimeter wave radar) according to a first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing a configuration example of the embodiment.
In this figure, the electronic scanning radar apparatus according to the present embodiment includes a receiving antenna 11-1n, a mixer 211-2n, a transmitting antenna 3, a distributor 4, a filter 51-5n, a SW (switch) 6, an ADC (A / D). Converter) 7, control unit 8, triangular wave generation unit 9, VCO 10, and signal processing unit 20.
The signal processing unit 20 includes a memory 21, a frequency separation processing unit 22, a peak detection unit 23, a peak combination unit 24, a distance detection unit 25, a speed detection unit 26, a correlation matrix calculation unit 28, an orientation detection unit 30, and an eigenvalue calculation unit. 31, a determination unit 32, and an angle range setting unit 50. Here, the angle range setting unit 50 is a person who estimates the angle range in which the target exists, has a configuration, and is a characteristic part of the present invention. In the first embodiment, as shown in FIG. 2, the angle range setting unit 50 includes a DBF processing unit 33 and a range detection unit 36.

Next, the operation of the electronic scanning radar apparatus according to the present embodiment will be described with reference to FIGS.
The receiving antennas 11 to 1n receive a reflected wave, i.e., a received wave, which is reflected from the target, with the transmitted wave reflected by the target.
Each of the mixers 21 to 2n mixes a transmission wave transmitted from the transmission antenna 3 and a signal obtained by amplifying the reception wave received at each of the reception antennas 11 to 1n by an amplifier to cope with each frequency difference. Generated beat signal.
The transmission antenna 3 transmits a transmission signal obtained by frequency-modulating the triangular wave signal generated by the triangular wave generation unit 9 in a VCO (Voltage Controlled Oscillator) 10 to the target as a transmission wave.
The distributor 4 distributes the frequency-modulated transmission signal from the VCO 10 to the mixers 21 to 2n and the transmission antenna 3.

Each of the filters 51 to 5n performs band limitation on the beat signals of Ch1 to Chn corresponding to the receiving antennas 11 to 1n generated in the mixers 21 to 2n, respectively, and beats band-limited to the SW (switch) 6 Output a signal.
The SW 6 sequentially switches the beat signals of Ch1 to Chn corresponding to the receiving antennas 11 to 1n that have passed through the filters 51 to 5n in response to the sampling signal input from the control unit 8, and performs ADC (A / A D converter) 7.
The ADC 7 performs A / D conversion on the beat signals of Ch1 to Chn corresponding to the receiving antennas 11 to 1n, which are input from the W6 in synchronization with the sampling signal, into a digital signal in synchronization with the sampling signal. The signal is converted and sequentially stored in the waveform storage area of the memory 21 in the signal processing unit 20.
The control unit 8 is constituted by a microcomputer or the like, and controls the entire electronic scanning radar apparatus shown in FIG. 1 based on a control program stored in a ROM (not shown).

<Principle to detect distance, relative speed, angle (azimuth)>
Next, the principle of detecting the distance, relative speed, and angle (azimuth) between the electronic scanning radar apparatus and the target used in the signal processing unit 20 in the present embodiment will be briefly described with reference to FIG.
FIG. 3 shows a transmission signal obtained by frequency-modulating the signal generated in the triangular wave generation unit 9 of FIG. 1 by the VCO 10 and a state in which the transmission signal is reflected by the target and input as a reception signal. The example of FIG. 3 shows a case where there is one target.
As can be seen from FIG. 3A, a received signal that is a reflected wave from the target is received with a delay in the right direction (time delay direction) in proportion to the distance from the target with respect to the signal to be transmitted. . Furthermore, it varies in the vertical direction (frequency direction) with respect to the transmission signal in proportion to the relative speed with the target. Then, after frequency conversion of the beat frequency obtained from FIG. 3A (Fourier transform, DCT (Discrete Cosine Transform), Hadamard transform, wavelet transform, etc.), as shown in FIG. In one case, each of the ascending region and the descending region has one peak value. Here, in FIG. 3A, the horizontal axis represents frequency and the vertical axis represents intensity.

The frequency resolution processing unit 22 performs discrete frequency analysis on the rising portion (upward) and the falling portion (downward) of the triangular wave from the sampled data of the beat signal stored in the memory 21 by, for example, Fourier transform. Convert frequency.
As a result, as shown in FIG. 3 (b), a graph of the signal level for each beat frequency subjected to frequency decomposition is obtained in the rising portion and the falling portion.
And the peak detection part 23 detects a peak value from the signal level for every beat frequency shown in FIG.3 (b), detects presence of a target, and beat frequency (both an increase part and a fall part) of a peak value. Is output as the target frequency.

Next, the distance detection unit 25 calculates a distance from the target frequency fu of the rising portion and the target frequency fd of the falling portion input from the peak combination unit 24 by the following formula.
r = {C · T / (2 · Δf)} · {(fu + fd) / 2}
Further, the speed detection unit 26 calculates a relative speed from the target frequency fu of the rising portion and the target frequency fd of the falling portion input from the peak combination unit 24 by the following formula.
v = {C / (2.f0)}. {(fu-fd) / 2}
In the formula for calculating the distance r and the relative velocity v,
C: speed of light Δf: frequency modulation width of triangular wave f0 : Triangular wave center frequency T: Modulation time (rising part / falling part)
fu: Target frequency in the rising part fd : Target frequency in the descending part

Next, as shown in FIG. 4, the receiving antennas 11 to 1n in the present embodiment are array-shaped antennas arranged at intervals d.
The receiving antennas 11 to 1n are incident on an incoming wave (incident wave, that is, a transmission wave transmitted from the transmission antenna 3) that is incident from an angle θ direction with respect to an axis perpendicular to the plane on which the antennas are arranged. The reflected wave from the target) is input.
At this time, the incoming waves are received at the same angle by the receiving antennas 11 to 1n.
A phase difference “dn ₋₁ · sin θ” obtained by the same angle, for example, the angle θ and the interval d between the antennas, is generated between the adjacent receiving antennas.
Using the above phase difference, the angle θ is detected by signal processing such as digital beam forming (DBF) or super-resolution algorithm that further Fourier transforms the antenna frequency in the time direction for each antenna. can do.

<Signal Processing for Received Wave (Incoming Wave) in Signal Processing Unit 20>
Next, the memory 21 stores time series data (ascending portion and descending portion) obtained by A / D converting the received signal in the waveform storage area by the ADC 7 for each of the antennas 11 to 1n. . For example, when 256 samples are sampled in each of the ascending portion and the descending portion, data of 2 × 256 × the number of antennas is stored in the waveform storage area.
The frequency resolution processing unit 22 converts each beat signal corresponding to each Ch1 to Chn (each antenna 11 to 1n) into a frequency with a preset resolution by, for example, Fourier transformation, and the like, and a frequency point indicating the beat frequency. The complex number data of the beat frequency is output. For example, if each of the ascending and descending parts has 256 sampled data for each antenna, it is converted into a beat frequency as complex frequency domain data for each antenna, and 128 in each of the ascending and descending parts. Complex number data (2 × 128 pieces × number of antennas data). The beat frequency is indicated by a frequency point.
Here, the difference between the complex number data for each antenna is only the phase difference depending on the angle θ, and the absolute values (reception intensity, amplitude, etc.) on the complex plane of each complex number data are equivalent.

Next, the frequency decomposition processing unit 22 performs frequency decomposition (time-axis Fourier transform) on the input beat signal, and outputs a frequency point indicating the beat frequency and complex number data to the DBF processing unit 33.
Then, the DBF processing unit 33 performs Fourier transform on the input complex number data corresponding to each antenna in the antenna arrangement direction, that is, performs spatial axis Fourier transform.
Then, the DBF processing unit 33 calculates spatial complex number data for each angle channel corresponding to the angle, that is, corresponding to the angular resolution, and outputs the data to the peak detection unit 23 for each beat frequency.

Accordingly, the spectrum indicated by the spatial complex number data (beat frequency unit) for each angle channel output from the DBF processing unit 33 depends on the arrival direction estimation of the received wave by the beam scanning resolution.
In addition, since the Fourier transform is performed in the antenna arrangement direction, the same effect as adding complex number data between angle channels can be obtained, and the S / N ratio is improved for complex number data for each angle channel. Therefore, the accuracy in detecting the peak value can be improved.
Both the complex number data and the spatial complex number data described above are calculated in both the rising and falling regions of the triangular wave.

Next, the peak detection unit 23 detects the peak value based on the spectrum intensity indicated by the spatial complex number data for each angle channel that is input, and outputs the peak value to the peak combination unit 24.
When the detection result is input, the peak combination unit 24 combines the beat frequency and the peak value in the ascending region and the descending region, and outputs the combined value to the distance detection unit 25 and the speed detection unit 26. Confirm.

Next, the distance detection unit 25 calculates the distance r to the target based on a numerical value obtained by adding the beat frequencies of the combinations of the ascending region and the descending region that are sequentially input.
Further, the speed detection unit 26 calculates the relative speed v with respect to the target based on the difference between the beat frequencies of the combinations of the ascending region and the descending region that are sequentially input.
Then, the correlation matrix calculation unit 28 selects the beat frequency frequency-resolved by the frequency decomposition processing unit 22 based on the frequency point of the beat frequency at the peak where the combination is determined, and either one of the rising part and the falling part in this combination. A correlation matrix corresponding to the beat frequency (the descending portion in the present embodiment) is generated.

  Next, the range detection unit 36 has a coarser resolution than the MUSIC or the like by an angle channel corresponding to the spatial complex number data of the same frequency point as the output to the correlation matrix calculation unit 28 in the pair determined by the pair determination unit 27. The angle range information is output to the azimuth detecting unit 30 as the (low) level azimuth information. For example, the range detection unit 36 compares the peak value of each angle channel with a preset threshold value, detects an angle channel having a peak value equal to or greater than this threshold value, and a plurality of channels in which the detected angle channel is set. When there are more than a few (for example, four angle channels) adjacent to each other, this range is output to the direction detection unit 30 as angle range information.

  By using the angle range information, the azimuth detecting unit 30 uses the spatial complex number data for each angle channel, as shown in FIGS. 6 (a) and 7 (a), when calculating the MUSIC spectrum. Compared to the case where there is no information on the angle channel, the detection direction range can be narrowed down to a narrow angle range, and the resolution of spectrum calculation of MUSIC can be increased.

Here, FIG. 6A and FIG. 7A are cases where 16-bit Fourier transform is performed in the channel direction (antenna direction) so that the angle-dependent Ch (channel) after DBF is 15 Ch. . Here, as described above, the range detection unit 36 narrows down the above-described angle range information, that is, narrows down when the value of the spectrum intensity exceeds the DBF level threshold in the range of 4 Ch (angle). The angle range information is output to the azimuth detecting unit 30 as a range.
Thereby, the azimuth detecting unit 30 analyzes the detection direction range narrowed down by the angle range information input from the range detecting unit 36 with high accuracy.

  In FIG. 7A, since the group of spectrum intensity values that exceed the DBF level threshold in a continuous angle range of 4 Ch is divided into two, the range detection unit 36 has the respective ranges (Ch3 to Ch6). , Ch10 to Ch13) as first angle range information (angle range in FIG. 7B) and second angle range information (angle range in FIG. 7C), the azimuth detecting unit 30 Output for.

Then, the azimuth detecting unit 30 detects the MUSIC spectrum separately (independently) separately from each of the first and second angular range information as the angular range for calculating the MUSIC spectrum. The analysis of the detection direction range narrowed down by the angle range information input from can be performed with high accuracy.
6A, 6B, and 7, the horizontal axis indicates the Ch number of the angle channel, and the vertical axis indicates the spectrum intensity for each Ch calculated by the DBF process.

<Super-resolution algorithm for estimating direction of arrival of received waves>
Next, the super-resolution algorithm for estimating the arrival direction of the received wave in the correlation matrix calculation unit 28, eigenvalue calculation unit 31, determination unit 32, and azimuth detection unit 30 will be described with reference to FIG. 8 taking MUSIC as an example. To do. FIG. 8 is a flowchart showing a general MUSIC process flow. Since the MUSIC process itself is generally used (for example, Non-Patent Documents 1 and 2, or Patent Documents 3 to 6), only the necessary parts in this embodiment will be described.

The frequency resolution processing unit 22 reads the beat signal based on the received wave stored in the memory 21 (step S101), and converts the frequency of the beat signal for each antenna (step S102).
Then, as already described, the correlation matrix calculation unit 28 calculates the frequency-resolved complex frequency domain data (hereinafter referred to as complex number data) corresponding to the target frequency point of the descending domain whose combination is determined by the pair determination unit 27. The frequency matrix is selected and read from the frequency resolution processing unit 22, and a correlation matrix indicating the correlation for each antenna is generated in the descending region (step S103).

For example, there are methods shown in FIG. 9A and FIG. 9B in the generation of the correlation matrix in the correlation matrix calculation unit 28 in step S103, which will be briefly described below.
In the method shown in FIG. 9A, the correlation matrix calculation unit 28 generates a correlation matrix (complex correlation matrix) as complex data (step S103_1), and only forward spatial average (Forward spatial average method) or forward / backward Processing is performed by spatial averaging (Forward-Backward spatial averaging method) (step S103_2).
The spatial average is obtained by dividing the number of antennas in the original array of receiving antennas into subarrays having a smaller number of antennas and averaging the subarrays. The basic principle of this spatial averaging method is that the phase relationship of correlated waves differs depending on the reception position. Therefore, if the correlation matrix is obtained by appropriately moving the reception point, the correlation of the correlation interference wave is suppressed by the average effect. . In general, a plurality of subarrays having the same arrangement are extracted from the entire array of receiving antennas without moving the array of receiving antennas, and the correlation matrix is averaged.

For example, as shown in FIG. 10, considering an array of nine receiving antenna antennas 11 to 1n (n = 9), the correlation matrix calculation unit 28 uses the following correlation matrix of the expression (1) below. With respect to CR ^f _f , a backward correlation matrix CR ^b _f of equation (2) is obtained, and the corresponding elements of the correlation matrix of equation (1) and the backward correlation matrix of equation (2) are expressed as CR ^fb _f = _{^{(CR f f + CR b f}} ) / 2
Is the average processing of the elements in the front / rear.
In this way, the correlation matrix CR ^fb _f obtained by the forward / backward averaging process is divided into subarrays and averaged to obtain a correlation matrix Rxx used for estimating the arrival direction of the received wave. That is, the correlation matrix obtained by the forward / backward spatial averaging process is expressed by the following equation.
Rxx = (CR ^fb1 _f + CR ^fb2 _f + CR ^fb3 _f ) / 3
Here, the correlation matrix calculation unit 28 divides the array of the nine receiving antennas 11 to 19 into three subarrays of seven antennas 11 to 17, 12 to 18, and 13 to 19, and calculates the matrix of each subarray. The correlation matrix Rxx is obtained by averaging the corresponding elements.

On the other hand, in the case of the spatial average of the front, the matrix of V ₁₁ to V ₉₉ may be the matrix of the formula (1) from W ₁₁ to W _99, and therefore, an example of V ₁₁ = W ₁₁ + W _99/2 is shown. The average of each element becomes unnecessary.
In applications that estimate the direction of arrival of received waves in radar, the transmitted waves transmitted from all incoming received waves are reflected waves reflected by the target, so the received wave data received for each antenna is strongly correlated. Correlation will be shown. Therefore, the result of eigenvalue calculation in the subsequent stage does not appear correctly. Therefore, the spatial average has the effect of suppressing the cross-correlation, extracting the auto-correlation, and correctly estimating the arrival wave direction.

Next, the correlation matrix calculation unit 30 performs unitary conversion for converting the correlation matrix of the complex number data spatially averaged by the above-described processing into a real number correlation matrix.
Here, by converting to a real number correlation matrix, the eigenvalue calculation with the heaviest calculation load in the subsequent steps can be calculated only for the real number, and the calculation load can be greatly reduced.
On the other hand, FIG. 9B is a type in which eigenvalue calculation in the next step is also performed with complex numbers without performing conversion to a real correlation matrix by unitary conversion as shown in FIG. 9A.
In step S103, each correlation matrix Rxx obtained in S103_3 in FIG. 9A and S103_2 in FIG. 9B is further set based on the maximum value of the correlation matrix (or the diagonal component of the correlation matrix). The element value may be normalized (= divided by the maximum value).

Next, the eigenvalue calculation unit 31 calculates the eigenvalue of the correlation matrix Rxx obtained in step S103 and the eigenvector corresponding thereto.
Rxxe = λe
Is calculated as an eigenvalue λ and an eigenvector e, and the eigenvalue λ is output to the azimuth detection unit 30 and to the determination unit 32 (step S104).
Then, the determination unit 32 estimates the number of incoming waves necessary for removing the signal component vector from the eigenvalue λ obtained by the eigenvalue calculation unit 31, and outputs the obtained number of incoming waves to the direction detection unit 30 (step) S105).

Here, the determination unit 32 performs the estimation of the number of incoming waves by an incoming wave estimation process described later.
Next, the azimuth detection unit 30 creates an angle spectrum by performing an inner product calculation of a vector including only a noise component, excluding the signal vector, and a direction vector for each azimuth angle set in advance. (Step S106). Thereby, directivity null can be matched with the arrival direction of a received wave.
At this time, the azimuth detection unit 30 includes only a noise component in the angle range indicated by the angle range information input from the range detection unit 36 described above, and a direction vector for each azimuth angle set inside. To calculate the angle spectrum P _MU (θ).
P _MU (θ) = a ^H (θ) a (θ) / {a ^H (θ) E _N E _N ^H a (θ)}
Here, a (θ) is a directional vector, E _N is the noise subspace eigenvectors, H is denotes the conjugate transpose.

Then, the azimuth detecting unit 30 detects a peak exceeding a preset threshold from the angle spectrum, thereby detecting the peak and calculating the incoming wave direction (angle θ) (step S107).
Further, the azimuth detecting unit 30 is positioned laterally with respect to the vertical axis of the antenna array in the electronic scanning radar apparatus by the angle (= the arrival direction of the received wave) and the distance calculated by the distance detecting unit 25. It can also be converted.
The above is a standard MUSIC, but in the MUSIC spectrum calculation in step S106, a method called Root-MUSIC that finds a solution from a root of a polynomial instead of a type that searches by a direction vector can also be used.
Here, Root-MUSIC is
a ^H (θ) E _N E _N ^H a (θ) = 0
This is a method for directly obtaining θ satisfying the condition, and is obtained without creating a spectrum.

Further, after step S107 in FIG. 8, processing for calculating received power and deleting unnecessary waves (unnecessary received wave data) may be added.
That is, the determination unit 32 compares the power that appears in the diagonal component of the matrix S in the following equation with a preset threshold value, detects whether the power exceeds the threshold value, and the power is the threshold value. Is determined as a necessary received wave, and when the power is less than or equal to this threshold, it is determined as an unnecessary received wave.
S = (A ^H A) ⁻¹ A ^H (Rxx−σ ² I) A (A ^H A) ⁻¹
Here, S is a correlation matrix of a received wave signal, A is a direction matrix, A ^H is a conjugate transpose matrix of A, I is a unit matrix, Rxx is a correlation matrix calculated by the correlation matrix calculation unit 28, and σ ² is noise The variance of the vector.

By adding the processing of the received power calculation and unnecessary wave deletion described above, when estimating the number of received waves in the estimation of the received wave number in step S105, it is possible to delete received waves that arrive unnecessarily by this processing. it can. Accordingly, it is possible to secure a margin for setting the threshold λth and the threshold λth ′ in the estimation process of the number of incoming waves, which will be described later (that is, to delete a received wave with insufficient received power without setting each threshold strictly). .
As described above, in the present embodiment, direction estimation is first performed using DBF (Digital Beam Forming) having a resolution lower than that of a super resolution algorithm such as MUSIC, and the angle range where the target exists is narrowed down. In this range, the direction estimation from the correlation matrix is performed by the super-resolution algorithm.

<Incoming wave estimation processing>
Next, the arrival wave estimation process in step S105 of FIG. 8 used for MUSIC spectrum detection in the azimuth detection unit 30 will be described with reference to FIG. The process for estimating the number of incoming waves shown in the flowchart of FIG. 11 is a process performed mainly by the determination unit 32 in FIG. 1 using the eigenvalue input from the eigenvalue calculation unit 31.
As already described in the flowchart of FIG. 8, at the time of entering step S105 in FIG. 8, the peak combination unit 24 detects the target, and the eigenvalue calculation unit 31 has already calculated the eigenvalues and eigenvalue vectors of the correlation matrix Rxx. ing.
Therefore, the determination unit 32 assumes that there is at least one incoming wave number, and substitutes 1 for the incoming wave number L (step S401).

Then, the determination unit 32 detects the maximum eigenvalue λa having the maximum value from the eigenvalues obtained from the correlation matrix, and determines all eigenvalues λx (x = 1, 2, 3,...) From the maximum eigenvalue λa. Dividing (determining the ratio of the maximum eigenvalue λa to all eigenvalues λx including the maximum eigenvalue λa), normalizing the eigenvalue λx, and obtaining λy (y = 1, 2, 3,... (Step S402). At this time, the determination unit 32 rearranges the normalized eigenvalues λy in descending order.
Next, the determination unit 32 compares the preset threshold value λth and the eigenvalue λy in order of decreasing eigenvalue λy (step S403), and detects that the eigenvalue λy is equal to or greater than the threshold value λth. The process proceeds to step S404.

Then, the determination unit 32 increments the incoming wave number L (adds 1), and returns the process to step S403.
On the other hand, when the determination unit 32 detects that the eigenvalue λy is less than the threshold value λth, it is not necessary to perform the subsequent comparison process between the eigenvalue λy and the threshold value λth (the eigenvalue λy from which the subsequent eigenvalue λy is currently compared). Because it is smaller, the process proceeds to step S405 (step S403).
Then, the determination unit 32 determines the current arrival wave number L as the detected arrival wave number, and outputs the determined arrival wave number L to the direction detection unit 30 (step S405).
In this arrival wave number estimation process, the determination unit 32 performs the above-described processing from step S401 to step S405 for each input of the eigenvalue from the eigenvalue calculation unit 31.

Also, as shown in the flowchart of FIG. 12, the determination unit 32 detects the maximum eigenvalue λa from the eigenvalue λx input from the eigenvalue calculation unit 31 before performing the arrival wave number estimation process.
Then, the determination unit 32 detects whether or not the detected maximum eigenvalue λa is greater than or equal to a preset threshold value λmax (step S400), and if it has detected that the maximum eigenvalue λa is greater than or equal to the threshold value λmax, When the arrival wave number processing after step S401 in FIG. 8 described above is performed and when it is detected that the maximum eigenvalue λa is less than the threshold value λmax, the arrival wave number estimation processing is not performed and the arrival wave number is detected with respect to the direction detection unit 30. Do not output L.
That is, even in the embodiment in which eigenvalues are obtained from the correlation matrix of all frequency points or a specific frequency point range, the arrival wave number estimation process can be canceled (stopped) in the estimation of the arrival wave number, Due to the influence of the path, erroneous estimation of the number of incoming waves can be avoided even when the reception level is low.

Next, another arriving wave number estimation process will be described with reference to the flowchart of FIG. In FIG. 13, unlike the flowcharts of FIGS. 11 and 12, normalization is not performed after the eigenvalue is calculated, but the correlation matrix calculation unit 30 is correlated as described in the description of the correlation matrix calculation unit 30. After dividing each element of the correlation matrix Rxx by the maximum value of the diagonal elements among the elements in the matrix Rxx and normalizing the elements, the eigenvalue calculation unit 31 may calculate the eigenvalue and the eigenvalue vector. .
In addition, the correlation matrix calculation unit 28 does not normalize the correlation matrix Rxx, and the eigenvalue calculation unit 31 calculates the eigenvalue and the eigenvalue vector after performing the above-described normalization process before calculating the eigenvalue. You may make it perform.

As a result, the precision of the floating-point calculation for eigenvalue calculation is improved, and the number of operations until convergence such as the Jacobian method and QR method, which are eigenvalue and eigenvalue vector calculation algorithms, can be reduced, thereby reducing the calculation time. be able to. In addition, it is not necessary to perform eigenvalue normalization processing in the arrival wave number estimation processing. Further, when the maximum value of all the elements including the diagonal elements among the elements in the correlation matrix Rxx is used as a standard for normalization, the normalization of the eigenvalue in step S402 in FIG. 11 is performed before step S501 in FIG. What is necessary is just to process.
In any case, the eigenvalue λx (x = 1, 2, 3,...) Calculated by the normalized correlation matrix is input to the determination unit 32, and the arrival wave number estimation process shown in the flowchart of FIG. 13 is started. The At this time, the determination unit 32 resets the incoming wave L to 0.

The determination unit 32 rearranges the input eigenvalues λx in descending order, and compares each eigenvalue λx with a preset threshold value λth ′ in descending order of the eigenvalue λx (step S501).
At this time, if the eigenvalue λx is greater than or equal to the preset threshold value λth ′, the determination unit 32 advances the process to step S502. If the eigenvalue λx is less than the preset threshold value λth ′, the determination unit 32 advances the process to step S503.
When the eigenvalue λx is equal to or greater than the preset threshold value λth ′, the determination unit 32 increments the number of incoming waves L (step S502), and returns the process to step S501.
Further, when the eigenvalue λx is less than the preset threshold λth ′, the determination unit 32 determines the current arrival wave number L as the estimated arrival wave number and outputs the estimated arrival wave number to the azimuth detection unit 30 (step S503).
In this arrival wave number estimation process, the determination unit 32 performs the above-described processing from step S501 to step S503 for each input of the eigenvalue from the eigenvalue calculation unit 31.

Further, as shown in FIG. 14, before the comparison process in step S501 of the flowchart in FIG. 13, a step S500 in which the peak value detected by the peak detector 23 after frequency decomposition is compared with a preset threshold value PEAK-th is performed. It may be provided.
Then, the determination unit 32 detects whether or not the peak value input from the peak detection unit 23 is equal to or greater than a preset threshold value PEAK-th (step S500), and the peak value is equal to or greater than the threshold value PEAK-th. 10 is performed, the number of incoming waves after step S501 in FIG. 10 already described is performed. On the other hand, when the peak value is detected to be less than the threshold value PEAK-th, the number of incoming waves is estimated. The incoming wave number L is not output to the azimuth detecting unit 30.
That is, even in the embodiment in which eigenvalues are obtained from the correlation matrix of all frequency points or a specific frequency point range, the arrival wave number estimation process can be canceled (stopped) in the estimation of the arrival wave number, Due to the influence of the path, erroneous estimation of the number of incoming waves can be avoided even when the reception level is low.
Furthermore, as shown in FIG. 15, instead of step S500 in FIG. 14, step S500 may be provided in which the maximum value of the diagonal elements in the obtained correlation matrix is compared with a preset threshold value.

16 and 17 are graphs showing a state in which the distribution of eigenvalues actually fluctuates for each distance (for each beat frequency). FIG. 16 shows the case where the incoming wave is 1 wave (arrival wave number 1), and FIG. 17 shows the case where the incoming wave is 2 waves (arrival wave number 2).
Here, the horizontal axis in FIGS. 16A and 17A indicates the distance, and the vertical axis indicates the eigenvalue. 16B and 17B, the horizontal axis indicates the distance, and the vertical axis indicates a value obtained by normalizing another eigenvalue λx with the maximum eigenvalue λa.
It can be seen from FIGS. 16 (a) and 17 (a) that there are regions where the eigenvalues are reduced due to the multipath at distances of about 65 m and 80 m from the target.
Also, as shown in FIGS. 16B and 17B, even in the normalized value, the fluctuation of the normalized value itself becomes large at the place where the multipath is received, and the arrival wave estimation is performed. In this case, the wrong number of incoming waves is estimated.

Accordingly, in the processing in step 400 of FIG. 13, step S500 of FIG. 14, and step S500 of FIG. 15, by inserting a configuration that does not perform arrival wave estimation, the arrival wave number estimation and direction detection in this correlation matrix are canceled. Thus, it is possible not to calculate an erroneous orientation detection result.
FIG. 16C shows the numerical value of the eigenvalue λx at the distance 100 (m) in FIG. 17A when the number of incoming waves is 1, and the eigenvalue λ1 in the signal space and other noise spaces It shows the numerical difference from a certain eigenvalue.
Conventionally, the arrival wave is estimated by setting the threshold Th for each distance using the eigenvalues of FIGS. 16A and 17A, but in the present embodiment, FIG. As shown in FIG. 17B and FIG. 17B, in order to normalize the eigenvalue and compare it with the threshold Th, as already described, the threshold λth (or threshold λth ′) is set as one numerical value common to all distances. And since it compares with the eigenvalue in all distances, the number of incoming waves can be estimated easily.
In addition, when the arrival wave number cannot be estimated, the direction detection unit 30 takes measures by a method of estimating the current position from the past distance, relative speed, and direction.

<Second Embodiment>
Next, an electronic scanning radar apparatus according to a second embodiment of the present invention will be described with reference to FIG. FIG. 18 is a block diagram illustrating a configuration example of an electronic scanning radar apparatus according to the second embodiment.
In the second embodiment, as in the first embodiment, orientation estimation for range setting is performed once using DBF (Digital Beam Forming) having a resolution lower than that of a super resolution algorithm such as MUSIC, and the target. The angle range is narrowed down.
The difference from the first embodiment is that the DBF value is subjected to IDBF (inverse DBF, that is, inverse space axis Fourier transform) to return it to complex data on the time axis, thereby improving the accuracy of the orientation estimation of the super resolution algorithm performed later. It is a configuration. The same components as those in the first embodiment shown in FIG. 2 are denoted by the same reference numerals, and only differences from the first embodiment will be described below.
In the present embodiment, a Ch (channel) deletion unit 34 and an IDBF processing unit 35 are added to the angle range setting unit 50 in the first embodiment.

As in the first embodiment, the DBF processing unit 33 performs spatial axis Fourier transform, and outputs spatial complex number data to the peak detection unit 23 and also outputs it to the Ch deletion unit 34.
Here, as shown in FIG. 6A and FIG. 7A, the DBF processing unit 33 performs spatial axis Fourier transform in the arrangement direction of the receiving antennas in the present embodiment, for example, with a resolution of 16 points. As a result, the spectrum of the angle unit of the 15 angle channels is generated and output to the Ch deletion unit 34 and the range detection unit 36.
Then, similarly to the first embodiment, the range detection unit 36 uses, as an angle range information, an azimuth detection unit that uses, as angle range information, a range of an angle channel having a spectrum intensity whose intensity is equal to or greater than a threshold from the spectrum of the angle unit of the angle channel. Output to 30.

Further, the Ch deletion unit 34 detects whether or not the spectrum level is adjacent and continuous in a preset angle range and exceeds a preset DBF threshold level, and does not exceed the DBF threshold. The process of replacing the spectrum of the angle channel with “0” is performed, and the spatial complex data shown in FIG. 6B, FIG. 7B, and FIG.
In the above-described processing, for example, if the adjacent four angle channels are continuously at a level that exceeds the DBF threshold, the Ch deletion unit 34 leaves the spectrum of these angle channels as the target exists, and sets other angles. The spectrum intensity at is replaced with “0”.

Then, the IDBF processing unit 35 narrows down the spectrum, that is, leaves only the data of the angle channel area that continuously exceeds the DBF threshold in the set number of angle channels, and replaces the intensity of other areas with “0”. The spatial complex number data is subjected to inverse spatial axis Fourier transform, converted back to time axis complex number data, and output to the correlation matrix calculating unit 28.
Then, since the correlation matrix calculation unit 28 calculates the correlation matrix from the input complex number data, it is possible to obtain a correlation matrix with good orthogonality by removing roadside objects and the like and reducing noise components. In FIG. 6C, a correlation matrix is created by the above-described method for the target group at the DBF resolution shown in FIG. 6B (actually, there is a possibility that there are two or more targets). This is an example in which the target is further separated by the super-resolution algorithm.
That is, the present invention does not perform angle estimation by peak estimation or the like in azimuth detection by DBF, but only extracts a range (target group) where one or more targets exist, and performs one or more of the above-described ones by subsequent azimuth detection. It is an arrival wave direction estimation algorithm that calculates the accurate number and angle of incoming waves by finely separating the presence of a target.

Further, as shown in FIG. 7A, when receiving a reception wave including reflection components from a plurality of target groups, the spatial complex data output from the DBF processing unit 33 includes DBF in a continuous angle channel. There will be multiple angular channel ranges that exceed the level.
Then, the Ch deletion unit 34 selects the spatial complex number data corresponding to the target frequency point of the descending region whose combination is determined by the pair determination unit 27, performs the above-described Ch (channel) deletion, and then performs the IDBF processing unit 35. Output to.

The IDBF processing unit 35 performs inverse spatial Fourier transform on the input spatial complex number data, and outputs the obtained time axis complex number data to the correlation matrix calculation unit 28.
Thereby, the correlation matrix calculation unit 28 calculates the correlation matrix from the input complex number data, and outputs the correlation matrix to the eigenvalue calculation unit 31 as the correlation matrix in the current detection cycle.
The subsequent process for estimating the number of incoming waves is the same as the process shown in FIGS.
With the above-described processing, the detection direction range can be narrowed in the same way as in the second embodiment when calculating the MUSIC spectrum in the MUSIC in the azimuth detecting unit 30, and the resolution can be increased.

That is, with the above-described configuration, the eigenvalue calculation unit 31 virtually receives the reception wave divided into the reflection components for each target group in the correlation matrix used for eigenvalue calculation. Even if received waves containing reflected components from more than that number of antennas and sub-arrays are received, it is possible to make calculations without error in eigenvalue calculation. It can be applied to Root-MUSIC or the like for calculating.
Further, since the entire detectable angle range is divided into a plurality of angle ranges, it is possible to assume a maximum value of the number of incoming waves (number of targets) in actual use within the divided narrow angle range. Therefore, it is possible to set a fixed number of incoming waves without estimating the number of incoming waves, and obtain an angle spectrum estimation or a solution for obtaining an angle.
In addition, after the direction of the current target is detected, the direction detection unit 30 stores the direction of the target in the memory 21 and reads it from the memory 21 as each cycle information after the next direction calculation cycle. The spectrum may be calculated by including the angle range around the target orientation of the past cycle in the angle range (angle range information input from the range setting unit 36).

<Third Embodiment>
In FIG. 1, the azimuth detecting unit 30 stores the azimuth of the target in the memory 21 after the azimuth of the current target is detected.
Then, the angle range setting unit 50 sets each cycle information after the next azimuth calculation cycle, reads the previous azimuth of the target from the memory 21, adds a numerical range set in advance before and after this azimuth as a center, An angle range centered on the azimuth obtained as a result of the previous detection cycle is set, and the angle range is output to the azimuth detection unit 30 as angle range information.
At this time, when the azimuth of each of the plurality of targets is stored in the memory 21 in the previous detection cycle, the angle range setting unit 36 reads the azimuth of each target from the memory 21 and obtains angle range information for each azimuth. Calculate and output to the direction detection unit 30.

The first to third embodiments have been described based on the configuration example used for the FMCW radar shown in FIG. 1, but can be applied to other antenna configurations of the FMCW scheme.
Also, the present invention can be applied to other systems other than the FMCW system such as multi-frequency CW and pulse radar. In this embodiment, the arrival wave number estimation and the direction detection are performed for the correlation matrix corresponding to the beat frequency of either the rising part or the falling part of the triangular wave. However, the direction detection is performed for each of the rising part and the falling part. Peak combination may be performed later. Furthermore, in this embodiment, the super-resolution algorithm MUSIC is described as an example of the azimuth detection unit. However, it can be applied to any detection algorithm based on the principle of estimating the number of incoming waves in the same way, such as the minimum norm method or the ESPRIT method Is possible.

  1, 2, and 18, a program for realizing the function of the signal processing unit 20 is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into a computer system, By performing the signal processing, the signal processing may be performed to detect the direction including the angle range setting processing shown in FIGS. 11 to 14 for calculating the angle range including the azimuth of the incoming wave. Here, the “computer system” includes an OS and hardware such as peripheral devices. The “computer system” includes a WWW system having a homepage providing environment (or display environment). The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Further, the “computer-readable recording medium” refers to a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included.

  The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

It is a block diagram which shows the structural example of the electronic scanning radar apparatus by embodiment of this invention. It is a block diagram which shows the structural example of the signal processing part 20 in the electronic scanning radar apparatus by the 1st Embodiment of this invention. It is a conceptual diagram explaining the production | generation of the beat signal in the rising area and falling area of a triangular wave with a transmission wave and a received wave. It is a conceptual diagram explaining the received wave in a receiving antenna. It is the result of frequency-decomposing the beat signal, and is a graph showing the beat frequency (horizontal axis) and its peak value (vertical axis). It is a graph for demonstrating the narrowing-down process of the angle range which calculates the MUSIC spectrum using DBF process. It is a graph for demonstrating the narrowing-down process of the angle range which calculates the MUSIC spectrum using DBF process. It is a flowchart for demonstrating the process of MUSIC. It is a flowchart which shows the substep performed in step S103 of the flowchart of FIG. It is a conceptual diagram explaining the process at the time of calculating the spatial average of a correlation matrix. 10 is a flowchart for explaining in detail the process of estimating the number of incoming waves performed in step S105 of FIG. 8. It is a flowchart explaining in detail the other estimation process of the number of incoming waves performed in step S105 of FIG. It is a flowchart explaining in detail the other estimation process of the number of incoming waves performed in step S105 of FIG. It is a flowchart explaining in detail the other estimation process of the number of incoming waves performed in step S105 of FIG. It is a flowchart explaining in detail the other estimation process of the number of incoming waves performed in step S105 of FIG. It is a graph which shows a response | compatibility with the eigenvalue in each distance and the distance in case an incoming wave is 1. FIG. It is a graph which shows a response | compatibility with the eigenvalue in each distance and the distance in case an incoming wave is 2. FIG. It is a block diagram which shows the structural example of the signal processing part 20 in the electronic scanning radar apparatus by the 2nd Embodiment of this invention.

Explanation of symbols

11, 1 n, receiving antenna 21, 2 n, mixer 3, transmitting antenna 4, distributor 51, 5 n, filter 6, SW
7 ... ADC
8 ... Control unit 9 ... Triangle wave generation unit 10 ... VOC
DESCRIPTION OF SYMBOLS 20 ... Signal processing part 21 ... Memory 22 ... Frequency decomposition processing part 23 ... Peak detection part 24 ... Peak combination part 25 ... Distance detection part 26 ... Speed detection part 27, 27B ... Pair determination part 28 ... Correlation matrix calculation part 30 ... Direction Detection unit 31 ... Eigen value calculation unit 32 ... Determination unit 33 ... DBF processing unit 34 ... Ch deletion unit 35 ... IDBF processing unit 36 ... Range detection unit 50 ... Angular range setting unit

Claims (9)

An electronic scanning radar device mounted on a moving body,
A transmission unit for transmitting a transmission wave;
A receiver configured with a plurality of antennas for receiving an incoming wave that is a reflected wave from a target of the transmission wave;
A beat signal generating unit that generates a beat signal having a frequency difference between the transmitted wave and the reflected wave;
A frequency decomposition processing unit that calculates complex number data by frequency-decomposing the beat signal into beat frequencies of a predetermined number of decompositions in time series;
An angle range setting unit for calculating an angle range in which a target exists from the complex data;
An electronic scanning radar apparatus comprising: an azimuth detecting unit that calculates an angle spectrum within the angle range. The angle range setting unit is
A DBF processing unit that performs digital beam forming on the complex number data in the antenna array direction, calculates a spectrum intensity for each angle channel, detects the presence of the target, and obtains orientation information;
The electronic scanning radar apparatus according to claim 1, further comprising: a range detection unit configured to set an angle range for calculating an angle spectrum based on data of a frequency axis on which the target exists and azimuth information. The angle range setting unit is
A channel that is divided into a plurality of groups according to the presence / absence of a target in the angle channel direction based on the spectrum intensity for each angle channel calculated by the DBF processing unit, and the spectrum intensity of the angle channel where no target exists is “0”. Delete part,
An IDBF processing unit that performs IDBF on the spectrum intensity for each angle channel, returns the complex intensity data to each antenna, and outputs the complex data as reproduction complex data;
3. The electronic device according to claim 2, wherein the range detection unit sets an angle range for calculating an angle spectrum based on the reproduction complex number data, direction information in which a target exists, and a received wave number estimation value. Scanning radar device. The angle range setting unit is
A channel that is divided into a plurality of groups according to the presence / absence of a target in the angle channel direction based on the spectrum intensity for each angle channel calculated by the DBF processing unit, and the spectrum intensity of the angle channel where no target exists is “0”. Delete part,
An IDBF processing unit that performs IDBF on the spectrum intensity for each angle channel, returns the complex intensity data for each antenna, and outputs it as reproduction complex data; and the azimuth detection unit estimates the reproduction complex data and the received wave number estimation. The electronic scanning radar apparatus according to claim 2, wherein a solution corresponding to an angle is calculated based on the value. The angle range setting unit is
A channel that is divided into a plurality of groups according to the presence / absence of a target in the angle channel direction based on the spectrum intensity for each angle channel calculated by the DBF processing unit, and the spectrum intensity of the angle channel where no target exists is “0”. Delete part,
An IDBF processing unit for performing IDBF on the spectrum intensity for each angular channel, returning the complex intensity data for each antenna, and outputting it as reproduction complex data;
Storage means for storing the orientation information of each target in the past orientation detection cycle,
The range detection unit sets an angle range for calculating an angle spectrum based on the reproduction complex number data, azimuth information of past azimuth detection cycles stored in the storage means, and a received wave number estimation value. 3. The electronic scanning radar apparatus according to claim 2, wherein   6. The electronic scanning radar apparatus according to claim 2, wherein the received wave number estimation value is a fixed value. The angle range setting unit is
Peak detection means for detecting the presence of a target from the peak value of the intensity of the frequency axis,
Storage means for storing the orientation information of each target in the past orientation detection cycle;
A range detection unit that limits the angle range based on azimuth information of past azimuth detection cycles stored in the storage unit and writes the obtained angle range to the storage unit. The electronic scanning radar device according to 1. A received wave direction estimation method by the electronic scanning radar apparatus according to any one of claims 1 to 7, which is mounted on a moving body,
A transmission process in which the transmitter transmits a transmission wave;
A reception process in which a reception unit includes a plurality of antennas that receive an incoming wave that is a reflected wave from the target of the transmission wave;
A beat signal generation process in which a beat signal generation unit generates a beat signal having a frequency that is a difference between the transmission wave and the reflected wave;
A frequency resolving process in which a frequency resolving unit calculates complex number data by frequency resolving the beat signal in time series into a predetermined number of beat frequencies.
An angle range setting process in which an angle range setting unit calculates an angle range in which a target exists from the complex number data; and
An azimuth detecting process in which an azimuth detecting unit calculates an angle spectrum within the angle range. A program for causing a computer to control the operation of estimating the direction of a received wave by the electronic scanning radar apparatus according to any one of claims 1 to 7 mounted on a moving body,
A transmission process in which the transmission unit transmits a transmission wave;
A reception process in which a reception unit receives an incoming wave that is a reflected wave from a target of the transmission wave by a plurality of antennas;
Beat signal generation processing in which a beat signal generation unit generates a beat signal having a difference frequency between the transmission wave and the reflected wave;
A frequency resolving process in which a frequency resolving unit calculates the complex number data by frequency resolving the beat signal into beat frequencies of a predetermined number of resolving times in time series;
An angle range setting process in which an angle range setting unit calculates an angle range in which a target exists from the complex number data;
A received wave direction estimation program, comprising: an azimuth detecting unit that calculates an azimuth spectrum within the angle range.

Images