Optimization theory-based radar waveform design for spectrally dense environments

Aubry, Augusto; Carotenuto, Vincenzo; Maio, Antonio De; Farina, A.; Pallotta, Luca

doi:10.1109/maes.2016.150216

Cited by 157 publications

(59 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the system architecture point of view, both SISO [6]- [8] and MIMO [9]- [13] configurations are considered in the literature. Both single carrier [6], [14] and multicarrier waveforms [5], [7], [15]- [18] have been employed. Most related papers propose systems in which the radar and the communications waveforms are distinct, while some propose systems where waveforms have dual purpose, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…When considering separate waveforms for radar and communications purposes various optimization strategies have been proposed. Many take radar-centric approaches (aiming at optimizing or adapting only the radar waveform) [3], [6], [11]- [14], [21], [22], whereas communications-centric approaches (aiming at optimizing or adapting only the communications waveform) have been considered in [3], [10], [16], [23]- [25]. A joint design strategy is adopted for example in [3], [26]- [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radar Waveform Optimization for Target Parameter Estimation in Cooperative Radar-Communications Systems

Bică

Koivunen

2019

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

The coexistence between radar and communications systems has received considerable attention from the research community in the past years. In this paper, a radar waveform design method for target parameter estimation is proposed. Target time delay parameter is used as an example. The case where the two systems are not co-located is considered. Radar waveform optimization is performed using statistical criteria associated with estimation performance, namely Fisher Information (FI) and Cramér-Rao Bound (CRB). Expressions for FI and CRB are analytically derived. Optimization of waveforms is performed by imposing constraints on the total transmitted radar power, constraints on the interference caused to the communications system, as well as constraints on the Subcarrier Power Ratio (SPR) of the radar waveform. The frequency domain SPR is different than the Peak to Average Power Ratio (PAPR) which is computed in time domain. It is shown, using simulation results, that the proposed optimization strategies outperform other strategies in terms of estimation error. It is also shown that the SPR constraint reduces the delay domain ambiguities.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radar Waveform Optimization for Target Parameter Estimation in Cooperative Radar-Communications Systems

Bică

Koivunen

2019

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

show abstract

“…Researchers have also looked at optimization theory based radar waveform design methods in spectrally dense environments that attempt to maximize some radar performance metrics (detection probability, ambiguity function features etc.) and keep interference to other in-band systems at a minimum [26][27][28] or impose constraints on the communications rate of other in-band systems [29]. Finally, research has also been done into radar waveform design in the presence of signal-dependent noise to maximize various other detection and information theoretic metrics such as the mutual information, and Kullback-Liebler divergence [30,31].…”

Section: Introductionmentioning

confidence: 99%

Radar Waveform Optimization for Joint Radar Communications Performance

et al. 2019

View full text Add to dashboard Cite

We develop and present a radar waveform design method that optimizes the spectral shape of the radar waveform so that joint performance of a cooperative radar communications system is maximized. The continuous water-filling (WF) spectral-mask shaping method presented in this paper is based on the previously derived spectral-mask shaping technique. However, the method presented in this paper is modified to utilize the continuous spectral water-filling algorithm to improve communications performance. We also introduce additional practical system constraints on the autocorrelation peak side-lobe-to-main-lobe ratio and radar waveform spectral leakage. Finally, we perform a numerical study to compare the performance of the continuous WF spectral-mask-shaping method with the previously derived method. The global estimation rate, which also accounts for non-local estimation errors, and the data rate capture radar and communications performance respectively.

show abstract

“…According to the different tasks of MIMO radar, radar performance can be improved by targeted MIMO waveform design. In [1], the authors provide a unified system approach that summarizes the latest results of waveform optimization and spectral compatibility requirements. The researchers in [2] investigated the optimization of the full-polarization radar transmission waveform and the receiver response to maximize either target detection or identification performance.…”

Section: Introductionmentioning

confidence: 99%

“…In the actual radar working environment, there are usually nonwhite noise interferences, such as colored noise, clutter, and so forth. Therefore, it is of great practical significance to study the optimal detection of compressed measurement signals in interference environments [1]. Since the concept of compressed sensing radar (CSR) was originally proposed in the groundbreaking work of [6], many works on CSR have been carried out [7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Waveform Optimization of Compressed Sensing Radar without Signal Recovery

Wang

Sun

2019

Information

View full text Add to dashboard Cite

Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.

show abstract

Optimization theory-based radar waveform design for spectrally dense environments

Cited by 157 publications

References 51 publications

Radar Waveform Optimization for Target Parameter Estimation in Cooperative Radar-Communications Systems

Radar Waveform Optimization for Target Parameter Estimation in Cooperative Radar-Communications Systems

Radar Waveform Optimization for Joint Radar Communications Performance

Waveform Optimization of Compressed Sensing Radar without Signal Recovery

Contact Info

Product

Resources

About