Application of Compressive Sensing in LFMCW Radar

Abstract

Compressive Sensing (CS) theory is a newly developed theory which combines the signal sampling and compression based on the sparsity characteristics of the signal. Applying CS theory in radar signal processing may lead to a reduction in sampling rate, complexity, power consumption, and cost. On the other hand, performance is a critical point to be considered. In the present paper, an important question of the worthy of applying CS in the signal processing of Linear Frequency Modulated Continuous Wave (LFMCW) radar is considered. Two approaches of CS are considered; Nyquist rate based approach, and pseudo random based approach. The detection performance of LFMCW radar signal processor using CS based approaches is compared to the traditional one which is based on Fast Fourier Transform (FFT) through Receiver Operating Characteristics (ROC) curves. Comparative analysis between CS approaches and the traditional one regarding the performance and complexity is presented.

Design and implementation of a new approach of LFMCW radar signal processing based on compressive sensing in azimuth direction

“…Application of CS in LFMCW radar signal processing is introduced in [10,12]. The signal will be acquired with a rate depends on Shannon theory and then transformed by Fourier transformation matrix to generate sparse signal.…”

“…random entries, it has been demonstrated that the RIP condition is satisfied with high probability to the following equation [3] M ≥ CKlog( N K ) (2) Where N is the total number of the original signal, M is the number of measurement vector such that M < N and C is a constant dependent on the total number of samples and the number of detected targets (K) [3]. According to [11,12], The target information (range and speed) contained in the original signal can be reconstructed from the compressed measurement using different reconstruction algorithms. According to [12], CAMP algorithm [10,13,14] gives the best result.…”

“…According to [11,12], The target information (range and speed) contained in the original signal can be reconstructed from the compressed measurement using different reconstruction algorithms. According to [12], CAMP algorithm [10,13,14] gives the best result. According to [11], a limitation is found in the number of detected targets in range.…”

“…Application of CS in LFMCW radar[12] 3. THE PROPOSED AZIMUTH DIRECTION CS-BASED LFMCW RADAR SIGNAL PROCESSOR The proposed approach, as shown inFig.…”

A Proposed Compressive Sensing Based LFMCW Radar Signal Processor

“…However, while the traditional LFMCW radars use well established processing algorithms and detection schemes, such as Fast Fourier Transform (FFT) and Constant False Alarm Rate (CFAR) detectors to extract target range and velocity, the reconstruction of the target scene from the CS measurements involves the use of highly nonlinear algorithms such as L1-minimization and Complex Approximate Message passing (CAMP). These algorithms have several free parameters that must be tuned properly in order to achieve good performance [6][7][8].…”