Search

In this chapter, the problem of joint transmit code and receive filter design has been considered to optimize the achieved SINR of extended targets in the presence of signal -dependent interference. Due to uncertainties about the TIR, a worst case optimization framework has been proposed over two different T1R uncertainty sets. The former contains T1R's samples corresponding to some TAAs, whereas the latter is equivalent to bounding the actual T1R within a spherical set. Additionally, a PAR constraint has been imposed on the transmitted waveform so as to ensure its practical implementation. The obtained non -convex optimization problem has been divided into two sequential relaxed SDP problems. Their corresponding solutions have then been exploited to synthesize the code -filter pair via randomization methods.

Two gains play key roles in recently developed‎ MIMO ‎wireless communication systems‎: ‎"spatial diversity‎" ‎gain and "spatial multiplexing‎" ‎gain‎. ‎The diversity gain refers to the capability to decrease the error rate of the MIMO channel‎, ‎while the multiplexing gain implicitly refers to the amount of increase in the capacity of the MIMO channel‎. ‎It has been shown that there is a fundamental tradeoff between these two types of gains‎, ‎meaning interplay between increasing reliability (via an increase in the diversity gain) and increasing data rate (via an increase in the multiplexing gain)‎. ‎On the other hand‎, ‎recently‎, ‎MIMO radars have attracted much attention for their superior ability to enhance the system's performance‎. ‎As a MIMO system‎, ‎it is expected that the mentioned diversity-multiplexing tradeoff exists in a MIMO radar‎, ‎too‎. ‎In this paper‎, ‎this tradeoff is studied and verified in MIMO radars with widely separated antennas‎. ‎In more details‎, ‎it will be shown that increasing dependency between transmit-receive links results in higher diversity gain and lower multiplexing gain‎, ‎and vice versa‎. ‎Then‎, ‎the optimal tradeoff is introduced‎, ‎i.e.‎, ‎the conditions that the MIMO radar system should have‎, ‎so that the diversity-multiplexing tradeoff is at its optimum point‎, ‎are driven‎.

Two gains play key roles in recently developed multiple-input–multiple-output (MIMO) wireless communication systems: ‘spatial diversity’ gain and ‘spatial multiplexing’ gain. The diversity gain (DG) refers to the capability to decrease the error rate of the MIMO channel, whereas the multiplexing gain (MG) implicitly refers to the amount of increase in the capacity of the MIMO channel. It has been shown that there is a fundamental tradeoff between these two types of gains, meaning interplay between increasing reliability (via an increase in the DG) and increasing data rate (via an increase in the MG). On the other hand, recently, MIMO radars have attracted much attention for their superior ability to enhance the system's performance. As an MIMO system, it is expected that the mentioned diversity-multiplexing tradeoff exists in an MIMO radar, too. In this study, first, these two gains are obtained in an MIMO radar system in a closed-form solution. The authors have shown that the DG of an MIMO radar is directly related to its detection performance and the MG represents its capability in estimating the target's parameters. Finally, the tradeoff between these two gains in an MIMO radar is studied and verified theoretically and through simulations.

In this paper, we consider an exponentially windowed sample covariance matrix (EWSCM) and propose an improved estimator for its eigenvalues. We use new advances in random matrix theory, which describe the limiting spectral distribution of the large dimensional doubly correlated Wishart matrices to find the support and distribution of the eigenvalues of the EWSCM. We then employ the complex integration and residue theorem to design an estimator for the eigenvalues, which satisfies the cluster separability condition, assuming that the eigenvalue multiplicities are known. We show that the proposed estimator is consistent in the asymptotic regime and has good performance in finite sample size situations. Simulation results show that the proposed estimator outperforms the traditional estimator, significantly.

HF radar systems are widely used for the measurement of ocean surface currents and wave parameters. Theoretical analysis of high frequency radio wave backscattering from the sea has been derived in the past. Also, the relationship between Bragg frequencies and surface currents is determined and has been proved by HF radars. It is known that when surface currents are present, the Bragg frequencies move to new frequencies. Their movement is proportional to the surface current in the direction toward radar. In addition, it is observed that sea surface current changes the second order part of the spectrum of radar reflection from the sea surface. In this paper, a theoretical analysis of high-frequency radio wave backscattering from the sea in the presence of constant surface current provides a formula for the second order part of the spectrum of sea echo. This resulted in similar formulas of others but the process of formula extraction is different. This method may be used for the determination of the second order spectra of radar cross section in the presence of other types of surface current.

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal-to-noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace-based beamformer. In this paper, a new signal-plus-interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal-to-interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms.

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. The objective of the present study is to propose a new adaptive beamformer which is robust against direction-of-arrival (DOA) mismatch and its convergence rate is not sensitive to the presence of the DS. This method is applicable to the arrays with specific structure such as the linear array. Our approach is based on the DS elimination from the training snapshots and the sub-array beamforming technique. To accomplish this goal, a blocking matrix which converts the primary data to the DS-free data is synthesized. The Synthesis process is based on the desired degree of freedom and the uncertainty of DOA of the DS. Using the signal-free data, the beamforming vector is calculated through the presented algorithm. Owing to elimination of the DS from the training snapshots and performing the adaptive operations in the sub-array level, our algorithm has high convergence rate and excellent performance even in cases with the small sample size. Simulations show that the proposed beamformer can achieve a much better performance in terms of output SINR compared to the existing ones.

Many papers have investigated phased array radars beam-forming so far. In these papers the power at a specific point of interest is written by a combination of transmitted power by means of each antenna. In this paper we will introduce a new approach which will use of cross correlation matric like MIMO radars to define beam-pattern of array and then by introducing an optimization problem to steering the available power at desire directions, it is shown that with an approach to optimum choosing of antenna placement, it is possible to concentrate the transmitter power on desire direction better than conventional phased array antennas with half wave length spacing. It should be say that some papers have investigated non uniform arrays but those papers usually use of multiple of half wave length in their elements spacing and in addition their approach to choosing these spaces is different and pretty more complicated from presented approach. Simulation is performed to analyze the presented algorithm and compare it's performance with conventional phased arrays with half wave length spacing for linear and planar array. MATLAB software is being employed for simulations.

In a MIMO (Multiple Input Multiple Output) radar system, proper design of transmit signal and receive filter is an advantageous tool to improve the detection performance. Thus, in this paper, we consider the problem of transmit code and receive filter design, in order to maximize the Signal to Noise Ratio (SNR) at the receiver while enforcing a similarity constraint between the transmit space-time code (STC) and a reference STC which has a desirable ambiguity function. We will show that by solving such constrained optimization problem through successive iterations, our proposed method leads to satisfactory results.

In this paper, we devise a joint code-filter design method to optimize the signal-to-interference-plus-noise ratio (SINR) associated with the colocated MIMO radars. We consider constant-modulus wavefrom design for moving target detection and propose a cyclic algorithm to deal with the design problem.

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the signal-to-interference-plus-noise ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach. Precisely, we model the uncertainty set as a finite collection of TIRs, obtained sampling the actual TIR at some aspect angles. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications, where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method.

In this paper, we consider the problem of transmit code design to optimize the detection probability of an extended target embedded in Gaussian signal-dependent interference. We model the target with a priori known target impulse response (TIR) structure along with an unknown reflection factor. As is known, the performance of the generalized likelihood ratio test (GLRT) detector, for the case at hand, monotonically increases with the signal-to-interference-plus-noise ratio (SINR). Hence, we deal with the code design problem via maximizing the SINR. We also enforce a peak-to-average power ratio (PAR) constraint on the sought code. We devise a cyclic method to tackle the highly non-convex design problem. Numerical results highlight the effectiveness of the proposed method to improve the detection performance of the system.

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the Signal-to-Interference plus Noise Ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach based on two different uncertainty sets. The former is composed of a finite collection of TIRs, obtained by sampling the actual TIR at some aspect angles; the latter is a spherical uncertainty set. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method in different scenarios.

A multiple input multiple output (MIMO) radar system, unlike a conventional phased array radar, can choose freely the signals which transmitted via its antennas to maximize the power around the locations of the specific targets, or more generally to approximate a given transmit beampattern, and also to minimize the cross-correlation of the signals reflected back to the radar by the targets of interest. In this paper we use of phase-coded signals and show how the above desirable features of MIMO radars with some operational consideration can help us to have a desire beampattern. We suppose that for a collection of phase-coded signals, the elements of transmitted signals cross-correlation matrix all be real and positive and then we will examine features of this matrix in beampattern.

In this article, we investigate the limiting spectral distribution of the sample covariance matrix (SCM) of weighted/windowed complex data. We use recent advances in random matrix theory and describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We obtain an approximation for the spectral distribution of the SCM obtained from windowed data. We also determine a condition on the coefficients of the window, under which the fragmentation of the support of noise eigenvalues can be avoided, in the noise-only data case. For the commonly used exponential window, we derive an explicit expression for the l.s.d of the noise-only data. In addition, we present a method to identify the support of eigenvalues in the general case of signal-plus-noise. Simulations are performed to support our theoretical claims. The results of this article can be directly employed in many applications working with windowed array data such as source enumeration and subspace tracking algorithms.

In this era of digital communication systems, different factors are included in efficiency of a transceiver system. Binary PAM systems are one of these transceiver systems. Many aspects of such a system has been considered yet but this field needs more research on because this is the base of any communication system. In this paper, effects of transmitter and receiver filters, noise, duty-cycle and time mismatch between transmitter and receiver in a binary baseband PAM transceiver system is analyzed. Simulation is performed to analyze the above effects on a typical communication system with 10 bits of transmit. MATLAB software is being employed for simulations.

A multiple-input multiple-output (MIMO) radar system., unlike a standard phased array radar., can choose freely the probing signals transmitted via its antennas to maximize the power around the locations of the targets of interest., or more generally to approximate a given transmit beam-pattern., or to minimize the cross-correlation of the signals reflected back to the radar by the targets of interest. In this paper it will be shown that how these more degrees of freedom to choose freely between probing signals in a MIMO radar can help to have an omnidirectional beam-pattern for a side-lobe blanker system., and it will be shown that with utilizing MIMO radars for a side-lobe blanker system., one can achieve immunity respect to faults that may occurred during build processes.

A multiple input multiple output (MIMO) radar system is an emerging research field which has attracted many studies in recent years. This type of radar, unlike conventional phased array radar, can transmit different probing signals via its antennas, that may be correlated or uncorrelated with each other. This waveform diversity offered by MIMO radar systems enable superior capabilities compared to conventional phased array radar. Many papers have introduced different ways for beamforming of MIMO radar. In this paper it is desirable to analysis some methods for beamforming of MIMO radars with planar arrays constellation and examine their 3D result beam-pattern of such constellations for covariance based MIMO radar waveforming method. MATLAB software is being employed for simulations.

One of non-cooperative illuminators recently considered for passive radar applications is the DVB-T (Digital Video Broadcasting- Terrestrial) station. The thumbtack ambiguity function of the DVB-T signal in addition to being stationary makes such signal a good candidate for such applications. However, certain ambiguities in its ambiguity function necessitates certain issues to be carefully considered when DVB-T signals are to be utilized in these scenarios. In this paper, after studying the origins of these ambiguities, we propose special processing schemes to resolve them.

The problem of detection of coherent radar signals with unknown Doppler shift in noise plus clutter, which is assumed to be gaussian is addressed. The authors solve the detection problem and find the optimum ALR detector. Since it includes a difficult integral, they derive several suboptimum detectors including CGLR, AALR and CALR. Comparison of these detectors via computer simulations shows that CALR and CGLR can have near-optimum performance whereas the common radar-detectors may have severe losses.