JP2010060353A - Radar system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To obtain a radar system for preventing a clutter from lingering by estimating a clutter center frequency corrected in a Doppler frequency when there occurs the folding of the Doppler frequently of the clutter. <P>SOLUTION: The radar system includes: a clutter estimating data extraction means 1 for extracting a signal for estimating the number of clutters and a center frequency from a reception signal of a pulse radar; a clutter number determination means 2 for estimating the number of clutters received from the extracted signal; a switch 3 for switching contents of clutter suppression carried out from results of determination of the number of clutters; a folding-corrected clutter center frequency estimation means 4 for using the extracted signal and results of determining the number of clutters and estimating the clutter center frequency by avoiding an estimation error occurring when the frequency folding of the clutter takes place; and a clutter suppression part 5 in which processing contents are determined depending on the number of clutters by the switch, the clutter center frequency estimation value transferred from the folding-corrected clutter center frequency estimation means is inputted and the clutter in the reception signal is suppressed. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a radar apparatus that detects a target by irradiating a target with radio waves and suppressing clutter (for example, unnecessary reflected echo due to ground, sea surface, clouds, rain, etc.) contained in a received signal. It is.

  The function of suppressing the clutter contained in the received signal of the pulse radar is a function necessary for the radar apparatus to accurately detect the target. In the conventional clutter suppression device, in addition to the fixed clutter eraser that suppresses the fixed clutter such as the ground clutter and the sea clutter, the moving clutter included in the output signal of the fixed clutter eraser looks like a fixed clutter. Therefore, an amplitude / phase corrector that corrects the amplitude and phase of the output signal, and a moving clutter included in the output signal of the amplitude / phase corrector (for example, a reflection source such as a weather clutter) A moving clutter eraser that suppresses (moving clutter) is mounted (for example, see Patent Document 1).

  The fixed clutter eraser constituting the conventional clutter suppression device suppresses clutter from a reflection source that does not move, such as a ground clutter or a sea clutter, or from a reflection source that is very slow. Therefore, if the received signal of the pulse radar includes a moving clutter such as a weather clutter, the clutter cannot be suppressed only by the fixed clutter canceller.

A conventional clutter suppression device is equipped with a moving clutter canceller so that a moving clutter such as a weather clutter can be suppressed. The actual situation of the fixed clutter canceller and the moving clutter canceller is a high-pass filter having a notch frequency of “0”. Examples are a single eraser with a transfer function of 1-z- ¹ and a double eraser with (1-z- ¹ ) ² .

  The fixed clutter canceller suppresses static clutter from the ground surface or building where the clutter Doppler center frequency is 0 (hereinafter, unless otherwise specified, the frequency means the Doppler frequency). On the other hand, the moving clutter canceller suppresses moving clutter that is a reflected wave from a moving object such as rain and clouds whose clutter center frequency is not zero. Since the center frequency of the moving clutter varies depending on weather conditions if it is a reflection echo from rain or clouds, it is necessary to estimate the value from the received signal.

  At this time, the frequency range in which the center frequency of the moving clutter is observed is limited to a pulse repetition frequency (hereinafter referred to as PRF: Pulse Repetition Frequency) according to the sampling theorem. In general, in search applications, radars with low PRF are often used to avoid false detection of target distances. On the other hand, the Doppler frequency folding is an allowable radar mode. A reflected wave having a frequency exceeding the PRF is observed at a frequency value different from the actual frequency due to so-called frequency folding. Therefore, it is conceivable that the frequency of the moving clutter is turned back depending on the weather conditions and the speed of the moving reflecting object.

  The clutter suppression processing by the filter is executed by moving the clutter center frequency using the estimated clutter center frequency or by adjusting the filter coefficient so that the filter stop band matches the clutter center frequency. Therefore, when the frequency folding of the clutter occurs, the clutter cannot be suppressed because the filter stop band and the actual clutter center frequency are different.

Japanese Patent Publication No. 3-2433

  Since the conventional radar apparatus is configured as described above, if the Doppler center frequency of the moving clutter is received outside the band defined by the pulse repetition frequency, the clutter center frequency cannot be estimated correctly. There is a problem that the target detection becomes difficult due to the disappearance of the clutter.

  The present invention has been made to solve the above-described problems. Even when the Doppler frequency aliasing of the clutter included in the received signal of the pulse radar occurs, the clutter center frequency estimation process for correcting this is performed. An object of the present invention is to obtain a radar device that can prevent the disappearance of clutter.

  The radar apparatus according to the present invention includes a clutter estimation data extraction means for extracting a signal for estimating the number of clutters and a center frequency thereof from a received signal of a pulse radar, and a signal selected by the clutter estimation data extraction means. Selected by the clutter number determination means for estimating the number of clutters received from the clutter, a switch for switching the contents of the clutter suppression processing performed from the clutter number determination result of the clutter number determination means, and the clutter estimation data extraction means. And the clutter number determination result of the clutter number determination means, the clutter center frequency estimating means with aliasing correction for estimating the clutter center frequency by avoiding the estimation error that occurs when the frequency aliasing of the clutter occurs, and The processing content is determined by the switcher according to the number of clutter, and the aliasing correction is performed. Enter the clutter central frequency estimation value transferred from the clutter central frequency estimation means, in which a filter processing unit for suppressing clutter in the received signal.

  According to the present invention, when the Doppler frequency aliasing of the clutter included in the received signal of the pulse radar occurs, the clutter center frequency estimation process that corrects this can be performed to prevent the clutter from disappearing.

Embodiment 1 FIG.
FIG. 1 is a block diagram showing a configuration of a radar apparatus according to Embodiment 1 of the present invention. The radar apparatus shown in FIG. 1 is selected by a clutter estimation data extracting means 1 for extracting a signal for estimating the number of clutters and its center frequency from a received signal of a pulse radar, and a clutter estimation data extracting means 1. Clutter number determination means 2 for estimating the number of clutters received from the signal, a switcher 3 for switching the contents of the clutter suppression processing performed from the clutter number determination result of the clutter number determination means 2, and clutter estimation data extraction means 1 The clutter center frequency estimation means with aliasing correction that estimates the clutter center frequency by avoiding the estimation error that occurs when the frequency aliasing of the clutter occurs by using the signal selected by the above and the clutter number judgment result of the clutter number judgment means 2 4 and the switching device 3 determines the processing contents according to the number of clutters, and the clutter with alias correction. A clutter suppression processing unit 5 that performs clutter suppression processing as a filter processing unit that receives the clutter center frequency estimation value transferred from the heart frequency estimation means 4 and suppresses clutter in the received signal. Thus, the clutter suppression processing unit 5 includes a unimodal clutter suppression filter 6 that is selected when the number of clutters is 1 and a bimodal clutter suppression filter 7 that is selected when the number of clutters is 2.

  FIG. 2 is a block diagram showing the relationship between the internal configuration of the clutter center frequency estimating means 4 with aliasing correction and the input signal. In FIG. 2, 10 is a received signal after the one-dimensional time-series signal received by the receiver is converted into two-dimensional data by the range bin number and the pulse hit number, and 11a and 11b are the clutter number determination process and A part of the received signal used for the clutter center frequency estimation processing is shown. The clutter center frequency estimation means 4 with aliasing correction is performed by the clutter estimation data extraction means 1 according to the clutter number determination result of the clutter number determination means 2. Clutter center frequency estimation means 12 for estimating the clutter center frequency by the maximum entropy method or the like using the selected signal, and correcting the frequency aliasing of the estimated clutter center frequency estimation value. Transfer from the clutter center frequency estimation means 12 It is possible to assume the clutter center frequency estimation value considering the clutter frequency aliasing. Clutter center frequency correction means 13 for calculating a correction value of a clutter center frequency estimation value, and data used to determine an appropriate estimation from among estimation value candidates transferred from the clutter center frequency correction means 13 Extracted, estimated value determining range bin extracting means 14 for selecting a range bin for performing suppression processing for determining the optimum value of the corrected clutter center frequency, and a signal transferred from the estimated value determining range bin extracting means 14 On the other hand, the clutter suppression processing is performed for each estimated value transferred from the clutter center frequency correcting means 13, and the simple clutter suppression processing unit 15 that performs the clutter suppression processing on the range bin extracted for estimation value determination; The clutter center frequency corrected as the optimum clutter center frequency estimate is determined from the result of the simple clutter suppression processing unit. And a best estimate selection means 16 for.

  FIG. 3 is a conceptual diagram for explaining the operation of the radar apparatus according to Embodiment 1 of the present invention. Next, the operation of the radar apparatus according to Embodiment 1 of the present invention will be described. In the first embodiment, description will be made assuming that the radar apparatus transmits pulses at equal intervals.

  As shown in FIG. 2, the radar received signal 10 can be expressed as a two-dimensional multi-cell data group formed from a range bin number and a pulse hit number. The range bin represents the distance from the radar, and the pulse hit represents the time elapsed in each range bin. Since the received signal is coherent in the pulse hit direction, the clutter suppression processing by the filter is performed in the pulse hit direction by one range bin. The same applies to the clutter center frequency estimation process. However, in a radar device used for search, it is necessary to detect a target from an unknown direction by shaking a beam at high speed, and the time for forming a reception beam in the same direction is short. Therefore, in most cases, a sufficient number of pulse hits cannot be obtained, and in frequency analysis using FFT or the like, the observation time is too short and sufficient estimation accuracy cannot be obtained.

  Therefore, processing using the characteristics of the clutter spatial distribution has been conventionally performed. In search radar, the distance width of one range bin is often about several tens of meters, and moving clutter, which is a reflected wave from a wide range of objects such as rain and clouds, is received over many range bins. . Therefore, in order to improve the clutter center frequency estimation accuracy, the clutter center frequency estimation process is performed from the reception signals 11a and 11b of the plurality of range bins using the spatial spread of the clutter as shown in FIG. ing.

  The power spectrum of the moving clutter can be expressed by a Gaussian function. As one of the methods for approximating a Gaussian spectrum, a method using an AR (Auto Regressive) model is well known, and several algorithms based on the maximum entropy method have been proposed as a calculation method. Since the maximum entropy method enables accurate spectrum estimation with a small number of data, the first embodiment is based on the clutter center frequency estimation method based on the maximum entropy method.

  As shown in FIG. 1, the received signal is branched and transferred to the clutter estimation data extraction means 1. FIG. 2 shows the relationship between the data extracted by the clutter estimation data extraction means 1 and the received signal. If the range bin number for performing the clutter suppression process is k, a plurality of surrounding range bins (the shaded portion) are selected as data for clutter estimation. At this time, the processing range bin and the clutter estimation range bin group are spaced apart by Δk. This is a process for reducing the influence of the range side lobe of the target signal. If the influence can be ignored, Δk may not be provided.

  The signal data extracted by the clutter estimation data extraction means 1 is first transferred to the clutter number determination means 2. The number of clutter referred to here is the number of peaks in the clutter spectrum, and the case where there is one large peak is called unimodal, and the case where there are two large peaks is called bimodal. In general, different types of clutter often exhibit different Doppler frequencies, so here the number of clutters is 1 for unimodal and 2 for bimodal.

As a method for determining the number of clutter, Sekiguchi and Harazawa described in “Adaptive Clutter Suppression by Combination of Multiple Notch Filters that Simplifies the Determination of Clutter Spectrum Single- and Double-peaks” (Science Technical Report SANE2002-5). There is a way. In this method, the number of clutters is estimated by comparing the absolute values of the poles of the AR model. The transfer functions H ₁ (z) and H ₂ (z) of the first-order AR model and the second-order AR model can be expressed as equations (1) and (2) using the AR coefficient.

Here, a ₁₁ is a primary AR coefficient, and a ₂₁ and a ₂₂ are secondary AR coefficients. The pole is the root of the denominator polynomial in the transfer function equations (1) and (2) of the AR model. The magnitude of the absolute value of this pole is closely related to the received clutter power, and the number of clutters is estimated as follows using the characteristics.

(A) When the pole of the primary AR model is small → no clutter exists (b) When the pole of the primary AR model is large and one of the poles of the secondary AR model is large → Unimodal clutter (c) 2 Both the poles of the next AR model are large → bimodal clutter. That is, the number of clutters is estimated by comparing the sizes of the poles of the AR model with each other.

  When the clutter number determination processing is completed, the reception signal extracted by the clutter estimation data extraction unit 1 is transferred to the clutter center frequency estimation unit 4 with aliasing correction. This signal data is input to the clutter center frequency estimation means 12. The center frequency of the clutter is estimated from the clutter number determination result transferred from the clutter number determination means 2 and the AR coefficient. The estimation method is as follows according to the determination result of the number of clutters.

(A) When the number of clutters is 1 (unimodal) A primary AR model is used. The transfer function H ₁ (z) using the AR coefficient a ₁₁ can be expressed by Expression (1). The center frequency f _{01 of the} clutter can be estimated from the phase term of the AR coefficient and can be obtained by the following equation.

  Here, Re represents extraction of the real part of the complex number, and Im represents extraction of the imaginary part of the complex number.

(B) When the number of clutters is 2 (bimodal) A secondary AR model is used. The transfer function H ₂ (z) using the AR coefficients a ₂₁ and a ₂₂ can be expressed by Expression (3). The center frequencies f ₀₁ and f ₀₂ of the clutter can be estimated from the phase term of the AR coefficient and can be obtained by the following equation.

Here, p _i is obtained from the following equation.

At this time, in order to suppress variations in the estimated value f _0i (r) of the clutter center frequency at the range bin number r, the received signal is divided into blocks of several tens of range bins (average processing range bin) as shown in Expression (7). Separately, the clutter center frequency calculated for each range bin is averaged within the range bin block.

  Here, i is a clutter identification number, B is the number of range bins in one block, and b is a block number.

  The clutter center frequency estimated by equation (7) is transferred to the clutter center frequency correction means 13.

In the processing so far, it is not known whether or not frequency folding has occurred at the center frequency of the clutter. FIG. 3 shows an image when the number of clutters is 2 and one of the clutters is folded. In the figure, the horizontal axis represents the Doppler frequency, and the vertical axis represents the spectral intensity. In the first embodiment, since radar that performs equal-interval pulse transmission is considered, avPRF, which means an average value of PRF, is equal to PRF. In FIG. 3, since the clutter 1 exists within ± avPRF / 2, the value estimated by the clutter center frequency estimation method corresponds to the clutter center frequency f ₁ actually received. On the other hand, since the clutter 2 has a Doppler frequency exceeding avPRF / 2, the value estimated by the clutter center frequency estimation method indicates the folded negative Doppler frequency region f ₂ −avPRF.

  If the received clutter frequency is known, it can be corrected to the value before folding, but it is actually difficult to know it.

Therefore, the clutter center frequency correction means 13 performs a correction process of the clutter center frequency estimation value in consideration of all possible possibilities. Assuming that the clutter center frequency is turned back within one time, the combination of the clutter 1 and clutter 2 center frequency estimation values and the clutter center frequency correction values f _c1 and f _c2 are as follows.

(A) No clutter 1 and clutter 2 are folded back. F _c1 = f ₁ , f _c2 = f ₂
(B) Clutter 1 is not folded, and clutter 2 is folded f _c1 = f ₁
f _c2 = f ₂ −avPRF (when f ₂ > 0)
f _c2 = f ₂ + avPRF (when f ₂ <0)
(C) Clutter 1 is folded, and clutter 2 is not folded f _c1 = f ₁ −avPRF (when f ₁ > 0)
f _c1 = f ₁ + avPRF (when f ₁ <0)
f _c2 = f ₂
(D) Both clutter 1 and clutter 2 are folded back. F _c1 = f ₁ −avPRF (when f ₁ > 0)
f _c1 = f ₁ + avPRF (when f ₁ <0)
f _c2 = f ₂ −avPRF (when f ₂ > 0)
f _c2 = f ₂ + avPRF (when f ₂ <0)

  If the correct combination is selected from among (a) to (d) and the clutter suppression process is performed, the suppression performance can be ensured regardless of the presence or absence of the folded clutter.

  In the determination of the optimal combination among the above (a) to (d), the clutter suppression process is performed in each case, and the combination having the best suppression performance is selected. The estimated value determination range bin extracting means 14 selects signal data for the suppression processing. Specifically, it is selected and extracted from a plurality of range bin groups selected by the clutter estimation data extraction means 1. Considering the processing load, it is better to select one range bin showing the largest received power and compare the results of suppression processing using only that range bin, but select multiple range bins with higher received power and perform suppression processing. It is realistic to average the output signal power among the selected range bins. That is, one or a plurality of range bins indicating a large received power are extracted from the range bins where clutter exists. If it is not acceptable to search for a range bin showing the maximum power, simply select the range bin located in the center of the plurality of range bins or the range bin closest to the radar. The range bin received signal may be selectively extracted.

  The signal data selected by the estimated value determination range bin extracting means 14 is transferred to the simple clutter suppression processing unit 15. Here, the clutter suppression processing is performed for all cases (a) to (d). However, since it is only necessary to compare the amount of clutter suppression, it is not always necessary to completely suppress clutter. Therefore, in consideration of the calculation load, the simple clutter suppression processing unit 15 may use a third-order filter for each of the clutter 1 and the clutter 2. Assuming that a transversal type FIR filter is used for the clutter suppression processing as the clutter suppression filter, each filter coefficient is calculated according to the following equation.

Here, M1, M2 are each clutter 1, the filter for clutter 2 suppression degree, _{h m} is the filter coefficient of the notch frequency 0 which is designed to achieve the desired amount of _{suppression, b1 m (k), b2} m ( k) represents an adjusted filter coefficient for suppressing clutter 1 and clutter 2, respectively, and k represents a processing range bin number for performing suppression processing.

  In order to suppress bimodal clutter, it is only necessary to connect a filter for suppressing each clutter independently. At this time, the order in which the filters are cascade-connected is irrelevant. Assuming that the suppression processing is performed in the order of filter 1 and filter 2, the suppression processing can be expressed by the following equation.

Here, x _k (n) is a received signal of range bin number k, and y1 _k (n) and y2 _k (n) are filter output signals for suppressing clutter 1 and clutter 2 respectively. The filter orders M1 and M2 are preferably set according to the received power and bandwidth of each clutter, but may be fixed at about 2 to 3 as described above.

Using the filters set in this manner, the suppression process is performed for each combination of clutter center frequency estimation values classified in the cases (a) to (d) described above. At this time, the average output power of each filter output signal at the final stage is defined as P _a , P _b , P _c , and P _d . If the signal data selected by the estimation value determination range bin extracting means 14 is a plurality of range bins, the average value of the output signal power of each range bin is obtained.

Next, the output signal average power values P _a , P _b , P _c , and P _d , which are processing results of the simple clutter suppression processing unit 15, are transferred to the optimum estimated value selection means 16. Here, the P _a , P _b , P _c , and P _d are compared with each other to search for a minimum value, and it is determined that the combination of the clutter center frequencies from which the result is obtained is a correct estimated value. For example, when _Pb is the minimum value, it is determined that the combination (b), that is, clutter 1 is not folded and clutter 2 is folded is correct. As a result, I transfers the f _c1 without reflection correcting the center frequency estimate of the clutter 1, a f _c2 in which the reflection correcting the center frequency estimate of the clutter 2 clutter suppression processing section 5.

  The clutter suppression processing unit 5 performs clutter suppression processing according to the clutter center frequency estimated value transferred from the optimum estimated value selection means 16 and the control signal transferred from the switch 3. The clutter suppression processing unit 5 includes a unimodal clutter suppression filter 6 and a bimodal clutter suppression filter 7. The unimodal clutter suppression filter 6 is a transversal type FIR filter, similar to the simple clutter suppression processing unit 15. The bimodal clutter suppression filter 7 has a configuration in which two transversal FIR filters are connected in cascade. Alternatively, the unimodal clutter suppression filter may be replaced with a filter connected in front of the bimodal clutter suppression filter 7. In other words, in the case of unimodal clutter, the output of the previous stage of the bimodal clutter suppression filter 7 may be used as the output signal of the clutter suppression processing unit 5 as it is.

  As described above, according to the first embodiment of the present invention, even when the Doppler frequency aliasing of the clutter, which is difficult in the clutter suppression processing in the conventional radar apparatus, occurs, the clutter center frequency estimation is corrected. By performing the processing, it is possible to always ensure the clutter suppression performance.

  In the above description, the case where the clutter is bimodal and has two spectral peaks is described, but the same applies to the case of unimodality. In the case of unimodality, the combination of clutter center frequencies considered by the clutter center frequency estimation means 4 with aliasing correction is only two patterns with and without aliasing.

Further, when the clutter in the received signal has three or more spectral peaks, if the Doppler frequency indicating each peak can be estimated, the clutter can be suppressed as in the first embodiment. Instead, a combination of considered clutter center frequency reflection correcting with clutter center frequency estimating means 4 is increased in two ways ^3. However, if the spectrum peak of the clutter is 3 or more, there is a concern that the probability that the pole of the denominator of the transfer function approximating the clutter spectrum by the AR model corresponds to the center frequency of the clutter is reduced. The frequency estimation process may be complicated.

  Further, although the transversal type FIR filter is used as the suppression filter of the simple clutter suppression processing unit 15 and the clutter suppression processing unit 5 in the above description, an IIR type filter or a lattice type filter may be used. However, when an IIR type filter is used, attention should be paid to the fact that the influence of the transient response of the filter occurs and the phase linearity is not necessarily guaranteed.

Embodiment 2. FIG.
In the first embodiment described above, the radar apparatus having the same pulse radio wave transmission interval has been described. However, in the second embodiment, a radar apparatus to which a stagger trigger system that repeatedly transmits pulse radio waves at several different transmission intervals is applied. Will be described. The stagger trigger method is used to avoid a blind phenomenon in which the Doppler frequency of the target signal is turned back and included in the filter stop band. When the stagger trigger method is applied, the transmission pulse interval becomes unequal, so the pulse hit interval in the received signal becomes unequal. In order to suppress a specific frequency component by using a filter for such a signal, it is desirable to use a time-varying coefficient filter having different coefficients at different pulse intervals.

  FIG. 4 is a block diagram showing the clutter suppression processing unit 20 of the radar apparatus according to the second embodiment of the present invention. The clutter suppression processing unit 20 replaces the clutter suppression processing unit 5 in the first embodiment. The clutter suppression processing unit 20 shown in FIG. 4 includes a unimodal clutter suppression time varying filter 21 and a bimodal clutter suppression time varying filter 22, and filters the received signal according to the control signal transferred from the switch 3. To process.

  FIG. 5 is a block diagram showing a time-varying coefficient transversal FIR filter as the bimodal clutter suppression time-varying filter 22. The bimodal clutter suppression time-varying filter 22 shown in FIG. 5 includes delay units 30 to 30 + M−1 that give a delay time corresponding to each pulse interval in consideration of different pulse intervals, and received signals or delay units 30 to 30 + M−. 1 is a time-varying filter that calculates a time-varying filter coefficient by using an estimated value transferred from a complex multiplier 40 to 40 + M that multiplies a signal transferred from 1 and a time-varying filter coefficient and a clutter center frequency estimating means 4 with aliasing correction The coefficient calculating means 50 and a complex adder 51 for calculating the sum of signals transferred from the complex multipliers 40 to 40 + M.

  FIG. 6 is a block diagram showing a clutter suppression filter in which a plurality of primary time-varying filters are cascade-connected as the bimodal clutter suppression time-varying filter 22. The bimodal clutter suppression time varying filter 22 shown in FIG. 6 uses the estimated values transferred from the primary time varying filters 70 to 72 and the clutter center frequency estimating means 4 with aliasing correction. Time-varying filter coefficient calculating means 60 to 62 for calculating the coefficient of the filter.

  As shown in FIG. 4, the clutter suppression processing unit 20 in the second embodiment includes a unimodal clutter suppression time varying filter 21 and a bimodal clutter suppression time varying filter 22, and is transferred from the switch 3. The received signal is filtered according to the control signal. Here, the unimodal clutter suppression time varying filter 21 is a time varying coefficient transversal FIR filter shown in FIG. 5, and the bimodal clutter suppressing time varying filter 22 is a time varying coefficient shown in FIG. The filter has the same configuration as the transversal type FIR filter, and forms a notch at two frequencies. Alternatively, two time-varying coefficient transversal FIR filters shown in FIG. 5 are connected in cascade. At this time, different filter coefficients and orders are used for the respective filters.

  Next, the operation of the radar apparatus according to Embodiment 2 of the present invention will be described. In the second embodiment, the operations other than the clutter suppression processing unit 20 are the same as those in the first embodiment, and therefore only the differences in the operation will be described.

The L types of pulse interval _{PRI 1, PRI 2, ···,} PRI L, PRI 1, PRI 2, the radar signal received by Staggered PRF varied repeatedly ... and x _k (t _n) I will represent it. Here, t ₁ = PRI ₁ , t ₂ = PRI ₁ + PRI ₂ ,..., T _L = PRI ₁ + PRI ₂ +... + PRI _L , t _{L + 1} = t _L + PRI ₁ ,. .

  The time-varying filter that constitutes the clutter suppression processing unit 20 can easily understand the configuration in which the clutter is suppressed by one filter regardless of the number of clutters for the convenience of the coefficient calculation method. A case where both the peak clutter suppression time-varying filter 22 are realized by one filter will be described.

  The time-varying filter coefficient calculation unit 50 calculates the filter coefficient by the following method. Since the case where the clutter is unimodal is included in the case of the bimodality, it is assumed here that the clutter is bimodal having two spectral peaks. The equivalent amplitude square characteristic of the stagger trigger method is defined as follows.

Here, N is the impulse response length of the filter, h _ln is the impulse response (filter coefficient) of the filter, L is the number of staggers (number of types of different pulse intervals), τ _i (i = 1, 2,..., N ) Is a delay time considering different pulse intervals.

The time δT is defined as in equation (14), and the delay operator z ⁻¹ is an element that gives a delay of δT.

However, PRI _l (l = 1, 2,..., L) is a pulse interval, and R _l (l = 1, 2,..., L) is a stagger ratio.

At this time, since δT indicates the stagger ratio 1, the delay operator for the delay of the stagger ratio R is expressed as z ^−R . Therefore, the transfer function C ₁ (z) of the equation (13) is expressed as the following equations (16) to (18).

Here, it is assumed that coefficients of a filter having M ₁ zeros and M ₂ zeros are calculated for the two clutter center frequencies f ₀₁ and f ₀₂ transferred from the clutter center frequency estimating means 4 with aliasing correction. .

First, with l fixed, the transfer function C _l (z) and the first derivative with respect to z of the transfer function C _l (z) to the (M ₁ -1) th derivative are expressed as z ₁ = exp [ Generate an equation that becomes 0 when j2πf ₀₁ δT].

Further, when the transfer function C _l (z) and the (M ₂ −1) -order derivative from the first-order derivative of z of the transfer function C _l (z) are z ₂ = exp [j2πf ₀₂ δT]. Generate an equation such that 0 is zero.

As a result, M ₁ + M ₂ = (N−1) equations are generated.

The time-varying filter coefficient calculation means 50 calculates a filter coefficient for a specific l by solving the (N-1) linear simultaneous equations. Although there is one equation less than the coefficient, if h _l0 is arbitrarily determined, the filter coefficient for a specific l can be calculated. If such filter coefficient calculation processing is repeatedly performed for l = 0, 1,..., L−1, all filter coefficients h _ln can be obtained.

The filter coefficient h _ln calculated as described above is transferred to the complex multipliers 40 to 40 + M. The complex multipliers 40 to 40 + M multiply the received signal that has passed through the delay units 30 to 30 + M−1 and the filter coefficient h _ln and transfer the result to the complex adder 51. The complex adder 51 performs addition of the complex signals transferred from the complex multipliers 40 to 40 + M, and transfers them as filter output signals.

In particular. By performing the convolution operations shown in equations (27) to (29), clutter included in the received signal x _k (t _n ) is suppressed. Note that y2 _k (t _n ) is an output signal of the bimodal clutter suppression time varying filter 22.

In the above description, the case of a bimodal clutter has been shown, but in the case of a unimodal clutter, the condition relating to the derivative of C _l (z) needs to be considered only for the equations (19) to (22).

When two clutters are suppressed by one time-varying filter, the number of simultaneous equations derived from the equations (19) to (26) becomes the sum of the filter orders assigned to the two clutters. Finding the filter coefficient by solving these simultaneous equations is equivalent to finding the inverse matrix of the (M ₁ + M ₂ ) -th order matrix, so that a heavy load is imposed on the radar device that performs real-time processing.

Therefore, a case will be described below where bimodal clutter is suppressed by a filter having two time-varying filters connected in cascade. Assume that the received signal is x _k (t _n ), the output signal of the preceding filter, that is, the input signal of the succeeding filter is u _k (t _n ), and the output signal of the succeeding filter is y 2 _k (t _n ). Further, the stagger number L is “3”, the preceding stage filter coefficients calculated by the time-varying filter coefficient calculating means 50 are h _n0 , h _n1 , h _n2 , h _n3 , and the _subsequent stage filter coefficients are g _n0 , g _n1 , g. Let _n2 and _gn3 . In this case, the output signal y2 _k (t _n ) of the subsequent time-varying filter at a certain time can be expressed by the following equation.

  From this, assuming that “*” is a symbol representing a convolution operation of two vectors surrounded by [], the impulse response when two time-varying coefficient filters are connected in cascade is as follows: . However, “+” in Expression (34) is a symbol representing vector addition when a numerical sequence obtained as a result of the convolution operation is regarded as a vector.

Next, the filter order of the preceding stage is M ₁ , the filter order of the _subsequent stage is M ₂ , and the result of the convolution operation obtained is e _lk (l = 0, 1,..., L−1; n = 0, 1 _,. .., M ₁ + M ₂ ), the above equation (34) can be expanded to a general form and expressed as the following equation.

In the equation (35), since the pulse interval periodically changes with the stagger number L, the filter coefficients g ₀₀ , g ₀₁ ,..., G _{0 at} times t ₀ , t _L , t _{2L,. , M2} correspond, the time _{t 1, t L + 1,} t 2L + 1, the ..., filter coefficients _{g 10, g 11, ···,} g 1, M2 corresponds, time _t 2, _the Filter coefficients g ₂₀ , g ₂₁ ,..., g _{2, M 2} correspond to t _{L + 2} , t _{2L + 2,.}

  At this time, the equivalent amplitude square characteristic E (f) of the filter is expressed by the following equation.

Next, a method for moving the notch frequency of the filter whose notch frequency is “0” designed in advance to an arbitrary frequency will be described. It is assumed that the filter coefficient of the notch frequency 0 used in the preceding time-varying filter is h ′ _ln , and the destination notch frequency is f ₀₁ . At this time, h _ln is recalculated as follows.

When the time δT is defined as in the above equation (14) and the delay operator z ⁻¹ is an element that gives a delay by the time δT, the transfer function of the entire filter when the time-varying coefficient filters are cascaded is It can be expressed as:

  When the exponent part of the equation (41) is replaced by the following equation (44), the equation (41) becomes the following equation (43).

  Further, when formula (35) is substituted into formula (43) and rearranged, formula (45) below is obtained.

However, m ₁₀ = 0.

In the transfer function E _l (z) of the equation (45), a derivative with respect to z is obtained, and when the transfer function E _l (z) is set to “0” in the following equation (46), that is, the equation ( 47) If - (50) is so established, it is possible to have a _{M 2} double zeros in f = f _02.

The derivative of the transfer function E _l (z) can be obtained from the following equations (51) to (53).

This generates M ₂ equations that are one less than the number of filter coefficients. To solve the M ₂ equations, g _l0 may be arbitrarily determined. Further, the two filters connected in cascade, in order to receiver noise power across the filter input and output so as not to change, the filter coefficients constants c _l of the following formula _{(54) g l0, g l1} , · .., G _{l, M2} may be multiplied.

  If the filter coefficient vector to be obtained is defined as the following equation (55) in order to represent the above relationship in a matrix, simultaneous equations shown in the following equation (56) are obtained.

In the above equation (56), A _l and vector b _l of the M ₂ × M ₂ matrix are represented by the following equations (57) and (58).

The above equation is calculated by setting an arbitrary value _gl0 to “1”. By solving the equation (56), the optimum filter coefficient for a specific l can be obtained. Thus, equation (56) l = 0,1, ··· , by solving L times for L-1, can be obtained filter coefficient of the second stage having _{M 2} double zeros in f = f _02.

In this way, by applying the filter coefficient calculated by the time-varying filter coefficient calculating means 50 to the bimodal clutter suppression time-varying filter 22 of the clutter suppression unit 20, f = f 0 ₁ and f = can form a notch f _02, it can also suppress clutter in Staggered PRF.

  However, as can be seen from Equation (57), when the filter order increases, the size of the matrix A increases and the amount of computation increases, which is often inconvenient for radar signal processing. Therefore, as shown in FIG. 6, a configuration in which primary time-varying filters are cascade-connected by the filter order is convenient from the viewpoint of computational complexity. If it is a primary filter, all the calculation of the filter coefficient is a scalar operation, and the matrix calculation becomes unnecessary.

Consider the case where two first-order time-varying filters are cascaded as shown in FIG. Assuming that the coefficients of the first-order time-varying filter 70 are h ₁₀ and h ₁₁ , the time-varying filter coefficient calculating means 60 calculates the filter coefficient according to the equation (59) and transfers it to the first-order time-varying filter 70. To do.

Assuming that the coefficients of the second-order first-order time-varying filter 71 are g ₁₀ and g ₁₁ , the time-varying filter coefficient calculating means 61 calculates the filter coefficient according to the equation (60), and calculates it as the first-order time-varying filter. Transfer to filter 71.

Here, g ₁₀ = 1 and z ₀ = exp [j2πf _{0 1} δT].

  For the third and subsequent stages, the coefficients of the first and second stages are converted into coefficients so that they can be regarded as one filter, and the calculation of Expression (60) may be repeated. Increasing the number of filters connected in cascade is equivalent to increasing the filter order, and the output signal at the final stage is a signal with suppressed clutter.

However, in this calculation method, it is known that desired filter characteristics cannot be obtained if time-varying filters using the same zero frequency are connected in cascade. For this reason, the zero point frequency used for coefficient calculation in Equation (60), that is, the estimated clutter center frequency f ₀₁ is actually z ₀ = exp [j2π (f) using f _{0 1} + δf (δf is a very small value). _{0 1} + δf) δT].

The same applies to the case of the bimodal clutter. When the zero point frequency is set to the same clutter center frequency f ₀₂ , it is necessary to calculate the filter coefficient as z ₀ = exp [j2π (f ₀₂ + δf) δT].

  As described above, according to the second embodiment of the present invention, even when the clutter suppression of the clutter, which is difficult in the clutter suppression processing in the conventional radar apparatus to which the stagger trigger method is applied, is corrected. By performing the clutter center frequency estimation process, the clutter suppression performance can always be ensured.

  In the second embodiment, the operation of the simple clutter suppression processing unit 15 is described as being the same as that of the first embodiment. However, the filter used for the suppression processing performed by the simple clutter suppression processing unit 15 is also a time-varying coefficient filter. If used, it can be expected that the determination accuracy of the aliasing correction is improved. If the increased amount of computation is acceptable, it is desirable to use a time-varying coefficient filter for the simple clutter suppression processing unit 15 as well.

Embodiment 3 FIG.
In the second embodiment described above, the case of the radar apparatus to which the stagger trigger method is applied has been described. However, instead of changing the transmission interval for each pulse as in the stagger trigger method, radio waves are transmitted at several types of pulse intervals. If the transmission interval is changed, the same effect can be expected in a radar apparatus that transmits pulse radio waves at regular intervals for a fixed time. In this case, if the pulse interval is equal in the same range bin, the clutter suppression filter described in the first embodiment may be used, and the filter coefficient may be changed for each range bin. Further, when the pulse interval is switched even in the same range bin, the time-varying filter described in the second embodiment may be used.

Embodiment 4 FIG.
As a method of avoiding the blind phenomenon of the target signal due to the filter blocking region, there is a method of changing the RF (Radio Frequency) of the transmission signal. Since the Doppler frequency of the received target signal changes when the transmission frequency changes, this is a mode in which clutter suppression processing and target detection processing are performed at a frequency at which blinding does not occur. In a pulse radar apparatus that transmits radio waves by switching several types of transmission frequencies, the clutter center frequency also changes by switching the transmission frequency. Therefore, a method of performing aliasing correction of the clutter center frequency using the time-varying filter of the second embodiment is used. If applied, the clutter suppression performance can be ensured even in a situation where the clutter center frequency is turned back.

It is a block diagram which shows the structure of the radar apparatus by Embodiment 1 of this invention. It is a block diagram which shows the relationship between the internal structure of the clutter center frequency estimation means 4 with a folding correction | amendment 4 shown in FIG. 1, and the input signal. It is a conceptual diagram for demonstrating operation | movement of the radar apparatus by Embodiment 1 of this invention. It is a block diagram which shows the clutter suppression process part 20 of the radar apparatus by Embodiment 2 of this invention. FIG. 5 is a configuration diagram showing a time-varying coefficient transversal FIR filter as the bimodal clutter suppression time-varying filter 22 shown in FIG. 4. FIG. 5 is a configuration diagram illustrating a clutter suppression filter in which a plurality of first-order time-varying filters are cascade-connected as the bimodal clutter suppression time-varying filter 22 illustrated in FIG. 4. FIG. 7 is a diagram in which two first-order time-varying filters 70 and 71 shown in FIG. 6 are connected in cascade.

Explanation of symbols

  1 Clutter estimation data extraction means, 2 clutter number determination means, 3 switch, 4 clutter center frequency estimation means with aliasing correction, 5 clutter suppression processing unit, 6 unimodal clutter suppression filter, 7 bimodal clutter suppression filter, 10 Received signals after being converted into two-dimensional data, 11a and 11b Part of received signals used for clutter number determination processing and clutter center frequency estimation processing, 12 clutter center frequency estimation means, 13 clutter center frequency correction means, 14 Estimated value determination range bin extracting means, 15 Simple clutter suppression processing section, 16 Optimal estimated value selection means, 20 Clutter suppression processing section, 21 Unimodal clutter suppression time varying filter, 22 Bimodal clutter suppression time varying filter, 30 ~ 30 + M-1 delay, 40 ~ 40 + M complex multiplier, 50 time-varying filter Coefficient calculation means, 51 complex adder, 70 to 72 primary time-varying filter, 60 to 62 time-varying filter coefficient calculation means.

Claims (7)

Clutter estimation data extraction means for extracting a signal for estimating the number of clutters and their center frequency from the received signal of the pulse radar;
A clutter number determination means for estimating the number of clutters received from the signal selected by the clutter estimation data extraction means;
A switcher for switching the content of the clutter suppression processing performed from the clutter number determination result of the clutter number determination means;
By using the signal selected by the clutter estimation data extraction means and the clutter number determination result of the clutter number determination means, a clutter center frequency estimation is performed by avoiding an estimation error that occurs when clutter frequency aliasing occurs. A clutter center frequency estimating means with correction;
The processing content is determined according to the number of clutters by the switch, and a filter processing unit that receives clutter center frequency estimation values transferred from the clutter center frequency estimation means with alias correction and suppresses clutter in the received signal; A radar device provided. The radar apparatus according to claim 1, wherein
The pulse radar is a stagger trigger type pulse radar that repeatedly transmits pulse radio waves at several different transmission intervals. The radar apparatus according to claim 1, wherein
The pulse radar is a pulse radar that transmits radio waves at several pulse intervals, but transmits pulse radio waves at regular intervals after changing the pulse transmission interval. The radar apparatus according to claim 1, wherein
The radar device according to claim 1, wherein the pulse radar is a pulse radar that transmits radio waves by switching several kinds of transmission frequencies. The radar apparatus according to any one of claims 1 to 4, wherein
The clutter center frequency estimating means with alias correction is,
Clutter center frequency estimation means for estimating the clutter center frequency using the signal selected by the clutter estimation data extraction means according to the clutter number determination result of the clutter number determination means,
A clutter center frequency correcting means for calculating a correction value of an assumed clutter center frequency estimated value considering a clutter frequency aliasing with respect to the clutter center frequency estimated value transferred from the clutter center frequency estimating means;
A range bin extracting means for estimating an estimated value for extracting data used for determining an appropriate estimate from candidates for estimated values transferred from the clutter center frequency correcting means;
A simple clutter suppression processing unit that performs a clutter suppression process for each estimated value transferred from the clutter center frequency correction unit with respect to the signal transferred from the fixed value determination range bin extraction unit;
A radar apparatus comprising: an optimum estimated value selecting means for determining an optimum clutter center frequency estimated value from a processing result of the simple clutter suppression processing unit. The radar apparatus according to claim 5, wherein
The estimation value determining range bin extracting means extracts a reception signal of one or a plurality of predetermined range bins. The radar apparatus according to claim 5, wherein
The estimation value determining range bin extracting means extracts one or a plurality of range bins indicating a large received power among the range bins in which clutter exists.

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