TY - GEN
T1 - Correlation-gradient-descent
T2 - 2020 IEEE International Radar Conference, RADAR 2020
AU - Alhujaili, Khaled
AU - Monga, Vishal
AU - Rangaswamy, Muralidhar
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020/4
Y1 - 2020/4
N2 - We consider the problem of designing sequences with good auto- and cross-correlation properties for multiple-input multiple-output (MIMO) radar systems. This design problem aims to minimize a polynomial function of the transmit waveforms. The problem becomes more challenging in the presence of practical constraints on the waveform such as the constant modulus constraint (CMC). The aforementioned challenge has been addressed in the literature by approximating the cost function and/or constraints, i.e. the CMC. In this work, we develop a new algorithm that deals with the exact non-convex cost function and CMC. In particular, we develop a new update method (Correlation-Gradient-Descent (CGD)) that employs the exact gradient of the cost function to design such sequences with guarantees of monotonic cost function decrease and convergence. Our method further enables descent directly over the CMC by invoking principles of optimization over manifolds. Experimentally, CGD is evaluated against state-of-the-art methods for designing uni-modular sequences with good correlation properties. Results reveal that CGD can outperform these methods while being computationally less expensive.
AB - We consider the problem of designing sequences with good auto- and cross-correlation properties for multiple-input multiple-output (MIMO) radar systems. This design problem aims to minimize a polynomial function of the transmit waveforms. The problem becomes more challenging in the presence of practical constraints on the waveform such as the constant modulus constraint (CMC). The aforementioned challenge has been addressed in the literature by approximating the cost function and/or constraints, i.e. the CMC. In this work, we develop a new algorithm that deals with the exact non-convex cost function and CMC. In particular, we develop a new update method (Correlation-Gradient-Descent (CGD)) that employs the exact gradient of the cost function to design such sequences with guarantees of monotonic cost function decrease and convergence. Our method further enables descent directly over the CMC by invoking principles of optimization over manifolds. Experimentally, CGD is evaluated against state-of-the-art methods for designing uni-modular sequences with good correlation properties. Results reveal that CGD can outperform these methods while being computationally less expensive.
UR - http://www.scopus.com/inward/record.url?scp=85090343310&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090343310&partnerID=8YFLogxK
U2 - 10.1109/RADAR42522.2020.9114771
DO - 10.1109/RADAR42522.2020.9114771
M3 - Conference contribution
AN - SCOPUS:85090343310
T3 - 2020 IEEE International Radar Conference, RADAR 2020
SP - 940
EP - 945
BT - 2020 IEEE International Radar Conference, RADAR 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 April 2020 through 30 April 2020
ER -