Forward Looking Radar Imaging by Truncated Singular Value Decomposition and Its Application for Adverse Weather Aircraft Landing

Abstract

The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry. Deconvolution method can realize the forward looking imaging but it often leads to the noise amplification in the radar image. In this paper, a forward looking radar imaging based on deconvolution method is presented for adverse weather aircraft landing. We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing. Then, we convert the forward looking radar imaging task into a corresponding deconvolution problem, which is solved in the framework of algebraic theory using truncated singular decomposition method. The key issue regarding the selecting of the truncated parameter is addressed using generalized cross validation approach. Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

Target Fast Reconstruction of Real Aperture Radar Using Data Extrapolation-Based Parallel Iterative Adaptive Approach

“…To reconstruct the targets based on the complete echo data in (9), the echo data within two beamwidths are recursively divided into coherent processing intervals (CPIs). The echo data can be transformed into (11) It is seen that the echo data of every two beamwidths are independent. Therefore, they can be processed in parallel.…”

“…To accurately and efficiently reconstruct the targets of the whole observation sector, the accurate echo model, the complete echo data, and fast inversion processing are indispensable [6], [10]. The echo model of RAR can be established in the time domain and frequency domain [11]- [14]. In the time domain, the echo data of RAR can be expressed as a convolution relationship between the antenna pattern and targets [15]- [17].…”

Imaging for a Forward Scanning Automotive Synthetic Aperture Radar

“…Note, Figures 19-22 essentially provide a qualitative analysis of the FS-SAR methodology. However, a quantitative analysis of this methodology can be obtained by plotting (17) for different angular ranges of the scan. Figure 23 provides such an analysis for varying ranges.…”

“…In [16], a truncated singular value decomposition (TSVD) based approach has been presented to enhance the spatial resolution. This approach was applied to FL-SAR in [17]. However, the key limitation in this approach is the truncation parameter which needs to be learnt.…”

Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging

“…An optimum finite impulse response (FIR) filter is developed in [ 16 ], which could improve azimuth resolution 23 times depending on high signal-to-noise ratio (SNR). In [ 17 , 18 , 19 ], a scheme of the truncated singular value decomposition (TSVD) method was proposed for radar forward-looking imaging and can significantly suppress the noise, whereas, the improvement for the resolution is limited and the number of reserved truncated values is difficult to determine.…”