Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar Space-Time Adaptive Processing

We consider the design of polyphase waveforms for ground moving target detection with airborne multiple-inputmultiple-output (MIMO) radar. Due to the constant-modulus and finite-alphabet constraint on the waveforms, the associated design problem is non-convex and in general NP-hard. To tackle this problem, we develop an efficient algorithm based on relaxation and cyclic optimization. Moreover, we exploit a reparameterization trick to avoid the significant computational burden and memory requirement brought about by relaxation. We prove that the objective values during the iterations are guaranteed to converge. Finally, we provide an effective randomization approach to obtain polyphase waveforms from the relaxed solution at convergence. Numerical examples show the effectiveness of the proposed algorithm for designing polyphase waveforms.Index Terms-Multiple-input-multiple-output (MIMO) radar, space time adaptive processing (STAP), signal-to-interferenceplus-noise-ratio (SINR), waveform design, polyphase waveforms.

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“…Next we choose the best polyphase waveform after randomization. One can use a selection method similar to that in [23], i.e.,…”

Section: Randomization Approachmentioning

confidence: 99%

Polyphase Waveform Design for MIMO Radar Space Time Adaptive Processing

Tang

Tuck

Stoica

2020

IEEE Trans. Signal Process.

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“…This is a joint design of transmit waveforms and receive filters for MIMO radar systems. The aim is to maximize the output SINR so that to achieve enhanced detection performance for slow-moving targets that might be obscured by clutter and jamming [2]. Here we review the basic joint optimization under energy constraint.…”

Section: Review Of Transceiver Joint Optimization With Fixed Pulse-tomentioning

confidence: 99%

“…The derivation process is similar to Reference [2], to jointly optimize the transceiver with fixed pulse-to-pulse waveform, while optimizing the receive filters with fixed waveform, regard (11) as a generalized Rayleigh quotient, then we get the optimal solution of receive filters:…”

Section: Review Of Transceiver Joint Optimization With Fixed Pulse-tomentioning

confidence: 99%

“…Multi-input multi-output (MIMO) radar is a type of radar that uses multiple transmit antennas and multiple receive antennas. MIMO radar makes full use of the radar's transmit freedom and can form a longer equivalent baseline [1,2], that means it have great potential in ground moving target indication (GMTI). MIMO system also provides more flexibility in the beampattern design, which makes the joint transceiver design of MIMO radar system to be possible [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The robust joint design of the transmit waveform and filter structure for polarimetric radar is discussed in Reference [33]. The joint design of transmit waveform and receive filters for MIMO radar STAP with fixed transmit waveform is discussed in Reference [2]. Reference [34] also discusses the transmit-receive filter design with covert communications focus.…”

Section: Introductionmentioning

confidence: 99%

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Joint Optimization of Transmit Waveform and Receive Filter with Pulse-to-Pulse Waveform Variations for MIMO GMTI

Sun

et al. 2019

Sensors

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Multi-input multi-output (MIMO) is usually defined as a radar system in which the transmit time and receive time, space and transform domain can be separated into multiple independent signals. Given the bandwidth and power constraints of the radar system, MIMO radar can improve its performance by optimize design transmit waveforms and receive filters, so as to achieve better performance in suppressing clutter and noise. In this paper, we cyclicly optimize the transmit waveform and receive filters, so as to maximize the output signal interference and noise ratio (SINR). From fixed pulse-to-pulse waveform to pulse-to-pulse waveform variations, we discuss the joint optimization under energy constraint, then extend it to optimizations under constant-envelope constraint and similarity constraint. Compared to optimization with fixed pulse-to-pulse waveform, the generalized optimization achieves higher output SINR and lower minimum detectable velocity (MDV), further improve the suppressing performance.

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