Name
Abstract | ||
|---|---|---|
This paper considers the problem of radar waveform design in the presence of colored Gaussian disturbance under a peak-to-average-power ratio (PAR) and an energy constraint. First of all, we focus on the selection of the radar signal optimizing the signal-to-noise ratio (SNR) in correspondence of a given expected target Doppler frequency (Algorithm 1). Then, through a max-min approach, we make robust the technique with respect to the received Doppler (Algorithm 2), namely we optimize the worst case SNR under the same constraints as in the previous problem. Since Algorithms 1 and 2 do not impose any condition on the waveform phase, we also devise their phase quantized versions (Algorithms 3 and 4, respectively), which force the waveform phase to lie within a finite alphabet. All the problems are formulated in terms of nonconvex quadratic optimization programs with either a finite or an infinite number of quadratic constraints. We prove that these problems are NP-hard and, hence, introduce design techniques, relying on semidefinite programming (SDP) relaxation and randomization as well as on the theory of trigonometric polynomials, providing high-quality suboptimal solutions with a polynomial time computational complexity. Finally, we analyze the performance of the new waveform design algorithms in terms of detection performance and robustness with respect to Doppler shifts. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSP.2011.2128313 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Doppler shift,computational complexity,concave programming,minimax techniques,quadratic programming,quantisation (signal),radar detection,Doppler frequency,Doppler shifts,NP-hard problems,PAR,SDP relaxation,SNR,colored Gaussian disturbance,detection performance,energy constraint,high-quality suboptimal solutions,max-min approach,nonconvex quadratic optimization programs,peak-to-average power ratio constraint,phase-quantized versions,polynomial time computational complexity,radar code optimization design,radar signal,radar waveform design,semidefinite programming,signal-to-noise ratio,trigonometric polynomials,waveform design algorithms,Approximation bound,nonconvex quadratic optimization,nonnegative trigonometric polynomials,radar waveform design,randomized algorithm,semidefinite programming relaxation,waveform diversity | Approximation algorithm,Mathematical optimization,Algorithm design,Signal-to-noise ratio,Waveform,Quadratic programming,Time complexity,Mathematics,Semidefinite programming,Computational complexity theory | Journal |
Volume | Issue | ISSN |
59 | 6 | 1053-587X |
Citations | PageRank | References |
45 | 2.08 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Maio | 1 | 421 | 33.27 |
| Yongwei Huang | 2 | 814 | 50.83 |
| Marco Piezzo | 3 | 155 | 8.84 |
| Shuzhong Zhang | 4 | 2808 | 181.66 |