LFM-based waveform design for cognitive MIMO radar with constrained bandwidth
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LFM-based waveform design for cognitive MIMO radar with constrained bandwidth Shuangling Wang† , Qian He*† and Zishu He
Abstract Waveform design is studied for a cognitive multiple-input multiple-output (MIMO) radar system faced with a combination of additive Gaussian noise and signal dependent clutter. The linear frequency modulation (LFM) signals are employed as transmitted waveforms. Based on the sensed statistics of the target and clutter-plus-noise, assuming the LFM waveforms transmitted at different transmitters can have different starting frequencies and bandwidths, these waveform parameters are designed to maximize the signal-to-clutter-plus-noise ratio at the receiver of the cognitive MIMO radar system. The constraints of the allowable range of operating frequency and total transmit energy are considered. We show that in the tested examples, the designed waveforms are nonorthogonal which leads to superior performance compared with that of the frequency spread LFM waveforms commonly used in the traditional MIMO radar systems. Keywords: Cognitive multiple-input multiple-output radar; Cognitive radar; Waveform design; Linear frequency modulation
1 Introduction The advantages of multiple-input multiple-output (MIMO) radar have drawn considerable attention in the last decade [1-7]. MIMO radar systems employ multiple antennas on both the transmit and receive sides. The antennas can be either co-located or widely separated. Geometry gains can be obtained for the former since the antennas are located in several different directions with respect to a target, while waveform gains can be produced for the latter by sending different waveforms with different antennas. Waveform design is a key issue in radar signal processing. The transmit waveforms of MIMO radar are usually optimized for specific goals, such as improving the signal-to-clutter-plus-noise ratio (SCNR) [8], increasing the resolution in the spatial and temporal domains, enhancing the detection performance [5], reducing the estimation error when approximating a desired beampattern [4], or maximizing the mutual information (MI) between the random target impulse response and the reflected waveforms [9]. *Correspondence: [email protected] † Equal contributors EE Department, University of Electronic Science and Technology of China, Xiyuan Road, Chengdu 611731 China
The concept of cognitive radar (CR) was proposed in [10] for optimizing the performance of a radar system faced with interference and the constraint of limited resources. The CR system can intelligently learn the state of the environment and store the information in the database. The stored information can be used as an available prior knowledge for the designs of radar systems and transmit waveforms, which is helpful for improving the performance of target detection and parameter estimation. There have been many researches on waveform design for CR systems [11-14]. In [11], the transmit signals are designed by minimizing the mean-square error of the estimate of the tar
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