Efficient waveform covariance matrix design and antenna selection for MIMO radar

Bose, Arindam; Khobahi, Shahin; Soltanalian, Mojtaba

doi:10.1016/j.sigpro.2021.107985

“…where ∂αn ∂pij and ∂βn ∂pij are given according to (5). As can be seen in ( 61), (62), and (64), analyzing the convexity of Ψ m (p ij ) depends on the parameters d, p 0i , p 0j , N q , and {θ ε }, which indicates that the analysis is restricted to the case where the mentioned parameters are known; i.e.…”

Section: Appendix B Proof Of the Modified Bussgang Law Formulamentioning

confidence: 99%

Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds— Part II: Non-Stationary Signals

Eamaz¹,

Yeganegi²,

Soltanalian³

2022

Preprint

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The recovery of the input signal covariance values from its one-bit sampled counterpart has been deemed a challenging task in the literature. To deal with its difficulties, some assumptions are typically made to find a relation between the input covariance matrix and the autocorrelation values of the one-bit sampled data. This includes the arcsine law and the modified arcsine law that were discussed in Part I of this work [2]. We showed that by facilitating the deployment of time-varying thresholds, the modified arcsine law has a promising performance in covariance recovery. However, the modified arcsine law also assumes input signals are stationary, which is typically a simplifying assumption for real-world applications. In fact, in many signal processing applications, the input signals are readily known to be non-stationary with a non-Toeplitz covariance matrix. In this paper, we propose an approach to extending the arcsine law to the case where one-bit ADCs apply time-varying thresholds while dealing with input signals that originate from a non-stationary process. In particular, the recovery methods are shown to accurately recover the time-varying variance and autocorrelation values. Furthermore, we extend the formulation of the Bussgang law to the case where non-stationary input signals are considered.

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“…Note that α n in ( 5) is directly scaled by the sampling threshold mean d. By setting α n = dα e , (32) can be written as where α n and β n are defined in (5). To guarantee the bound in (33), we should have:…”

Section: Fitness Analysis Of the Proposed Approximationsmentioning

confidence: 99%

Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part II: Non-Stationary Signals

Eamaz¹,

Yeganegi²,

Soltanalian³

2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

The recovery of the input signal covariance values from its one-bit sampled counterpart has been deemed a challenging task in the literature. To deal with its difficulties, some assumptions are typically made to find a relation between the input covariance matrix and the autocorrelation values of the one-bit sampled data. This includes the arcsine law and the modified arcsine law that were discussed in Part I of this work [2]. We showed that by facilitating the deployment of time-varying thresholds, the modified arcsine law has a promising performance in covariance recovery. However, the modified arcsine law also assumes input signals are stationary, which is typically a simplifying assumption for real-world applications. In fact, in many signal processing applications, the input signals are readily known to be non-stationary with a non-Toeplitz covariance matrix. In this paper, we propose an approach to extending the arcsine law to the case where one-bit ADCs apply time-varying thresholds while dealing with input signals that originate from a non-stationary process. In particular, the recovery methods are shown to accurately recover the time-varying variance and autocorrelation values. Furthermore, we extend the formulation of the Bussgang law to the case where non-stationary input signals are considered.

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“…• An LOS link as illustrated in Fig. 2 is present between the radar and target with the IRS channel strength vector defined in (19) denoted by r LOS . • There is a NLOS or virtual LOS link between the target and the radar as shown in Fig.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The goal of a radar system is to acquire (or preserve) the maximum amount of information from the desirable sources in the environment; where in fact, the transmit signal can be viewed as a medium that collects information [19], [20]. The target detection and estimation performance of the radar system is shown to be improved by a judicious design of the probing signals and processing schemes.…”

Section: Introductionmentioning

confidence: 99%

IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces

Esmaeilbeig¹,

Mishra²,

Soltanalian³

2021

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The intelligent reflecting surface (IRS) technology has recently attracted a lot of interest in wireless communications research. An IRS consists of passive reflective elements capable of tuning the phase, amplitude, frequency and polarization of the impinging waveforms. Given such desirable properties, the wireless channel characteristics can be controlled and optimized for specific signal design and processing needs-thus promising significant potential in radar applications. In this paper, we establish the theoretical foundations for introducing IRS into a radar system and study the potential to improve target parameter estimation. More specifically, we will investigate the deployment of IRS in cases where the line-of-sight (LOS) link is weak or blocked by obstructions. We demonstrate that the IRS can provide a virtual or non-line-of-sight (NLOS) link between the radar and target leading to an enhanced radar performance. The effectiveness of such an IRS-provided virtual link in estimating the moving target parameters is illustrated under both optimized and non-optimized IRS scenarios. Numerical simulations indicate that the IRS can enhance the target parameter estimation when the LOS link is weaker than ∼ 10 −1 in relative strength in comparison with the NLOS link.

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Efficient waveform covariance matrix design and antenna selection for MIMO radar

Cited by 28 publications

References 30 publications

Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds— Part II: Non-Stationary Signals

Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds— Part II: Non-Stationary Signals

Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part II: Non-Stationary Signals

IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces

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