The DOA estimation in array signal processing is one of the important and emerging research area. The effectiveness of this direction of arrival estimation greatly determines the performance of smart antennas. It works on the digitized output from each sensor array. The estimation results for coherent signals, broadband signals and multiple signals are of greater consideration. Various information of the parameters relating to a particular wave can be obtained by analyzing the incoming wave that is received by a sensor or an array of sensors. The accuracy in the estimation of direction of arrival is very crucial in array signal processing. DOA estimation has vital applications in radar, sonar, seismology, earthquake, astronomy, biomedicine and communication. This paper presents an overview of direction of arrival estimation using the MUSIC algorithm. This algorithm is a peak search method to estimate the arrival angle. The paper emphasizes on the factors that can affect the results of the estimation process and describes the concept of estimating the arrival angle. The MATLAB simulations shown here highlight the factors that can improve accuracy. The factors include the array element spacing, number of array elements, number of snapshots and the signal incidence angle difference. Attempts are made to bring about better resolution and contrast. As the conventional MUSIC algorithm is found to have less efficiency for coherent signals, better simulations using an improved MUSIC algorithm in provided in this paper.

MUSIC and improved MUSIC algorithm to estimate direction of arrival

The thesis consists of two parts. The first part is devoted to the theory of dual-orthogonal polarimetric radar signals with continuous waveforms. The thesis presents a comparison of the signal compression techniques, namely correlation and de-ramping methods, for the dual-orthogonal sophisticated signals. The novel time-frequency representation of beat signals in frequency modulated continuous wave (FM-CW) polarimetric radar with simultaneous measurement of scattering matrix elements is shown. Since the sounding sophisticated signals usually have large time-bandwidth product, the observed object motion can result in specific bandwidth effects in the scattered signals. A novel wide-band correlation processing for overcoming the limitations due to these bandwidth effects is proposed for polarimetric radar signals. Also a novel wideband model of the de-ramped signals is described. The second part of the thesis is devoted to advanced processing in polarimetric radar with continuous waveforms (and focus on polarimetric FM-CW radar). A novel technique for continuous “quasi-simultaneous” measurement of the elements of the scattering matrix, which can provide high isolation level between the branches in the radar receiver channels, has been proposed in this PhD thesis. The ambition to increase the radar performance, namely to improve the radar range resolution, has led to the development of a novel flexible de-ramping processing applicable in single-channel and in polarimetric FM-CW radar. The problem of isolation in the polarimetric FM-CW radar receiver is especially acute. A novel method allowing to mitigate the problem is developed. The modeling results of the proposed method show its high efficiency.

/pdf/processing-of-dual-orthogonal-cw-polarimetric-radar-signals-30frxhzp8b.pdf

Processing of dual-orthogonal cw polarimetric radar signals

In this paper MUSIC algorithm by using of multi-input multi-output (MIMO) arrays is investigated and its performance is compared with conventional arrays. The parameters involved in this comparison are: the number of the snapshots and array elements, and the SNR values. MIMO configuration with spatially orthogonal signal transmission is equivalent to additional virtual sensors which extend the array aperture with virtual spatial tapering. These virtual sensors can be used to form narrower beams with lower sidelobes and, therefore, provide higher performance in target detection, angular resolution, and angular estimation accuracy. Simulation results on prototype data showed the superiority of MUSIC algorithm performance; that is without requirement to increase the SNR value or the elements and snapshots number, high performance is simply possible by means of MIMO arrays.

MUSIC algorithm for DOA estimation using MIMO arrays

In this paper, Cramer-Rao lower bounds (CRLBs) for estimation of signal parameters related to periodically moving objects in pulse-based ultra-wideband (UWB) systems are presented. The results also apply to estimation of vital parameters, such as respiration rate, using UWB signals. In addition to obtaining the CRLBs, suboptimal estimation algorithms are also presented. First, a single-path channel with additive white Gaussian noise is considered, and closed-form CRLB expressions are obtained for sinusoidal object movements. Also, a two-step suboptimal algorithm is proposed, which is based on time delay estimation via matched filtering followed by least-squares estimation, and its asymptotic optimality property is shown in the limit of certain system parameters. Then, a multipath environment is considered, and exact and approximate CRLB expressions are derived. Moreover, suboptimal schemes for parameter estimation are studied. Simulation studies are performed for the estimation of respiration rates in order to evaluate the lower bounds and performance of the suboptimal algorithms for realistic system parameters.

/pdf/theoretical-limits-for-estimation-of-periodic-movements-in-326zft45rc.pdf

Theoretical Limits for Estimation of Periodic Movements in Pulse-Based UWB Systems

The application of the superresolution multiple signal classification (MUSIC) algorithm is extended to overcome the Doppler frequency estimation limitations due to the Fourier transform- (FT-) based techniques for ultrawideband (UWB) random noise radar signals. Simulated and experimental results are compared for single and multiple moving targets within a range bin, for narrowband (200 MHz) and UWB (1 GHz) noise radar signals. The simulated percentage error in Doppler estimation is parameterized as a function of the velocity ratio of two targets in a given range bin.

Superresolution Doppler Estimation using UWB Random Noise Signals and MUSIC

The clutter return presents a severe problem in target detection for radar systems. Conventional radar systems assume the clutter is stationary, with energy concentrated in the low frequency domain. To minimize the effect of clutter, radar systems use a fixed high pass filter (HPF) to eliminate the clutter. When the target Doppler frequency is below the cutoff frequency of the high pass filter, or the clutter energy does not concentrate on the low frequency domain, the performance of radar system is severely degraded. This paper proposes an adaptive clutter suppression scheme. This scheme provides more efficient clutter suppression for non-stationary clutter and provides the ability to detect a slow moving target. The computer simulation is presented to compare the performance of this adaptive clutter suppression technique and the fixed clutter suppression filter.

Adaptive radar clutter suppression

The angular resolution of conventional antenna is limited by the antenna mainlobe beamwidth. To reduce the antenna mainlobe beamwidth by conventional approach requires either using larger antenna or operating at a higher frequency. Both approaches may be undesirable for the application of airborne radar systems. This report suggests using a sensor array to perform space-time processing to enhance the angular resolution of antenna. By using the eigenanalysis method, the time sequence of sensor array outputs are decomposed into the signal and noise subspace and noise only subspace. The two subspaces are orthogonal to each other. Using the generalized MUltiple SIgnal Classification (MUSIC) method, closed spaced targets within the antenna mainlobe may be resolved. The theoretical background is discussed in this report. Computer simulation results are presented to verify the super resolution capability of this signal processing algorithm.

Direction of arrival estimation using super-resolution algorithm

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Adaptive radar clutter suppression

Direction of arrival estimation using super-resolution algorithm