Multistatic moving target detection in unknown coloured Gaussian interference

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

In the non-Gaussian clutter modeled as independent and identically distributed spherically invariant random vectors, three estimators of sample covariance matrix (SCM), normalized sample covariance matrix (NSCM) and the corresponding recursive estimator (NSCM-RE) are analyzed. Based on the uniform theorem, three corresponding adaptive normalized matched filters (ANMF) are evaluated from the standpoint of constant false alarm rate (CFAR) property. The theoretical results demonstrate that the SCM-ANMF is only CFAR to the normalized clutter covariance matrix (NCCM); the NSCM-ANMF is only CFAR to the clutter power level; and the NSCM-RE-ANMF with finite number of iterations is still not CFAR to the NCCM. To ensure CFAR property of ANMF, an adaptive estimator (AE) is devised. Moreover, with AE as the initialization matrix for the iterations, the AE-RE is proposed. With finite number of iterations, the corresponding AE-REANMF guarantees CFAR property to both of the NCCM and the clutter power level. Finally, the performance assessment conducted by Monte Carlo simulation confirms the effectiveness of the proposed detectors.

Traditional space-time adaptive processing (STAP) is a strategy for clutter suppression in airborne radar, which requires a large number of computational complexity and secondary data. In order to address the problem, reduced-dimension (RD) STAP is generally used. We propose a novel RD STAP through searching the best channels as the auxiliary channels to cancel the interference. Based on the estimation of the clutter Fourier basis vectors offline, a parameter named angle-Doppler correlation coefficient (ADC 2) is constructed to evaluate the capability of each auxiliary channel in clutter suppression, and the best sets of RD channels can be selected. The proposed algorithm can achieve the best detection performance with the fixed number of auxiliary channel. When the degrees of freedom (DOF) are restricted to a small value, only one auxiliary channel is needed to guarantee the SINR loss less than 3 dB. Therefore, the requirement of the training sample can be reduced, which makes the proposed approach more suitable for the heterogeneous clutter environments.

This research presents two new space-time adaptive processing (STAP) algorithms; a two-dimensional non-statistical method and a hybridisation of this approach with statistically based methods. The non-statistical algorithm developed here allows filtering of uncorrelated interference, such as discrete interferers, within the range cell of interest. However, the performance of these algorithms in homogeneous correlated interference scenarios is inherently inferior to traditional statistical STAP algorithms. The proposed hybrid algorithm alleviates this drawback by implementing a second stage of statistical adaptive processing. This paper illustrates the advantages of using a two stage adaptive process to combine the direct data domain and statistical algorithms. The work presented in this paper brings together two different aspects of STAP research: statistical and direct data domain processing. In doing so, this research fulfils an important need in the context of practical STAP processing.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

The advantages and limitations of coherent multiple-input multiple-output (MIMO) radar have been discussed widely. In practical systems, nonidealities often impose the dominant limitation to system performance. Consequently, successful high-performance systems typically employ adaptive techniques. Because of the higher potential performance of MIMO systems, they become even more sensitive to the system model if adaptivity is not employed. The quintessential example of the need for adaptivity is by ground-moving-target indictor (GMTI) radars, which employ space-time adaptive processing (STAP). As an initial step toward a more general study of test statistic robustness, we study an approximation to STAP, based on requiring test statistic invariance to clutter localization about an angle-Doppler operating point. We consider the effects of angle and velocity mismatch on clutter mitigation performance. Furthermore, we investigate the implications of MIMO waveform cross-antenna contamination by using invariance.

The space-time adaptive processing (STAP) in the airborne multiple-input multiple-output (MIMO) radar with nested structure is studied. According to the special structures of arrays and transmitting pulse interval, a virtual space-time snapshot vector with longer length is constructed. Both the virtual antenna array and the virtual pulse train formed by the nested array and nested pulse train are successive and hole-free. Then a Hermitian matrix is built to estimate the full rank clutter covariance matrix in airborne MIMO radar with nested structure. For the increasing DoFs, the STAP method can achieve higher upper bound in the airborne MIMO radar with nested structure than the STAP in the traditional airborne MIMO radar, which facilitates the clutter suppression and target detection. Simulation results verify the proposed STAP method.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

In this paper, a robust statistical method called projection statistics (PS) is developed into an TV-dimensional complex form required to detect strong nonhomogeneities, or TV-dimensional outlier vectors (i.e., outliers), in space-time adaptive processing (STAP) radar training data, where N is the number of degrees of freedom (DOF) associated with the STAP scenario. The PS technique does not require the estimation of a covariance matrix, as is the case with the generalized inner product (GIP) test. Rather, it uses robust estimators of location (the median) and scale (the median absolute deviation from the median (MAD)) to identify and then excise strong nonhomogeneities in STAP training data that are capable of rendering traditional nonhomogeneity detection (NHD) techniques, such as the GIP, ineffective. Shown in the results section of this paper is sample matrix inversion (SMI) STAP performance using three separate NHD techniques: 1) a power test for high power outlier snapshots, 2) a GIP test, and 3) a PS-based test. The performance when not using any form of NHD is shown for comparison. It is clear from the results that the PS NHD method provides an SMI-STAP processor the best training data selection in the multi channel airborne radar measurements (MCARM) [B.N.S Babu et al., 1996] based radar data scenario studied here, and does so across all range cells of the scenario.

Consider a multiple input multiple output (MIMO) system with pulse amplitude modulation (PAM), where each transmit antenna is allowed to choose a different transmission delay. (We refer to this system as a MIMO system with asynchronous PAM.) Such MIMO systems have been studied before for increasing diversity and avoiding unintentional beamforming. We quantify the capacity/outage capacity improvement due to asynchronism and the corresponding spectral leakage (if any) for MIMO point-to-point and MIMO broadcast channels. We show that under commonly used transmitter spectral mask constraints, the channel has unbounded degrees of freedom. Synchronous PAM exploits only a finite number of these degrees of freedom, and asynchronism helps to improve capacity. For most cases of interest, two distinct shifts attain most of the capacity improvement. These gains are robust to interference from adjacent carriers. Analysis of the spectrum of the asynchronous PAM suggests that optimal water-filling based schemes are useful for improving capacity only in directional links due to possible spectral leakage, while uniform power allocation leads to capacity improvement without any extra spectral leakage.

The constant false alarm rate property in transformed noncoherent detection processes

We address the problem of training data corruption in space-time adaptive processing (STAP) for ground moving target indication (GMTI) radar scenarios characterized by high densities of ground targets. A site-specific clutter simulation is used to demonstrate the impact that target signals in the training data have on STAP performance. Measured MCARM data results are presented that reveal similar performance trends as those observed in the simulations. A strategy for mitigating the deleterious effects of targets in the training data using a priori knowledge of the radar environment (e.g., locations of roads) to edit the training data is presented.

Space-time adaptive processing (STAP) algorithms typically consist of a data transformation step to reduce the number of degrees of freedom and a sampling step wherein radar returns from adjacent range bins are used to estimate interference statistics. The reduction in the number of degrees of freedom, inadequate sample support, presence of target in sampled data, and range dependence of interference are some of the main reasons for STAP performance loss. In this paper, we present an approach to target detection and localization that mitigates these performance losses using the well-known stochastic realization algorithm from system identification theory. We first identify a state space model from the radar return data in range-pulse domain for a given range bin, and then perform detection and localization using the identified state space matrices. As interference statistics are not directly computed and since there is no sampling from adjacent range bins, this approach is more robust to sample support issues, target in training and range dependence of clutter. A numerical comparison of the approach with beam-space post-Doppler STAP using simulated data is given.

The research of closed form expressions for the pdf of the lth ordered eigenvalue of a Wishart matrix has received a great attention in the past years owing to its applications in the performance analysis of multiple input multiple output (MIMO) systems in fading environments. Although several closed form expressions for this pdf were obtained in the past years, to the authors' knowledge, no one was very friendly for further analysis. We propose a methodology to obtain the pdf for the lth largest eigenvalue whose expression is given as a sum of terms xβe-xδ. This expression is easily usable to obtain closed form results for the performance of many MIMO systems, such as, for instance, MIMO beamforming, and MIMO with Singular Value Decomposition (SVD). The methodology is valid for both uncorrelated and correlated central Wishart, allowing the investigation of MIMO systems with uncorrelated and correlated Rayleigh fading. © 2008 IEEE.

When a flying object tracks a target, the location and velocity of the target are estimated based on a target detection process using radar. In general, the constant false alarm rate (CFAR) is used in the target detection process. However, if a high power interference wave is received in addition to the object signal, the target cannot be detected, so that tracking fails. This is caused by an uncontrolled increase in the threshold level at the time of target detection due to the interference component mixed in the reference cells. After the CFAR process, tracking of the target is possible by the use of a tracking filter as long as the existing target signal component is detected, even if the interference wave is evaluated as the target signal component. In this paper, an interference adaptive CFAR is discussed in which the CFAR process is carried out and the interference component mixed in the reference cell is detected and eliminated. The relationship between the threshold coefficient and the false alarm probability and a computation method for the CFAR loss are formulated in this paper. By means of the derived relationships, several case studies are carried out and the nature of the interference adaptive CFAR is discussed. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 1, 80(10): 29–37, 1997