Abstract
The work presented in this paper is about implementing a frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) positioning radar and a sparse spectrum fitting (SpSF) algorithm for range and angular measurements. First, we designed a coherent FMCW MIMO radar system working in the S-band with low power consumption that consists of four transmitter and four receiver antennas and has the ability to extend its virtual aperture; thus, this system can achieve a higher resolution than conventional phased array radars. Then, the SpSF algorithm was designed for estimating the distance and angle of the targets in the FMCW MIMO radar. Due to the fact that the SpSF algorithm can exploit the spatial sparsity diversity of a signal, the SpSF algorithm that is applied in the designed MIMO radar system can achieve a better estimation performance than the multiple signal classification (MUSIC) and Capon algorithms, especially in the context of small snapshots and low signal-to-noise ratios (SNRs). The simulated and experimental results are used to prove the effectiveness of the designed MIMO radar and the superior performance of the algorithm.
Highlights
Since the concept of a multiple-input multiple-output (MIMO) radar [1] was proposed in 2004, it has attracted large amounts of attention of researchers
After using space-time signal processing, the results showed that the ability of the MIMO radar to suppress spatial clutter was improved by 35 dB, a result that verified the superior performance of the MIMO system
Radar, the implemented frequency-modulated continuous wave (FMCW) MIMO radar system is described in Section 3, the sparse spectrum fitting (SpSF) algorithm is described in Section 4, the simulation and experimental results are presented in Section 5; and Section 6 draws the conclusions
Summary
Since the concept of a multiple-input multiple-output (MIMO) radar [1] was proposed in 2004, it has attracted large amounts of attention of researchers. After using space-time signal processing, the results showed that the ability of the MIMO radar to suppress spatial clutter was improved by 35 dB, a result that verified the superior performance of the MIMO system These radars are relatively large and have high power consumption. A signal model of a monostatic MIMO sparse frequency FMCW radar was established in [21], and a modified 2D iterative adaptive approach (IAA) algorithm for range-azimuth processing was presented and verified with simulated data. Based on experiment and real data, a sparse spectrum fitting (SpSF) algorithm is designed for estimating the distance and angle of a target via the use of the the sparsity of signals, which verify the effectiveness of the designed MIMO radar system and the superior performance of the algorithm. The paper is organized as follows: Section 2 describes the signal model of the FMCW MIMO radar, the implemented FMCW MIMO radar system is described in Section 3, the SpSF algorithm is described in Section 4, the simulation and experimental results are presented in Section 5; and Section 6 draws the conclusions
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