Joni Polili Lie

A Practical Simple Geometry and Gain/Phase Calibration Technique for Antenna Array Processing

A calibration technique is proposed in this paper for an arbitrary array. This technique estimates the array sensor gain/phase and geometry with a set of simultaneous equations formed by using the MUSIC null spectrum property. Note that the technique does not use iterative calculation in estimating the array parameters and hence it has no convergent problem; however, it requires that n directions of arrival (DOAs) of signal sources to be known to calibrate the array which is perturbed n-dimensionally. The efficacy of the method is demonstrated by means of simulations and on experimental data collected with an antenna array operating in high-frequency radio band.

Adaptive Uncertainty Based Iterative Robust Capon Beamformer Using Steering Vector Mismatch Estimation

To overcome the signal-to-interference-and-noise ratio (SINR) performance degradation in the presence of large steering vector mismatches, we propose an iterative robust Capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector (SV) based on conventional robust Capon beamformer (RCB) formulation that uses an uncertainty sphere to model the mismatch between the actual and presumed SV. At each iteration, the adaptive uncertainty algorithm self-adjusts the uncertainty sphere according to the estimated mismatch SV. This estimation is derived based on the geometrical interpretation of the mismatch and can be expressed as a simple closed-form expression as a function of the presumed SV and the signal-subspace projection. The other variant of the proposed algorithm that uses a flat ellipsoid to model the mismatch is also proposed. Simulation results show that the proposed approaches offer better interference suppression capability and achieve higher output SINR, as compared to other diagonal-loading-based approaches.

Robust Adaptive Beamforming in Partly Calibrated Sparse Sensor Arrays

Two new approaches to adaptive beamforming in sparse subarray-based partly calibrated sensor arrays are developed. Each subarray is assumed to be well calibrated, so that the steering vectors of all subarrays are exactly known. However, the intersubarray gain and/or phase mismatches are known imperfectly or remain completely unknown. Our first approach is based on a worst-case beamformer design which, in contrast to the existing worst-case designs, exploits a specific structured ellipsoidal uncertainty model for the signal steering vector rather than the commonly used unstructured uncertainty models. Our second approach is based on estimating the unknown intersubarray parameters by maximizing the output power of the minimum variance beamformer subject to a proper constraint that helps to avoid trivial solution of the resulting optimization problem. Different modifications of the second approach are developed for the cases of gain-and-phase and phase-only intersubarray distortions.

Ultra wideband direction finding using digital channelization receiver architecture

In this paper, UWB direction finding technique using digital channelization receiver architecture Is proposed. The basic idea of this technique is to split the UWB array output into multiple frequency channels and then down-convert each channel into much lower frequency, hence allowing the low sampling rate ADC to be used. Estimation of the DoA of UWB source is achieved by forming a direction finding function using the spectral information from channelizers output. A thorough performance analysis from simulation results is presented to verify the proposed technique and to investigate the effect of the systems parameters on the performance

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector. The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed steering vectors can be due to the error in the position (geometry) and/or in the look direction estimate. In this paper, we propose a difierential evolution (DE) based robust adaptive beamforming that is able to achieve near optimal performance even in the presence of geometry error. Initially, we estimate an optimal steering vector by maximizing and minimizing the signal power in and out of the desired signals angular range, respectively. Then, we estimate the look direction and reconstruct the covariance matrix. Based on the obtained steering vector, estimate for look direction and reconstructed covariance matrix, near optimal output SINR, can be obtained with the increase in the input SNR without observing any saturation even in the presence of geometry error. Numerical simulations are presented to demonstrate the e-cacy of the proposed algorithm.

Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction for Look Direction Mismatch

The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation of the covariance matrix. The inaccurate estimation of the covariance matrix is due to the limited data samples and presence of desired signal components in the training data. The mismatch between the actual and presumed steering vectors can be mainly due to the error in the look direction estimate. In this paper, we propose a novel algorithm to estimate the look direction and to reconstruct the covariance matrix so that near optimal performance without the efiect of saturation can be achieved as the input SNR increases. Numerical results also show that all existing beamforming algorithms sufier from saturation efiect as the input SNR increases.

Multiple UWB Emitters DOA Estimation Employing Time Hopping Spread Spectrum

In this paper, UWB Direction of Arrival (DoA) estimation using channelization receiver architecture proposed in [1] is extended to cover the case of multiple UWB emitters employing Time Hopping Spread Spectrum (TH-SS) multiple access technique. The DoA estimation is based on the spectral lines extracted from the channelizer. When considering the multiple-emitter case, these spectral lines are dependent on the hopping sequences assigned to the emitters (as derived in [2]). The principle behind the proposed method is to set the channelizer’s frequencies at which only the spectral lines from the desired UWB emitter are present while those from the other emitters are absent. This requires a proper design of the hopping sequences that govern the transmission of all emitters. First, an additive white Gaussian noise channel is considered to demonstrate the fundamental principle. Then, the estimation in a realistic multipath channel is addressed. Simulation results show that the direction finding function successfully indicates the DoA of the desired signal when the channelizer’s frequencies are set for the detection of that signal.

NEAR OPTIMAL ROBUST ADAPTIVE BEAMFORMING APPROACH BASED ON EVOLUTIONARY ALGORITHM

The presence of desired signal in the training data for sample covariance matrix calculation is known to lead to a substantial performance degradation, especially when the desired signal is the dominant signal in the training data. Together with the uncertainty in the look direction, most of the adaptive beamforming solutions are unable to approach the optimal performance. In this paper, we propose an evolutionary algorithm (EA) based robust adaptive beamforming that is able to achieve near optimal performance. The essence of the idea is to shape the array beam response such that it has maximum response in the desired signal’s angular range and minimum response in the interferences’ angular range. In addition, the approach introduces null-response constraints deduced from the array observation to achieve better interference cancelation performance. As a whole, the proposed optimization is solvable using an improved variant of the differential evolution (DE) algorithm. Numerical simulations are also presented to demonstrate the efficacy of the proposed algorithm.

UWB ranging with high robustness against dominant jammer and multipath

The problem of reduced ranging resolution of an ultra wideband (UWB) localization system due to interference and multipath is addressed in this letter. We then propose a solution to this problem. The solution originates from the use of a tunnel diode in a leading-edge pulse detection scheme. The tunnel diode transforms the received UWB pulse to another form whereby the level difference between the desired pulse and the noise is increased. This transformation results in a better performance of the detection. However, this method fails in the presence of a dominant jammer, hence, we propose an envelope detector to be added to the structure of the detection in order to recover the performance. The simulation result shows that the proposed method maintains good ranging resolution and outperforms the conventional analog correlation method in a low signal-to-interference ratio (SIR) environment.

Single Antenna Power Measurements Based Direction Finding

In this paper, the problem of estimating direction-of-arrival (DOA) of multiple uncorrelated sources from single antenna power measurements is addressed. Utilizing the fact that the antenna pattern is bandlimited and can be modeled as a finite sum of complex exponentials, we first show that the problem can be transformed into a frequency estimation problem. Then, we explain how the annihilating filter method can be used to solve for the DOA in the noiseless case. In the presence of noise, we propose to use Cadzow denoising that is formulated as an iterative algorithm derived from exploiting the matrix rank and linear structure properties. Furthermore, we have also derived the Cramér-Rao Bound (CRB) and reviewed several alternative approaches that can be used as a comparison to the proposed approach. From the simulation and experimental results, we demonstrate that the proposed approach significantly outperforms other approaches. It is also evident from the Monte Carlo analysis that the proposed approach converges to the CRB.