Name
Title | ||
|---|---|---|
Design of phase codes for radar performance optimization with a similarity constraint |
Abstract | ||
|---|---|---|
This paper deals with the design of coded waveforms which optimize radar performances in the presence of colored Gaussian disturbance. We focus on the class of phase coded pulse trains and determine the radar code which approximately maximizes the detection performance under a similarity constraint with a prefixed radar code. This is tantamount to forcing a similarity between the ambiguity functions of the devised waveform and of the pulse train encoded with the prefixed sequence. We consider the cases of both continuous and finite phase alphabet, and formulate the code design in terms of a nonconvex, NP-hard quadratic optimization problem. In order to approximate the optimal solutions, we propose techniques (with polynomial computational complexity) based on the method of semidefinite program (SDP) relaxation and randomization. Moreover, we also derive approximation bounds yielding a "measure of goodness" of the devised algorithms. At the analysis stage, we assess the performance of the new encoding techniques both in terms of detection performance and ambiguity function, under different choices for the similarity parameter. We also show that the new algorithms achieve an accurate approximation of the optimal solution with a modest number of randomizations. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSP.2008.2008247 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
pulse train,radar code,code design,optimal solution,similarity parameter,optimize radar performance,ambiguity function,similarity constraint,prefixed radar code,radar performance optimization,phase code,detection performance,design optimization,randomization,constraint optimization,covariance matrix,quadratic optimization,gaussian noise,approximation algorithms,computational complexity,quadratic programming | Ambiguity function,Radar,Similitude,Approximation algorithm,Mathematical optimization,Algorithm,Quadratic programming,Prefix code,Mathematics,Constrained optimization,Computational complexity theory | Journal |
Volume | Issue | ISSN |
57 | 2 | 1053-587X |
Citations | PageRank | References |
39 | 2.50 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio De Maio | 1 | 721 | 48.03 |
| Silvio De Nicola | 2 | 72 | 5.40 |
| Yongwei Huang | 3 | 814 | 50.83 |
| Zhi-Quan Luo | 4 | 7506 | 598.19 |
| Shuzhong Zhang | 5 | 2808 | 181.66 |