Estimation of FM signal parameters in impulse noise environments
Signal Processing
Multicomponent chirp signals analysis using product cubic phase function
Digital Signal Processing
Detection and estimation of spectral change
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
A new class of multilinear functions for polynomial phase signal analysis
IEEE Transactions on Signal Processing
Adaptive algorithm for chirp-rate estimation
EURASIP Journal on Advances in Signal Processing
Parameter estimation of phase-modulated signals using Bayesian unwrapping
IEEE Transactions on Signal Processing
A new parameter estimation method of linear frequency modulation signal
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Efficient estimation of a narrow-band polynomial phase signal impinging on a sensor array
IEEE Transactions on Signal Processing
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Performance analysis of an autocorrelation based frequency tracker for LFM and QFM signals
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
New approach for ISAR imaging of ship target with 3D rotation
Multidimensional Systems and Signal Processing
Performance of instantaneous frequency rate estimation using high-order phase function
IEEE Transactions on Signal Processing
Are genetic algorithms useful for the parameter estimation of FM signals?
Digital Signal Processing
Fast communication: On the Cramér-Rao bound for polynomial phase signals
Signal Processing
| Hi-index | 35.70 |
This paper describes a fast algorithm that can be used for estimating the parameters of a quadratic frequency modulated (FM) signal. The proposed algorithm is fast in that it requires only one-dimensional (1-D) maximizations. The optimal maximum likelihood method, by contrast, requires a three-dimensional (3-D) maximization, which can only be realized with an exhaustive 3-D grid search. Asymptotic statistical results are derived for all the estimated parameters. The amplitude estimate is seen to be optimal, whereas the phase parameters are, in general, suboptimal. Of the four phase parameter estimates, two approach optimality as the signal-to-noise ratio (SNR) tends to infinity. The other two have mean-square errors that are within 50% of the theoretical lower bounds for high SNR. Simulations are provided to support the theoretical results. Extensions to multiple components and higher order FM signals are also discussed.