Abstract
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-interference-plus-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.
Highlights
Radar and communication systems have been widely studied as two independent entities [1]
Different from the work in [22], where only transmit beamforming (TB) technique is adopted to realize both functions simultaneously, in this work, we propose an iterative transmit and receive beamforming (ITRB) technique for two purposes: 1) synthesize transmit beamformers with different sidelobe levels (SLLs), which enables information streams towards the fusion center (FC) while keeping the radar main beam at a desired magnitude; 2) obtain a higher radar signal-to-interferenceplus-noise ratio (SINR) gain with the constraint of available transmit power
Where θr,j and φr, ̃j denote the direction of arrival (DOA) of the signals transmitted by the j-th andj-th integrated radar and communication system (IRCS) ( ̃j = j), respectively; A θr,j = b θr,j aT θr,j with a θr,j ∈ CNT ×1 and b θr,j ∈ CNR×1 are the transmit/receive array steering vectors, respectively; A φr, ̃j, θr,j = b φr, ̃j aT θr,j ; uk,j denotes the transmit beamformer on the k-th subcarrier associated with the j-th IRCS; αdenotes the target impulse response, which is assumed to be zero mean Gaussian random [48], [50], [51]; αr,k,j is the channel coefficient of the target associated with the k -th subcarrier; α
Summary
Radar and communication systems have been widely studied as two independent entities [1]. The authors in [14], [15] analyze the problem of power minimization-based radar waveform design considering the existence of a communication signal on the same band. Different from the work in [22], where only transmit beamforming (TB) technique is adopted to realize both functions simultaneously, in this work, we propose an iterative transmit and receive beamforming (ITRB) technique for two purposes: 1) synthesize transmit beamformers with different SLLs (each SLL is mapped to a unique communication symbol), which enables information streams towards the FC while keeping the radar main beam at a desired magnitude; 2) obtain a higher radar SINR gain with the constraint of available transmit power. We derive the CRBs of the TOA/DOA estimation for the IRCS, which are determined by the conditions of radar and communication channels simultaneously. Notations: Superscripts (x)T and (x)† denote transpose and complex conjugate transpose of x, respectively; C denotes the set complex number; CN and CN×N denote the set of N × 1 vectors and N × N matrices with complex entries, respectively; E{x} denotes statistical expectation; IN is the N × N identity matrix; |X| and X represent the modulus and norm of X, respectively; det (X) and tr (X) indicate the determinant and trace of the matrix X, respectively; diag{x1, . . . , xn} is the diagonal matrix with diagonal entries x1, . . . , xn; blkdiag{X1, . . . , Xn} is the block diagonal matrix with diagonal blocks X1, . . . , Xn; vec{x1, . . . , xn} is the vector obtained by stacking up x1, . . . , xn
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
