Bijit Halder

Computationally efficient angle estimation for signals with known waveforms

This paper presents a large sample decoupled maximum likelihood (DEML) angle estimator for uncorrelated narrowband plane waves with known waveforms and unknown amplitudes arriving at a sensor array in the presence of unknown and arbitrary spatially colored noise. The DEML estimator decouples the multidimensional problem of the exact ML estimator to a set of 1-D problems and, hence, is computationally efficient. We shall derive the asymptotic statistical performance of the DEML estimator and compare the performance with its Cramer-Rao bound (CRB), i.e., the best possible performance for the class of asymptotically unbiased estimators. We will show that the DEML estimator is asymptotically statistically efficient for uncorrelated signals with known waveforms. We will also show that for moderately correlated signals with known waveforms, the DEML estimator is no longer a large sample maximum likelihood (ML) estimator, but the DEML estimator may still be used for angle estimation, and the performance degradation relative to the CRB is small. We shall show that the DEML estimator can also be used to estimate the arrival angles of desired signals with known waveforms in the presence of interfering or jamming signals by modeling the interfering or jamming signals as random processes with an unknown spatial covariance matrix. Finally, several numerical examples showing the performance of the DEML estimator are presented in this paper. >

Efficient estimation of closely spaced sinusoidal frequencies using subspace-based methods

Both experience and analysis show that the widely used subspace methods, such as MUSIC and ESPRIT, perform poorly when applied to estimate closely spaced sinusoidal frequencies. This severely limits the resolution of subspace methods for frequency estimation. In this letter, we present a simple interleaving technique that significantly improves the performance of subspace based methods in the case of closely spaced frequencies. Simulation results show that the improved performance is comparable to the corresponding Cramer-Rao bound (CRB).

Unconditional maximum likelihood approach for blind estimation of digital signals

In contrast to conventional array processing, in many applications, such as mobile communications, the concept of a parametric array manifold is ill defined. In mobile communications the loss of a well defined array manifold can be attributed to the complex propagation environment consisting of multiple local scatterers near the mobile and remote dominant scatterers, as well as other co-channel signals. In such applications, estimation methods developed in the conventional setting of array processing are of little use and blind estimation of the transmitted signals is of real importance. We present an unconditional maximum likelihood (UML) approach for blind estimation of multiple co-channel digital BPSK signals received by an antenna array, along with the array response matrix A. Based on the idea of fixed point iteration, an efficient algorithm is derived to obtain the UML estimate of A and the maximum a posteriori (MAP) estimates of the digital signals. Simulation results are presented to demonstrate the improved performance of the proposed UML method over the conventional conditional ML (CML) methods. An upper bound on the bit error rate is also presented.

Efficient implementation of echo canceller for applications with asymmetric rates

In conventional full duplex wireline systems digital echo cancellers are commonly used to suppress echo. For applications with asymmetric rates the complexity and performance of the echo canceller depend on the implementation of the rate matching function. When the transmit rate is lower than the receive rate, the traditional approach of resampling before filtering is inefficient. We show that efficient implementation can be obtained by reversing the order of resampling and filtering. We propose two new echo canceller structures based on scalar and vector error signals and develop associated adaptive algorithms.

An efficient non-iterative method for estimating the angles of arrival of known signals

The vast majority of existing high resolution angle of arrival (AOA) estimators are designed for the case of completely unknown signal waveforms. However, in many applications, such as mobile communications, the receiver has access to the structure of the incoming signals. By exploiting this extra information, a considerable improvement in estimation accuracy and/or computational complexity can be achieved. Herein, we propose a simple two-step procedure for the case of perfectly known waveforms (up to gain and phase). Despite its low complexity, the method can operate in the presence of arbitrary noise fields including interfering signals. Furthermore, if the signals of interest are uncorrelated, the proposed technique yields statistically efficient AOA estimates.<<ETX>>

Propagation diversity enhancement to the subspace-based line detection algorithm

In the context of fitting straight lines to noisy images, the subspace-based line detection algorithm (SLIDE) offers two benefits over the conventional Hough transform method: low computational complexity and high resolution of the estimates. These improvements are due to the fact that the line fitting problem is converted to an equivalent problem of fitting exponentials to a time series, which can then be solved efficiently by using subspace methods like ESPRIT. The SLIDE algorithm establishes this equivalence by transforming the two dimensional binary image to a single observation vector using a propagation scheme. The difficulty with having a single observation vector in this approach is that the total number of snapshots may not be adequately large. This limits the estimation accuracy and degrades the performance of the detection algorithm (which estimates the number of present lines in the image). In this paper we propose to utilize multiple observation vectors to circumvent the problem of inadequate number of snapshots. The challenge with the multiple observation vector approach is how to combine these vectors to form a covariance matrix that possesses the desired structure. We overcome this difficulty by considering only a specific set of observation vectors along with an interleaving technique. Simulation results show that this technique significantly improves the efficiency of the detection algorithm as well as the accuracy of the estimates of the line angles.