TY - GEN
T1 - Ambiguity function shaping via quartic descent on the complex circle manifold
AU - Alhujaili, Khaled
AU - Monga, Vishal
AU - Rangaswamy, Muralidhar
PY - 2019/4
Y1 - 2019/4
N2 - Controlling the range-Doppler response, i.e. Ambiguity Function (AF) continues to be of great interest in cognitive radar. The design problem is known to be a nonconvex quartic function of the transmit radar waveform. This AF shaping problem becomes even more challenging in the presence of practical constraints on the transmit waveform such as the Constant Modulus Constraint (CMC). Most existing approaches address the aforementioned challenges by suitably modifying or relaxing the design cost function and/or CMC. In a departure from such methods, we develop a solution that involves direct optimization over the non-convex complex circle manifold, i.e. the CMC set. We derive a new update strategy (Quartic-Gradient-Descent (QGD)) that computes an exact gradient of the quartic cost and invokes principles of optimization over manifolds towards an iterative procedure with guarantees of monotonic cost function decrease and convergence. Experimentally, QGD can outperform state of the art approaches for shaping the ambiguity function under CMC while being computationally less expensive.
AB - Controlling the range-Doppler response, i.e. Ambiguity Function (AF) continues to be of great interest in cognitive radar. The design problem is known to be a nonconvex quartic function of the transmit radar waveform. This AF shaping problem becomes even more challenging in the presence of practical constraints on the transmit waveform such as the Constant Modulus Constraint (CMC). Most existing approaches address the aforementioned challenges by suitably modifying or relaxing the design cost function and/or CMC. In a departure from such methods, we develop a solution that involves direct optimization over the non-convex complex circle manifold, i.e. the CMC set. We derive a new update strategy (Quartic-Gradient-Descent (QGD)) that computes an exact gradient of the quartic cost and invokes principles of optimization over manifolds towards an iterative procedure with guarantees of monotonic cost function decrease and convergence. Experimentally, QGD can outperform state of the art approaches for shaping the ambiguity function under CMC while being computationally less expensive.
UR - http://www.scopus.com/inward/record.url?scp=85073120532&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073120532&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2019.8835538
DO - 10.1109/RADAR.2019.8835538
M3 - Conference contribution
T3 - 2019 IEEE Radar Conference, RadarConf 2019
BT - 2019 IEEE Radar Conference, RadarConf 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Radar Conference, RadarConf 2019
Y2 - 22 April 2019 through 26 April 2019
ER -