TY - GEN
T1 - Modulation classification using a neural tree network
AU - Farrell, K. R.
AU - Mammone, Richard
PY - 1993/12/1
Y1 - 1993/12/1
N2 - A new classifier is presented for estimating the modulation type for digitally modulated signals. The new classifier is known as the neural tree network (NTN). The NTN is a self-organizing, hierarchical classifier that implements a sequential linear decision strategy. The NTN does not require a statistical analysis of the features, as do Bayesian methods or decision trees. The NTN also allows for a more flexible partitioning of feature space than the prior classification methods. The features used for modulation classification are obtained from an autoregressive model of the signal. These features include the instantaneous frequency, bandwidth, and derivative of the instantaneous frequency. The modulation types to be estimated are continuous wave, binary and quadrature phase shift keying, and binary and quadrature frequency shift keying. The experiment results show the NTN to perform well for low carrier to noise ratio (CNR) input signals, in addition to outperforming the decision tree classifier.
AB - A new classifier is presented for estimating the modulation type for digitally modulated signals. The new classifier is known as the neural tree network (NTN). The NTN is a self-organizing, hierarchical classifier that implements a sequential linear decision strategy. The NTN does not require a statistical analysis of the features, as do Bayesian methods or decision trees. The NTN also allows for a more flexible partitioning of feature space than the prior classification methods. The features used for modulation classification are obtained from an autoregressive model of the signal. These features include the instantaneous frequency, bandwidth, and derivative of the instantaneous frequency. The modulation types to be estimated are continuous wave, binary and quadrature phase shift keying, and binary and quadrature frequency shift keying. The experiment results show the NTN to perform well for low carrier to noise ratio (CNR) input signals, in addition to outperforming the decision tree classifier.
UR - http://www.scopus.com/inward/record.url?scp=0027885722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027885722&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0027885722
SN - 0780309537
T3 - Proceedings - IEEE Military Communications Conference
SP - 1028
EP - 1032
BT - Proceedings - IEEE Military Communications Conference
A2 - Anon, null
PB - Publ by IEEE
T2 - Proceedings of the 12th Annual IEEE Military Communications Conference
Y2 - 12 October 1993 through 14 October 1993
ER -