A long-time phase correlation accumulation method with joint constraint of operation complexity and detection probability

Abstract

The application provides a long-time phase accumulation method with combined constraints of operation complexity and detection probability, belongs to the technical field of radar detection, and is mainly used for solving the range-Doppler migration problem of a radar when the radar is used for gazing at a weak maneuvering target. The application adopts Keystone transformation to eliminate range migration, optimizes the number of acceleration channels under the combined condition constraints of operation complexity and detection probability, and adopts double-stage acceleration channel fine Doppler demodulation technology to eliminate Doppler migration based on the optimization, so that long-time phase accumulation is realized.

Description

Technical Field

[0001] This invention relates to the field of radar detection technology. Background Technology

[0002] Detecting weak targets in cluttered environments has always been a common challenge in the radar field. Based on radar's staring detection of weak targets, employing long-term coherent accumulation technology to efficiently gather target echo energy and precisely separate the target from clutter through high Doppler resolution presents an excellent solution for weak target detection. During staring detection, the radar's CPI period is long, and the target can easily cross multiple range-Doppler resolution cells within a single CPI, causing the target energy to be dispersed in the RD domain, resulting in range-Doppler migration. Long-term coherent accumulation technology can effectively focus target energy in the RD domain, thus providing high-quality target information for robust detection of weak targets.

[0003] Typical long-term coherent accumulation techniques employ Keystone transform to eliminate range migration and Doppler compensation to eliminate Doppler migration. For radially uniformly accelerated targets, Doppler compensation essentially involves first demodulating the quadratic phase term using the estimated radial acceleration of the target, and then focusing the target energy using a traditional coherent accumulation algorithm. Common Doppler compensation methods include Doppler demodulation, fractional Fourier transform (FrFT), polynomial phase transform (PPT), and LVD algorithms. These algorithms have extremely high computational complexity and are difficult to implement in engineering. The long-term coherent accumulation methods disclosed in patents such as CN106970371B and CN110824439B all employ parameter search methods. Since high-precision target acceleration estimation depends on fine-grained parameter search steps, the computational complexity of these algorithms increases dramatically with the increasing accuracy requirements of acceleration estimation. Summary of the Invention

[0004] To address the range-Doppler migration problem in radar staring at weak maneuvering targets and improve radar's detection capability in strong clutter environments, this invention proposes a long-term coherent accumulation method with joint constraints on computational complexity and detection probability. By jointly constraining computational complexity and detection probability, the parameter search range is optimized. Under the condition that the target detection probability is not reduced, the optimal parameter search interval is obtained by minimizing computational complexity, which significantly reduces the algorithm complexity and is easy to implement in engineering.

[0005] The technical solution for achieving this invention is as follows: Keystone transform is used to eliminate distance migration; the number of acceleration channels is optimized through joint constraints of computational complexity and detection probability; and based on this, a two-stage acceleration channel fine Doppler demodulation technique is employed to eliminate Doppler migration, thereby achieving long-term coherent accumulation. The specific steps are as follows:

[0006] Step 1: Use Keystone transform to eliminate target distance migration;

[0007] Step 2: Optimize the number of acceleration channels based on the criterion of minimizing computational complexity;

[0008] Step 3: Optimize the number of acceleration channels based on detection probability constraints;

[0009] Step 4: Dual-stage acceleration channel Doppler demodulation enables long-term coherent accumulation.

[0010] Compared with the prior art, the present invention has the following significant advantages: by jointly constraining the acceleration search range by combining computational complexity and detection probability, the optimal acceleration search interval is obtained by minimizing computational complexity under the condition that the target detection probability is not reduced, which significantly reduces the algorithm complexity and is easy to implement in engineering.

[0011] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0012] Figure 1 A flowchart of a long-term coherent accumulation method with joint constraints on computational complexity and detection probability;

[0013] Figure 2 Energy distribution map of the target range-velocity domain after traditional coherent accumulation processing;

[0014] Figure 3 The target range-velocity domain energy distribution map after long-term coherent accumulation processing. Detailed Implementation

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of protection of the present invention is not limited by the implementing regulations.

[0016] Combination Figure 1 This invention describes a long-term coherent accumulation method that combines computational complexity and detection probability constraints. For example... Figure 1 As shown, a preferred embodiment of the present invention includes the following steps:

[0017] Step 1: Use Keystone transform to eliminate target distance migration;

[0018] Step 2: Optimize the number of acceleration channels based on the criterion of minimizing computational complexity;

[0019] Step 3: Optimize the number of acceleration channels based on detection probability constraints;

[0020] Step 4: Dual-stage acceleration channel Doppler demodulation enables long-term coherent accumulation.

[0021] Step 2 includes the following steps:

[0022] Step 2-1: Preset the number of coherent pulses N and the upper limit of the acceleration range a max Maximum unambiguous speed V max Radar pulse repetition period T r The computational complexity of the two-stage Doppler demodulation process is O(N). a ):

[0023] O(N a ) = N a +(4a max / (N a -1))(1 / (2V max / (N(N-1)T r ))) (1);

[0024] In the formula, N a This represents the number of primary acceleration channels.

[0025] Step 2-2: Solve for the optimal number of first-stage acceleration channels P based on the minimum computational complexity criterion:

[0026]

[0027] In the formula, N a This represents the number of primary acceleration channels.

[0028] Step 3 includes the following steps:

[0029] Step 3-1: Initialize the optimal number of first-level acceleration channels K = P under the joint constraints of computational complexity and detection probability;

[0030] Step 3-2: For the acceleration interval [-a] max ,a max K acceleration values ​​were obtained through uniform sampling:

[0031] a k =-a max +k·2a max / K,k=0,1,...,K-1 (3);

[0032] In the formula, k is the index number of the first-level acceleration channel.

[0033] Steps 3-4: In the distance cell where the target is located, for each a k Doppler demodulation processing is performed on radar echoes that have had their range migration eliminated in the slow time domain:

[0034]

[0035] In the formula, x(n) represents the slow-time domain sample of the target echo after eliminating range migration, and t n λ represents slow time, and λ represents wavelength.

[0036] Steps 3-5: Demodulate the Doppler sample data x d (a k Perform coherent accumulation on n) and search for the acceleration channel index number corresponding to the coarse estimate of the target radial acceleration.

[0037]

[0038] In the formula, FFT[·] is the Fast Fourier Transform.

[0039] Step 3-6: Detection probability constraint decision: If satisfied If the test is successful, the test is successful; otherwise, the test fails. The number of successful tests is counted after multiple trials to estimate the probability of successful detection.

[0040]

[0041] In the formula, N e N represents the total number of trials. d The number of successful detections is represented by △a1, which is the interval between adjacent first-order acceleration channels.

[0042] Steps 3-7: If satisfied Then output the optimal number of first-level acceleration channels K under the joint constraints of computational complexity and detection probability, and go to step 4-1; otherwise, let K = K+1, and go to step 3-2.

[0043] Step 4 includes the following steps:

[0044] Step 4-1: Based on the optimal number of first-level acceleration channels K output in Step 3-7, obtain the first-level acceleration channel index numbers according to Steps 3-2 to 3-5. And calculate the number M of the second-order acceleration channels:

[0045]

[0046] In the formula, For floor function, N is the number of phase pulses, and V is the number of phase pulses. max For the maximum unambiguous speed, T r This is the pulse repetition period.

[0047] Step 4-2: Based on the first-level acceleration channel index number Reposition the acceleration range to Further analysis of the acceleration range Fine-grained uniform sampling yields M acceleration values:

[0048]

[0049] Step 4-3: For each a m The radar echoes, after range migration elimination, are subjected to fine Doppler demodulation in the slow time domain, and the sample data after fine Doppler demodulation are coherently accumulated.

[0050]

[0051] In the formula, x(n) represents the slow-time domain sample of the echo after eliminating distance migration, and t n λ is the slow time, λ is the wavelength, and v is the radial velocity.

[0052] Step 4-4: Search for the acceleration channel index number corresponding to the accurate estimate of the target radial acceleration.

[0053]

[0054] Steps 4-5: Output the long-term coherent accumulation results:

[0055]

[0056] Example:

[0057] Assume the radar transmitted waveform has a pulse width of 3.5 μs, a bandwidth of 35 μs, a wavelength of 0.01 m, a complex sampling rate of 30 Msps, a coherent pulse number of 4096, and an acceleration range of -10 m / s. 2 ~10m / s 2 The target begins moving radially towards the radar from the 600th range cell, with a speed of 50 m / s and an acceleration of 8 m / s². 2 The target detection probability is constrained to 0.8.

[0058] From the perspective of computational complexity, in order to ensure that the energy diffusion of the target does not exceed a single Doppler resolution unit after long-term coherent accumulation processing, the single-stage demodulation technology requires the construction of 83 acceleration channels, while the present invention only needs to construct 27 acceleration channels while maintaining the target detection probability, reducing the computational complexity by 67.47%.

[0059] From the perspective of long-term cumulative performance analysis, Figure 2 The energy distribution of the target in the range-velocity domain after processing by the traditional coherent accumulation method. Figure 3 The image shows the energy distribution of the target in the range-Doppler domain after long-term coherent accumulation processing according to this invention. It can be seen that traditional coherent accumulation algorithms cannot eliminate the target range-Doppler migration effect, while the method proposed in this invention can efficiently focus the target energy.

Claims

1. A long-term coherent accumulation method with joint constraints on computational complexity and detection probability, characterized in that: Step 1: Use Keystone transform to eliminate target distance migration; Step 2: Optimize the number of acceleration channels based on the criterion of minimizing computational complexity; Step 3: Optimize the number of acceleration channels based on detection probability constraints; Step 4: Dual-stage acceleration channel Doppler demodulation enables long-term coherent accumulation; Step 2 includes the following steps: Step 2-1: Preset the number of coherent pulses N and the upper limit of the acceleration range. Maximum unambiguous speed Radar pulse repetition period The computational complexity of the two-stage Doppler demodulation process is obtained. : （1）； In the formula, This refers to the number of first-order acceleration channels; Step 2-2: Solve for the optimal number of first-stage acceleration channels P based on the minimum computational complexity criterion: （2）； Step 3 includes the following steps: Step 3-1: Initialize the optimal number of first-level acceleration channels K=P under the joint constraints of computational complexity and detection probability; Step 3-2: Acceleration range K acceleration values ​​were obtained through uniform sampling: （3）； In the formula, k is the index number of the first-level acceleration channel; Step 3-3: In the distance cell where the target is located, for each... Doppler demodulation processing is performed on radar echoes that have had their range migration eliminated in the slow time domain: （4）； In the formula, To eliminate slow-time domain samples of target echoes after distance migration, For slow time, Wavelength; Steps 3-4: Demodulating the Doppler sample data Perform coherent accumulation and search for the acceleration channel index number corresponding to the coarse estimate of the target radial acceleration. : （5）； In the formula, For Fast Fourier Transform; Step 3-5: Detection probability constraint decision: If satisfied If the result is positive, the detection is successful; otherwise, the detection fails. Repeat the experiment multiple times, count the number of successful detections, and estimate the detection probability. : （6）； In the formula, The total number of trials, The number of successful tests. The interval between adjacent acceleration channels is one level. Steps 3-6: If satisfied Then output the optimal number of first-level acceleration channels K under the joint constraints of computational complexity and detection probability, and go to step 4-1; otherwise, let K=K+1, and go to step 3-2. Step 4 includes the following steps: Step 4-1: Based on the optimal number of first-level acceleration channels K output in Step 3-6, obtain the first-level acceleration channel index numbers according to Steps 3-2 to 3-4. And calculate the number M of the second-order acceleration channels: （7）； In the formula, For floor function, N is the number of parameter pulses. To achieve the maximum unambiguous speed, The pulse repetition period; Step 4-2: Based on the first-level acceleration channel index number Reposition the acceleration range to Further analysis of the acceleration range Fine-grained uniform sampling yields M acceleration values: （8）； Step 4-3: For each The radar echoes, after range migration elimination, are subjected to fine Doppler demodulation in the slow time domain, and the sample data after fine Doppler demodulation are coherently accumulated. （9）； In the formula, To eliminate slow-time domain samples of echoes after distance migration, For slow time, For wavelength, Radial velocity; Step 4-4: Search for the acceleration channel index number corresponding to the accurate estimate of the target radial acceleration. : （10）； Steps 4-5: Output the long-term coherent accumulation results: （11）。

Images