Showing papers in "Iet Radar Sonar and Navigation in 2012"
TL;DR: The authors devise constrained optimisation procedures that sequentially improve the Signal to Interference plus Noise Ratio (SINR), accounting for a similarity constraint between the transmitted signal and a prescribed radar waveform.
Abstract: In this study, the authors consider the problem of cognitive transmit signal and receive filter design for a point-like target embedded in a high-reverberating environment. The authors focus on phase-only waveforms, sharing either a continuous or a finite alphabet phase, hence they devise constrained optimisation procedures that sequentially improve the Signal to Interference plus Noise Ratio (SINR), accounting for a similarity constraint between the transmitted signal and a prescribed radar waveform. The computational complexity of the proposed algorithms is linear with the number of iterations and polynomial with the receive filter length. At the analysis stage, the performance of the techniques is assessed in the presence of a homogeneous clutter scenario.
128 citations
TL;DR: In this article, the authors describe the development sequence, the development history, IFM design and performance principles, its general use and limitations in modern electronic warfare (EW) systems and end with a projection for the future.
Abstract: Quadrature phase discriminators, the core of wideband instantaneous frequency measurement (IFM) receivers and multiple base-line interferometry for accurate direction finding (DF), were invented in 1957, by S.J. Robinson of Mullard Research Laboratories (MRL). Digital instantaneous frequency measurement (DIFM) receivers have been universally used operationally for wideband monitoring of radar environments in naval, airborne and ground-based electronic support measures (ESM) systems all over the world for over 50 years. Their importance is such that many countries have developed their own IFM manufacturing capability with just minor architectural changes to the original design. This study describes the invention sequence, the development history, IFM design and performance principles, its general use and limitations in modern electronic warfare (EW) systems and ends with a projection for the future.
96 citations
TL;DR: In this paper, a novel adaptive algorithm in fractional Fourier transform (FRFT) domain is proposed, which combines statistic-based and FRFT-based detection method, which provides less error and faster convergence.
Abstract: Attention has been focused on the moving target detection in heavy sea clutter. On the basis of detection model of moving target with fluctuant amplitudes, a novel adaptive algorithm in fractional Fourier transform (FRFT) domain is proposed, which combines statistic-based and FRFT-based detection method. FRFT has good energy concentration property on linear frequency modulation (LFM) signal with the optimal transform angle, which is determined by calculating spectral kurtosis (SK) in FRFT domain. Grading iterative search method is used for good accuracy of parameter estimation and fast calculation speed. A novel adaptive line enhancer (ALE) in FRFT domain is proposed to suppress sea clutter and improve signal-to-clutter ratio (SCR), which provides less error and faster convergence. Leakage factor is introduced into the update equation of weight vector to reduce `memory effect` and step size is normalised by the power of input signal with better convergence characteristic. In the end, both X-band and S-band real sea clutter is used for verification and the results present that the proposed algorithm has good convergence property and small mean square error (MSE). Weak moving target in low SCR environment (SCR = -6 dB) can be well detected and estimated, which indicates the effectiveness of the algorithm.
94 citations
TL;DR: The results indicate that the Bayesian formalism can provide a sharp and sparse image absence of side-lobes, which is the common problem in conventional imaging methods and has fewer artifacts compared with the previous version of CS-based methods.
Abstract: To achieve high-resolution two dimension images, synthetic aperture radar (SAR) with ultra wide-band faces considerably technical challenges such as long data collection time, huge amount of data storage and high hardware complexity In these years, several imaging modalities based on compressive sensing (CS) have been proposed which can provide high-resolution images using significantly reduced number of samples However, the CS-based methods are sensitive to noise and clutter In this study, a new imaging modality based on Bayesian compressive sensing (BCS) is proposed along with a novel compressed sampling scheme Clutter, which the previous CS-based methods not considered, is also included in this study This new imaging scheme requires minor change to traditional system and allows both range and azimuth compressed sampling Also, the Bayesian formalism accounts for additive noise encountered in the compressed measurement process Experiments are carried out with noisy and cluttered imaging scenes to verify the new imaging scheme The results indicate that the Bayesian formalism can provide a sharp and sparse image absence of side-lobes, which is the common problem in conventional imaging methods and has fewer artifacts compared with the previous version of CS-based methods
78 citations
TL;DR: The authors propose a solution based on the use of inverse synthetic aperture radar (ISAR) processing to solve the problem of non-cooperative target imaging and the effectiveness of the proposed method is tested on Cosmo-Skymed spotlight SAR data of maritime targets.
Abstract: Non-cooperative moving targets typically appear defocused in synthetic aperture radar (SAR) images as they are non-stationary during the coherent processing interval. Typically, the problem of refocusing moving targets in SAR scenes is addressed as a problem of the target's motion estimation and compensation. In this study, the problem is addressed as a problem of non-cooperative target imaging and the authors propose a solution based on the use of inverse synthetic aperture radar (ISAR) processing to solve this problem. Taking into consideration the advantages of SAR processing, the problem is tackled starting from formed SAR images, where non-cooperative targets images are firstly detected, then backprojected to the received data domain and reprocessed as ISAR data. The effectiveness of the proposed method is then tested on Cosmo-Skymed spotlight SAR data of maritime targets.
74 citations
TL;DR: The concerns are with the calculation of monostatic and bistatic ambiguity function (AF) of a quadrature phase shift keying (QPSK) signal where the pulses are shaped with a root raised cosine (RRC) filter.
Abstract: The concerns are with the calculation of monostatic and bistatic ambiguity function (AF) of a quadrature phase shift keying (QPSK) signal where the pulses are shaped with a root raised cosine (RRC) filter. The monostatic and bistatic modified Cramer-Rao lower bounds (MCRLBs) for the estimation of target range and velocity are also derived and analysed. The QPSK modulation is used in the downlink of a universal mobile telecommunications system (UMTS) base station, hence the results of our analysis provide a useful tool to asses the performance of a passive coherent location (PCL) system where the non-co-operative transmitter of opportunity is a UMTS base station. The actual growing coverage of UMTS signals on the international territory makes multistatic radar configuration feasible, therefore these results can also be exploited for the dynamical selection of the transmitter in a multistatic radar system where multiple UMTS base stations are used.
73 citations
TL;DR: In this article, the feasibility of classifying different human activities using ultra-wide band (UWB) radar was investigated, and a support vector machine (SVM) was used to classify the activities based on the signatures.
Abstract: The authors investigate the feasibility of classifying different human activities using ultra-wide band (UWB) radar. Eight human subjects performing eight different activities are measured using a UWB radar. The eight activities include walking, running, rotating, punching, jumping, transitioning between standing and sitting, crawling and standing still. The dimension of the UWB returns is reduced using principal component analysis (PCA). The time-varying UWB signatures are characterised within a time window through observing the variation of the PCA coefficients. A support vector machine (SVM) is used to classify the activities based on the signatures. A multi-class classification is implemented using a one-versus-one method. Optimal parameters for the SVM are found through a 4-fold cross-validation. The resulting classification accuracy is found to be more than 85%. The potential of classifying human activities with different ground planes and with cluttered environments is also investigated. To extract more information regarding the target motion, human walking style classification with the developed method is also discussed.
70 citations
TL;DR: The proposed algorithm can achieve lower computational complexity than the Capon algorithm, while not debasing the performance of the angle estimation, and it has close angle estimation performance to the CrameŕRao bound (CRB).
Abstract: In this study, the authors consider the direction of arrival (DOA) estimation problem for a monostatic multiple-input multiple-output (MIMO) radar. A DOA estimation algorithm for the monostatic MIMO radar using Capon and the reduced-dimension transformation is proposed. The proposed algorithm can achieve lower computational complexity than the Capon algorithm, while not debasing the performance of the angle estimation. The proposed algorithm has better angle estimation performance than the Capon algorithm and estimation of signal parameters via rotational invariance techniques algorithm, and it has close angle estimation performance to the CrameŕRao bound (CRB). The variance of the estimation error and the CRB of the DOA estimation are derived. Simulation results present the usefulness of the proposed algorithm.
66 citations
TL;DR: The authors present a computationally efficient method for designing practical radar transmit waveforms that maximize the signal-to-interference-plus-noise ratio (SINR) in a known additive coloured Gaussian noise environment that relies on a parametrisation of the phase perturbations of a linear frequency modulated waveform.
Abstract: The authors present a computationally efficient method for designing practical radar transmit waveforms that maximize the signal-to-interference-plus-noise ratio (SINR) in a known additive coloured Gaussian noise environment. The problem reduces to a non-linear constrained optimisation in which the authors seek the SINR-maximising transmit waveform that has a specified envelope and an acceptable autocorrelation sequence (ACS). The waveform ACS can be constrained either directly or indirectly. The direct approach involves forcing the ACS magnitude below a specified level at each lag. This provides the greatest control over the waveform ACS, but it is too computationally demanding for many realistic problem sizes. Indirect methods of ACS constraint can be computationally less demanding, but they afford only inexact control over the waveform ACS. The leading indirect approach, which relies on the so-called similarity constraint, requires significantly fewer calculations than the direct approach, but it provides significantly less SINR improvement. The indirect approach presented here relies on a parametrisation of the phase perturbations of a linear frequency modulated waveform. This approach requires fewer calculations than the direct approach, and can provide more SINR improvement than the similarity constraint approach. As such, this new approach may be preferable when computation time is limited.
57 citations
TL;DR: A new approach of parameters estimation for the cubic phase signal is presented, and combined with the range-instantaneous-Doppler and range- Instantaneous-chirp-rate algorithms, the high-quality instantaneous ISAR images can be obtained.
Abstract: For inverse synthetic aperture radar (ISAR) imaging of manoeuvring target, the received signal in a range bin is very complicated, which will degrade the azimuth focusing quality greatly. In this study, the author characterises the received signal as multi-component cubic phase signal, and a new approach of parameters estimation for the cubic phase signal is presented. Combined with the range-instantaneous-Doppler and range-instantaneous-chirp-rate algorithms, the high-quality instantaneous ISAR images can be obtained. The results of simulated and real data demonstrate the validity of the new method proposed.
56 citations
TL;DR: This work proposes a novel UWB navigation system that permits accurate mobile robot (MR) navigation in indoor environments and reaches an accuracy that outperforms traditional sensors technologies used in robot navigation, such as odometer and sonar.
Abstract: Typical indoor environments contain multiple walls and obstacles consisting of different materials. As a result, current narrowband radio frequency (RF) indoor navigation systems cannot satisfy the challenging demands for most indoor applications. The RF ultra wideband (UWB) system is a promising technology for indoor localisation owing to its high bandwidth that permits mitigation of the multipath identification problem. This work proposes a novel UWB navigation system that permits accurate mobile robot (MR) navigation in indoor environments. The navigation system is composed of two sub-systems: the localisation system and the MR control system. The main contributions of this work are focused on estimation algorithm for localisation, digital implementation of transmitter and receiver and integration of both sub-systems that enable autonomous robot navigation. For sub-systems performance evaluation, statics and dynamics experiments were carried out which demonstrated that the proposed system reached an accuracy that outperforms traditional sensors technologies used in robot navigation, such as odometer and sonar.
TL;DR: In digital radio mondiale (DRM)-based high frequency (HF) passive bistatic radar, target echoes need to be detected against the direct-path wave and multipath echoes, and a novel approach is proposed to reject these echoes.
Abstract: In digital radio mondiale (DRM)-based high frequency (HF) passive bistatic radar, target echoes need to be detected against the direct-path wave and multipath echoes. A novel approach is proposed to reject the direct-path wave and multipath echoes. The signal is projected into a subspace orthogonal to the clutter subspace carrier by carrier, exploiting the fact that the direct-path wave and multipath echoes at the same carrier in orthogonal frequency division multiplexing (OFDM) waveform are totally correlated. Simulation and experimental results following the theory analysis verify the performance of the new approach.
TL;DR: A new tracking algorithm for multistatic sonar systems, where the measurements collected by different sensors are sent to a fusion centre, which relies on the main idea behind the track-before-detect paradigm and estimates target positions by maximising the likelihood function of the available measurements.
Abstract: In this study, the authors propose a new tracking algorithm for multistatic sonar systems, where the measurements collected by different sensors are sent to a fusion centre. The proposed algorithm relies on the main idea behind the track-before-detect paradigm, which consists of processing data from several consecutive pings, and estimates target positions by maximising the likelihood function of the available measurements. The authors assume that one manoeuvring target is present within the surveillance area. The preliminary performance assessment, carried out on simulated scenarios, shows that the proposed algorithm has acceptable performance also when the probability of detection per sensor is low (in the order of 0.3) and measurement errors are significant.
TL;DR: An extended processing is proposed, which makes it possible to detect manoeuvring targets without the loss of the signal-to-noise ratio and an estimate of the bistatic acceleration is available, which can greatly facilitate tracking of manoeuvring target detection.
Abstract: In this study, the problem of detection and parameter estimation of manoeuvring targets in passive radar is addressed. Classical processing scheme used in passive radars works well under certain conditions. If the conditions are not met, such as in the case of detection of highly manoeuvring targets, performance degradation can be expected. In this study, an extended processing is proposed, which makes it possible to detect manoeuvring targets without the loss of the signal-to-noise ratio. Moreover, an estimate of the bistatic acceleration is available, which can greatly facilitate tracking of manoeuvring targets. The proposed methods are tested using real-life data recorded with an FM-based passive radar.
TL;DR: In this paper, the fractal property of the frequency spectrum of the real sea clutter was analyzed and the effect of the length of the time series and fast Fourier transform (FFT) was analyzed.
Abstract: This study mainly describes the fractal property of the frequency spectrum of the sea clutter and the application of the obtained fractal characteristic in frequency domain to the constant-false-alarm-rate (CFAR) target detection within sea clutter. First, this study takes fractional Brownian motion (FBM) for example, and the spectrum of the FBM is proved to be fractal theoretically on condition that the time series of the FBM is fractal. This argument lays the foundation for the application of fractal theory to the frequency spectrum. Next, X - and S -band real radar data are used for the verification of the fractal property of the real sea clutter frequency spectrum. Finally, the effects of the length of the time series and fast Fourier transform (FFT) are analysed in detail. The results show that the frequency spectrum of the real sea clutter is fractal in the statistical sense and the frequency Hurst exponents of clutter range bins and target range bins are distinguishable. Therefore the frequency Hurst exponent is used for the CFAR target detection within sea clutter.
TL;DR: A novel M OCO algorithm based on entropy minimisation is proposed, which is named by minimum entropy MOCO (ME-MOCO), which is efficient for the capability of exploiting the fast Fourier transform through the processing chain.
Abstract: Spotlight synthetic aperture radar (SAR) is an effective way to obtain finer azimuth resolution than the achievement in strip-map mode with the same physical antenna. In general, for spotlight SAR imaging, high azimuth resolution requires long synthetic aperture length. However, in practical airborne applications, because of the inevitable atmospheric turbulence during the long flight trajectory, more complicated motion error is induced that severely degrades the focusing quality of the SAR image. It makes motion compensation (MOCO) for high-resolution spotlight (HR-Spotlight) SAR imagery more difficult than that for other SAR systems. To tackle the HR-Spotlight SAR data contaminated by the complicated motion error, a novel MOCO algorithm based on entropy minimisation is proposed, which is named by minimum entropy MOCO (ME-MOCO). In this approach, by fitting a polynomial to the motion error, the entropy of a focused image is utilised as the optimisation function of the polynomial coefficients. Attributed to damped Newton method, a modified strategy is designed, which results in that the data-driven ME-MOCO algorithm estimates high order polynomial parameters accurately and efficiently. Besides, the proposed algorithm is efficient for the capability of exploiting the fast Fourier transform through the processing chain. Real data experiments and comparisons demonstrate the effectiveness and superiority of the proposal.
TL;DR: The authors use the Rihaczek distribution and the Hough transform to concentrate the energy in time-frequency plane and derive two new characteristic features to improve the probabilities of successful recognition (PSRs) to recognise the classical low probability of intercept (LPI) radar signals.
Abstract: It is an important work to classify the modulation type of the intercepted radar signal for an electronic intelligence (ELINT) receiver in a non-cooperative environment. The authors use the Rihaczek distribution (RD) and the Hough transform (HT) to concentrate the energy in time-frequency plane and derive two new characteristic features, namely the ratio of the minimum to the maximum of the HT and the peak number of the HT of the real part of the RD, to improve the probabilities of successful recognition (PSRs) to recognise the classical low probability of intercept (LPI) radar signals. The first feature is especially suitable for the linear frequency modulation (LFM), whereas the second one is specifically designed for frequency shift keying (FSK). The choice of thresholds and the effects of signal parameters are analysed. Simulations show that the PSRs can reach 90% when the signal-to-noise ratio (SNR) is above -4 dB. The proposed algorithm is better than the previous algorithms by just using ambiguity function.
TL;DR: In this article, the adaptive beamformer orthogonal rejection test (ABORT) was used to detect distributed targets in the presence of homogeneous and partially homogeneous Gaussian disturbance with unknown covariance matrix.
Abstract: This study deals with the problem of detecting distributed targets in the presence of homogeneous and partially homogeneous Gaussian disturbance with unknown covariance matrix. The proposed detectors improve the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of distributed targets, which makes it possible to decide whether some observations contain a useful target or a signal belonging to the orthogonal complement of the useful subspace. At the design stage, the authors resort to either the plain generalised likelihood ratio test (GLRT) or ad hoc design procedures. Remarkably, the considered criteria lead to receivers ensuring the constant false alarm rate (CFAR) property with respect to the unknown quantities. Moreover, authors’ derivations show that the ad hoc detector for a partially homogeneous environment coincides with the generalised adaptive subspace detector. The performance assessment conducted by Monte Carlo simulation has confirmed the effectiveness of the newly proposed detection algorithms.
TL;DR: In this article, the Space-time Radon-Fourier Transform (STRFT) was proposed for wideband digital array radar (DAR), which is a coherent detection technique that jointly realized wideband DBF, range compression and long-time coherent integration.
Abstract: This study proposes the Space-time Radon-Fourier transform (STRFT) for wideband digital array radar (DAR). It is a novel coherent detection technique that jointly realise wideband digital beamforming (DBF), range compression and long-time coherent integration. The likelihood ratio detector (LRT) is first derived based on the three-dimensional (3D) signal model of moving targets, which is the output of the STRFT on the 3D range-compressed echoes. Some transform properties of the proposed STRFT are also discussed. This include the 3D impulse response, 3D translational invariance, multi-target linear additivity, linear SNR gain in additive white Gaussian noise (AWGN), as well as the 3D correlation function of transformed AWGN. Also, the fast implementation of STRFT is proposed in the element-pulse-range frequency domain, which is realised via the two dimensional Chirp-Z transform (CZT) based on fast Fourier transform (FFT) operators. The proposed method may remarkably reduce the computational burden for modern wideband DAR. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed methods.
TL;DR: An interacting multiple model (IMM) particle filtering method using multiple time-of-arrival (TOA) measurements from several transmitter-receiver pairs is proposed and evaluated and obtains better estimates of position, velocity and acceleration.
Abstract: This study investigates the problem of manoeuvring target tracking with passive coherent location (PCL) radar. The main challenge stems from the non-linearities of the dynamic state space and the non-Gaussian measurement noise. The contribution of the study is twofold. First, to deal with the problem of manoeuvring target tracking in the presence of glint noise, an interacting multiple model (IMM) particle filtering (PF) method using multiple time-of-arrival (TOA) measurements from several transmitter-receiver pairs are proposed and evaluated. Second, because tracking precision is sensitive to the configuration of transmitters and receiver stations, the authors provide insight into the influence of the configuration of transmitters and receiver stations on the tracking methods of PCL radar. Simulations illustrate the effectiveness of the proposed method. Compared with conventional IMM algorithm and PF algorithm in different glint noise environments and various configurations of transmitters and receiver stations, the proposed method obtains better estimates of position, velocity and acceleration.
TL;DR: In this article, the detection problem of multiple-input multiple-output (MIMO) radar in the presence of a compound-Gaussian clutter is considered and two new detectors based on the Rao and Wald criteria are devised under the known covariance matrix.
Abstract: This study considers the detection problem of multiple-input multiple-output (MIMO) radar in the presence of a compound-Gaussian clutter. Two new detectors based on the Rao and Wald criteria are devised under the known covariance matrix. The theoretical expression for the probability of false alarm is developed and constant false alarm (CFA) rate behaviour is remarked on. Furthermore, the fully adaptive approximated Rao and Wald tests are investigated in place of the exact covariance matrix with a suitable estimator using secondary data. Finally, several numerical simulations with typical parameters are provided and discussed.
TL;DR: In this paper, a constrained total least squares (CTLS) algorithm for estimating the position and velocity of a moving source with sensor location uncertainties that uses the time difference of arrival and frequency difference of measurements of a signal received at a number of sensors is proposed.
Abstract: In this study, a constrained total least-squares (CTLS) algorithm for estimating the position and velocity of a moving source with sensor location uncertainties that uses the time difference of arrival and frequency difference of arrival measurements of a signal received at a number of sensors is proposed. The CTLS method, as a natural extension of LS when noise occurs in all the data and the noise components of the coefficients are linearly dependent, is more appropriate than the LS method for the above problem. By utilising the Lagrange multipliers technique, the known relation between the intermediate variables and the source localisation coordinates has been exploited to constrain the solution. In addition, the Lagrange multipliers can be obtained efficiently and robustly, which can allow real-time implementation as well as ensure global convergence. After a perturbation analysis, the bias and covariance of the proposed CTLS algorithm are also derived, indicating that the proposed CTLS algorithm is an unbiased estimator, and it could achieve the Cramer Rao lower bound (CRLB) when the measurement noise and the sensor location errors are sufficiently small. The simulation results show that the proposed estimator achieves remarkably better performance than the TLS and two-step weighted least squares approach, which makes it possible that the CRLB is attained at a sufficiently high noise level before the threshold effect occurs.
TL;DR: The issue of ship detection from HFSWR images is introduced and an overview of the MCA approach is given, the algorithm used for target detection is depicted, and comparisons with a classical constant false-alarm rate (CFAR) detection method are given.
Abstract: In this study, high-frequency surface wave radar (HFSWR) is considered for target detection. These systems, commonly used for oceanographic purposes, are of interest in maritime surveillance because of their long range detection capabilities compared with conventional microwave radar. Unfortunately, the received signals are strongly polluted by different noises. In this contribution a target detection method based on morphological component analysis (MCA) is investigated. Basically, MCA is a source separation technique based on multiscale transforms and the sparsity representation. The authors goal is to extract the target signatures from the range-Doppler image and then to take the final decision through a simple rule. This study introduces the issue of ship detection from HFSWR images and gives an overview of the MCA approach. Then, the algorithm used for target detection is depicted. Comparisons with a classical constant false-alarm rate (CFAR) detection method, the so-called greatest of cell averaging-CFAR, are given through receiver operating characteristic curves computed from simulated data.
TL;DR: This study considers how to estimate and improve such a multi-sensor multi-network positioning system's reliability and estimate its accuracy.
Abstract: This study discusses a simple multi-sensor multi-network positioning system that integrates global positioning satellite (GPS) measurements, accelerometers and a digital compass with wireless network localisation utilising a pedestrian motion model and dead reckoning. The feasibility of the multi-technology system for seamless outdoor to indoor pedestrian navigation is discussed with the emphasis on reliability issues and adaptability requirements. The multi-sensor multi-network positioning system is developed for challenging navigation environments such as indoors and deep urban canyons. This study considers how to estimate and improve such a multi-sensor multi-network system's reliability and estimate its accuracy. An outdoor to indoor pedestrian test is conducted. Adaptive filtering performance of the multi-technology solution as well as general measurement quality monitoring and error detection when an over-determined solution is at hand is shown. Reliability estimation utilising adaptation in the form of environment detection to estimate the final positioning accuracy is also presented.
TL;DR: This study addresses the design of optimal waveform for detecting extended target in signal-dependent interference, using the probability of detection as a cost function, and proposes methods for designing optimal, suboptimal and asymptotic optimal waveforms for detecting rank-one target.
Abstract: In this study, the authors address the design of optimal waveform for detecting extended target in signal-dependent interference, using the probability of detection as a cost function. The authors model the received signal in frequency domain and derive the optimum Neyman-Pearson (NP) detector. Considering the distribution of test statistic is rather complicated, the authors use a mathematically tractable form to approximate and derive an analytical expression for the probability of detection with regard to the transmitted waveform. This lays the foundation for the subsequent waveform optimisation. Finally, the authors focus on rank-one target, and propose methods for designing optimal, suboptimal and asymptotic optimal waveforms for detecting rank-one target.
TL;DR: After developing the optimum detector for a general case, exact closed-form expressions are derived for the probability of detection and false alarm as the derived expressions have complicated form, their interpretation is not tractable in the general case.
Abstract: This study is concerned with the performance analysis of detection problem in statistical multiple-input multiple-output radars for Gaussian interference. This subject has been addressed in some publications for such special cases as white Gaussian noise and orthogonal transmission. However, theoretical performance analysis of optimum detector for general case including coloured Gaussian interference and arbitrary transmission signal has not been reported yet. In the present study, after developing the optimum detector for a general case, exact closed-form expressions are derived for the probability of detection and false alarm. As the derived expressions have complicated form, their interpretation is not tractable in the general case. Therefore lower and upper Chernoff bounds are obtained to provide better insight into the detector performance. Furthermore, the effect of various parameters on the detector performance is investigated by Monte-Carlo simulations. Numerical analysis shows a high degree of consistency between the theoretical and Monte-Carlo simulation results.
TL;DR: In this article, an oblique projection polarisation filter (OPPF) is proposed for interference suppression in high-frequency surface wave radar (HFSWR), which can be constructed from the polarisation subspaces of the target signal and those of the interference or directly from experimental data.
Abstract: Polarisation filtering is a valid approach for interference suppression in high-frequency surface wave radar (HFSWR) and other systems. Based on the fundamental principle of the oblique projection and polarised filtering, an oblique projection polarisation filter (OPPF), which can be constructed from the polarisation subspaces of the target signal and those of the interference or directly from experimental data, is proposed in this study. Generalised methods for constructing the OPPF operators (theoretical OPPF and improved OPPF) are provided and the impact on the performance caused by the estimation errors is also discussed. Numerical results from simulation and experimental data demonstrate that the proposed filter is an effective means of interference cancellation. It is proved that OPPF is an extension of the conventional polarised filter, whereas the improved OPPF is more suitable for the situation where the interference is unknown.
TL;DR: An optimal detector in the sense of generalised likelihood ratio test for coherent radar detection in a compound K -distributed clutter environment is re-studied, and a few properties of the detector are derived showing potential of its implementation in radar systems.
Abstract: An optimal detector in the sense of generalised likelihood ratio test (GLRT) for coherent radar detection in a compound K -distributed clutter environment is re-studied in detail. A few properties of the detector are derived showing potential of its implementation in radar systems. To the optimal detector, sea spikes (abnormally high radar returns from the sea surface) are unlikely to trigger false alarms, leading to a significant improvement in target detection. Two suboptimal detectors, aiming at saving computational cost, are proposed, and their performance is compared with the GLRT detector. The detector is assessed using true radar sea clutter data, and the problem of sea spikes causing excessive false alarms is identified and discussed.
TL;DR: This paper proposes a scheme that addresses the association problem in an efficient way while using a single receiver in the case of a multi-input single-output (MISO) system, a subset of MIMO systems, in general.
Abstract: Efficient combination of multiple input multiple output (MIMO) and passive coherent location (PCL) ideas is expected to improve performance of localisation schemes. While using multiple antennas at transmit and receive sides provides spatial diversity, it is possible to obtain similar performance by using multiple transmitters, already transmitting standard signals (such as digital TV) in the environment (also known as illuminators of opportunity). However, in this case, it is not always possible to ensure that signals of different transmitters are orthogonal to each other. In such cases, resolving signals of multiple transmitters reflected from multiple objects is not a trivial problem. One such scenario arises when transmitters of a single frequency network (SFN) are used. Consequently, in order to obtain the desired diversity gain, it is necessary to develop proper techniques to assign each echo arriving at the receiver to a given transmitter and a specific object to be localised. In this paper, we propose a scheme that addresses the association problem in an efficient way. Also, we consider the case of multiple antennas only at the transmit side while using a single receiver, which is the case of a multi-input single-output (MISO) system, a subset of MIMO systems, in general.
TL;DR: In this paper, a renormalization scheme was proposed to rearrange the weights assigned to each target in Gaussian mixture probability hypothesis density (GM-PHD) recursion.
Abstract: The Gaussian mixture probability hypothesis density (GM-PHD) filter has recently been devised as a closed-form recursion for PHD filter for multiple target tracking. The GM-PHD filter works successfully when targets do not move near each other. However, the estimation performance of the GM-PHD filter degrades when targets are in close proximity, such as occlusion condition. In this study, the authors propose a novel approach to improve this drawback. The proposed method employs a renormalisation scheme to rearrange the weights assigned to each target in GM-PHD recursion. Simulation results achieved for different clutter rates and different probabilities of detection show that the proposed approach significantly improves the overall estimation performance compared with the original GM-PHD filter.