Signal reception device, signal reception method, signal reception program, and recording medium

Abstract

This signal reception device comprises a plurality of signal reception units (11) to (1N) which, in response to high-frequency outputs corresponding to incoming waves received by each of a plurality of antenna elements, each subject the high-frequency output from the corresponding antenna element to signal processing and output a complex signal vector in a digital format, and a signal processing device including: a correlation matrix calculating unit (21) that calculates a correlation matrix using the complex signal vectors from the plurality of signal reception units (11) to (1N); a generalized tapered matrix calculating unit (22) that calculates a generalized tapered matrix using a characteristic function of a probability density function; an adaptive weight calculating unit (26) that calculates weight vectors for forming a null width using the correlation matrix calculated by the correlation matrix calculating unit (21) and the generalized tapered matrix calculated by the generalized tapered matrix calculating unit (22); and an adaptive beam forming unit (27) that calculates an adaptive beam using the weight vectors calculated by the adaptive weight calculating unit (26) on the complex signal vectors from the plurality of signal reception units (11) to (1N).

Description

Signal receiving device, signal receiving method, signal receiving program, and recording medium

[0001] The present disclosure relates to a signal receiving device, a signal receiving method, a signal receiving program, and a recording medium for receiving a transmission signal from a target radio wave source from incoming waves received by each of a plurality of antenna elements.

[0002] When a transmission signal from a target radio wave source is received from an incoming wave received by each of a plurality of antenna elements, a method for extracting and receiving the transmission signal from the target radio wave source from the incoming wave in the presence of a clutter source that emits an interfering wave or a jamming wave is known from Non-Patent Documents 1 and 2.

[0003] Non-Patent Document 1 discloses a method called CMT (Covariance Matrix Taper). The CMT method disclosed in Non-Patent Document 1 defines a tapered matrix in which each element is expressed in the form of a SINC function and incorporates the matrix into weights for adaptive beamforming, thereby expanding the width of the null beam (null width) to be formed and reducing degradation of suppression performance even when the clutter source moves.

[0004] Furthermore, Non-Patent Document 2 discloses a method also called a differential constraint type adaptive array. The method disclosed in Non-Patent Document 2, also called a differential constraint type adaptive array, is a method for expanding the width of the null beam to be formed by adding a constraint condition such that the gradient of the null beam in the direction of the clutter source becomes zero.

[0005] J.R. Guerci, “Theory and application of covariance matrix tapers for robust adaptive beamforming,” IEEE Transactions on Signal Processing, vol. 47, no. 4, pp. 977-985, Apr 1999. Alex B. Gershman, U. Nickel and JF Bohme, “Adaptive beamforming algorithms with robustness against jammer motion,” IEEE Transactions on Signal Processing, vol. 45, no. 7, pp. 1878-1885, July 1997.

[0006] The methods disclosed in Non-Patent Document 1 and Non-Patent Document 2 both have the problem that while the null width is expanded to deal with moving clutter sources, the signal-to-interference-plus-noise ratio (SINR) deteriorates.

[0007] The present disclosure has been made in consideration of the above-mentioned points, and aims to provide a signal receiving device that can suppress degradation of SINR and reduce degradation of suppression performance even in the presence of moving clutter sources, and that can flexibly respond to received signals.

[0008] A signal receiving device according to the present disclosure includes a signal processing device having a plurality of signal receiving units that perform signal processing on the high frequency outputs from the corresponding antenna elements in response to high frequency outputs corresponding to arriving waves received by each of the plurality of antenna elements to output complex signal vectors in digital format, a correlation matrix calculation unit that calculates a correlation matrix using the complex signal vectors from the plurality of signal receiving units, a generalized tapered matrix calculation unit that calculates a generalized tapered matrix using a characteristic function of a probability density function, an adaptive weight calculation unit that calculates a weight vector for forming a null width using the correlation matrix calculated by the correlation matrix calculation unit and the generalized tapered matrix calculated by the generalized tapered matrix calculation unit, and an adaptive beam forming unit that calculates adaptive beams using the complex signal vectors from the plurality of signal receiving units and the weight vectors calculated by the adaptive weight calculation unit.

[0009] According to the present disclosure, it is possible to flexibly respond to extraction of a transmission signal from a target radio wave source from an arriving wave, and it is possible to suppress deterioration of SINR and reduce deterioration of suppression performance even in the presence of a moving clutter source.

[0010] 1 is a block diagram showing a schematic configuration of a signal receiving device according to a first embodiment. FIG. 2 is a configuration diagram showing a hardware configuration of a signal processing device in the signal receiving device according to the first embodiment. FIG. 3 is a flowchart explaining the operation of the signal processing device in the signal receiving device according to the first embodiment. FIG. 4 is a diagram showing an example of a probability density function when the probability distribution is a uniform distribution in the signal receiving device according to the first embodiment. FIG. 5 is a diagram showing an example of an SINR evaluation result when the probability distribution is defined as a non-CMT (DCMP) in the signal receiving device according to the first embodiment. FIG. 6 is a diagram showing an example of an SINR evaluation result when the probability distribution is defined as a uniform distribution (CMP) shown in FIG. 4 in the signal receiving device according to the first embodiment. FIG. 7 is a diagram showing an example of an SINR evaluation result when the probability distribution is defined as a differentially constrained type in the signal receiving device according to the first embodiment. FIG. 8 is a diagram showing an example of an SINR evaluation result when the probability distribution is defined as a Gaussian distribution in the signal receiving device according to the first embodiment. FIG. 9 is a diagram showing an example of an SINR evaluation result when the probability distribution is defined as a Laplace distribution in the signal receiving device according to the first embodiment. 1 is a diagram illustrating an example of an evaluation result of SINR when the probability distribution is defined as a Cauchy distribution in a signal receiving device according to embodiment 1. FIG. 2 is a block diagram illustrating a schematic configuration of a signal receiving device according to embodiment 2. FIG. 3 is a flowchart illustrating the operation of a signal processing device in a signal receiving device according to embodiment 2.

[0011] Embodiment 1. A signal receiving device according to embodiment 1 will be described with reference to Figures 1 to 10. The signal receiving device according to embodiment 1 is a signal receiving device mounted on an adaptive array antenna device. The signal receiving device according to embodiment 1 is a signal receiving device that suppresses clutter sources and efficiently receives desired signals even in the presence of clutter sources moving at high speed, such as aircraft, in other words, it is a signal receiving device that suppresses clutter signals from clutter sources, extracts desired signal components, and forms adaptive beams.

[0012] As shown in FIG. 1, the signal receiving device according to the first embodiment includes a plurality of signal receiving units 1 １ ~1 Ｎ and a signal processing device 2. N is a natural number equal to or greater than 2.１ ~1 Ｎ Each of these antennas receives radio frequency (RF) output corresponding to an incoming wave from a plurality of (N) antenna elements (not shown) arranged in a straight line, on a plane, or on a curved surface, and performs various signal processing such as amplification, band-pass filtering (filtering), and frequency conversion on the RF output from the corresponding antenna element to generate an analog signal.

[0013] Signal receiving unit 1 １ ~1 Ｎ The analog signal generated by each of the signal receiving units 1 is a complex signal having an in-phase component and a quadrature component. １ ~1 Ｎ Each of the N antenna elements converts the generated complex signal, which is an analog signal, into a received signal, which is a complex baseband signal in a digital format, to obtain a complex signal vector x(t). The signal processing device 2 outputs the RF output corresponding to the incoming wave received by each of the N antenna elements to N signal receiving units 1. １ ~1 Ｎ Therefore, the complex signal vector x(t) input to the signal processing device 2 is an N-dimensional vector.

[0014] The signal processing device 2 receives incoming waves through a plurality of antenna elements, and receives a plurality of signal receiving units 1 １ ~1 Ｎ The signal processing device 2 suppresses clutter signals from clutter sources from a complex signal vector x(t) obtained from the signal processing device 2, extracts desired signal components, and forms an adaptive beam. The signal processing device 2 includes a correlation matrix calculation unit 21, a generalized tapered matrix calculation unit 22, a characteristic function calculation unit 23, a proportional coefficient setting unit 24, a constant setting unit 25, an adaptive weight calculation unit 26, and an adaptive beam forming unit 27.

[0015] The correlation matrix calculation unit 21 receives a plurality of signal from the signal receiving units 1. １ ~1 Ｎ From the complex signal vector x(t) from the above, a signal correlation matrix R is calculated, which is a matrix M N shown in the following equation (1).

[0016]

[0017] In the above formula (1), N is the total number of signal samples, and n is an index indicating each time. The correlation matrix R of the signal is originally expressed as the expected value shown on the middle side of the above formula (1), but if the number of samples is finite, it can be expressed in the form of a sum shown on the right side of the above formula (1).

[0018] The generalized tapered matrix calculation unit 22 calculates a generalized tapered matrix having elements that are characteristic functions calculated from a set probability density function, i.e., characteristic functions of the probability distribution calculated by applying a Fourier transform to the set probability distribution. The generalized tapered matrix calculation unit 22 calculates the generalized tapered matrix using the characteristic function, proportional coefficients and constant terms related to the characteristic function. The generalized tapered matrix calculation unit 22 calculates the generalized tapered matrix using the characteristic function, proportional coefficients and constant terms related to the characteristic function, and information on the difference in position between the antenna elements.

[0019] The generalized tapered matrix calculation unit 22 calculates a generalized tapered matrix T using a CMT (Covariance Matrix Taper) method from the characteristic function from the characteristic function calculation unit 23, the proportionality coefficient and complex vector indicating the constant term for the characteristic function from the proportionality coefficient setting unit 24, the difference information between the antenna elements in terms of the characteristic function, and the constant β from the constant setting unit 25. Ｇ―ＣＭＴ When the antenna elements are arranged in a one-dimensional arrangement, the generalized tapered matrix calculation unit 22 calculates the generalized tapered matrix T Ｇ―ＣＭＴ is expressed as a generalized tapered matrix [T Ｇ―ＣＭＴ ] ｍｎ Calculate by finding the following.

[0020]

[0021] In the above equation (2), P is a probability density function, φ Ｐ is the characteristic function of the probability density function from the characteristic function calculation unit 23, d ｍ is a complex vector (complex coefficient) of the mth component, which is a proportionality coefficient from the proportionality coefficient setting unit 24 for the characteristic function, d ｎ is a complex vector (complex coefficient) of the nth component, which is a proportional coefficient from the proportional coefficient setting unit 24 for the characteristic function, Δx ｍｎis the difference (distance difference) information between the positions of the mth antenna element and the nth antenna element, and β is a constant from the constant setting unit 25. In short, the generalized tapered matrix calculation unit 22 calculates the generalized tapered matrix T Ｇ―ＣＭＴ Calculate.

[0022] The characteristic function φ calculated by the characteristic function calculation unit 23 Ｐ The characteristic function calculation unit 23 defines a probability density distribution (function) P(u) centered at the origin, which is followed by a random variable u (a random variable in one direction in the case of a one-dimensional arrangement), and calculates a characteristic function φ of the probability density function P(u) by Fourier transforming the probability density function P(u). Ｐ is calculated by the following formula (3).

[0023]

[0024] When the antenna elements are arranged in a three-dimensional arrangement, the generalized tapered matrix calculation unit 22 calculates the generalized tapered matrix T Ｇ―ＣＭＴ is a generalized tapered matrix [T Ｇ―ＣＭＴ ] ｍｎ Calculate by finding the following.

[0025]

[0026] In the above equation (4), P(u), P(v), and P(w) are probability density functions set for the random variables u, v, and w in the three-dimensional directions (X-direction, Y-direction, and Z-direction coordinates), respectively, and φ Ｐ（ｕ）、 φ Ｐ（ｖ）、 φ Ｐ（ｗ） is the characteristic function of the probability density function from the characteristic function calculation unit 23 calculated by Fourier transforming each of the probability density functions P(u), P(v), and P(w) using the above formula (3), and Δx ｍｎ is the difference (distance difference) information between the mth antenna element and the nth antenna element in the X coordinate (X direction), Δy ｍｎ is the difference (distance difference) information between the mth antenna element and the nth antenna element in the Y coordinate (Y direction), Δz ｍｎis the difference (distance difference) information between the positions of the mth antenna element and the nth antenna element in the Z coordinate (Z direction). In other words, even when the antenna elements are arranged in three dimensions, the generalized tapered matrix calculation unit 22 calculates the generalized tapered matrix T Ｇ―ＣＭＴ Calculate.

[0027] The adaptive weight calculation unit 26 calculates the correlation matrix R (the right side of the above equation (1)) calculated by the correlation matrix calculation unit 21 and the generalized taper matrix T calculated by the generalized taper matrix calculation unit 22. Ｇ―ＣＭＴ Using the above, a weight vector W for forming a null width shown in the following equation (5) is obtained. Ｇ―ＣＭＴ Calculate.

[0028] In the above formula (5), a(θ ０ ) is the steering vector in the desired direction. The above equation (5) is the generalized tapered matrix T Ｇ―ＣＭＴ Using the weight vector W Ｇ―ＣＭＴ is calculated, it is a more generalized version of the weights by the CMT described in Non-Patent Document 1 and the differential constrained adaptive array described in Non-Patent Document 2 in addition to the conventional DCMP.

[0029] The adaptive beam forming unit 27 receives a plurality of signal receiving units 1 １ ~1 Ｎ The weight vector W calculated by the adaptive weight calculation unit 26 is applied to the complex signal vector x(t) from Ｇ―ＣＭＴ Using the above, the adaptive beam (signal) y(t) shown in the following equation (6) is calculated.

[0030] As is clear from the above equation (6), the adaptive signal y(t) is １ ~1 Ｎ For a complex signal vector x(t) from Ｇ―ＣＭＴ As a result, the signal receiving device according to the first embodiment can suppress clutter signals from clutter sources even in a cluttered environment and extract signals that form an adaptive beam of the desired component.

[0031] The hardware configuration for realizing the signal processing device 2 having the correlation matrix calculation unit 21, the generalized tapered matrix calculation unit 22, the characteristic function calculation unit 23, the proportional coefficient setting unit 24, the constant setting unit 25, the adaptive weight calculation unit 26, and the adaptive beam forming unit 27 includes a processor 31, a memory 32, an input interface 33, an output interface 34, and a signal path (bus) 35, as shown in FIG. 2 .

[0032] The processor 31 is realized by a single or multiple processors including an LSI (Large Scale Integrated circuit) such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array), or a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) that executes a computer program.

[0033] The memory 32 includes a program memory that stores various programs for implementing the signal processing functions of the signal processing device 2, a work memory that is used when the processor 31 executes signal processing, and a memory into which data used in the signal processing is expanded. The memory 32 uses a plurality of semiconductor memories such as a read-only memory (ROM) and a synchronous dynamic random access memory (SDRAM).

[0034] Next, the operation of the signal receiving device according to the first embodiment will be described with reference to Fig. 3. In step ST1, the RF outputs corresponding to the incoming waves received by the respective antenna elements are transmitted to the plurality of signal receiving units 1. １ ~1 Ｎ The signal processing device 2 acquires a complex signal vector x(t), which is a complex baseband signal in a digital format obtained by performing various signal processing. Step ST1 is a step in which the signal processing device 2 acquires a received signal.

[0035] In step ST2, the correlation matrix calculation unit 21 calculates the correlation matrix of the plurality of signal receiving units 1 １ ~1 Ｎ From the complex signal vector x(t) from the above, the correlation matrix R of the signal expressed by the above equation (1) is calculated. Step ST2 is a step for calculating the correlation matrix R.

[0036] In step ST3, the characteristic function calculation unit 23 sets the probability distribution. That is, the characteristic function calculation unit 23 calculates the characteristic function φ Ｐ The probability density function P is set to calculate the probability distribution. The probability distribution is defined as one of Gaussian distribution, Laplace distribution, Cauchy distribution, uniform distribution shown in FIG. 4, differential constraint, and non-CMT (DCMP) depending on the application, and the probability density function P is set based on the definition. Step ST3 is a step for setting the probability distribution set by the characteristic function calculation unit 23.

[0037] In step ST4, the characteristic function calculation unit 23 performs a Fourier transform on the probability density function P based on the probability distribution set, i.e., defined in step ST3, to obtain a characteristic function φ of the probability density function P. Ｐ is calculated by the above formula (3). Step ST4 is a step in which the characteristic function calculation unit 23 calculates the characteristic function.

[0038] In step ST5, the proportionality coefficient setting unit 24 calculates the generalized tapered matrix T Ｇ―ＣＭＴ The proportional coefficient d is set as the complex vector (complex coefficient) d used in the above equation (2). ｍ , d ｎ The proportionality coefficient d is 1 when the probability distribution is Gaussian, Laplace, Cauchy, uniform, or non-CMT (DCMP). Ｎ×１ and the differential constraint is set as follows: Ｎ×１ represents an N-dimensional vector with all elements being 1.

[0039] Step ST5 is a step for setting the proportionality coefficient set by the proportionality coefficient setting unit 24.

[0040] In step ST6, the constant setting unit 25 calculates the generalized tapered matrix T Ｇ―ＣＭＴ The constant β used in the calculation of is set. The constant β is the constant β used in the above equation (2). The constant β is 0 if the probability distribution is a Gaussian distribution, a Laplace distribution, a Cauchy distribution, a uniform distribution, or a non-CMT (DCMP), and is 1 if the distribution is differentially constrained. Step ST6 is a step for setting the constant set by the constant setting unit 25.

[0041] In step ST7, the generalized tapered matrix calculation unit 22 calculates the characteristic function φ calculated in step ST4 using the probability density function P set in step ST3. Ｐ and the generalized tapered matrix T shown in the above equation (2) using the proportional coefficient d set in step ST5 and the constant β set in step ST6. Ｇ―ＣＭＴ The taper matrix for the probability distribution set by is calculated.

[0042] Step ST7 is a step of calculating a generalized tapered matrix by the generalized tapered matrix calculation unit 22. Step ST7 is a step of calculating a characteristic function φ calculated using the set probability density function P calculated in step ST4. Ｐ and the generalized tapered matrix T Ｇ―ＣＭＴ This is a step of calculating a taper matrix for the probability distribution set by

[0043] When the probability distribution is defined as non-CMT (DCMP), the probability density function P is a Dirac delta function in step ST3, and the proportionality coefficient d is 1 in step ST5. Ｎ×１ In step ST6, the constant β is set to 0. The generalized taper matrix T calculated by the generalized taper matrix calculation unit 22 using the above equation (2) is Ｇ―ＣＭＴ is a matrix with all elements being 1.

[0044] As a result, in step ST8, the adaptive weight calculation unit 26 calculates the generalized tapered matrix T Ｇ―ＣＭＴIn the matrix where all elements are 1, the weight vector W Ｇ―ＣＭＴ The calculated weight vectors match the weight vectors of the DCMP. Step ST8 is a step of calculating adaptive weights by the adaptive weight calculation unit 26. Step ST8 is a step of calculating the correlation matrix R calculated in step ST2 and the tapered matrix T for the set probability distribution calculated in step ST7. Ｇ―ＣＭＴ Using the weight vector W Ｇ―ＣＭＴ This is the step of calculating

[0045] When the probability distribution is defined as the uniform distribution shown in FIG. 4, the probability density function P is set to a function of the uniform distribution in step ST3, and the proportional coefficient d is set to 1 in step ST5. Ｎ×１ In step ST6, the constant β is set to 0. The generalized taper matrix T calculated by the generalized taper matrix calculation unit 22 using the above equation (2) is Ｇ―ＣＭＴ is expressed by the following equation (8).

[0046] The above equation (8) coincides with the tapered matrix of the CMT described in Non-Patent Document 1.

[0047] As a result, in step ST8, the adaptive weight calculation unit 26 calculates the generalized tapered matrix T calculated in the above equation (8). Ｇ―ＣＭＴ In this case, the weight vector W is calculated using the above equation (5). Ｇ―ＣＭＴ When calculated, it coincides with the weight vector of the CMT described in Non-Patent Document 1.

[0048] When the probability distribution is defined as a differential constraint, the probability density function P is set to a Dirac delta function in step ST3, the proportional coefficient d is set to the proportional coefficient shown in the above formula (7) in step ST5, and the constant β is set to 1 in step ST6. The generalized taper matrix T calculated by the generalized taper matrix calculation unit 22 using the above formula (2) is Ｇ―ＣＭＴ is expressed by the following equation (9).

[0049]

[0050] As a result, in step ST8, the adaptive weight calculation unit 26 calculates the generalized tapered matrix T Ｇ―ＣＭＴIn this case, the weight vector W is calculated using the above equation (5). Ｇ―ＣＭＴ The calculated weights are consistent with the differential constraint type adaptive weights described in Non-Patent Document 2.

[0051] The weight vector W calculated by the adaptive weight calculation unit 26 Ｇ―ＣＭＴ The reason why the correlation matrix R used in the adaptive weights described in Non-Patent Document 2 is the same as the differential constraint type adaptive weights described in Non-Patent Document 2 will be explained below. diff can be transformed into the following equation (10).

[0052]

[0053] In the above equation (10), the matrix B(θ) is a diagonal matrix having the components of the vector b(θ) in its diagonal elements, and the vector b(θ) is a one-dimensional vector corresponding to the angular differential of the steering vector. diff The mn-th component of the generalized tapered matrix T calculated in the above equation (9) is the same as that of the generalized tapered matrix T Ｇ―ＣＭＴ By using the above, the same as the differential constraint described in Non-Patent Document 2 is obtained.

[0054] When the probability distribution is defined as a Gaussian distribution, the probability density function P is converted to a Gaussian distribution function P shown in the following equation (11) in step ST3. Ｇ (u), the proportional coefficient d is 1 in step ST5. Ｎ×１ , the constant β is set to 0 in step ST6.

[0055] In the above equation (11), σ is a constant, and the variance of the Gaussian distribution is σ ２ It can be expressed as:

[0056] The generalized taper matrix calculation unit 22 calculates the generalized taper matrix T Ｇ―ＣＭＴ（Gauss） is expressed by the following equation (12).

[0057]

[0058] In step ST8, the adaptive weight calculation unit 26 calculates the generalized tapered matrix T calculated in the above equation (12). Ｇ―ＣＭＴ（Gauss）In this case, the weight vector W is calculated using the above equation (5). Ｇ―ＣＭＴ（Gauss） is calculated, the following equation (13) is obtained.

[0059]

[0060] When the probability distribution is defined as a Laplace distribution, the probability density function P is converted to the Laplace distribution function P shown in the following equation (14) in step ST3. Ｌ (u), the proportional coefficient d is 1 in step ST5. Ｎ×１ , the constant β is set to 0 in step ST6.

[0061] In the above equation (14), b is a constant, and the variance of the Laplace distribution is b ２ It can be expressed as:

[0062] The generalized taper matrix calculation unit 22 calculates the generalized taper matrix T Ｇ―ＣＭＴ（Laplace） is expressed by the following equation (15).

[0063]

[0064] In step ST8, the adaptive weight calculation unit 26 calculates the generalized tapered matrix T calculated in the above equation (15). Ｇ―ＣＭＴ（Laplace） In this case, the weight vector W is calculated using the above equation (5). Ｇ―ＣＭＴ（Laplace） is calculated, the following equation (16) is obtained.

[0065]

[0066] When the probability distribution is defined as a Cauchy distribution, the probability density function P is converted to the Cauchy distribution function P shown in the following equation (17) in step ST3. ｃ (u), the proportional coefficient d is 1 in step ST5. Ｎ×１ , the constant β is set to 0 in step ST6.

[0067] In the above equation (17), γ is a constant, and the variance of the Cauchy distribution is γ ２ It can be expressed as:

[0068] The generalized taper matrix calculation unit 22 calculates the generalized taper matrix T Ｇ―ＣＭＴ（Cauchy） is expressed by the following equation (18).

[0069]

[0070] In step ST8, the adaptive weight calculation unit 26 calculates the generalized tapered matrix T calculated in the above equation (18). Ｇ―ＣＭＴ（Cauchy） In this case, the weight vector W is calculated using the above equation (5). Ｇ―ＣＭＴ（Cauchy） is calculated, the following equation (19) is obtained.

[0071]

[0072] The generalized tapered matrix T Ｇ―ＣＭＴ is calculated, and the weight vector W is calculated using the above equation (5). Ｇ―ＣＭＴ By calculating the adaptive weights, it is possible to accommodate any of the probability distributions, such as Gaussian distribution, Laplace distribution, Cauchy distribution, uniform distribution shown in FIG. 4, differential constraint, and non-CMT (DCMP), and it is possible to calculate various types of adaptive weights. Therefore, it is possible to flexibly accommodate various clutter sources, such as moving clutter sources, while suppressing a decrease in the signal-to-interference-plus-noise ratio (SINR).

[0073] In step ST9, the adaptive beam forming unit 27 receives the plurality of signal receiving units 1. １ ~1 Ｎ The weight vector W calculated by the adaptive weight calculation unit 26 is applied to the complex signal vector x(t) from Ｇ―ＣＭＴ The adaptive beam (signal) y(t) shown in the above equation (6) is calculated using the above equation, and is output as a received signal in response to a transmitted signal from a target radio wave source. １ ~1 Ｎ This is a multiplication step in which the received signal vector obtained by the adaptive weight calculation unit 26 is multiplied by the weight vector calculated by the adaptive weight calculation unit 26.

[0074] The signal reception method in steps ST1 to ST9 is performed by the processor 31 executing processing in accordance with a program stored in the memory 32, particularly in the ROM. That is, the program stored in the memory 32 includes the steps of: calculating a correlation matrix from a plurality of complex signal vectors; calculating a tapered matrix for a probability distribution set by a generalized tapered matrix using a characteristic function calculated using a set probability density function, a set proportional coefficient, and a set constant; calculating a weight vector using the correlation matrix and the tapered matrix for the set probability distribution; and calculating adaptive beams using the weight vector for the plurality of complex signal vectors.

[0075] Next, the results of investigating the SINR characteristics when the probability distribution is set to each of Gaussian distribution, Laplace distribution, Cauchy distribution, uniform distribution shown in FIG. 4, differential constraint, and non-CMT (DCMP) in the signal receiving device according to the first embodiment will be described with reference to FIGS. 5 to 10.

[0076] The change in SINR when there is a 1-degree shift in the clutter direction relative to the clutter source was investigated for each probability distribution. In Figures 5 to 10, the horizontal axis (Angle) indicates the direction of arrival of the interference wave, the vertical axis (JNR) indicates the interference wave power, and the color depth indicates the calculated SINR evaluation result.

[0077] Fig. 5 shows the evaluation results of SINR when the probability distribution is defined as non-CMT (DCMP). Fig. 6 shows the evaluation results of SINR when the probability distribution is defined as the uniform distribution (CMT (Conventional)) shown in Fig. 4. Fig. 7 shows the evaluation results of SINR when the probability distribution is defined as differential constraint. Fig. 8 shows the evaluation results of SINR when the probability distribution is defined as Gaussian distribution. Fig. 9 shows the evaluation results of SINR when the probability distribution is defined as Laplace distribution. Fig. 10 shows the evaluation results of SINR when the probability distribution is defined as Cauchy distribution.

[0078] As is clear from the investigation results shown in Figures 5 to 10, when the probability distribution is defined as the uniform distribution, differential constraint, Gaussian distribution, Laplace distribution, and Cauchy distribution shown in Figure 4, the SINR is higher than when it is defined as non-CMT (DCMP). In particular, when the probability distribution is defined as the Gaussian distribution and Laplace distribution, the SINR is excellent.

[0079] Note that, when the probability distribution is defined as the Cauchy distribution, the SINR is not higher than when it is defined as the Gaussian distribution and the Laplace distribution. One of the reasons for this is thought to be that the tails of the distribution are thicker than those of the Gaussian distribution and the Laplace distribution, making it a special distribution in which the expected value and variance cannot be defined.

[0080] The reason why the probability distribution defined as non-CMT (DCMP) is lower than that defined as other probability distributions is thought to be because no null width occurs in the interference wave direction and it is easily affected by the movement of clutter sources. However, when the probability distribution is defined as non-CMT (DCMP), if there is no displacement (movement) of the clutter source, it follows the maximum S / N standard and therefore shows the best SINR characteristics.

[0081] When the probability distribution is defined as a Gaussian distribution and a Laplace distribution, the adaptive weight calculation unit 26 calculates the generalized tapered matrix T calculated in the above equation (12) as Ｇ―ＣＭＴ（Gauss） In this case, the weight vector W obtained by the above equation (13) using the above equation (5) is Ｇ―ＣＭＴ（Gauss） , and the adaptive weight calculation unit 26 calculates the generalized tapered matrix T Ｇ―ＣＭＴ（Laplace） In this case, the weight vector W obtained by the above equation (16) using the above equation (5) is Ｇ―ＣＭＴ（Laplace） Each has characteristics between the case where the probability distribution is defined as DCMP and the case where it is defined as CMT, and as a result, while forming a null width, the degradation of SINR is reduced more than with CMT, and it is considered that the SINR is superior.

[0082] The signal receiving device according to the first embodiment includes a plurality of signal receiving units 1 １ ~1 Ｎ a correlation matrix calculation unit 21 that calculates a correlation matrix R using a complex signal vector x(t) from Ｐ a generalized taper matrix calculation unit 22 that calculates a generalized taper matrix using the correlation matrix R calculated by the correlation matrix calculation unit 21 and the generalized taper matrix T calculated by the generalized taper matrix calculation unit 22; Ｇ―ＣＭＴ weight vector W for forming the null width using Ｇ―ＣＭＴ and a plurality of signal receiving units 1 １ ~1 Ｎ The weight vector W calculated by the adaptive weight calculation unit 26 is applied to the complex signal vector x(t) from Ｇ―ＣＭＴ Since the signal processing device includes an adaptive beam forming unit 27 that calculates an adaptive beam using the above equation, it is possible to more flexibly form an adaptive beam pattern for forming a null width in the interference wave direction.

[0083] Furthermore, the signal receiving device according to the first embodiment can obtain good SINR characteristics even in the presence of interference waves. In particular, the signal receiving device according to the first embodiment has excellent SINR characteristics when the probability distribution is defined as a Gaussian distribution and a Laplace distribution, and can suppress clutter sources and efficiently receive a desired signal even in the presence of clutter sources moving at high speed, such as aircraft.

[0084] Second Embodiment A signal receiving device according to a second embodiment will be described with reference to Fig. 11 and Fig. 12. The signal receiving device according to the first embodiment calculates the generalized tapered matrix T Ｇ―ＣＭＴ The adaptive weight calculation unit 26 calculates the weight vector W Ｇ―ＣＭＴ Calculate.

[0085] In contrast to this, the signal receiving device according to the second embodiment uses the generalized tapered matrix T Ｇ―ＣＭＴ11 and 12, the same reference numerals as those in FIGS. 1 to 4 denote the same or corresponding parts.

[0086] The signal receiving device according to the second embodiment includes a diagonal weight matrix setting unit 28, so that the generalized tapered matrix T calculated by the generalized tapered matrix calculation unit 22 Ｇ―ＣＭＴ When becomes irregular, the generalized tapered matrix T Ｇ―ＣＭＴ By adding the diagonal weighting matrix D to the matrix, the tapered matrix can be treated as a regular matrix, and degradation of the SINR characteristics can be prevented.

[0087] As shown in FIG. 11, the signal receiving device according to the second embodiment includes a plurality of signal receiving units 1 １ ~1 Ｎ The signal processing device 2A includes a correlation matrix calculation unit 21, a generalized tapered matrix calculation unit 22, a characteristic function calculation unit 23, a proportional coefficient setting unit 24, a constant setting unit 25, a diagonal weighting matrix setting unit 28, an adaptive weight calculation unit 26A, and an adaptive beam forming unit 27.

[0088] The diagonal weight matrix setting unit 28 calculates the weight vector W Ｇ―ＣＭＴ generalized tapered matrix T Ｇ―ＣＭＴ When becomes irregular, the generalized tapered matrix T Ｇ―ＣＭＴ A diagonal weighting matrix D is set that can treat the tapered matrix as regular by adding it to the above. The diagonal weighting matrix D is a matrix of the same size as the correlation matrix R calculated by the correlation matrix calculation unit 21.

[0089] The diagonal weighting matrix D may be a value αI obtained by multiplying a unit matrix I of the same size as the correlation matrix R by a coefficient α. Alternatively, the diagonal weighting matrix D may be a matrix of the same size as the correlation matrix R with random numbers entered in each diagonal element.

[0090] The adaptive weight calculation unit 26A calculates the correlation matrix R (the right side of the above equation (1)) calculated by the correlation matrix calculation unit 21 and the generalized taper matrix T calculated by the generalized taper matrix calculation unit 22. Ｇ―ＣＭＴ and the diagonal weight matrix D set by the diagonal weight matrix setting unit 28, the weight vector W for forming the null width shown in the following equation (20) is Ｇ―ＣＭＴ Calculate.

[0091]

[0092] Weight vector W Ｇ―ＣＭＴ By using the above equation (20) to calculate the above, the regularity of the inverse matrix part is guaranteed, and therefore, the method has good robust characteristics.

[0093] The hardware configuration for realizing the signal processing device 2 having the correlation matrix calculation unit 21, the generalized tapered matrix calculation unit 22, the characteristic function calculation unit 23, the proportionality coefficient setting unit 24, the constant setting unit 25, the diagonal weighting matrix setting unit 28, the adaptive weight calculation unit 26A, and the adaptive beam forming unit 27 includes a processor 31, a memory 32, an input interface 33, an output interface 34, and a signal path (bus) 35 shown in FIG. 2 , similar to the signal receiving device according to the first embodiment.

[0094] Next, the operation of the signal receiving device according to the second embodiment will be described with reference to FIG. 12. The operations from step ST1 to step ST7 are the same as those from step ST1 to step ST7 of the signal receiving device according to the first embodiment, and therefore the description thereof will be omitted. In step ST7A, the diagonal weight matrix setting unit 28 calculates the weight vector W Ｇ―ＣＭＴ Step ST7A is a step in which the diagonal weight matrix setting unit 28 sets the diagonal weight matrix D used to calculate the following equation:

[0095] In step ST8, the adaptive weight calculation unit 26A calculates the generalized tapered matrix T Ｇ―ＣＭＴ The weight vector W is calculated by adding the diagonal weight matrix D to the above equation (20). Ｇ―ＣＭＴIn step ST8, the adaptive weight calculation unit 26A calculates the generalized taper matrix T calculated by the generalized taper matrix calculation unit 22 using the above formula (2) when the setting of the probability distribution is defined as non-CMT (DCMP) in the same manner as described in the first embodiment. Ｇ―ＣＭＴ When a matrix with all elements being 1 is used and the probability distribution is defined as a uniform distribution as shown in FIG. 4, the generalized tapered matrix T Ｇ―ＣＭＴ Using the above equation (8) and defining the probability distribution as a differential constraint, the generalized tapered matrix T Ｇ―ＣＭＴ Using the above equation (9) and defining the probability distribution as a Gaussian distribution, the generalized tapered matrix T Ｇ―ＣＭＴ As shown in the above formula (12), T Ｇ―ＣＭＴ（Gauss） When the probability distribution is defined as a Laplace distribution using the generalized tapered matrix T Ｇ―ＣＭＴ As shown in the above formula (15), T Ｇ―ＣＭＴ（Laplace） When the probability distribution is defined as the Cauchy distribution, the generalized tapered matrix T Ｇ―ＣＭＴ As shown in the above formula (18), T Ｇ―ＣＭＴ（Cauchy） is used.

[0096] Step ST9 is the same as step ST9 of the signal receiving device according to the first embodiment. １ ~1 Ｎ This is a multiplication step in which the received signal vector obtained by the adaptive weight calculation unit 26 is multiplied by the weight vector calculated by the adaptive weight calculation unit 26.

[0097] The signal reception method in steps ST1 to ST9 is performed by the processor 31 executing processing in accordance with a program stored in the memory 32, particularly in the ROM. That is, the program stored in the memory 32 includes the steps of: calculating a correlation matrix from a plurality of complex signal vectors; calculating a tapered matrix for a probability distribution set by a generalized tapered matrix using a characteristic function calculated using a set probability density function, a set proportionality coefficient, and a set constant; calculating a weight vector using the correlation matrix, the tapered matrix for the set probability distribution, and the set diagonal weighting matrix; and calculating adaptive beams using the weight vector for the plurality of complex signal vectors.

[0098] The signal receiving device according to the second embodiment has the same effects as the signal receiving device according to the first embodiment, and in addition, the weight vector W Ｇ―ＣＭＴ Since the diagonal weight matrix setting unit 28 sets the diagonal weight matrix D used in the calculation of the generalized tapered matrix T Ｇ―ＣＭＴ Even if the signal becomes irregular, the deterioration of the SINR characteristic can be prevented.

[0099] It should be noted that the embodiments may be freely combined, any of the components of the embodiments may be modified, or any of the components of the embodiments may be omitted.

[0100] The signal receiving device according to the present disclosure is suitable as a signal receiving device in an antenna device provided in a radar device, and is particularly suitable as a signal receiving device in an antenna device for selectively extracting target echoes in a radar device such as a weather radar.

[0101] 1 １ ~1 Ｎ Signal receiving unit, 2 signal processing device, 21 correlation matrix calculation unit, 22 generalized tapered matrix calculation unit, 23 characteristic function calculation unit, 24 proportional coefficient setting unit, 25 constant setting unit, 26 adaptive weight calculation unit, 27 adaptive beam forming unit, 28 diagonal weight matrix setting unit.

Claims

1. Multiple signal receiving units that process the high-frequency output from each of the multiple antenna elements corresponding to the incoming wave received by each of the multiple antenna elements to output a complex signal vector in digital format, A signal processing device having: a correlation matrix calculation unit that calculates a correlation matrix using complex signal vectors from the plurality of signal receiving units; a generalized taper matrix calculation unit that calculates a generalized taper matrix using the characteristic function of the probability density function; an adaptive weight calculation unit that calculates weight vectors for forming a null width using the correlation matrix calculated by the correlation matrix calculation unit and the generalized taper matrix calculated by the generalized taper matrix calculation unit; and an adaptive beam formation unit that calculates an adaptive beam using the complex signal vectors from the plurality of signal receiving units and the weight vectors calculated by the adaptive weight calculation unit. A signal receiving device equipped with the following features.

2. The signal receiving device according to claim 1, wherein the characteristic function of the probability density function is the characteristic function of the probability density function obtained by defining a probability density function centered on the origin that the random variable follows, and then performing a Fourier transform on the defined probability density function.

3. The signal receiving device according to claim 1 or claim 2, wherein the generalized tapered matrix calculated by the generalized tapered matrix calculation unit is calculated using the characteristic function of the probability density function, the proportionality coefficient and constant relating to the characteristic function.

4. The signal receiving device according to claim 1 or claim 2, wherein the generalized tapered matrix calculated by the generalized tapered matrix calculation unit is calculated using the characteristic function of the probability density function, the proportionality coefficient and constant relating to the characteristic function, and the difference information of the position between the antenna elements.

5. The signal receiving device according to claim 4, wherein the generalized tapered matrix is ​​calculated by adding the constant to the product of each element of the characteristic function, the proportionality coefficient, and the difference information of the positions between the antenna elements.

6. The aforementioned probability density function is a Gaussian distribution function based on the setting that the probability distribution is a Gaussian distribution. The proportionality constant is a multi-dimensional vector in which all elements are 1. The above constant is 0. The signal receiving device according to claim 3.

7. The aforementioned probability density function is a Laplace distribution function based on the setting that the probability distribution is a Laplace distribution. The proportionality constant is a multi-dimensional vector in which all elements are 1. The above constant is 0. The signal receiving device according to claim 3.

8. The signal receiving device according to claim 1 or 2, wherein the adaptive weight calculation unit calculates the weight vector using the correlation matrix calculated by the correlation matrix calculation unit and the generalized taper matrix calculated by the generalized taper matrix calculation unit, as well as the diagonal weight matrix.

9. The signal receiving device according to claim 8, wherein the diagonal weight matrix is ​​an identity matrix of the same size as the correlation matrix calculated by the correlation matrix calculation unit.

10. The signal receiving device according to claim 8, wherein the diagonal weight matrix is ​​a matrix of the same size as the correlation matrix calculated by the correlation matrix calculation unit, with random numbers inserted into each diagonal element.

11. Each of the multiple signal receiving units processes the high-frequency output from the corresponding antenna element for the incoming wave received by each of the multiple antenna elements, and outputs a complex signal vector in digital format. The steps include: a correlation matrix calculation unit in a signal processing device calculates a correlation matrix from the plurality of complex signal vectors; The generalized tapered matrix calculation unit in the signal processing device calculates a tapered matrix for a probability distribution set by a generalized tapered matrix using a characteristic function calculated using a set probability density function, a set proportionality coefficient, and a set constant. The adaptive weight calculation unit in the signal processing device calculates a weight vector using the correlation matrix and the tapered matrix for the set probability distribution. The adaptive beam forming unit in the signal processing device calculates an adaptive beam using the weight vector in the complex signal vector, A signal receiving method comprising the following features.

12. The signal receiving method according to claim 11, wherein the adaptive weight calculation unit in the signal processing device calculates the weight vector using the correlation matrix, the taper matrix, and a diagonal weight matrix.

13. Procedure for calculating a correlation matrix from multiple complex signal vectors, A procedure for calculating a taper matrix for a probability distribution defined by a generalized taper matrix using a characteristic function calculated using a defined probability density function, a defined proportionality coefficient, and a defined constant, and A procedure for calculating weight vectors using the correlation matrix and the tapered matrix for the set probability distribution, A procedure for calculating an adaptive beam using the weight vector in the complex signal vector, A signal receiving program that causes a computer to execute a command.

14. Procedure for calculating a correlation matrix from multiple complex signal vectors, A procedure for calculating a taper matrix for a probability distribution defined by a generalized taper matrix using a characteristic function calculated using a defined probability density function, a defined proportionality coefficient, and a defined constant, and A procedure for calculating weight vectors using the correlation matrix and the tapered matrix for the set probability distribution, A procedure for calculating an adaptive beam using the weight vectors for the plurality of complex signal vectors, A storage medium that stores a program that causes a computer to execute a command.