Multi-channel ATI-SAR optimal fusion moving target detection method under strong clutter background

Abstract

The invention provides a multi-channel ATI-SAR optimal fusion moving target detection method under a strong clutter background, and the method comprises the steps: firstly carrying out the local detection through a group of multi-baseline clutter removal ATI phase testing and AMF amplitude testing, and then designing global detection optimization based on an optimal fusion rule, and carrying out the global detection. According to the method, the maximum target detection performance can be realized under a constant false alarm probability. In order to facilitate implementation, the invention develops a cognitive detection framework without priori target knowledge. According to the invention, the spatial degree of freedom is fully utilized, and the information fed back by the environment is fully utilized, so that the detection performance of the weak and small target with low signal-to-noise ratio and small radial speed in the strong clutter background is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of radar technology, and further relates to a moving target detection method of multi-channel along-track interferometry-synthetic aperture radar ATI-SAR (Along-Track Interferometry-Synthetic Aperture Radar) optimal fusion under strong clutter background in the field of radar target detection technology. The present invention can be used in the ATI-SAR system to detect small targets with low signal-to-noise ratio and slow speed under strong clutter background. Background Art

[0002] The ATI-SAR system is a special form of multi-channel SAR system, which realizes multi-frame SAR image observation of the same scene by configuring two or more SAR imaging channels in the direction of the platform's motion track. Compared with the single-channel system, the multi-channel system increases the spatial degrees of freedom of the signal and can more effectively suppress clutter, thereby improving the detection performance of low signal-to-clutter ratio moving targets. However, under the influence of complex scenes and system errors, the multi-channel system will produce serious signal-to-noise ratio loss in the process of adaptive clutter suppression, resulting in a decrease in clutter suppression capability. Therefore, the existing multi-channel ATI-SAR system is still insufficient in terms of spatial freedom utilization, especially in the strong clutter background, slow target detection under low signal-to-noise ratio is still very difficult.

[0003] Gao Gui et al. proposed a moving target detection method IMP (Interferometric Magnitude Phase) in their paper "The CFAR detection of ground moving targetsbased on a joint metric of SAR interferogram's magnitude and phase" (IEEE Transactions on Geoscience and Remote Sensing, vol.50, no.9, pp.3618-3624, 2012) that combines the interferometric amplitude and interferometric phase of SAR images along the track. The implementation process of this method is to first perform interference processing on the SAR image data of the two channels along the track, and then use the signal amplitude and ATI phase after interference to construct the detection statistic to detect the SAR image and obtain the moving target. However, the shortcomings of this method are that for multi-channel synthetic aperture radar systems, the utilization rate of spatial degrees of freedom is low. In addition, the estimation performance of the ATI phase detection statistic is seriously deteriorated under low signal-to-noise ratio conditions, and it is sensitive to channel errors and system noise. Therefore, these factors make it difficult for this method to detect slow, low signal-to-noise ratio ground moving targets under strong clutter background;

[0004] CH Gierull, I. Sikaneta et al. proposed a two-step detection method in their paper "Two-step detector for RADARSAT-2's experimental GMTI mode" (IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 436–454, 2013). The implementation process of this method is that the SAR image is initially detected in the first stage using the signal amplitude information after multi-channel adaptive clutter suppression, and then the SAR image is detected in the second step using the two-channel along-track interferometric ATI phase. The final detection result is the result of the logical AND of the two-step detection. However, the method still has the disadvantage that the ATI phase detection statistic in the second step detection only uses the echo data of two channels, which wastes the spatial freedom for the multi-channel radar system, and the estimation performance of the ATI phase statistic is seriously deteriorated under low signal-to-noise ratio conditions, resulting in its detection performance being unrobust in the strong clutter background. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and propose a moving target detection method for multi-channel ATI-SAR optimal fusion under strong clutter background, so as to solve the problem of difficulty in detecting slow targets with low spatial degrees of freedom and low signal-to-noise ratio in multi-channel ATI-SAR system under strong clutter background in the prior art.

[0006] The technical solution for achieving the purpose of the present invention is that the present invention utilizes AMF detection and multi-baseline clutter removal ATI phase detection to form a multi-scale optimal fusion target detector, performs high false alarm rate estimation on the initialized target parameters, utilizes the statistical characteristics of each detection amount, and designs global optimization according to the optimal fusion rule to determine the optimal local detection threshold. Then, the local detection results are optimally fused to make potential target judgments, and potential targets are detected as much as possible. Then, the maximum likelihood method can be used to adaptively estimate the parameters of the potential target, update the target parameters, and finally, according to the parameters of the potential target, adaptive target judgment is performed through a multi-scale optimal fusion target detector and global detection optimization to obtain the final target. Since the present invention makes full use of the spatial degrees of freedom and the information fed back by the environment, the problem of difficulty in detecting slow small targets with low signal-to-noise ratio under a strong clutter background is effectively improved.

[0007] The specific implementation steps of the present invention include the following:

[0008] S1: Use N SAR images to construct an M+1 scale detector, where the value of N is equal to the number of radar channels, M=N(N-1) / 2;

[0009] S2: Initialize each detector to obtain the target parameter K, perform the global detection optimization method on all detectors, and obtain the optimal local false alarm probability corresponding to each detector in the automatic perception stage;

[0010] S3: by estimating the corresponding statistical characteristics of each detector, obtaining the corresponding local detection threshold according to the optimal local false alarm probability and the corresponding statistical characteristics, using the corresponding local detection threshold to perform local detection on each detection amount of each detector, obtaining the binary detection result corresponding to each detector, and weighted fusion of the binary detection results corresponding to each detector to obtain the global detection amount of the target; using the optimal fusion rule, perform global detection on the global detection amount of the target to determine the potential target;

[0011] S4: Adaptively estimate the detection amount corresponding to each detector for each potential target through the maximum likelihood method, and estimate the target parameters of each potential target; use the target parameters of each potential target and the set global detection false alarm rate to perform the same global detection optimization method as step S2 to obtain the optimal local false alarm probability corresponding to each detector in the enhanced detection stage;

[0012] S5: Using the same method as step S3, obtain the global detection amount of each potential target; using the same optimal fusion rule as step S3, perform global detection on each potential target to determine the real target.

[0013] Furthermore, the M+1 scale detectors are composed of 1 detector for AMF amplitude detection and M detectors for ATI phase detection.

[0014] Furthermore, the moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background is characterized in that the AMF amplitude detection quantity is expressed as:

[0015]

[0016] Where T represents the AMF amplitude detection quantity, n represents the number of views, ∑ represents the summation operation, k represents the ordinal number of the pixel in the SAR image, u represents the normalized filter weight vector, (·) H represents the conjugate transpose operation, z represents the data vector of the complex image point of the pixel to be detected, σ cn Represents the minimum resolvable noise power.

[0017] Furthermore, the de-cluttered ATI phase detection quantity is expressed as:

[0018]

[0019] in, represents the phase detection amount after the ATI processing of the lth channel and the mth channel, arg[·] represents the phase angle, y l represents the n-view DPCA processing results of the lth channel and the 1st channel, represents the conjugate complex number of the n-view DPCA processing result of the m-th channel and the first channel, z l z1(k) and z2(k) represent the kth pixel value in the SAR image corresponding to the lth channel and the 1st channel respectively.

[0020] Further, the initialization of each detector refers to respectively initializing the AMF amplitude detection amount, the target de-cluttering ATI phase estimation detection amount, and the correlation coefficient between the target channels in the target parameter K;

[0021] Initialize the AMF amplitude estimation detection quantity according to the following formula:

[0022]

[0023] in, represents the initialization detection quantity of the AMF amplitude estimation, σ s represents the minimum resolvable signal power, represents the space-time steering vector, σ n represents the minimum resolvable noise power, k scnr =σ s / σ cn Indicates the minimum resolvable signal-to-noise ratio of the array channel;

[0024] Initialize the target de-cluttered ATI phase estimation detection quantity according to the following formula:

[0025]

[0026] in, represents the AIT phase estimation initialization detection quantity corresponding to the lth channel and the mth channel, [·] π represents the phase folding operation, v m represents the maximum unambiguous velocity in the airspace, λ represents the radar wavelength, and d l,m represents the baseline length, V represents the target radial velocity;

[0027] Initialize the space-time steering vector according to the following formula:

[0028]

[0029] in, represents the initialization space-time steering vector, exp(·) represents the exponential operation with the natural constant e as the base, and i represents the symbol of the imaginary unit;

[0030] Initialize the correlation coefficient between target channels according to the following formula:

[0031]

[0032] in, represents the correlation coefficient initialized between the target lth channel and the mth channel, ρ0 represents a constant, ρ0=0.99.

[0033] Furthermore, the steps of the global detection optimization method are as follows:

[0034] In the first step, the following optimization problem is established to obtain a set of optimal local detection false alarm probabilities:

[0035]

[0036] Among them, |·| represents the absolute value operation, P d represents the detection probability of global detection, P f represents the false alarm probability of global detection, r represents the global false alarm probability set according to the receiver performance requirements, and P fj represents the false alarm probability of the jth detection unit;

[0037] The second step is to calculate the global detection false alarm probability P corresponding to each set of local detection false alarm probability candidate values. f for:

[0038]

[0039] Among them, L represents the number of candidate values ​​of local detection false alarm probability exceeding the global detection threshold η in each group G The number of G =log(P(H0) / P(H1)), log represents the logarithmic operation with base 10, P(H1) and P(H0) represent the prior probability of the existence or non-existence of the moving target, ∏· represents the symbol of the multiplication operation, D 1,l , D 0,l Respectively represent the set of identifiers in the lth group that exceed the local detection threshold and the set of identifiers that do not exceed the local detection threshold, l = 1, 2, ..., L, u j Represents the detection result of the local detection unit, u j = +1 indicates that the local detection unit determines that there is a target, u j = -1 indicates that the local detection unit determines that there is no target, 1-P fk represents the correct judgment probability of the kth detection unit;

[0040] The third step is to calculate the global detection probability P corresponding to the candidate value of the local detection false alarm probability. d for:

[0041]

[0042] Among them, P dj represents the detection probability of the jth detection unit, 1-P dk represents the missed detection probability of the kth detection unit;

[0043] The fourth step is to traverse each candidate value of the local detection false alarm probability, repeat the second and third steps and record the global false alarm probability and the detection probability until the optimal local detection false alarm probability of the optimization problem described in the first step is obtained.

[0044] Furthermore, the step of obtaining the corresponding local detection threshold according to the optimal local false alarm probability and the corresponding statistical characteristics is as follows:

[0045] The first step is to use the following formula to calculate the probability density function of the AMF amplitude detection quantity T under the background of non-uniform clutter:

[0046]

[0047] Among them, f AMF (·) represents the probability density function of the AMF amplitude detection quantity T, t represents the AMF amplitude detection quantity, represents the equivalent view number, χ represents the non-uniformity of clutter, χ>0, the larger the χ value, the more uniform the clutter scene is, and the χ value is estimated by the second-order statistic m2 of the AMF amplitude detection quantity. H0 represents the hypothesis without a target, Γ(·) represents the Gamma function;

[0048] When there is a moving target signal, according to the following formula, let its corresponding AMF amplitude detection quantity be ω, and the probability density function of the AMF amplitude detection quantity T is:

[0049]

[0050] Where t represents the AMF amplitude detection quantity, H1 represents the hypothesis of the existence of the target, and 2F1(·) represents the Gaussian hypergeometric function;

[0051] Given the false alarm probability P f0 , according to the following formula, the corresponding amplitude detection threshold is obtained:

[0052]

[0053] in, Represents the calculation of the probability density function f from the threshold η0 to positive infinity AMF The cumulative probability of (·);

[0054] The second step is to remove the clutter ATI phase detection quantity under the clutter background according to the following formula: The probability density function of is:

[0055]

[0056] Among them, f p (·) represents the noise-free ATI phase detection amount The probability density function of represents the mean of the ATI phase of the cluttered samples, Represents the correlation coefficient between channel l and channel m. According to the following formula, it is estimated

[0057]

[0058] in, Represents the estimated value of the correlation coefficient between channel l and channel m;

[0059] When there is a moving target signal, the corresponding ATI phase detection value is ATI phase detection with noise removal The probability density function of is:

[0060]

[0061] Given the false alarm probability P fj , the corresponding detection threshold η j It can be obtained by the following formula

[0062]

[0063] Here, |·| represents an absolute value operation.

[0064] Furthermore, the steps of performing local detection on each detection amount of each detector using the corresponding local detection threshold to obtain the binary detection result corresponding to each detector are as follows:

[0065] The first step is to perform local detection: for the AMF amplitude detection, determine whether the target exists according to T≥η0, where η0 represents the detection threshold; for any de-cluttered ATI phase detection, determine whether the target exists according to Determine whether the target exists; among them, represents the ATI phase detection value of the jth detection unit, η j represents the detection threshold of the jth detection unit;

[0066] In the second step, the amount of detection that passes the local limit is recorded as u j = +1, the detection amount that fails to pass the local detection threshold is recorded as u j=-1, j = 0, 1, 2, … M, then the binary detection result corresponding to each detector is obtained.

[0067] Furthermore, the use of the optimal fusion rule refers to the optimal fusion rule based on Chair and Varshney.

[0068] Furthermore, the global detection amount of the target obtained by weighted fusion of the binary detection results corresponding to each detector is obtained by the following formula:

[0069]

[0070] Among them, a j Represents the weighted fusion coefficient obtained from the detection credibility of the local detection quantity.

[0071] Furthermore, the steps of adaptively estimating the detection amount corresponding to each detector for each potential target and estimating the target parameters of each potential target by the maximum likelihood method are as follows:

[0072] In the first step, the mean of the AMF amplitude estimation detection values ​​of all potential targets is used as the AMF amplitude estimation detection value;

[0073] In the second step, the estimated value of the ATI phase estimation detection quantity of each potential target is estimated according to the following formula:

[0074]

[0075] in, represents the estimated value of the qth potential target de-cluttered AIT phase detection quantity of the lth channel and the mth channel;

[0076] The third step is to estimate the channel correlation coefficient of each potential target according to the following formula:

[0077]

[0078] in, represents the estimated value of the channel correlation coefficient between the lth channel and the qth potential target of the mth channel, E[·] represents the expected operation, z l Represents the complex signal data of the lth channel, z m Represents the complex signal data of the mth channel.

[0079] Compared with the prior art, the present invention has the following advantages:

[0080] First, since the present invention uses N SAR images to construct a group of multi-scale detectors, namely, AMF test and multi-baseline clutter removal ATI phase test, and estimates the respective distribution characteristics of the multi-scale detectors, the receiver performance of each local test can be correctly predicted, and the optimal local detection threshold is determined by global optimization through optimal fusion rule design, which overcomes the problem of excessively high false alarm probability of moving target detection and deterioration of system MDV caused by isolated strong clutter residues under strong clutter background, so that the present invention has the ability to detect low signal-to-noise ratio and slow-moving targets, and improves the performance of multi-channel ATI-SAR in detecting weak and small targets with low signal-to-noise ratio and small radial velocity in strong clutter background.

[0081] Second, the present invention develops a cognitive detection framework that does not require prior target knowledge. Since the information fed back from the environment is more accurate than fixed information, the real target is retained, and the false positive target will be eliminated. This enables the present invention to achieve multi-channel ATI-SAR optimal fusion moving target detection under strong clutter background, and effectively improves the detection performance of weak targets with low signal-to-noise ratio and small radial velocity in a strong clutter background. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a flow chart of an embodiment of the present invention;

[0083] Figure 2 is a comparison diagram of the estimated error of the conventional ATI phase and the de-cluttered ATI phase of the present invention as a function of the input signal-to-noise ratio (SCNR);

[0084] Figure 3 This is a curve of the detection probability of the comparison method changing with the false alarm probability when the signal-to-noise ratio of the moving target is 10dB. DETAILED DESCRIPTION

[0085] The present invention will be described in more detail below in conjunction with the accompanying drawings and embodiments.

[0086] See attached Figure 1 The specific implementation steps of the present invention are further described in detail with reference to the accompanying drawings.

[0087] Step 1: Construct AMF detection quantity.

[0088] Under the unknown target-oriented constraint, a robust clutter suppression method is adopted, and its filter weight vector w opt Obtained by the following formula:

[0089]

[0090] Where R represents the maximum likelihood estimate of the clutter plus noise covariance matrix, a s represents the velocity steering vector of the moving target, ε represents the velocity steering vector set of the target of interest, v m Indicates the maximum unambiguous speed in the airspace.

[0091] The normalized filter weight vector obtained by solving is

[0092] After clutter suppression, the average output power of the background is estimated to be σ cn 2 =u H Such as.

[0093] After normalizing the filter output power, the amplitude detection value is:

[0094]

[0095] Where z represents the complex image data vector of the pixel to be detected, n represents the number of views, ∑ represents the summation operation, (·) H represents the conjugate transpose operation, H0 represents the hypothesis that there is no target, H1 represents the hypothesis that there is a target, and η1 represents the amplitude detection threshold.

[0096] Step 2: construct a multi-baseline clutter-free ATI phase detection quantity.

[0097] Step 2.1, for an N-channel ATI-SAR system, take the first channel as the reference channel, perform n-view DPCA processing on the lth channel and the first channel to obtain the signal after cancellation:

[0098]

[0099] Among them, y l represents the n-view DPCA processing result of the lth channel and the 1st channel, z l (k) represents the data corresponding to the kth pixel on the SAR image of the lth channel, and z1(k) represents the data corresponding to the kth pixel on the SAR image of the 1st channel. For N channels, N-1 independent cancellation signals can be obtained by performing n-view DPCA processing results, which are expressed as y l (k), l = 2, 3, ..., N, k represents the pixel number on the SAR image.

[0100] Step 2.2, using the cancellation signals to perform n-view ATI processing on each pair, we can obtain M = (N-2)(N-2) / 2 independent ATI phase detection quantities to remove clutter, with y l (k) and y m (k) Example, the corresponding de-cluttered AIT phase detection quantity is expressed as:

[0101]

[0102] Step 2.3, after the N-1 cancellation signals are processed by S21 and S22, the corresponding M=(N-2)(N-2) / 2 independent de-cluttered ATI phase detection quantities are:

[0103]

[0104] Step 3: Estimate statistical properties.

[0105] Step 3.1, under the background of non-uniform clutter, the probability density function of the S14 amplitude detection quantity T is:

[0106]

[0107] in, represents the equivalent view number, χ represents the non-uniformity of the clutter, χ>0, the larger the χ value, the more uniform the clutter scene. The χ value is estimated by the second-order statistic m2 of the amplitude detection quantity, ν=(2m2n-(n+1)2) / (m2n-(n+1)2), Γ(·) represents the Gamma function.

[0108] Step 3.2, when there is a moving target signal, let its amplitude detection value be ω, and the probability density function of the S14 amplitude detection value T be:

[0109]

[0110] Where 2F1(·) represents the Gaussian hypergeometric function.

[0111] Step 3.3, under the clutter background, remove the clutter ATI phase detection quantity The probability density function of is:

[0112]

[0113] in, represents the mean of the ATI phase of the cluttered samples, Represents the correlation coefficient between channel l and channel m.

[0114] Step 3.4, The estimation formula is

[0115] Step 3.5, when there is a moving target signal, set the corresponding ATI phase detection value of the clutter removal to ATI phase detection with noise removal The probability density function of is:

[0116]

[0117] Step 4: Initialize target parameters.

[0118] The estimated detection quantity of AMF is initialized as:

[0119]

[0120] Among them, σ s represents the minimum resolvable signal power, represents the space-time steering vector, σ n represents the minimum resolvable noise power, k scnr =σ s / σ cn It represents the minimum resolvable signal-to-noise ratio of the array element channel.

[0121] Target clutter removal ATI phase estimation detection quantity Initialized to:

[0122]

[0123] in,[·] π represents the phase folding operation, λ represents the radar wavelength, and d l,m represents the baseline length, and V represents the target radial velocity.

[0124] Space-time Steering Vector Initialized to:

[0125]

[0126] Here, exp(·) represents an exponential operation with the natural constant e as the base, and i represents the symbol of the imaginary unit.

[0127] Initialize the correlation coefficient between the target lth channel and the mth channel:

[0128]

[0129] Wherein, ρ0 represents a constant, and in the embodiment of the present invention, ρ0=0.99.

[0130] Step 5: Global detection optimization.

[0131] Global detection optimization is performed using target parameters initialized by S4 and high false alarm.

[0132] For each set of local detection false alarm probabilities, it is not necessarily possible to obtain a good global detection result. According to maximizing the global detection probability, the following optimization problem is established to obtain a set of optimal local detection false alarm probabilities:

[0133]

[0134] Among them, |·| represents the absolute value operation, P f and P dThey represent the global false alarm probability and detection probability, r represents the false alarm probability given according to the receiver performance requirements, and P fj represents the false alarm probability of the jth detection unit.

[0135] Given a set of candidate values ​​of local detection false alarm probability, the false alarm probability of global detection corresponding to S52 is:

[0136]

[0137] Where L represents the number of candidates for the local false alarm probability that exceed the global detection threshold, and D 1,l and D 0,l Respectively represent the set of identifiers that exceed the local detection threshold and the set of identifiers that do not exceed the local detection threshold in these L cases, l = 1, 2, ..., L, u j and u k Represents the detection result of the local detection unit, u j = +1 indicates that the local detection unit determines that there is a target, u k = -1 indicates that the local detection unit determines that there is no target, 1-P fk Represents the correct judgment probability of the kth detection unit.

[0138] The corresponding global detection probability is:

[0139]

[0140] Among them, P d It represents the detection probability of global detection corresponding to the candidate value of local detection false alarm probability.

[0141] By traversing each local detection false alarm probability candidate value, the final optimized detection result can be obtained through S53 and S54.

[0142] Step 6: Identify potential targets.

[0143] For AMF local detection, given the false alarm probability P f0 , the corresponding amplitude detection threshold η0 and false alarm probability are as follows:

[0144]

[0145] Among them, f AMF (·) represents the probability density function of the AMF amplitude detection quantity.

[0146] For AMF local detection, under the detection threshold η1, the corresponding detection probability is:

[0147]

[0148] For any ATI phase detection quantity, Determine whether the target exists, η j Indicates the detection threshold.

[0149] Given the false alarm probability P fj , the corresponding detection threshold η j It can be obtained by the following formula:

[0150]

[0151] At the detection threshold η j The corresponding detection probability is calculated as follows:

[0152]

[0153] The optimal local detection P obtained after S5 global detection optimization fa Next, perform local detection.

[0154] Based on the local detection results, the detection amount passing the local limit is recorded as u j = +1, the detection amount that fails to pass the local detection threshold is recorded as u j =-1, j = 0, 1, 2, ... M, then the global detection quantity of the moving target is expressed as: Y = a0u0 + a1u1 + a2u2 + ... + a M u M ; a j It means that the weighting coefficient is determined by the detection reliability of the local detection quantity:

[0155]

[0156] The global detection is expressed as: Among them, η G represents the global detection threshold, η G =log(P(H0) / P(H1)), P(H1) and P(H0) respectively represent the prior probability of the existence or non-existence of the moving target. Without loss of generality, P(H1)=P(H0)=0.5.

[0157] Perform potential target determination and output potential target results.

[0158] Step 7: Adaptively estimate target parameters.

[0159] According to the potential target results, assuming there are Q potential targets, the target parameters are adaptively estimated for each potential target according to the maximum likelihood method.

[0160] is the average value of the AMF detection of Q potential targets, and the AMF detection of each target is:

[0161] Adaptive Estimation Among them, q∈{1,2,…,Q}.

[0162] Adaptive Estimation Among them, q∈{1,2,…,Q}.

[0163] Step 8: Update target parameters and optimize global detection;

[0164] The latent target parameters from step 7 are used to perform global detection optimization.

[0165] For each set of local detection false alarm probabilities, it is not necessarily possible to obtain a good global detection result. According to maximizing the global detection probability, the following optimization problem is established to obtain a set of optimal local detection false alarm probabilities:

[0166]

[0167] Among them, P f and P d They represent the global detection false alarm probability and detection probability respectively, and r represents the false alarm probability given according to the receiver performance requirements.

[0168] Given a set of candidate values ​​of local detection false alarm probability, the false alarm probability of global detection corresponding to S82 is:

[0169]

[0170] The corresponding global detection probability is:

[0171]

[0172] Where L represents the number of candidates for the local false alarm probability that exceed the global detection threshold, and D 1,l and D 0,l They represent the set of identifiers that exceed the local detection threshold and the set of identifiers that do not exceed the local detection threshold in these L cases, l=1,2,…,L.

[0173] By traversing each local detection false alarm probability candidate value, the final optimized detection result can be obtained through S84.

[0174] Step 9: Determine the final goal.

[0175] For AMF local detection, given the false alarm probability P f0 The corresponding relationship between the amplitude detection threshold η0 and the false alarm probability is as follows:

[0176]

[0177] For AMF local detection, under the detection threshold η1, the corresponding detection probability is:

[0178]

[0179] For any ATI phase detection quantity, Determine whether the target exists, where η j Indicates the detection threshold.

[0180] Given the false alarm probability P fj , the corresponding detection threshold η j It can be obtained by the following formula:

[0181]

[0182] At the detection threshold η j Next, calculate the corresponding detection probability according to the following formula:

[0183]

[0184] The optimal local detection P obtained after global detection optimization in step 8 fa Next, perform local detection.

[0185] Based on the local detection results, the detection amount passing the local limit is recorded as u j = +1, the detection amount that fails to pass the local detection threshold is recorded as u j =-1, j = 0, 1, 2, ... M, then the global detection quantity of the moving target is expressed as: Y = a0u0 + a1u1 + a2u2 + ... + a M u M , where a j It means that the weighting coefficient is determined by the detection reliability of the local detection quantity:

[0186]

[0187] S98: Global detection is expressed as:

[0188]

[0189] Among them, η G represents the global detection threshold, η G =log(P(H0) / P(H1)), P(H1) and P(H0) represent the prior probability of the existence or non-existence of the moving target, respectively. Without loss of generality, P(H1)=P(H0)=0.5;

[0190] S99: Make a final target determination and output the final target result.

[0191] The effect of the present invention is further described below in conjunction with simulation experiments:

[0192] 1. Simulation experiment conditions:

[0193] The environment of the simulation experiment of the present invention is: MATLAB R2021a, Intel(R)Core(TM)2Duo CPU 16GHz, and Windows 10 Professional Edition.

[0194] 2. Simulation content and result analysis:

[0195] The effectiveness of the algorithm is verified using simulation data.

[0196] The scene parameters in the simulation experiment are shown in Table 1. When the radar is working, the antenna transmits with full aperture and receives with sub-aperture. After the four channels receive the echo signals, SAR imaging processing, radiation error correction, terrain interferometry phase compensation and channel registration processing are performed respectively to obtain four SAR images. Then, the AMF detection method in the literature (F. Robey, D. Fuhrmann, E. Kelly, and R. Nitzberg, “A CFAR adaptive matched filter detector,” IEEE Transactions on Aerospace and Electronic Systems, vol. 28, no. 1, pp. 208–216, 1992.), the ATI phase detection method in the literature (CH Gierull, “Closed-form expressions for InSAR sample statistics and its application to non-Gaussian data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 3967–3980, 2021.), and the ATI phase detection method in the literature (CH Gierull, I. Sikaneta, and D. Cerutti-Maori, “Two-step detector for RADARSAT-2's experimental GMTI mode,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 3967–3980, 2021.) were used respectively. The two-step detection method in RemoteSensing, vol.51, no.1, pp.436–454, 2013.) and the method proposed in the present invention are used to detect moving targets. Through 1 million Monte Carlo simulation experiments, the detection probability (Pd) and false alarm probability (Pfa) of the two-step detection method are statistically compared.

[0197] Table 1 Simulation experiment scenario parameters

[0198] name Numeric Radar wavelength 0.02 Platform speed 100m / s Baseline length 1m Number of channels 4 Clutter scene non-uniformity 3 CNR Changes between 20dB and 50dB Equivalent View Number 2 Moving target radial velocity 2m / s

[0199] Figure 2 : is a comparison diagram of the estimated error of the traditional ATI phase and the de-cluttered ATI phase of the present invention as a function of the input signal-to-noise ratio (SCNR) in the simulation experiment of the present invention, wherein: Figure 2 The right vertical axis in represents the output SCNR of the moving target after DPCA processing.

[0200] Figure 3 It is a curve showing the variation of detection probability with false alarm probability of the comparison method when the signal-to-noise ratio of the moving target in the simulation experiment of the present invention is 10 dB.

[0201] from Figure 2 It can be seen that compared with the traditional ATI phase, the estimation error of the clutter-free ATI phase of the present invention is smaller, especially when the moving target input signal-to-noise ratio (SCNR) is low, the accuracy is higher. Figure 3 It can be seen that under the same false alarm probability, compared with the AMF detection method, the ATI detection method and the AMF+ATI two-step detection method, the proposed method can obtain a better detection rate, indicating that the proposed method has better detection performance.

[0202] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A moving target detection method based on optimal fusion of multi-channel ATI-SAR under strong clutter background, characterized in that: Using multi-channel ATI-SAR data, a multi-scale optimal fusion target detector was generated, and a cognitive detection framework without prior target knowledge was developed; the steps of the detection method include the following: S1: Use N SAR images to construct an M+1 scale detector, where the value of N is equal to the number of radar channels, M=N(N-1) / 2; S2: Initialize each detector to obtain the target parameter K, perform the global detection optimization method on all detectors, and obtain the optimal local false alarm probability corresponding to each detector in the automatic perception stage; S3: by estimating the corresponding statistical characteristics of each detector, obtaining the corresponding local detection threshold according to the optimal local false alarm probability and the corresponding statistical characteristics, using the corresponding local detection threshold to perform local detection on each detection amount of each detector, obtaining the binary detection result corresponding to each detector, and weighted fusion of the binary detection results corresponding to each detector to obtain the global detection amount of the target; using the optimal fusion rule, perform global detection on the global detection amount of the target to determine the potential target; S4: Adaptively estimate the detection amount corresponding to each detector for each potential target through the maximum likelihood method, and estimate the target parameters of each potential target; use the target parameters of each potential target and the set global detection false alarm rate to perform the same global detection optimization method as step S2 to obtain the optimal local false alarm probability corresponding to each detector in the enhanced detection stage; S5: Using the same method as step S3, obtain the global detection amount of each potential target; using the same optimal fusion rule as step S3, perform global detection on each potential target to determine the real target.

2. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 1 is characterized in that: The M+1 scale detectors in step S1 are composed of 1 detector for AMF amplitude detection and M detectors for ATI phase detection.

3. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 2 is characterized in that: The AMF amplitude detection quantity is expressed as: Where T represents the AMF amplitude detection quantity, n represents the number of views, ∑ represents the summation operation, k represents the ordinal number of the pixel in the SAR image, u represents the normalized filter weight vector, (·) H represents the conjugate transpose operation, z represents the data vector of the complex image point of the pixel to be detected, σ cn Represents the minimum resolvable noise power.

4. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 3 is characterized in that: The de-cluttered ATI phase detection quantity is expressed as: in, represents the phase detection amount after the ATI processing of the lth channel and the mth channel, arg[·] represents the phase angle, y l represents the n-view DPCA processing results of the lth channel and the 1st channel, represents the conjugate complex number of the n-view DPCA processing result of the m-th channel and the first channel, z l z1(k) and z2(k) represent the kth pixel value in the SAR image corresponding to the lth channel and the 1st channel respectively.

5. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 4, characterized in that: Initializing each detector in step S2 means respectively initializing the AMF amplitude detection amount, the target de-cluttering ATI phase estimation detection amount, and the correlation coefficient between the target channels in the target parameter K; Initialize the AMF amplitude estimation detection quantity according to the following formula: in, represents the initialization detection quantity of the AMF amplitude estimation, σ s represents the minimum resolvable signal power, represents the space-time steering vector, σ n represents the minimum resolvable noise power, k scnr =σ s / σ cn Indicates the minimum resolvable signal-to-noise ratio of the array channel; Initialize the target de-cluttered ATI phase estimation detection quantity according to the following formula: in, represents the AIT phase estimation initialization detection quantity corresponding to the lth channel and the mth channel, [·] π represents the phase folding operation, v m represents the maximum unambiguous velocity in the airspace, λ represents the radar wavelength, and d l,m represents the baseline length, V represents the target radial velocity; Initialize the space-time steering vector according to the following formula: in, represents the initialization space-time steering vector, exp(·) represents the exponential operation with the natural constant e as the base, and i represents the symbol of the imaginary unit; Initialize the correlation coefficient between target channels according to the following formula: in, represents the correlation coefficient initialized between the target lth channel and the mth channel, ρ0 represents a constant, ρ0=0.

99.

6. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 5, characterized in that: The steps of the global detection optimization method in step S2 are as follows: In the first step, the following optimization problem is established to obtain a set of optimal local detection false alarm probabilities: Among them, |·| represents the absolute value operation, P d represents the detection probability of global detection, P f represents the false alarm probability of global detection, r represents the global false alarm probability set according to the receiver performance requirements, and P fj represents the false alarm probability of the jth detection unit; The second step is to calculate the global detection false alarm probability P corresponding to each set of local detection false alarm probability candidate values. f for: Among them, L represents the number of candidate values ​​of local detection false alarm probability exceeding the global detection threshold η in each group G The number of G =log(P(H0) / P(H1)), log represents the logarithmic operation with base 10, P(H1) and P(H0) represent the prior probability of the existence or non-existence of the moving target, ∏· represents the symbol of the multiplication operation, D 1,l , D 0,l Respectively represent the set of identifiers in the lth group that exceed the local detection threshold and the set of identifiers that do not exceed the local detection threshold, l = 1, 2, ..., L, u j and u k Represents the detection result of the local detection unit, u j = +1 indicates that the local detection unit determines that there is a target, u k = -1 indicates that the local detection unit determines that there is no target, 1-P fk represents the correct judgment probability of the kth detection unit; The third step is to calculate the global detection probability P corresponding to the candidate value of the local detection false alarm probability. d for: Among them, P dj represents the detection probability of the jth detection unit, 1-P dk represents the missed detection probability of the kth detection unit; The fourth step is to traverse each candidate value of the local detection false alarm probability, repeat the second and third steps and record the global false alarm probability and the detection probability until the optimal local detection false alarm probability of the optimization problem described in the first step is obtained.

7. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 6, characterized in that: The steps of obtaining the corresponding local detection threshold according to the optimal local false alarm probability and the corresponding statistical characteristics in step S3 are as follows: The first step is to use the following formula to calculate the probability density function of the AMF amplitude detection quantity T under the background of non-uniform clutter: Among them, f AMF (·) represents the probability density function of the AMF amplitude detection quantity T, t represents the AMF amplitude detection quantity, represents the equivalent view number, χ represents the non-uniformity of clutter, χ>0, the larger the χ value, the more uniform the clutter scene is, and the χ value is estimated by the second-order statistic m2 of the AMF amplitude detection quantity. H0 represents the hypothesis without a target, Γ(·) represents the Gamma function; When there is a moving target signal, according to the following formula, let its corresponding AMF amplitude detection quantity be ω, and the probability density function of the AMF amplitude detection quantity T is: Where t represents the AMF amplitude detection quantity, H1 represents the hypothesis of the existence of the target, and 2F1(·) represents the Gaussian hypergeometric function; Given the false alarm probability P f0 , according to the following formula, the corresponding amplitude detection threshold is obtained: in, Represents the calculation of the probability density function f from the threshold η0 to positive infinity AMF The cumulative probability of (·); The second step is to remove the clutter ATI phase detection quantity under the clutter background according to the following formula: The probability density function of is: Among them, f p (·) represents the noise-free ATI phase detection amount The probability density function of represents the mean of the de-cluttered ATI phase of the cluttered samples, Represents the correlation coefficient between channel l and channel m. According to the following formula, it is estimated in, Represents the estimated value of the correlation coefficient between channel l and channel m; When there is a moving target signal, the corresponding ATI phase detection value is ATI phase detection with noise removal The probability density function of is: Given the false alarm probability P fj , the corresponding detection threshold η j It can be obtained by the following formula: Here, |·| represents an absolute value operation.

8. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 7, characterized in that: The steps of performing local detection on each detection amount of each detector using the corresponding local detection threshold in step S3 to obtain the binary detection result corresponding to each detector are as follows: The first step is to perform local detection: for the AMF amplitude detection, determine whether the target exists according to T≥η0, where η0 represents the detection threshold; for any de-cluttered ATI phase detection, determine whether the target exists according to Determine whether the target exists; among them, represents the ATI phase detection value of the jth detection unit, η j represents the detection threshold of the jth detection unit; In the second step, the amount of detection that passes the local limit is recorded as u j = +1, the detection amount that fails to pass the local detection threshold is recorded as u j =-1, j = 0, 1, 2, … M, then the binary detection result corresponding to each detector is obtained.

9. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 8, characterized in that: The optimal fusion rule in step S3 refers to the optimal fusion rule based on Chair and Varshney.

10. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 9, characterized in that: The global detection amount of the target obtained by weighted fusion of the binary detection results corresponding to each detector in step S3 is obtained by the following formula: Among them, a j Represents the weighted fusion coefficient obtained from the detection credibility of the local detection quantity.

11. The moving target detection method of multi-channel ATI-SAR optimal fusion under strong clutter background according to claim 10, characterized in that: The steps of adaptively estimating the detection amount corresponding to each detector for each potential target and estimating the target parameters of each potential target in step S4 by the maximum likelihood method are as follows: In the first step, the mean of the AMF amplitude estimation detection values ​​of all potential targets is taken as the AMF amplitude estimation detection value; In the second step, the estimated value of the ATI phase estimation detection quantity of each potential target is estimated according to the following formula: in, represents the estimated value of the qth potential target de-cluttered AIT phase detection quantity of the lth channel and the mth channel; The third step is to estimate the channel correlation coefficient of each potential target according to the following formula: in, represents the estimated value of the channel correlation coefficient between the lth channel and the qth potential target of the mth channel, E[·] represents the expected operation, z l Represents the complex signal data of the lth channel, z m Represents the complex signal data of the mth channel.

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