Spectrally Constrained MIMO Radar Waveform Design Based on Mutual Information

In order to further exploit the potential of joint multi-antenna radar-communication (Rad-Com) system, we propose two transmission techniques respectively based on separated and shared antenna deployments. Both techniques are designed to maximize weighted sum rate (WSR) and probing power at target's location under average power constraints at antennas such that the system can simultaneously communicate with downlink users and detect the target within the same frequency band. Based on a Weighted Minimized Mean Square Errors (WMMSE) method, the separated deployment transmission is designed via semidefinite programming (SDP) while the shared deployment problem is solved by majorization-minimization (MM) algorithm. Numerical results show that shared deployment outperforms separated deployment in radar beamforming. The tradeoffs between WSR and probing power at target are compared among both proposed transmissions and two practically simpler dual-function implementations i.e., time division and frequency division. Results show that although separated deployment has an advantage of realizing spectrum sharing, it experiences a performance loss compared with frequency division, while shared deployment outperforms both and surpasses time division in certain conditions.

Section: A Coexistence Of Existing Radar and Communication Devicesmentioning

confidence: 99%

Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff

Clerckx

Zhang

2020

“…are the transmit/receive array steering vectors, respectively; A ϕ r,j , θ r,j = b ϕ r,j a T θ r,j ; u k,j denotes the transmit beamformer on the k-th subcarrier associated with the j-th IRCS;ᾱ denotes the target impulse re-sponse, which is assumed to be zero mean Gaussian random [48], [50], [51]; α r,k,j is the channel coefficient of the target associated with the k-th subcarrier; α I,k,j is the channel coefficient from thej-th IRCS to the target, to the j-th IRCS; n r,k,j ∈ C N R ×1 is additive white Gaussian noise (AWGN) vector with zero mean and covariance matrix σ 2 n,r,k,j I N R . The received signal y r,k,j (t; ) is filtered through the receive beamformer v k,j ∈ C N R ×1 , which outputs [18], [52], [53]:…”

Section: Signal Modelmentioning

confidence: 99%

Cramer-Rao Bounds of Localization Estimation for Integrated Radar and Communication System

Tian

2020

Instead of only considering the radar estimation error in the traditional radar system (TRS), for the integrated radar and communication system (IRCS), we investigate the Cramer-Rao bound (CRB) of the localization estimation, which is influenced by both radar estimation error and communication transmission error. The functions of radar and communication are operated simultaneously by embedding the communication symbols into the multicarrier radar waveforms. Firstly, we derive the CRB of time/direction of arrival (TOA/DOA) estimation. To minimize the estimation error, we maximize the signal-to-interferenceplus-noise ratio (SINR) of radar by iteratively optimizing the radar transmit and receive beamformers (with the constraint of available transmit power). Then, the CRB of localization estimation is derived using hybrid TOA/DOA measurement. The local CRBs from different IRCSs are fused according to the linear fusion rule at the fusion center (FC). Finally, numerical results demonstrate that the additional estimation errors for the IRCS are mainly determined by the channel conditions of communication and available transmit power; the estimation accuracy for both IRCS and TRS can be improved through the iterative transmit and receive beamforming (ITRB) technique. INDEX TERMS Cramer-Rao bound, localization estimation, multicarrier waveform, beamforming, integrated radar and communication system.

“…Although the two optimal solutions lead to different power allocation strategies, both require the transmission waveform to match the target/noise characteristics. Moreover, Tang synthesizes the globally optimal waveforms by maximizing the MI in spectrally crowded environments [29].…”

Section: A Background and Motivationmentioning

confidence: 99%

Closed-Form Asymptotic Approximation of Target’s Range Information in Radar Detection Systems

Luo

et al. 2020

The echoes of the radar systems can provide useful information about the target, including range, velocity, shape, and angular direction. Extensive studies have utilized the information of the target to improve system performance, whereas the problem of a favorable closed-form asymptotic approximation of the target's range information (RI) is seldom investigated. In this paper, we address the problem of obtaining a closed-form asymptotic approximation of the target's RI in all SNR regions for radar detection systems. The RI is formulated as the mutual information (MI) between the random range and the received signals with complex additive white Gaussian noise (CAWGN). The basis of our scheme is to employ the a posteriori probability density function (PDF) of the range to extract RI from the echoes. We show the a posteriori PDF is a function of the autocorrelation function (ACF) of signal and the cross-correlation function (CCF) of signal-noise. By dividing the integration interval of the a posteriori PDF into the signal-noise interval and noise interval, a closed-form asymptotic approximation of RI is derived based on the probability of range distinguishability and the normalized a posteriori entropies of low and high signal-to-noise ratio (SNR). The results also reveal an interesting relationship between the RI and the uncertainty of the target. As special cases, the closed-form approximations of RI in low and high SNR are obtained. Moreover, for the problem of slightly looser in medium SNR approximation, a better approximation is derived based on a linear model approach. Numerical results are presented to validate the proposed theory and verify the effectiveness of the proposed approximation.