A method for detecting weak sea surface targets based on Doppler spectrum

Abstract

The application discloses a sea surface weak target detection method based on Doppler spectrum, comprising: obtaining a radar echo signal; wherein a distance unit where a target is located is known, a distance unit adjacent to the distance unit is taken as a protection unit, and the rest of the distance units are taken as pure sea clutter units; the distance unit where the target is located is taken as a to-be-detected unit, the pure sea clutter units are taken as reference units, and Doppler amplitude spectrum of the to-be-detected unit and Doppler amplitude spectrum of the reference unit are calculated; a median function of sea clutter Doppler amplitude spectrum, variation, and median normalized Doppler amplitude spectrum are determined; a non-zero interval length, a vector entropy, and global accumulation are calculated as a feature vector of sea clutter, and a training set is constructed; based on the training set and in combination with a preset false alarm rate, a fast convex hull algorithm is used to construct a detector; after obtaining a radar echo signal of an unknown target, for each distance unit, whether a target exists in a signal sequence corresponding to the distance unit is determined through the constructed detector.

Description

Technical Field

[0001] The invention belongs to the field of radar signal processing, and in particular relates to a sea surface weak target detection method based on Doppler spectrum. Background Art

[0002] Detecting weak targets at sea is an important but challenging task for high-resolution surface surveillance radars. The difficulties arise from three main aspects: First, the sea clutter received by high-resolution surface surveillance radars has strong non-Gaussian characteristics, making it difficult to accurately model them statistically; second, these weak targets, with their small radar cross-sections and weak radar echoes, are easily submerged in the strong clutter background characterized by sea spikes; and third, these weak targets interact with the waves, resulting in complex motion states, making it difficult to parameterize the target echoes.

[0003] Feature-based detection methods are an important means of effectively detecting such targets. These methods combine multiple differential features with learning algorithms to detect targets. Existing feature-based detection methods fall into two main categories: one focusing on the extraction of differential features, and the other on the design of detection algorithms. While the current focus is on the extraction of differential features, existing methods have high requirements for signal-to-clutter ratios. When faced with targets with low or even negative signal-to-clutter ratios and radial velocity, detection performance is insufficient. Furthermore, radar systems require real-time processing capabilities, and even computationally complex detection methods with good results cannot meet practical requirements. Therefore, it is necessary to develop a method that can efficiently detect targets with low or even negative signal-to-clutter ratios and radial velocity. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for detecting weak targets on the sea surface based on Doppler spectrum, so as to overcome the problem that the existing technology is limited by the signal-to-noise ratio and the requirement of real-time processing of the radar system, and the detection effect cannot meet the requirements when facing moving targets with low signal-to-noise ratio and radial velocity.

[0005] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0006] A method for detecting weak targets on the sea surface based on Doppler spectrum, comprising:

[0007] Acquire a radar echo signal; wherein the range unit where the target is located in the radar echo signal is known, the range units adjacent to the range unit are used as protection units, and the remaining range units are used as pure sea clutter units;

[0008] The range unit where the target is located is taken as the unit to be detected, and the pure sea clutter unit is taken as the reference unit, and the Doppler amplitude spectrum of the unit to be detected and the Doppler amplitude spectrum of the reference unit are calculated;

[0009] Using the Doppler amplitude spectrum of the reference unit, the median function of the sea clutter Doppler amplitude spectrum is calculated;

[0010] Determine the variation based on the Doppler amplitude spectrum of the reference unit and the median function of the Doppler amplitude spectrum of the sea clutter;

[0011] Calculate the median normalized Doppler amplitude spectrum using the median function of the Doppler amplitude spectrum of the reference unit and the sea clutter Doppler amplitude spectrum;

[0012] The maximum non-zero interval length of the first characteristic of each sea clutter is calculated by using the median normalized Doppler amplitude spectrum and the zero point. The median normalized Doppler amplitude spectrum is further normalized, and the second eigenvector entropy of each sea clutter is calculated based on the normalized result. The third characteristic global accumulation of each sea clutter is calculated using the median normalized Doppler amplitude spectrum.

[0013] Calculate the eigenvector of each sea clutter in all reference cells and construct a training set;

[0014] Based on the training set and in combination with a preset false alarm rate, a detector is constructed using a fast convex hull algorithm;

[0015] After acquiring the radar echo signal of the unknown target, for each range unit, the constructed detector is used to determine whether there is a target in the signal sequence corresponding to the range unit.

[0016] Furthermore, the calculation of the Doppler amplitude spectrum of the unit to be detected and the Doppler amplitude spectrum of the reference unit is expressed as:

[0017]

[0018] Where Z(f d ), Z p (f d ) represent the Doppler amplitude spectrum of the unit to be detected and the reference unit, respectively, z(n), z p (n) represents the signal sequence corresponding to the unit to be detected and the reference unit in the radar echo signal, n represents the nth sampling point, f d is the Doppler frequency, T r is the pulse repetition period, p represents the pth reference unit, p = 1, 2, …, P, and P is the number of reference units.

[0019] Furthermore, the calculating of the median function of the sea clutter Doppler amplitude spectrum using the Doppler amplitude spectrum of the reference unit includes:

[0020]

[0021] where Z p (f d) is the Doppler amplitude spectrum of the reference unit, medain{·} means to find the median value;

[0022] The determining of the variation based on the Doppler amplitude spectrum of the reference unit and the median function of the sea clutter Doppler amplitude spectrum includes:

[0023]

[0024] in, is the median function of the sea clutter Doppler amplitude spectrum, For deterioration;

[0025] The median normalized Doppler amplitude spectrum is calculated by using the median function of the Doppler amplitude spectrum of the reference unit and the sea clutter Doppler amplitude spectrum, which is expressed as:

[0026]

[0027] Where max{·} means finding the maximum value.

[0028] Furthermore, the maximum non-zero interval length NZL is expressed as follows:

[0029]

[0030] in represents the median normalized Doppler amplitude spectrum Y(f d ) corresponds to the Doppler frequency of the maximum value, δ1 and δ2 represent Y(f d )exist The nearest zero points on both sides of express The maximum non-zero interval to which it belongs, length{·} represents the length of the sequence.

[0031] Furthermore, the expression of the vector entropy VE is as follows:

[0032]

[0033] in, represents the maximum and minimum normalized Y(f d ), max(f d )、min(f d ) are the Doppler frequencies f d The maximum and minimum values ​​of .

[0034] Furthermore, the expression of the global accumulation GA is as follows:

[0035]

[0036] Furthermore, based on the training set and in combination with a preset false alarm rate, a detector is constructed using a fast convex hull algorithm, which is expressed as:

[0037]

[0038] Where Ω represents the distribution of the training set S in the feature space, |Ω| represents the volume of the feature space, min(·) represents the minimum value, #{·} represents the number of elements in the set, r represents the rth sea clutter, P f is the false alarm rate, [·] indicates rounding.

[0039] Furthermore, after acquiring the radar echo signal of the unknown target, for each range unit, determining whether there is a target in the signal sequence corresponding to the range unit by means of the constructed detector includes:

[0040]

[0041] Where Q represents the number of triangular faces on the three-dimensional convex hull surface of the detector, (v q 1 ,v q 2 ,v q 3 ) represents the three vertices of the qth triangle of the three-dimensional convex hull, det(·) represents the calculation of the determinant, and max{·} represents the maximum value. ξ represents the eigenvector of each echo in the range unit. When the statistic η≤0, it means that the eigenvector corresponding to the echo falls inside the three-dimensional convex hull formed by the detector, and the echo is judged to be sea clutter. When the statistic η>0, it means that the eigenvector corresponding to the echo falls outside the three-dimensional convex hull formed by the detector, and the echo is judged to contain a target.

[0042] After all echoes in each distance unit are determined, it is determined whether there is a target in the detected distance unit based on the proportion of echoes containing the target.

[0043] A target detection device comprises a processor, a memory and a computer program stored in the memory; when the processor executes the computer program, the method for detecting weak sea targets based on Doppler spectrum is implemented.

[0044] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the method for detecting weak sea targets based on Doppler spectrum is implemented.

[0045] Compared with the prior art, the present invention has the following technical features:

[0046] The present invention overcomes the problem that existing methods cannot achieve a balance between high detection probability and low computational cost for weak targets with low signal-to-clutter ratio and radial velocity. It effectively suppresses the clutter level through median normalization and extracts differential features. At the same time, it accurately controls the false alarm rate based on the convex hull learning algorithm, completes the design of the detector, and ultimately achieves effective detection of weak targets with low signal-to-clutter ratio and radial velocity at sea at a low computational cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of the process of the present invention, wherein the white part is the training process, and the gray part is the testing process in a certain embodiment;

[0048] Figure 2 are the median normalized Doppler amplitude spectra of sea clutter and echoes with targets; (a) is the Doppler amplitude spectrum of sea clutter, (b) is the Doppler amplitude spectrum of echoes with targets, (c) is the median function, (d) is the variogram, (e) is the median normalized Doppler amplitude spectrum of sea clutter, and (f) is the median normalized Doppler amplitude spectrum of echoes with targets;

[0049] Figure 3 The separability of the three features in the feature space based on the normalized Doppler amplitude spectrum, where (a) is the case at 0.512s of observation time, (b) is the case at 1.024s of observation time, (c) is the case at 2.048s of observation time, and (d) is the case at 4.096s of observation time;

[0050] Figure 4 The convex hull decision region formation process is shown in Figure 1, where (a) is the initial convex hull formed by the pure clutter feature vector in the feature space, and (b) is the convex hull (decision region) and false alarm points that meet the false alarm rate requirements after training.

[0051] Figure 5 The detection performance of the proposed detector is compared with that of the traditional three-feature detector, the time-frequency three-feature detector, and the SVM-based detector on the CSIR radar database in this field.

[0052] Figure 6 This figure compares the detection performance of the detector proposed in the present invention with that of the traditional three-feature detector, the time-frequency three-feature detector, and the SVM-based detector on the unmanned aerial vehicle (UAV) radar database; among them, (a) is the detector based on the traditional three-feature detector, (b) is the detector based on the time-frequency three-feature detector, (c) is the SVM-based detector, and (d) is the detector proposed in the present invention. DETAILED DESCRIPTION

[0053] In view of the fact that existing methods are difficult to detect weak targets with negative signal-to-clutter ratio but radial velocity, the differential features extracted in the present invention (maximum non-zero interval length, vector entropy and global accumulation) have low computational complexity and are all spectral features, which are suitable for targets with negative signal-to-clutter ratio but radial velocity. Secondly, the present invention uses median and variation for normalization, which can effectively suppress the clutter level and has better robustness to outliers compared to the commonly used mean and variance normalization. The present invention aims to improve the detection probability of weak targets on the sea surface while accurately controlling the false alarm rate, while reducing the computational cost.

[0054] The present invention provides a method for detecting weak targets on the sea surface based on Doppler spectrum, which is characterized by comprising the following steps:

[0055] Step 1: Obtain a radar echo signal; wherein the range unit where the target is located in the radar echo signal is known, and the range units adjacent to the range unit are used as protection units, and the remaining range units are used as pure sea clutter units.

[0056] The radar echo signal reflected by the sea surface and received by the radar receiver is obtained through the prior database to obtain an M×N dimensional data matrix. N is the number of range cells of the radar echo signal, and M is the length of the signal sequence in the range cell. The range cell is the basic unit in the radar echo signal used to represent the fixed distance range between the target and the radar. In this scheme, since the radar echo signal comes from the prior database, the range cell where the target is located is known. The range cells adjacent to the range cell where the target is located are used as protection cells. Except for the protection cells and the range cell where the target is located, the remaining range cells can be considered as pure sea clutter cells.

[0057] For example, if a detection scenario has 11 range cells and the target is located in the 9th range cell, the 8th and 11th range cells are protected cells, and the remaining range cells are treated as pure sea clutter cells. Because the echo from the target's range cell affects the surrounding range cells, its neighboring range cells are used as protection cells.

[0058] Step 2: Take the range unit where the target is located as the unit to be detected and the pure sea clutter unit as the reference unit, and calculate the Doppler amplitude spectrum of the unit to be detected and the Doppler amplitude spectrum of the reference unit.

[0059]

[0060] Where Z(f d ), Z p (f d ) represent the Doppler amplitude spectrum of the unit to be detected and the reference unit, respectively, z(n), z p(n) represents the signal sequence corresponding to the unit to be detected and the reference unit in the radar echo signal, n represents the nth sampling point, f d is the Doppler frequency, T r is the pulse repetition period (PRT), p represents the pth reference unit, p = 1, 2, ..., P, and P is the number of reference units.

[0061] Step 3: Calculate the median function of the sea clutter Doppler amplitude spectrum using the Doppler amplitude spectrum of the reference unit.

[0062] Assuming the sea surface is locally uniform, the sea clutter characteristics within the signal sequence of the reference cell are essentially the same as those within the signal sequence of the cell to be detected. Therefore, the reference cell can be used to estimate the clutter characteristics. A pure sea clutter cell contains multiple sea clutter waves, and the number of sea clutter waves depends on the observation duration. For example, in the 1993 IPIX database, with an observation duration of 0.512 seconds, each pure sea clutter cell contains 1021 sea clutter waves.

[0063] Using the Doppler amplitude spectrum Z of the reference unit p (f d ) calculates the median function of the sea clutter Doppler amplitude spectrum Specifically expressed as:

[0064]

[0065] Where medain{·} means finding the median.

[0066] Step 4: Determine the variation based on the Doppler amplitude spectrum of the reference unit and the median function of the sea clutter Doppler amplitude spectrum.

[0067]

[0068] Step 5: Calculate the median normalized Doppler amplitude spectrum using the median function of the Doppler amplitude spectrum of the reference unit and the sea clutter Doppler amplitude spectrum:

[0069]

[0070] Where max{·} means finding the maximum value.

[0071] Step 6: Calculate the first feature of each sea clutter by using the median normalized Doppler amplitude spectrum and the zero point: the maximum non-zero interval length NZL, which is expressed as follows:

[0072]

[0073] in represents the median normalized Doppler amplitude spectrum Y(f d) corresponds to the Doppler frequency of the maximum value, δ1 and δ2 represent Y(f d )exist The nearest zero points on both sides of express The maximum non-zero interval to which it belongs, length{·} represents the length of the sequence.

[0074] Step 7: The median normalized Doppler amplitude spectrum is further normalized, and the second feature of each sea clutter is calculated based on the normalized result: vector entropy VE, which is expressed as follows:

[0075]

[0076] in, represents the maximum and minimum normalized Y(f d ), max(f d )、min(f d ) are the Doppler frequencies f d The maximum and minimum values ​​of .

[0077] Step 8: Use the median normalized Doppler amplitude spectrum to calculate the third feature of each sea clutter: global accumulation GA, which is expressed as follows:

[0078]

[0079] In step 9, the first to third features are used as the feature vectors of each sea clutter based on the Doppler amplitude spectrum.

[0080] z r =[NZL,VE,GA] (8)

[0081] Among them, z r represents the eigenvector of the rth sea clutter in the pure sea clutter unit.

[0082] Step 10: Calculate the eigenvector of each sea clutter in all reference cells and construct a training set S.

[0083]

[0084] The eigenvector of the rth sea clutter is represented by z in the above formula. r =[z 1,r ,z 2,r ,z 3,r ], R is the total number of sea clutter contained in all reference cells.

[0085] Step 11: Based on the training set and in combination with a preset false alarm rate, a detector is constructed using a fast convex hull algorithm, as follows:

[0086]

[0087] Where Ω represents the distribution of the training set S in the feature space, |Ω| represents the volume of the feature space, min(·) represents the minimum value, #{·} represents the number of elements in the set, r represents the rth sea clutter, P f is the false alarm rate, [·] indicates rounding.

[0088] Step 12, in actual application, after obtaining the radar echo signal of the unknown target, for each range unit, the constructed detector is used to determine whether there is a target in the signal sequence corresponding to the range unit. Specifically:

[0089]

[0090] Where Q represents the number of triangular faces on the three-dimensional convex hull surface of the detector, (v q 1 ,v q 2 ,v q 3 ) represents the three vertices of the qth triangle of the three-dimensional convex hull, det(·) represents the calculation of the determinant, and max{·} represents the maximum value; ξ represents the eigenvector of each echo in the distance unit; when the statistic η≤0, it means that the eigenvector corresponding to the echo falls inside the three-dimensional convex hull formed by the detector, and the echo is determined to be sea clutter; when the statistic η>0, it means that the eigenvector corresponding to the echo falls outside the three-dimensional convex hull formed by the detector, and the echo is determined to be an echo containing a target; after all the echoes in each distance unit are determined, the presence of a target in the detected distance unit is determined based on the proportion of echoes containing targets.

[0091] Example:

[0092] In one embodiment of the present invention, 10 sets of data from the CSIR radar database and data from the UAV radar database were used. The comparison of the detector proposed in the present invention with the detector based on the traditional three features, the detector based on the time-frequency three features, and the detector based on the SVM on the CSIR radar database is shown as follows: Figure 5 As shown in Figure 2, the comparison of the proposed detector with the traditional three-feature detection, the time-frequency three-feature detection and the SVM-based detector on the UAV radar database is shown in Figure 2. Figure 6 shown.

[0093] (1) In the CSIR radar database, the length of each pulse used is 512 and the false alarm rate is 0.001, as shown in Figure 5As shown in the figure, in descending order of detection performance, the order is the detector proposed in the present invention, the detector based on the time-frequency three features, the detector based on the traditional three features, and the detector based on SVM. The detector proposed in the present invention achieved the highest average detection probability of 0.902, while the results of the other three detectors were 0.897, 0.755, and 0.693, respectively. In addition, when the results of the proposed detector are comparable, its computational cost is much lower than that of the detector based on the time-frequency three features with the second best performance. In the detector based on the time-frequency three features, the complexity of the smoothed pseudo-Wigner-Ville distribution is O(NKlog2K), where N is the number of pulses and K is the length of the time window. In contrast, the complexity of the proposed Doppler feature is O(N(P+1)), where P is the number of reference units.

[0094] (2) Compared with the results of CSIR radar database, the detector proposed in this invention has the highest detection result on UAV radar database, reaching 0.9376, followed by the detector based on time-frequency three features, the detector based on SVM and the detector based on traditional three features. Figure 6 In the above example, the target trajectory can only be detected by the detector proposed in this invention and the detector based on the three features of time and frequency.

[0095] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for detecting weak targets on the sea surface based on Doppler spectrum, characterized in that: include: Acquire radar echo signals; The range unit where the target is located in the radar echo signal is known, and the range units adjacent to the range unit are used as protection units, and the remaining range units are used as pure sea clutter units; The range unit where the target is located is taken as the unit to be detected, and the pure sea clutter unit is taken as the reference unit, and the Doppler amplitude spectrum of the unit to be detected and the Doppler amplitude spectrum of the reference unit are calculated; Using the Doppler amplitude spectrum of the reference unit, the median function of the sea clutter Doppler amplitude spectrum is calculated; Determine the variation based on the Doppler amplitude spectrum of the reference unit and the median function of the Doppler amplitude spectrum of the sea clutter; Calculate the median normalized Doppler amplitude spectrum using the median function of the Doppler amplitude spectrum of the reference unit and the sea clutter Doppler amplitude spectrum; The length of the first characteristic maximum non-zero interval of each sea clutter is calculated by median-normalized Doppler amplitude spectrum and zero point; The median normalized Doppler amplitude spectrum is further normalized, and the second eigenvector entropy of each sea clutter is calculated based on the normalized result; The third characteristic global accumulation of each sea clutter is calculated using the median normalized Doppler amplitude spectrum; Calculate the eigenvector of each sea clutter in all reference cells and construct a training set; Based on the training set and in combination with a preset false alarm rate, a detector is constructed using a fast convex hull algorithm; After acquiring the radar echo signal of the unknown target, for each range unit, the constructed detector is used to determine whether there is a target in the signal sequence corresponding to the range unit.

2. The method for detecting weak targets on the sea surface based on Doppler spectrum according to claim 1, characterized in that: The calculation of the Doppler amplitude spectrum of the unit to be detected and the Doppler amplitude spectrum of the reference unit is expressed as: (1) in 、 Represent the Doppler amplitude spectrum of the unit to be detected and the reference unit respectively, 、 Respectively represent the signal sequences corresponding to the unit to be detected and the reference unit in the radar echo signal, Indicates the sampling points, is the Doppler frequency, is the pulse repetition period, Indicates the Reference units, =1,2,…, , is the number of reference cells.

3. The method for detecting weak targets on the sea surface based on Doppler spectrum according to claim 1, wherein: The method of calculating the median function of the sea clutter Doppler amplitude spectrum by using the Doppler amplitude spectrum of the reference unit includes: (2) in is the Doppler amplitude spectrum of the reference unit, medain{·} means finding the median value; The determining of the variation based on the Doppler amplitude spectrum of the reference unit and the median function of the sea clutter Doppler amplitude spectrum includes: (3) in, is the median function of the sea clutter Doppler amplitude spectrum, For deterioration; The median normalized Doppler amplitude spectrum is calculated by using the median function of the Doppler amplitude spectrum of the reference unit and the sea clutter Doppler amplitude spectrum, which is expressed as: (4) Where max{·} means finding the maximum value.

4. The method for detecting weak targets on the sea surface based on Doppler spectrum according to claim 1, wherein: The expression of the maximum non-zero interval length NZL is as follows: (5) in, is the Doppler frequency, is the pulse repetition period, represents the median normalized Doppler amplitude spectrum The Doppler frequency corresponding to the maximum value of and express exist The nearest zero points on both sides of express The largest non-zero interval to which , length {·} indicates the length of the sequence.

5. The method for detecting weak targets on the sea surface based on Doppler spectrum according to claim 1, characterized in that: The expression of vector entropy VE is as follows: (6) in, represents the Doppler amplitude spectrum normalized to the median The result after normalization of maximum and minimum values ​​is: 、 Doppler frequencies The maximum and minimum values ​​of .

6. The method for detecting weak targets on the sea surface based on Doppler spectrum according to claim 1, characterized in that: The expression of global accumulation GA is as follows: (7) in, 、 Doppler frequencies The maximum and minimum values ​​of represents the median normalized Doppler amplitude spectrum.

7. The method for detecting weak targets on the sea surface based on Doppler spectrum according to claim 1, characterized in that: Based on the training set and combined with the preset false alarm rate, a detector is constructed using the fast convex hull algorithm, which is expressed as: (11) Where Ω represents the training set S The distribution in the feature space, |Ω| represents the volume of the feature space, min(·) represents the minimum value, #{·} represents the number of elements in the set, Indicates the first The eigenvector of sea clutter, is the total amount of sea clutter contained in all reference cells, P f is the false alarm rate, [·] indicates rounding.

8. The method for detecting weak targets on the sea surface based on Doppler spectrum according to claim 1, characterized in that: After obtaining the radar echo signal of the unknown target, for each range unit, a detector is constructed to determine whether a target exists in the signal sequence corresponding to the range unit, including: (12) in, Q The number of triangular faces representing the three-dimensional convex hull surface of the detector, ( v q 1 ,v q 2 ,v q 3 ) represents the three-dimensional convex hull q The three vertices of a triangle, det(·) means calculating the determinant, and max{·} means finding the maximum value; Represents the characteristic vector of each echo in the range unit; when the statistic η When ≤0, it means that the eigenvector corresponding to the echo falls inside the three-dimensional convex hull formed by the detector, and the echo is judged to be sea clutter; when the statistic η When >0, it means that the eigenvector corresponding to the echo falls outside the three-dimensional convex hull formed by the detector, and the echo is determined to be an echo containing a target; After all echoes in each distance unit are determined, it is determined whether there is a target in the detected distance unit based on the proportion of echoes containing the target.

9. A target detection device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor executes the computer program, the method for detecting weak targets on the sea surface based on Doppler spectrum according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program; wherein: When the computer program is executed by a processor, the method for detecting weak targets on the sea surface based on Doppler spectrum according to any one of claims 1 to 8 is implemented.

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