A radar optimized station layout method based on actual installation performance

Abstract

The present invention discloses a method for optimizing the radar station layout based on the actual installation performance. This method starts from four aspects: establishing the antenna pattern based on measured data, establishing an adaptive radar actual installation model based on measured data, calculating the cloud data of the radar detection range, and evaluating the layout effect, providing computing power guarantee for solving the problem of optimizing the radar station layout based on measured data. Compared with the traditional radar networking station layout model, which is too simple, has a relatively fixed mode, and the site location is often affected by the actual environment, etc., this method can better simulate the performance of the actual installed radar, form a detection area / defense area vulnerability surface using cloud data, facilitate the evaluation of the layout effect, and provide reasonable suggestions for quickly, intuitively, and accurately describing the layout effect.

Description

Technical Field

[0001] The present invention belongs to the field of radar, and particularly relates to a method for optimizing the radar station layout based on the actual installed performance. Background Art

[0002] The optimization of radar station layout is a key content for the effective operation of the radar networking system. Its goal is to maximize the combat effectiveness of the radar networking system by comprehensively considering influencing factors such as radar performance, field conditions, electromagnetic environment, and combat missions under the given radar resources and combat area, and to achieve full coverage of the specified area as much as possible.

[0003] Generally, the optimization of radar station layout should meet the following five principles: (1) seamless coverage of the defense area; (2) blind area filling between radars; (3) appropriate redundant coverage in azimuth and altitude layers; (4) avoidance of co-frequency interference; (5) effective coverage of key areas. Based on the above principles, scholars have successively proposed relatively classic radar networking station layout models: linear mode, circular mode, and multi-channel defense mode. The linear mode distributes radars in a straight line, but there are problems such as scattered equipment and low efficiency; the circular mode is convenient for information transmission, but there is a problem of indistinguishable primary and secondary searches; the multi-channel defense mode realizes hierarchical deployment, but it is suitable for the key area defense mode.

[0004] The traditional radar networking station layout model is too simple and the mode is relatively fixed. Often, the site location is affected by the actual environment and cannot reach the ideal station layout position; or the problem of optimizing the station layout is transformed into a mathematical problem of solving the optimal solution. However, due to actual factors such as terrain occlusion, radar performance degradation, and ground reflection in the actual environment, these data are often difficult to obtain or are too complex to be described by mathematical formulas, and the practicality is limited. Therefore, it is necessary to develop a practical method for optimizing the radar station layout based on the measured radar data. Summary of the Invention

[0005] The present invention proposes a method for optimizing the radar station layout based on the actual installed performance, which provides computing power guarantee for solving the problem of optimizing the radar station layout based on the measured data.

[0006] The technical solution for realizing the present invention is as follows: A method for optimizing the radar station layout based on the actual installed performance, characterized in that the steps are as follows:

[0007] Step 1: Establish an antenna pattern based on the measured data.

[0008] Step 2: Based on the measured data and in combination with the antenna pattern, establish an adaptive radar actual installation model.

[0009] Step 3: Calculate the cloud data of the radar detection range based on the radar actual installation model to obtain the point cloud data within the radar detection range.

[0010] Step 4: Based on the point cloud data within the radar detection range, perform radar array layout and analyze the layout effect.

[0011] Compared with the prior art, the significant advantages of the present invention are as follows:

[0012] (1) Aiming at the requirement of optimizing the station layout for actual radars, the present invention can better simulate the performance of installed radars.

[0013] (2) By constructing the point cloud data of the detection area, the present invention forms a detection area / defense area vulnerability surface, which is convenient for evaluating the layout effect.

[0014] (3) The present invention can effectively integrate the measured radar data into the optimization of the station layout, enabling it to quickly, intuitively, and accurately describe the layout effect and provide reasonable suggestions for the layout. Brief Description of the Drawings

[0015] Figure 1 It is a flowchart of a method for optimizing the radar station layout based on the actual installation performance of the present invention.

[0016] Figure 2 It is the irregular antenna pattern of the installed radar of the present invention.

[0017] Figure 3 It is a point cloud map of the network detection of two radars considering terrain occlusion.

[0018] Figure 4 It is a result map of the defense area analysis. Detailed Embodiment

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Next, the detailed embodiment, as well as the technical difficulties and inventive points of the present invention, will be further introduced in conjunction with the design examples.

[0021] Combined with Figures 1 to 4 , a method for optimizing the radar station layout based on the actual installation performance is as follows:

[0022] Step 1: Establish an antenna pattern based on measured data

[0023] The radar detection performance is determined by the radar equation, as shown in the following formula:

[0024]

[0025] Where P Ra is the transmitting power of the radar, G Ra is the gain of the radar antenna, σ is the target scattering area, R is the distance between the target and the radar antenna, λ is the radar wavelength, and P r is the received echo power.

[0026] In the above formula, the main factor affecting the detection performance is the antenna gain. This gain has directivity, and for different types and shapes of radar antennas, their antenna patterns are also different. Even if the radar models are the same, with the use of the equipment, there will be certain differences in their respective patterns. And this kind of difference causes a large difference between the actual installed equipment and the theoretical model. Therefore, for the antenna pattern, it is necessary to approximate the real situation. To adapt to the measured data, this method proposes a method of using the measured power diagram to obtain the radar antenna pattern data at fixed points. That is, according to the typical inflection point data, the antenna pattern G' Ra (α, θ) is dynamically generated by using the polynomial spline interpolation method, where α is the azimuth angle and θ is the elevation angle. Then the received power P' r (α, θ) corresponding to the azimuth angle α and the elevation angle θ is:

[0027]

[0028] Step 2: Based on the measured data and combined with the antenna pattern, establish an adaptive radar actual installation model

[0029] After the radar receives the electromagnetic wave reflected by the target, it needs to go through matched filtering, coherent integration, and subsequent detection processing to form point track data. Then, through radar data processing, the target track information is obtained. Whether the target can be reliably detected depends crucially on whether the received target reflection can exceed the minimum detectable power S min . Therefore, when P' r (α, θ) is equal to S min , the maximum operating distance R max (α, θ) of the radar at the azimuth angle α and the elevation angle θ can be obtained:

[0030]

[0031] However, the above formula is an ideal situation, and the actual results of the actual installed equipment are often lower than the above results. To enable the radar model to adapt to the modeling needs of the actual installed equipment, a gain loss term L s is introduced. L s is used to describe the loss of the system, that is, the correction formula (3) is:

[0032]

[0033] In the above formula, L s is a simplified treatment, that is, the entire system loss is represented by a variable, and Ls Its size can be determined by reverse calculation based on the actual factory installation data. That is, according to the azimuth angle α and the elevation angle θ, for a scattering cross-sectional area of 1 m 2 The effective radar detection range R of the target m , substituting into formula (4), L can be deduced s as follows:

[0034]

[0035] In principle, the loss should be related to the azimuth angle and the elevation angle, but these effects can be ignored compared to the effects of the radiation pattern. Therefore, the results R of N actual measurements mn can be statistically averaged as the final measurement result of L s .

[0036]

[0037] If there are too many actual measurement points, the method of cluster analysis can be used to eliminate outliers, which can eliminate the influence of individual abnormal values on the results.

[0038] Step 3: Calculate the cloud data of the radar detection range based on the actual radar installation model to obtain the point cloud data within the radar detection range.

[0039] Using high-level data, the terrain occlusion situation of a given point can be calculated. Considering that the radar coverage area is large and the terrain occlusion has a great influence on the detection results, through trade-off, the occlusion can be calculated in units of the radar spatial resolution unit, so as to form the point cloud data of the radar detection range. The calculation steps are as follows:

[0040] S3.1. Determine the number of range cells K according to the range resolution of the radar and the maximum detection range of the radar; note that at this time, the maximum detection range obtained by K refers to the detection range corresponding to the maximum of the radiation pattern. Compared with other azimuth angles and elevation angles, its maximum detection range is smaller than this value.

[0041] S3.2. Quantize the radar detection direction into M azimuth resolution cells according to the azimuth resolution of the radar;

[0042] S3.3. Solve and calculate the maximum elevation angle of the radar beam occlusion for each range cell in the quantized azimuth direction to form an occlusion angle matrix B (M×K dimension).

[0043] S3.4. Determine the quantized height layer L of the detection height according to the maximum height and height interval of the radar detection. For example, if the maximum height is 10,000 meters and the height interval is 500 meters, then L is 20, corresponding to the height range of [500, 10,000] meters;

[0044] S3.5. According to a certain direction and altitude layer, combined with the antenna pattern, form a detection matrix T (M×K×L dimensions) for ideal detection. The matrix element being 1 indicates that the radar power can reach, and being 0 indicates that the radar power cannot reach;

[0045] S3.6. Apply the masking angle matrix B to T (M×K×L dimensions). For the k-th range cell and the m-th azimuth cell, if T(m,k,l) < B(m,k), modify T(m,k,l) to 0, and thus obtain the point cloud data within the radar detection range.

[0046] Step Four: According to the point cloud data within the radar detection range, perform radar array deployment and analyze the deployment effect.

[0047] S4.1 Generate the detection area / defense area vulnerability surface

[0048] Since the point cloud data within the radar detection range forms the three-dimensional structure of the radar detection area, but as each detection unit has a larger unit volume as the distance increases, there are certain errors in accurately calculating the defense area coverage, that is, the geometric relationship between a point cloud data and other points in its neighborhood is still unclear. Therefore, it is necessary to reconstruct the point cloud data, that is, construct the point cloud with the same value (1 or 0) into a data type with complex geometric topological relationships according to a certain shape criterion. On this basis, fit each topological surface to form the detection area surface (i.e., the point cloud map composed of value 1) and the defense area vulnerability surface (the point cloud map composed of value 0). In this paper, the classical Delaunay triangulation and Voronoi polygons are used to construct the geometric topological relationships between points. First, randomly find an initial Delaunay triangle in a large number of point data, and then continuously expand outward based on this. For points that do not meet the Voronoi polygon criterion and cannot form a Delaunay triangle, they are directly removed.

[0049] S4.2 Station layout optimization processing

[0050] According to the detection defense area requirements and the radar installation site requirements (such as road, flat ground size, etc.), form an effective set P of station layout positions, including P spare sites. According to the available radar equipment, form a radar deployment library R, including R radars, among which there are A types (R≥A).

[0051] The optimization processing steps are as follows:

[0052] S4.2.1. According to the detection task requirements, when arranging stations, give priority to selecting the radar with strong detection performance that best meets the detection task indicators. Its deployment position is where the masking influence within the detection defense area is small, and it is close to the edge of the detection defense area (for the case where the station layout point is outside the defense area) or located at the center of the defense area (for the case where the station layout point is inside the defense area) to maximize its detection ability.

[0053] S4.2.2. Run the method for constructing the detection area / defense area vulnerability surface, and determine whether there are vulnerabilities when detecting a specified defense area. If there are vulnerabilities, go to S4.2.3; if there are no vulnerabilities, the specified index task is completed.

[0054] S4.2.3. If there are vulnerabilities or the specified index task is not completed, execute S4.2.1 in the remaining radars until the index is completed. If the requirements still cannot be met after the radar deployment is completed, prompt that there are defense area vulnerabilities in the system and it cannot be completed.

[0055] The experiment uses the radar model based on measured data proposed in the article, and considers the terrain occlusion effect, and simulates and analyzes a station layout result. Figure 2 It is the irregular antenna pattern of the actual installed radar. This pattern is obtained by the user clicking the mouse to complete the input of several known data, and the calculation of the remaining points is completed by interpolation. Figure 3 It is the point cloud map of the networked detection of two radars considering terrain occlusion, where different closed lines represent different heights. Figure 4 Give the input results of the optimization analysis. In the example, for a given defense area, there are two vulnerabilities, and the longitude and latitude of the vulnerabilities are given, and suggestions are also given.

[0056] Specifically, the algorithm first reads the data and constructs an initial triangular network model according to the position relationship of the data. Then the data is processed in blocks.

Claims

1. A radar optimal station layout method based on actual installation performance, characterized in that The steps are as follows: Step 1: Based on the measured data, establish the antenna pattern through interpolation; Step 2: Based on the measured data and combined with the antenna pattern, calculate the maximum operating range R(α,θ) of the radar at the azimuth angle α and elevation angle θ according to the radar equation, and then establish an adaptive radar in-service model; max (α,θ), and then establish an adaptive radar in-service model; Step 3: Based on the maximum detection range of the radar, range resolution, azimuth resolution, maximum detection height of the radar, height interval, as well as the antenna pattern and radar beam occlusion angle, obtain the point cloud data within the radar detection range; Step 4: According to the point cloud data within the radar detection range, perform surface fitting to obtain the detection area surface and the defense area vulnerability surface, so that the shielding effect within the radar detection defense area is small, and it is close to the edge of the detection defense area or located in the center of the defense area. Taking the maximization of its detection ability as the optimization goal, perform radar array deployment and analyze the array deployment effect.

2. The method for optimizing radar station layout based on actual installation performance according to claim 1, wherein: In Step 1, specifically as follows: According to the typical inflection point data, the antenna pattern G' is dynamically generated by means of polynomial spline interpolation Ra (α, θ), where α is the azimuth angle and θ is the elevation angle; Then the received power P' corresponding to the azimuth angle α and the elevation angle θ r (α, θ) is: Among them, P Ra is the transmitting power of the radar, G Ra is the radar antenna gain, σ is the target scattering area, R is the distance between the target and the radar antenna, λ is the radar wavelength, P r is the received echo power, G' Ra (α, θ) represents the dynamically generated antenna pattern.

3. The method for optimizing the radar station layout based on the actual installation performance according to claim 2, wherein: In Step 2, specifically as follows: After the radar receives the electromagnetic wave reflected by the target, it needs to go through matched filtering, coherent integration, and subsequent detection processing to form point track data, and then through radar data processing, obtain the target track information; When P' r (α, θ) is equal to the minimum detectable power S min a gain loss term L is introduced s and the maximum operating range R max (α, θ) of the radar is as follows: wherein, R m represents the effective radar detection range for a target with a scattering cross-sectional area of 1 m 2 at azimuth angle α and elevation angle θ; the results of N actual measurements are R mn ; If there are too many measured points, use the method of cluster analysis to eliminate outliers and eliminate the influence of individual abnormal values on the results.

4. The method for optimizing the radar station layout based on the actual installation performance according to claim 3, characterized in that In Step 3, specifically as follows: S3.1: According to the range resolution of the radar and the maximum detection range of the radar, determine the number of range cells K; The maximum detection distance obtained is the detection distance corresponding to the maximum of the pattern. Compared with other azimuths and elevations, its maximum detection distance is less than this value; S3.2: According to the azimuth resolution of the radar, quantify the radar detection direction into M azimuth resolution units; S3.3: At the quantified azimuth directions, solve and calculate the maximum elevation angle of each range cell to the radar beam occlusion to form an M×K-dimensional occlusion angle matrix B; S3.4: According to the maximum detection height of the radar and the height interval, determine the quantified height layer L of the detection height; S3.5: According to a certain direction and height layer, combined with the antenna pattern, form an ideal detection M×K×L-dimensional detection matrix T, where the matrix element is 1 indicating that the radar power can reach, and 0 indicating that the radar power cannot reach; S3.6: Apply the occlusion angle matrix B to T. If for the kth range cell and the mth azimuth cell, if T(m,k,l)<B(m,k), modify T(m,k,l) to 0, and then obtain the point cloud data within the radar detection range.

5. The method for optimizing the radar station layout based on the actual installation performance according to claim 4, characterized in that In Step 4, specifically as follows: S4.1: Generate the detection area / defense area vulnerability surface; Reconstruct the point cloud data within the radar detection range, that is, construct the point cloud with the same value into a data type with a complex geometric topology relationship according to a certain shape criterion, and on this basis, fit each topological surface to form the detection area surface and the defense area vulnerability surface; S4.2 Station layout optimization processing According to the requirements of the detection defense area and the requirements of the radar installation site, form an effective set of station layout positions P, including P standby sites; According to the available radar equipment, form a radar deployment library; The optimization processing steps are as follows: S4.2.1: According to the needs of the detection task, when arranging the stations, give priority to selecting the radar with strong detection performance that best meets the detection task indicators. Its deployment position is where the shielding effect within the detection defense area is small, and it is close to the edge of the detection defense area or located in the center of the defense area to maximize its detection ability; S4.2.

2. Run the method for constructing the detection area surface / defense area vulnerability surface, and determine whether there are vulnerabilities when detecting a specified defense area. If there are vulnerabilities, go to S4.2.3; if there are no vulnerabilities, the specified index task is completed. S4.2.

3. If there are vulnerabilities or the specified index task is not completed, execute S4.2.1 in the remaining radars until the index is completed. If the requirements still cannot be met after the radar deployment is completed, prompt that there are defense area vulnerabilities in the system and it cannot be completed.

6. The method for optimizing the radar station layout based on the actual installation performance according to claim 5, characterized in that In S4.1, the detection area surface contains a point cloud map composed of values 1, and the defense area vulnerability surface contains a point cloud map composed of values 0.

7. The method for optimizing the radar station layout based on the actual installation performance according to claim 5, characterized in that, In S4.1, the classical Delaunay triangular mesh and Voronoi polygon are used to construct the geometric topological relationship between points. First, a random initial Delaunay triangle is found in a large amount of point data, and then it is continuously expanded outward based on this. Points that do not meet the criteria of the Thiessen polygon and cannot form a Delaunay triangle are directly excluded.

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