Abstract
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.
Highlights
The advantages of multiple-input multiple-output (MIMO) radar have drawn considerable attention in the last decade [1,2,3,4,5,6,7]
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]
Unlike the frequency spread (FS) linear frequency modulation (LFM) signals usually adopted in MIMO radars [24,25], where each of the transmit LFM signals has identical bandwidth and transmit energy and spaced starting frequencies, we propose to construct the transmit waveforms as a set of LFM signals whose starting frequencies, bandwidths, and energies can be different and are to be optimized
Summary
The advantages of multiple-input multiple-output (MIMO) radar have drawn considerable attention in the last decade [1,2,3,4,5,6,7]. During the learning process of a cognitive MIMO radar system, the information of the environment, such as the prior knowledge of clutter and target impulse responses and noise, are collected by multiple receive antennas, which are transferred to multiple. In [18], a waveform optimization approach is provided for cognitive MIMO radar based on the MI between the target impulse response and the received echoes and the MI between successive backscatter signals. 1. Set values for the target and clutter impulse response covariances Rht and Rhc and noise covariance Rz based on the learning output of the cognitive MIMO radar. The temporal correlation matrix CT is considered by the conventional time-varying autoregressive (TVAR) [31,32] modeling (see [33] for details)
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