An interferometric radiometer target imaging method and system

Abstract

The application discloses an interferometric radiometer target imaging method and system, and belongs to the technical field of passive microwave radiation measurement and imaging; in actual target detection, the target occupies less pixel points in a field of view, and is usually sparse, therefore, the application regards the target signal as a sparse signal, constructs a measurement operator of a target brightness temperature map, sufficiently utilizes prior information of target distribution, greatly reduces the requirement for the quality of a measurement sample, and reduces the complexity of the system; on the basis, the application fully considers the relationship between the connection relationship of various hardware parts of the system and the visibility data, and optimizes the connection relationship between the hardware of the system and reduces the number of system devices while improving the imaging quality; based on this, the application can realize high imaging quality with low system complexity, and solves the technical problems of poor imaging quality and high system complexity of the existing interferometric radiometer target.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of passive microwave radiometric measurement and imaging, and more particularly, relates to an interferometric radiometer target imaging method and system. BACKGROUND

[0002] The current interferometric radiometer technology applied to target detection still generally follows the technical achievements of microwave remote sensing, and has not formed a targeted and systematic comprehensive aperture radiometric measurement technology applied to target detection. Unlike the field of microwave remote sensing, the imaging targets of the interferometric comprehensive aperture radiometer for target detection are mainly aircraft, ships and other air or sea surface targets, and the detection performance is mainly improved through the contrast difference between the target and the background, without the requirement of absolute brightness temperature information of the whole scene. Generally speaking, the target occupies fewer pixel points and has spatial distribution characteristics, and the system resolution requirement is higher. However, in the current target detection application, the system design and imaging algorithm are consistent with the remote sensing application, and when the system resolution is improved, the hardware complexity, signal transmission and processing complexity of the system are greatly increased, the system complexity is difficult to reduce, and the imaging quality is difficult to improve. The higher system complexity and poorer imaging quality have hindered the further development of the interferometric radiometer in target detection and identification. SUMMARY

[0003] In view of the above defects or improvement needs of the prior art, the present application provides an interferometric radiometer target imaging method and system to solve the technical problem of achieving higher imaging quality with lower system complexity in the prior art.

[0004] In order to achieve the above-mentioned purpose, in a first aspect, the present application provides an interferometric radiometer target imaging method, comprising: inputting the visibility data measured by the interferometric radiometer into a target imaging model for solving to obtain a target brightness temperature map;

[0005] The method for constructing the target imaging model comprises:

[0006] A relationship model between the signals received by the radiometer and the system connection matrix is constructed, and the relationship model is substituted into the mapping relationship between the signals received by the radiometer and the visibility data to obtain a compressed interferometric radiometer measurement model; wherein the system connection matrix is used to represent the connection relationship of each hardware part of the radiometer;

[0007] Based on the sparse distribution characteristics of the target, a metric operator of the target brightness temperature map is constructed;

[0008] A target imaging model is constructed with the minimization of the metric operator as the optimization objective and the compressed interferometric radiometer measurement model as the constraint.

[0009] Further preferably, the relationship model between the system connection matrix and the signals received by the radiometer is:

[0010] y = ΨAs + n

[0011] where y is the signal received by the interferometric radiometer; Ψ is the system connection matrix; A is the steering vector; s is the vector representation of the target original signal of the interferometric radiometer; and n represents the measured noise signal.

[0012] Further preferably, the above target imaging model is:

[0013]

[0014] s.t.||S r ⊙V v -FT|| F ≤δ

[0015] where f(T) is the metric operator of the target brightness temperature map; T is the target brightness temperature map; f(·) is the sparsity distribution characteristic metric function; S r is the sampling operator, which satisfies W r represents the redundant averaging operator of the interferometric radiometer, vec(.) represents the matrix vectorization operation, Ψ is the system connection matrix, represents the tensor product, ⊙ represents the Hadamard product, S is the visibility data distribution of the interferometric radiometer; V v is the visibility data; F represents the Fourier operator; ||·|| F is the Frobenius norm; and δ represents the size of the noise level.

[0016] Further preferably, the method for solving the target brightness temperature map in the above target imaging model comprises:

[0017] converting the target imaging model into a weighted target function updating the target brightness temperature map iteratively after initializing the target brightness temperature map until the C value reaches the minimum;

[0018] where λ is the generalized weight; g(·) is the function converted from the sparsity distribution characteristic metric function f(·) by using the convex relaxation method; W g is the weight matrix which changes with the number of iterations; is the i th weight value in the weight matrix W g in the k th iteration; is the i th pixel point in the target brightness temperature map T obtained in the k th iteration, h(·) represents the neighborhood weighting function, c is a preset constant, and ε is a preset normal number.

[0019] Further preferably, when the sparse distribution characteristic of the target is a spatial sparse distribution characteristic, f(·)=||·||0;

[0020] When the sparse distribution characteristic of the target is the gradient sparse distribution characteristic, f(·)=||.|| TV

[0021] When the sparse distribution characteristic of the target is a structured sparse distribution characteristic, f(·)=||.|| 01 .

[0022] Further preferably, the above hardware part includes: an antenna, a channel, an analog-to-digital converter and a cross-correlator.

[0023] In a second aspect, the present invention provides an interferometric radiometer that performs target imaging using the interferometric radiometer target imaging method provided in the first aspect of the present invention.

[0024] In a third aspect, the present invention provides an interferometric radiometer target imaging system, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the interferometric radiometer target imaging method provided in the first aspect of the present invention when executing the computer program.

[0025] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the interferometric radiometer target imaging method provided in the first aspect of the present invention.

[0026] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0027] 1. The present invention provides an interferometric radiometer target imaging method. Considering that in actual target detection, the target occupies fewer pixels in the field of view and usually exhibits sparse characteristics, the present invention regards the target signal as a sparse signal and constructs a metric operator for the target brightness temperature map. By making full use of the prior information of the target distribution, the requirements for the quality of the measurement sample are greatly reduced, thereby reducing the complexity requirements of the system. On this basis, the present invention fully considers the relationship between the connection relationship of each hardware part of the system and the visibility data, and uses this as a constraint. While improving the imaging quality, the connection relationship between the system hardware can be further optimized, the number of system components can be reduced, and thus the complexity can be reduced. Based on this, the present invention can achieve higher imaging quality with lower system complexity.

[0028] 2. Furthermore, the interferometric radiometer target imaging method provided by the present invention takes into account the problem of excessive penalty for large brightness temperature values ​​in the direct solution process of the target imaging model constructed with minimization of the metric operator as the optimization goal and a compressed interferometric radiometer measurement model as the constraint. Therefore, the concept of generalized reweighting is introduced, and a weight matrix that changes with the number of iterations is introduced for the target brightness temperature map item of the model. During each iteration, the weight value of the weight matrix is ​​adaptively adjusted based on the target brightness temperature map result under the current iteration, with a larger penalty for small brightness temperature values ​​and a smaller penalty for large brightness temperature values, thereby avoiding the problem of excessive penalty for large brightness temperature values, improving the contrast of the target brightness temperature map, and further improving the quality of the target image. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram showing the connection relationship between the various hardware components of the radiometer provided in Example 1 of the present invention;

[0030] Figure 2 Flowchart of the interferometric radiometer target imaging method provided in Example 1 of the present invention;

[0031] Figure 3 A diagram showing the framework of a compressed interferometric radiometer according to embodiment 1 of the present invention;

[0032] Figure 4 Schematic diagram of the original sparse array structure and the sampling distribution diagram of the original sparse array provided in Example 1 of the present invention; wherein (a) is a schematic diagram of the original sparse array structure; (b) is a schematic diagram of the sampling distribution of the original sparse array;

[0033] Figure 5 is the target scene distribution map provided by Example 1 of the present invention;

[0034] Figure 6 This is a simulation result diagram based on a compressed interferometer radiometer under the condition of a sampling rate of 60% provided in Example 1 of the present invention, and a comparison diagram with various existing methods; among them, (a) is a distribution diagram of elements in the system connection matrix; (b) is a target brightness temperature map obtained by solving using the discrete Fourier transform method (DFT); (c) is a target brightness temperature map obtained by solving using a general reweighted reconstruction algorithm; (d) is a target brightness temperature map obtained by solving using the generalized reweighted algorithm provided by the present invention. DETAILED DESCRIPTION

[0035] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0036] Embodiment 1,

[0037] An interferometric radiometer target imaging method, comprising: inputting visibility data measured by an interferometric radiometer into a target imaging model for solving to obtain a target brightness temperature map;

[0038] The method for constructing the target imaging model comprises:

[0039] A relationship model between the signal received by the radiometer and the system connection matrix is constructed, and the relationship model is substituted into the mapping relationship between the signal received by the radiometer and the visibility data to obtain a compressed interferometric radiometer measurement model;

[0040] Based on the sparse distribution characteristics of the target, a measurement operator of the target brightness temperature map is constructed;

[0041] A target imaging model is constructed with the minimization of the measurement operator as an optimization objective and the compressed interferometric radiometer measurement model as a constraint.

[0042] The system connection matrix is used to represent the connection relationship of each hardware part of the radiometer; wherein the hardware parts include: antennas, channels, analog-to-digital converters, cross-correlators and the like. Specifically, as shown in Figure 1 The connection relationship of each hardware part of the radiometer is shown in the figure; when constructing the system connection matrix, first, each hardware part of the interferometric radiometer system is modeled respectively, and the matrix of each hardware part is constructed, for example, Figure 1 The matrix of the antenna part can be represented as [antenna 1, antenna 2,..., antenna M], the matrix of the channel part can be represented as [channel 1, channel 2,..., channel M], the matrix of the analog-to-digital converter part can be represented as [ADC 1, ADC 2,..., ADC M ], and the matrix of the cross-correlator part can be represented as [correlator 1, correlator 2,..., correlator N]; then the system connection matrix is constructed according to the corresponding connection matrix between the hardware. For example, the connection matrix ACH M×M between the antenna part and the channel part is ij ach M×Mand the connection matrix ACO between the analog-to-digital converter part and the cross-correlator part M×N ; finally determine the system connection matrix as ACH M×M ×CHA M×M ×ACO M×N It should be noted that since there is a certain connection relationship between some hardware, and the connection relationship between some hardware needs to be controlled by the corresponding switch to determine, therefore, part of the value in the connection matrix can be determined based on the determined connection relationship, and part of the value needs to be obtained by solving the target imaging model to obtain its optimal result. The present application fully considers the relationship between the connection relationship of each hardware part of the system and the visibility data, and uses this as a constraint to improve the imaging quality while further optimizing the connection relationship between the system hardware, reducing the number of system devices, and thus reducing the complexity.

[0043] It should be noted that the synthetic aperture interferometric radiometer obtains the target brightness temperature through the transform domain measurement, and the most basic unit of signal reception and processing is the baseline, that is, the spatial frequency domain sampling. There is a lot of redundancy in the spatial frequency domain sampling of the traditional interferometric radiometer, and each sampling point exists once according to the traditional synthetic aperture principle. From the perspective of spatial frequency domain sampling:

[0044] V(μ i ,ν i )=V c (μ,ν)S(μ,ν)

[0045] Among them

[0046]

[0047]

[0048] The discrete form can be expressed as:

[0049] V=V c ⊙S=FT+n

[0050] Among them, S represents the visibility data distribution of the interferometric radiometer, that is, the sampling matrix, which determines the array size and field of view of the system.

[0051] In order to construct the relationship between the system complexity and the spatial frequency domain, the system connection matrix is constructed, and the relationship model between the system connection matrix and the signal received by the radiometer is obtained from the perspective of array signal processing:

[0052] y=ΨAs+n

[0053] Among them, y is the signal received by the interferometric radiometer; Ψ is the system connection matrix; A is the steering vector; s is the vector representation of the target original signal of the interferometric radiometer; n represents the noise signal of the measurement.

[0054] The cross-correlation operation of the signal received by the radiometer can obtain the covariance matrix R:

[0055] R = E[yy H ] = ΨAR s (ΨA) H + δI

[0056] wherein E[.] represents the cross-correlation operation; R s is a diagonal matrix, representing the power value distribution of the target; (.) H represents the conjugate transpose of the matrix. δ represents the size of the noise level.

[0057] Since there is a mapping relationship between the covariance matrix and the visibility data [R] i,j = V(u ij , v ij ), i, j = 1,..., N, N 2 is the number of field of view pixels, therefore, the relationship between the system sampling matrix and the system connection matrix can be obtained:

[0058]

[0059] wherein W r represents the redundant average operator of the interferometric radiometer, represents the tensor product, represents the Hadamard product, S r is a spatial frequency domain resampling set (system sampling matrix), which is an optimization variable of the optimization connection matrix, and is used to reduce the system complexity; when S r all elements are 1, at this time, it represents the spatial frequency domain distribution of the traditional synthetic aperture radiometer; S is the visibility data distribution of the interferometric radiometer.

[0060] In combination with the discrete representation of the visibility data, S r ⊙ V v = FT + n, and the measurement model of the compressed interferometric radiometer is obtained as: S r ⊙ V v = FT + n, wherein V v is the visibility data; F represents the Fourier operator; T is the target brightness temperature map; n represents the measured noise signal; S r satisfies

[0061] In actual target detection, the target occupies less pixel points in the field of view, and usually presents prior sparse characteristics. Analyzing the distribution properties of the interferometric radiometer target, combining the distribution characteristics of the target, a general description operator of the target prior information can be constructed based on the norm theory, and a measurement operator f(T) of the target brightness temperature map is obtained. The compression sensing theory shows that the sparse signal can be reconstructed with fewer measurements. Therefore, based on the compression sensing theory, a general framework for reconstructing the image of the sparse interferometric radiometer can be obtained, so as to construct the functional constraint of the target radiation signal (target brightness temperature map) and the measurement model, and establish the target imaging model based on the target prior information.

[0062] Specifically, the target imaging model takes the minimization of the measurement operator as the optimization target, and takes the compression interferometric radiometer measurement model as the constraint, and the specific expression is:

[0063]

[0064] s.t.||S r ⊙V v -FT|| F ≤δ

[0065] Wherein, f(T) is the measurement operator of the target brightness temperature map; T is the target brightness temperature map; f(·) is the sparse distribution characteristic measurement function; S r is the sampling operator, which satisfies W r represents the redundant averaging operator of the interferometric radiometer, vec(.) represents the matrix vectorization operation, and Ψ is the system connection matrix, represents the tensor product, and ⊙ represents the Hadamard product, S is the visibility data distribution of the interferometric radiometer; V v is the visibility data; F represents the Fourier operator; ||·|| F is the Frobenius norm; and δ represents the size of the noise level.

[0066] Further, in actual target detection, the target usually presents sparse distribution characteristics such as spatial sparse distribution characteristics, gradient sparse distribution characteristics and structured sparse distribution characteristics in the field of view. In an optional embodiment, when the sparse distribution characteristics of the target are spatial sparse distribution characteristics, f(·) = ||·||0; when the sparse distribution characteristics of the target are gradient sparse distribution characteristics, f(·) = ||·||1; and when the sparse distribution characteristics of the target are structured sparse distribution characteristics, f(·) = ||·||2. TV 01 .

[0067] Further, when solving the target imaging model, a reweighted reconstruction algorithm, a discrete Fourier transform algorithm and the like can be used for solving. ​

[0068] In order to improve the accuracy of solving the target brightness temperature map, in an optional embodiment, the present application proposes a generalized reweighting algorithm to solve the target brightness temperature map in the above target imaging model, specifically comprising:

[0069] Converting the target imaging model into a weighted target function After initializing the target brightness temperature map, the target brightness temperature map is iteratively updated until the C value reaches the minimum;

[0070] Wherein, λ is the generalized weight; g(·) is the function converted by the convex relaxation method to the sparse distribution characteristic measurement function f(·); W g is the weight matrix that changes with the number of iterations; is the ith weight value in the weight matrix W g in the kth iteration; is the ith pixel point in the target brightness temperature map T obtained in the kth iteration, h(·) represents the neighborhood weighting function, c is a predetermined constant, and ε is a predetermined normal number.

[0071] In order to further illustrate the target imaging method of the interferometric radiometer provided by the present application, a specific embodiment will be described in detail below:

[0072] In this embodiment, considering the spatial sparse distribution characteristics of the target, a specific target imaging model is constructed.

[0073] First, the sparse characteristic analysis is performed: assuming that the target is composed of K pixel points, for each baseline, the following can be obtained:

[0074]

[0075] Through analysis, it is found that K<<N 2 , that is, the number of target pixel points is much smaller than the number of field of view pixels. It can be known that the target presents the characteristics of sparse distribution in the spatial domain. At this time, the image reconstruction model based on the spatial sparse characteristics can be obtained.

[0076]

[0077] s.t.||S r ⊙V v -FT||2≤δ

[0078] supp(S r )=Ω

[0079] Wherein, Ω is used to represent the distribution of elements in the system connection matrix.

[0080] Then the problem property is analyzed, which is difficult to obtain stable solution. In this embodiment, the convex relaxation method is used to convert the above problem into the following optimization problem:

[0081]

[0082] The problem excessively punishes large brightness temperature values, so the concept of generalized reweighting is introduced, and the above model is represented as:

[0083]

[0084] Wherein, represents the weight, c represents the constant, ε represents the normal number, prevents the calculation error, h(T k ) represents the generalized reweighting method.

[0085] In order to further illustrate the target imaging method of the interferometric radiometer provided by the present application, another specific embodiment is described in detail below:

[0086] As Figure 2 shown is a flow chart of the target imaging method of the interferometric radiometer in this embodiment, which specifically includes:

[0087] (1) Using the array signal processing theory and the spatial frequency sampling theory, the correspondence between the complexity of the compressed interferometric radiometer system and the sampling theory of the interferometric radiometer is constructed, and the general framework of the compressed interferometric radiometer is established, as Figure 3 shown.

[0088] As Figure 4 shown, the regular hexagonal array commonly used for target detection of the interferometric radiometer is selected as the original sparse array in this embodiment, and the number of array elements is 72, wherein, Fig. (a) represents the structure of the original sparse array, and Fig. (b) represents the equivalent sampling distribution of the original sparse array.

[0089] (2) According to the sparse distribution characteristics of the target, as Figure 5 shown, the compressed interferometric radiometer target imaging model is obtained by using the compressed interferometric radiometer framework.

[0090] (3) In this embodiment, the generalized reweighting algorithm is used for solving.

[0091] Figure 6 represents the simulation result graph of the compressed interferometric radiometer under the condition that the sampling rate is 60%, and is compared with various existing methods, wherein, Fig. (a) is a distribution graph of elements in the system connection matrix; Fig. (b) is a target brightness temperature graph obtained by using the discrete Fourier transform method (DFT) for solving; Fig. (c) is a target brightness temperature graph obtained by using the general reweighting reconstruction algorithm for solving; Fig. (d) is a target brightness temperature graph obtained by using the generalized reweighting algorithm provided by the present application for solving.Figure 6 The horizontal and vertical coordinates in (b) represent the direction cosine coordinate system. Figure 6 It can be seen that the profile of the target in figure (c) is partially missing, and part of the information of the target is lost, mainly because the brightness temperature value of the target in the image is excessively punished. The overall profile of the target in figure (d) is relatively complete and clear. Therefore, it can be seen that the method provided by the present application has better imaging effect than the prior art.

[0092] In summary, the present application constructs an interferometric radiometer framework, introduces a compression matrix into the interferometric radiometer system, establishes the relationship between the sampling matrix and the compression matrix based on the sampling theory, establishes a generalized framework of the compression interferometric radiometer, regards the target signal as a sparse signal based on the analysis of the target distribution characteristics, obtains a target imaging model, constructs a target imaging model of the compression interferometric radiometer based on the compression interferometric radiometer framework and the target imaging model, and proposes a generalized reweighting concept to recover the target signal, and obtains the result of target imaging. The present application can improve the quality of the target image and reduce the complexity of the interferometric radiometer system, solves the technical problems of poor target imaging quality and high system complexity of the interferometric radiometer, and has high practical application value.

[0093] Embodiment 2,

[0094] An interferometric radiometer adopts the target imaging method of the interferometric radiometer provided in the first aspect of the present application for target imaging.

[0095] The related technical solutions are the same as those in embodiment 1, and will not be repeated here.

[0096] Embodiment 3,

[0097] An interferometric radiometer target imaging system comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to execute the target imaging method of the interferometric radiometer provided in embodiment 1 of the present application.

[0098] The related technical solutions are the same as those in embodiment 1, and will not be repeated here.

[0099] Embodiment 4,

[0100] A computer readable storage medium comprises a stored computer program, wherein the computer program controls the device where the storage medium is located to execute the target imaging method of the interferometric radiometer provided in embodiment 1 of the present application when the computer program is run by a processor.

[0101] The related technical solutions are the same as those in embodiment 1, and will not be repeated here.

[0102] Those skilled in the art can easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An interferometric radiometer target imaging method, characterized in that: include: The visibility data measured by the interferometric radiometer is input into the target imaging model for solution to obtain the target brightness temperature map; The method for constructing the target imaging model comprises: Constructing a relationship model between the signal received by the radiometer and the system connection matrix, and substituting the relationship model into the mapping relationship between the signal received by the radiometer and the visibility data to obtain a compressed interferometric radiometer measurement model; wherein the system connection matrix is ​​used to represent the connection relationship between the various hardware components of the radiometer; Based on the sparse distribution characteristics of the target, a measurement operator for the target brightness temperature map is constructed; Constructing a target imaging model with minimization of the metric operator as an optimization goal and the compressed interferometric radiometer measurement model as a constraint; The target imaging model is: in, is the measurement operator of the target brightness temperature map; is the target brightness temperature map; is the sparse distribution characteristic measurement function; is a sampling operator that satisfies , represents the redundant averaging operator of the interferometric radiometer, represents matrix vectorized operations, is the system connection matrix, represents the tensor product, represents the Hadamard product, is the visibility data distribution of the interferometric radiometer; is visibility data; represents the Fourier operator; is the Frobenius norm; Indicates the size of the noise level.

2. The interferometric radiometer target imaging method according to claim 1, wherein: The relationship model between the system connection matrix and the signal received by the radiometer is: in, is the signal received by the interferometric radiometer; It is the system connection matrix; is the guiding vector; is the vector representation of the target original signal of the interferometric radiometer; represents the measured noise signal.

3. The interferometric radiometer target imaging method according to claim 1, wherein: The method for solving the target brightness temperature map in the target imaging model includes: Convert the target imaging model into a weighted objective function ; After initializing the target brightness temperature map, iteratively update the target brightness temperature map until C The value reaches the minimum; in, ; is the generalized weight; To use convex relaxation method to measure the sparse distribution characteristics function The function after conversion; is the weight matrix that changes with the number of iterations; For the k The weight matrix at the iteration The i weight values; , For the k The target brightness temperature map obtained under the iteration The i pixels, represents the neighborhood weighting function, is a preset constant, The default positive number.

4. The interferometric radiometer target imaging method according to any one of claims 1 to 3, wherein: When the sparse distribution characteristics of the target are spatial sparse distribution characteristics, ; When the sparse distribution characteristics of the target are gradient sparse distribution characteristics, When the sparse distribution characteristics of the target are structured sparse distribution characteristics, .

5. The interferometric radiometer target imaging method according to any one of claims 1 to 3, wherein: The hardware part includes: an antenna, a channel, an analog-to-digital converter and a cross-correlator.

6. An interferometric radiometer, characterized in that Target imaging is performed using the interferometric radiometer target imaging method described in any one of claims 1 to 5.

7. An interferometric radiometer target imaging system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the interferometric radiometer target imaging method according to any one of claims 1 to 5 is executed.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the interferometric radiometer target imaging method according to any one of claims 1 to 5.

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