Threshold extension of the modified FBLP algorithm

Two methods are presented for extending the threshold characteristics of the modified forward-backward linear prediction (MFBLP) algorithm due to Tufts and Kumaresan (1982). This algorithm estimates angles of arrival of plane waves onto linear arrays of sensors. The first technique proposed, referred to as modified-modified FBLP (M2FBLP), offers 4- to 6-dB threshold extensions over MFBLP for a large number of snapshots, for a particular set of simulation parameters. The second technique, called optimized FBLP (opt-FBLP), offers similar threshold extension when the numer of snapshots is low. The opt-FBLP method is most successful when the number of incident signal components K is 2. The method is therefore viewed as being particularly applicable to the low-angle tracking problem in radar, since it has been shown that there is strong justification to fix the value of K at 2 in this situation. The increase in computational complexity required for M2-FBLP over MFBLP is virtually negligible. For opt-FBLP, the increase in computational complexity is minimal for K=2.

[1]  Hong Wang,et al.  Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources , 1985, IEEE Trans. Acoust. Speech Signal Process..

[2]  R. Kumaresan,et al.  Estimating the Angles of Arrival of Multiple Plane Waves , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[3]  S. DeGraaf,et al.  Improving the resolution of bearing in passive sonar arrays by eigenvalue analysis , 1981 .

[4]  A M Despain,et al.  Low-Angle Radar Tracking , 1976 .

[5]  Wickramaratna Bandula Dahanayake Super Precision Adaptive Array Processing and Systolic Array Structures , 1987 .

[6]  J. Litva,et al.  New angle-of-arrival estimator: comparative evaluation applied to the low-angle tracking radar problem , 1988 .

[7]  D. H. Brandwood,et al.  Noise-space projection: MUSIC without eigenvectors , 1987 .

[8]  Yoram Bresler,et al.  Exact maximum likelihood parameter estimation of superimposed exponential signals in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..

[9]  Ramdas Kumaresan,et al.  Improved spectral resolution II , 1980, ICASSP.

[10]  S. Haykin,et al.  Maximum-likelihood receiver for low-angle tracking radar. Part 2: The nonsymmetric case , 1982 .

[11]  J.P. Reilly,et al.  A real-time high-resolution technique for angle-of-arrival estimation , 1987, Proceedings of the IEEE.

[12]  Thomas Kailath,et al.  Eigenstructure methods for direction of arrival estimation in the presence of unknown noise fields , 1986, IEEE Trans. Acoust. Speech Signal Process..

[13]  Steven M. Kay,et al.  Frequency estimation by principal component AR spectral estimation method without eigendecomposition , 1988, IEEE Trans. Acoust. Speech Signal Process..

[14]  Ralph Otto Schmidt,et al.  A signal subspace approach to multiple emitter location and spectral estimation , 1981 .

[15]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[16]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[17]  R. Kumaresan,et al.  Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood , 1982, Proceedings of the IEEE.