Jointly Compensated Imaging Algorithm of Inverse Synthetic Aperture Lidar Based on Nelder-Mead Simplex Method

Shengjie, 刘盛捷 Liu; Hanchu, 付翰初 Fu; Kai, 魏凯 Wei; Yudong, 张雨东 Zhang

doi:10.3788/aos201838.0711002

Cited by 6 publications

(3 citation statements)

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“…Therefore, algorithms that rely on strong reference points, such as phase gradient self-focusing [3] and spatial correlation algorithms [4] , are not applicable in this case. Additionally, parameter search-based estimation algorithms suffer from drawbacks such as high computational complexity, low estimation accuracy, and the influence of cross-terms [5][6][7] . To address these limitations, non-search parameter estimation algorithms have been developed and proven effective [8,9] .…”

Section: Introductionmentioning

confidence: 99%

Motion compensation imaging algorithm for inverse synthetic aperture LiDAR of complex moving targets

Li,

jin,

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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The inverse synthetic aperture LiDAR (ISAL) system demonstrates excellent performance in various application scenarios, including long-range target imaging and recognition. However, due to the micrometer scale of the ISAL system carrier, compensating for the motion error of high-speed complex motion targets becomes challenging. Linear frequency modulation (LFM) signals are not enough to describe these targets accurately, as they can be better represented by high-order polynomial phase signals. Nevertheless, the presence of second-order and higher phase components can greatly affect the coherent synthesis between pulses. To tackle this issue, we propose a non-searching fast cubic phase signal estimation algorithm based on the fourth-order instantaneous autocorrelation function. This algorithm aims to estimate the second-order and third-order coefficients of the rotational error signals using the non-uniform Fourier transform. By accounting for advection compensation, we model the phase of the signal of the complex motion target as the third-order form of the slow time. The Fourier transform of the fourth-order instantaneous autocorrelation function presents challenges, such as the nonlinear coupling between the slow time and delay time, as well as the nonuniform sampling of the delay time. We suggest utilizing the nonuniform Fourier transform to complete the two-dimensional transformation and estimate the obtained second-order and third-order coefficients. In comparison to the traditional range doppler (RD) algorithm, our proposed algorithm achieves better image point focusing and enhances the energy aggregation of scattering points by approximately four times. Simulation results validate the effectiveness of this algorithm.

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Section: Introductionmentioning

confidence: 99%

Motion compensation imaging algorithm for inverse synthetic aperture LiDAR of complex moving targets

Li,

jin,

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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“…However, the Quasi-Newton method may fall into local optima, which is why Ref. 8 has further improved error compensation accuracy by combining the simplex method and Newtonian method to globally estimate and jointly compensate the translational and rotational phase errors. Nevertheless, in most cases, the problem that needs to be optimized is either non-derivable or difficult to derive.…”

Section: Introductionmentioning

confidence: 99%

Adaptive motion error compensation method based on bat algorithm for maneuvering targets in inverse synthetic aperture LiDAR imaging

Li,

Jin,

et al. 2023

Opt. Eng.

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Motion error compensation is a crucial aspect of processing inverse synthetic aperture light detection and ranging data. Motion phase error occurs mainly due to the relative motion between the target and the system, as well as vibration of either the system or the target, which significantly affects the image quality of optical synthetic aperture radar. Since spatial targets usually have a non-cooperative motion state with a high degree of motion parameter uncertainty, accurate estimation of cross-range phase error becomes challenging due to the presence of envelope tilt effect. We propose an adaptive compensation method that handles motion errors of maneuvering targets by estimating and compensating various types of errors introduced by the target motion process. Using the geometric and signal models to analyze error components, a compensation model is established that uses envelope contrast and image entropy as fitness functions. The bat algorithm is employed to solve this error model. Simulation and outdoor experimental results demonstrate that the proposed algorithm offers higher accuracy and better stability compared to traditional optimization algorithms.

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“…As a result, a CNR threshold for PGA to be successful was obtained. More recently, Shengjie et al [ 14 ] proposed a joint compensation imaging algorithm based on the Nelder–Mead simplex and quasi‐Newton methods, which yielded accurate estimates of the motion parameters of the target and obtained a well‐focused high‐resolution 2‐D ISAL image. Furthermore, a three‐dimensional ISAL imaging algorithm for processing spinning targets such as space debris has been reported by Di.…”

Section: Introductionmentioning

confidence: 99%

Simulating the Scattering Echo and Inverse Synthetic Aperture Lidar Imaging of Rough Targets

Xue

Cao

et al. 2022

Annalen der Physik

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Inverse synthetic aperture lidar (ISAL) has overcome the optical diffraction limit and realized radar imaging with centimeter-level resolution at ultra-long distances. Taking a two-dimensional Gaussian rough surface as an example, a method for calculating the scattered echo under the Kirchhoff approximation based on two-dimensional Fourier transform is proposed. ISAL imaging of planar rough targets (square and circular plates) is realized using the intensity and phase information of the scattered echo. Moreover, the ISAL imaging of large rough convex targets (cone) is obtained through a coordinate transformation, and the influence of different rough targets on ISAL imaging is analyzed, revealing that as the roughness decreases, the proportion of scattering produced by coherent scattering increases, resulting in a sharper target edge, whereas increasing target roughness leads to the image energy distribution becoming more uniform.

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Jointly Compensated Imaging Algorithm of Inverse Synthetic Aperture Lidar Based on Nelder-Mead Simplex Method

Cited by 6 publications

References 0 publications

Motion compensation imaging algorithm for inverse synthetic aperture LiDAR of complex moving targets

Motion compensation imaging algorithm for inverse synthetic aperture LiDAR of complex moving targets

Adaptive motion error compensation method based on bat algorithm for maneuvering targets in inverse synthetic aperture LiDAR imaging

Simulating the Scattering Echo and Inverse Synthetic Aperture Lidar Imaging of Rough Targets

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