Suppression approach to main-beam deceptive jamming in FDA-MIMO radar using nonhomogeneous sample detection

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit-receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit-receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach

In-Field Demonstration of a Photonic Coherent MIMO Distributed Radar Network

This paper reports an in-field experiment of a photonics-based coherent MIMO radar network. The use of photonics guarantees the coherence of the transmitted and received RF signals, and allows remoting the antennas exploiting deployed optical fibers, thus a MIMO approach can be applied on a network of widely distributed coherent radars. In the in-field experiment, a photonics-based radar core connects two transmitters and two receivers, with 100-MHz bandwidth signals in X-band, observing a collaborative target. The results demonstrate an improvement in radar precision, and envisage real applications wherever fiber is available for deploying the radar network

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment

Unit Circle Roots Based Sensor Array Signal Processing

As technology continues to rapidly evolve, the presence of sensor arrays and the algorithms processing the data they generate take an ever-increasing role in modern human life. From remote sensing to wireless communications, the importance of sensor signal processing cannot be understated. Capon\u27s pioneering work on minimum variance distortionless response (MVDR) beamforming forms the basis of many modern sensor array signal processing (SASP) algorithms. In 2004, Steinhardt and Guerci proved that the roots of the polynomial corresponding to the optimal MVDR beamformer must lie on the unit circle, but this result was limited to only the MVDR. This dissertation contains a new proof of the unit circle roots property which generalizes to other SASP algorithms. Motivated by this result, a unit circle roots constrained (UCRC) framework for SASP is established and includes MVDR as well as single-input single-output (SISO) and distributed multiple-input multiple-output (MIMO) radar moving target detection. Through extensive simulation examples, it will be shown that the UCRC-based SASP algorithms achieve higher output gains and detection probabilities than their non-UCRC counterparts. Additional robustness to signal contamination and limited secondary data will be shown for the UCRC-based beamforming and target detection applications, respectively

Subspace-Based Detector For Distributed Mmwave MIMO Radar Sensors

peer reviewedIn this paper, we present a generic signal model applicable to various distributed radar setups, encompassing both phased array (PA) and MIMO radar configurations. We consider a range of waveform modulation methods, including TDM, BPM, DDM, and fast time CDM. We devise a GLRT based detector for scenarios where the interference consists of colored noise plus a signal in a low-rank subspace and prove that the designed detector is CFAR. We demonstrate that when the CPI time is similar for the systems, the PA radar system exhibits better detection performance than MIMO, irrespective of the waveform modulation approach adopted. However, if the CPI time of the PA system is divided to the number of transmit waveforms utilized in the MIMO radar case (to account for the time needed for a PA radar to scan all angles), then in the presence of non-uniform interference, MIMO techniques, except TDM, surpass the performance of PA. Conversely, in cases of uniform interference, the performance of both MIMO techniques and PA are equivalent.U-AGR-7062 - BRIDGES2020/15407066/MASTERS (01/07/2021 - 30/06/2024) - MYSORE RAMA RAO Bhavani

Single data set detection for multistatic doppler radar

The aim of this thesis is to develop and analyse single data set (SDS) detection algorithms that can utilise the advantages of widely-spaced (statistical) multiple-input multiple-output (MIMO) radar to increase their accuracy and performance. The algorithms make use of the observations obtained from multiple space-time adaptive processing (STAP) receivers and focus on covariance estimation and inversion to perform target detection. One of the main interferers for a Doppler radar has always been the radar’s own signal being reflected off the surroundings. The reflections of the transmitted waveforms from the ground and other stationary or slowly-moving objects in the background generate observations that can potentially raise false alarms. This creates the problem of searching for a target in both additive white Gaussian noise (AWGN) and highly-correlated (coloured) interference. Traditional STAP deals with the problem by using target-free training data to study this environment and build its characteristic covariance matrix. The data usually comes from range gates neighbouring the cell under test (CUT). In non-homogeneous or non-stationary environments, however, this training data may not reflect the statistics of the CUT accurately, which justifies the need to develop SDS methods for radar detection. The maximum likelihood estimation detector (MLED) and the generalised maximum likelihood estimation detector (GMLED) are two reduced-rank STAP algorithms that eliminate the need for training data when mapping the statistics of the background interference. The work in this thesis is largely based on these two algorithms. The first work derives the optimal maximum likelihood (ML) solution to the target detection problem when the MLED and GMLED are used in a multistatic radar scenario. This application assumes that the spatio-temporal Doppler frequencies produces in the individual bistatic STAP pairs of the MIMO system are ideally synchronised. Therefore the focus is on providing the multistatic outcome to the target detection problem. It is shown that the derived MIMO detectors possess the desirable constant false alarm rate (CFAR) property. Gaussian approximations to the statistics of the multistatic MLED and GMLED are derived in order to provide a more in-depth analysis of the algorithms. The viability of the theoretical models and their approximations are tested against a numerical simulation of the systems. The second work focuses on the synchronisation of the spatio-temporal Doppler frequency data from the individual bistatic STAP pairs in the multistatic MLED scenario. It expands the idea to a form that could be implemented in a practical radar scenario. To reduce the information shared between the bistatic STAP channels, a data compression method is proposed that extracts the significant contributions of the MLED likelihood function before transmission. To perform the inter-channel synchronisation, the Doppler frequency data is projected into the space of potential target velocities where the multistatic likelihood is formed. Based on the expected structure of the velocity likelihood in the presence of a target, a modification to the multistatic MLED is proposed. It is demonstrated through numerical simulations that the proposed modified algorithm performs better than the basic multistatic MLED while having the benefit of reducing the data exchange in the MIMO radar system

Impact of scene decorrelation on geosynchronous SAR data focusing

We discuss the effects of the clutter on geosynchronous SAR systems exploiting long integration times (from minutes to hours) to counteract for two-way propagation losses and increase azimuth resolution. Only stable targets will be correctly focused whereas unstable targets will spread their energy along azimuth direction. We derive here a generic model for the spreading of the clutter energy based on the power spectral density of the clutter itself. We then assume the Billingsley Intrinsic Clutter Motion model, representing the clutter power spectrum as an exponential decay, and derive the expected GEOSAR signal-to-clutter ratio. We also provide some results from a Ground Based RADAR experiment aimed at assessing the long-term clutter statistics for different scenarios to complement the Internal Clutter Motion model, mainly derived for windblown trees. Finally, we discuss the expected performances of two GEOSAR systems with different acquisition geometries.Peer ReviewedPostprint (published version

Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications

We consider a spectral sharing problem in which a statistical (or widely distributed) multiple-input-multiple-output (MIMO) radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system concurrently operate within the same frequency band. Prior works on joint MIMO-radar-MIMO-communications (MRMC) systems largely focus on either colocated MIMO radars, half-duplex MIMO communications, single-user scenarios, omit practical constraints, or MRMC co-existence that employs separate transmit/receive units. In this paper, we present a co-design framework that addresses all of these issues. In particular, we jointly design the statistical MIMO radar codes, uplink (UL)/downlink (DL) precoders of in-band full-duplex multi-user MIMO communications, and corresponding receive filters using our proposed metric of compounded-and-weighted sum mutual information. This formulation includes practical constraints of UL/DL transmit powers, UL/DL quality-of-service, and peak-to-average-power ratio. We solve the resulting highly non-convex problem through a combination of block coordinate descent and alternating projection methods. Extensive numerical experiments show that our methods achieve monotonic convergence in a few iterations, improve radar target detection over conventional codes, and yield a higher achievable data rate than standard precoders.Comment: 20 pages, 8 figures, 1 tabl