Non-coherent Multiple-input Multiple-output Radar Research Articles

Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas

In this article, the problem of simultaneously detecting and localizing multiple targets in homogeneous noise environment is considered for non-coherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming that the a prior knowledge of target number is available, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching the parameters of interest in a high-dimensional state space. However, the complexity of this solution increases exponentially with the number $G$ of targets. Besides, if the number of targets is unknown, a multi-hypothesis testing strategy to verify all the possible hypotheses on target number is required, which further complicates this method. In order to devise computationally feasible methods for practical applications, we split the high-dimensional maximization into $G$ disjoint sub-optimization problems by sequentially detecting targets and then clearing their interference for the subsequent detection of remaining targets. In this way, we further propose two fast and robust suboptimal solutions which allow to trade performance for a much lower implementation complexity. In addition, the multi-hypothesis testing is no longer required when target number is unknown. Simulation results show that the proposed algorithms can correctly detect and accurately localize multiple targets even when targets lie in the same range bins. Experimental data recorded by three small radars are also provided to demonstrate the efficacy of the proposed algorithms.

Location algorithms for moving target in non‐coherent distributed multiple‐input multiple‐output radar systems

In this study, for the problem of estimating the location and speed of a moving target in the non-coherent multiple-input multiple-output (MIMO) radar systems with widely separated antennas, the authors propose two new methods, in which the parameters used are the joint of bearing, elevation, frequency-of-arrival (FOA) and time-of-arrival (TOA). The two proposed methods are based on non-coherent MIMO radar systems, but method 1 centralises all measuring parameters in one linear equation and processes together, while method 2 divides the measurements into several groups according to the different transmitter-receiver pairs. In this study, the authors assume that bearing, elevation, FOA and TOA parameters have already been measured by a preprocessing algorithm. For the both methods, an initial guess is acquired through a best linear unbiased estimator. Then for method 1, a more explicit solution can be acquired by employing a maximum likelihood estimator for decorrelation, while the method 2 applies maximum likelihood estimation of a first-order Taylor expansion for a better solution. The simulations show that these two methods are effective and both of them can attain to Cramer-Rao lower bound at sufficiently moderate noise conditions.

Cramér–Rao bounds for L‐band digital aeronautical communication system type 1 based passive multiple‐input multiple‐output radar

In this study, of concern is the achievable estimation accuracy for a non-coherent passive multiple-input multiple-output (MIMO) radar employing orthogonal frequency-division multiplexing (OFDM) based signals of opportunity. The motivation behind this investigation is the surveillance aspect of the air trac management modernisation process currently taking place in civil aviation. To this end, the surveillance capability of the OFDM-based L-band digital aeronautical communication system type 1 (LDACS1) is analysed. The extension of the LDACS1 communication system towards surveillance is of great importance since it would enable an alternative, non-cooperative surveillance application, and, together with the already validated navigation extension, establish a fully integrated LDACS1-based communication, navigation, and surveillance technology. The modied Cramer-Rao bound on the joint estimation of target location and velocity for a non-coherent passive MIMO radar, employing the OFDM-based LDACS1 signals as signals of opportunity, is investigated. The analysis is carried out assuming one target, multiple and widely separated transmit and receive antennas, orthogonal waveforms, independent reection coecients, and zero-mean white Gaussian noise. Closed-form expressions for the relevant elements of the modied Fisher information matrix are obtained. The results can be used to assess the estimation accuracy of a multiple antenna passive radar system employing any OFDM-based signals of opportunity.

An Improved Algebraic Solution for Moving Target Localization in Noncoherent MIMO Radar Systems

We propose an improved method for moving target localization with a noncoherent multiple-input multiple-output (MIMO) radar system having widely separated antennas. The method is based on the well-known two-stage weighted least squares (2SWLS) method, but in contrast to the recently proposed Group-2SWLS, it requires only one reference transmitter (or receiver) without grouping and combining. This change allows us to obtain a closed-form solution which is less likely to be degraded by bias. Furthermore, the proposed method can easily utilize not only time-of-arrival (TOA) and frequency-of-arrival (FOA) data but also time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) data. We also introduce new auxiliary variables for the purpose of numerical stability; our method using the auxiliary variables is shown to be numerically more stable than the Group-2SWLS, while attaining the Cramer–Rao lower bound (CRLB) at higher noise levels. The adoption of auxiliary variables requires no additional computations in contrast to the adoption of the concept of Turbo-2SWLS for the same purpose.

Target Localization and Tracking in Noncoherent Multiple-Input Multiple-Output Radar Systems

For a noncoherent multiple-input multiple-output (MIMO) radar system, the maximum likelihood estimator (MLE) of the target location and velocity, as well as the corresponding Cramer-Rao lower bound (CRLB) matrix, is derived. MIMO radar's potential in localization and tracking performance is demonstrated by adopting simple Gaussian pulse waveforms. Due to the short duration of the Gaussian pulses, a very high localization performance can be achieved, even when the matched filter ignores the Doppler effect by matching to zero Doppler shift. This leads to significantly reduced complexities for the matched filter and the MLE. Further, two interactive signal processing and tracking algorithms, based on the Kalman filter and the particle filter (PF), respectively, are proposed for noncoherent MIMO radar target tracking. For a system with a large number of transmit/receive elements and a high signal-to-noise ratio (SNR) value, the Kalman filter (KF) is a good choice; while for a system with a small number of elements and a low SNR value, the PF outperforms the KF significantly. In both methods, the tracker provides predictive information regarding the target location, so that the matched filter can match to the most probable target locations, reducing the complexity of the matched filter and improving the tracking performance. Since tracking is performed without detection, the presented approach can be deemed as a track-before-detect approach. It is demonstrated through simulations that the noncoherent MIMO radar provides significant tracking performance improvement over a monostatic phased array radar with high range and azimuth resolutions. Further, the effects of coherent integration of pulses are investigated for both the phased array radar and a hybrid MIMO radar, where only the pulses transmitted and received by colocated transceivers are coherently integrated and the other pulses are combined noncoherently. It is shown that the hybrid MIMO radar achieves significant tracking performance improvement when compared with the phased array radar, by using the extra Doppler information obtained through coherent pulse integration.

Target estimation using MIMO radar with multiple subcarriers

This paper analyzes the effect of waveform parameters on the joint target location and velocity estimation by a noncoherent multiple input multiple output (MIMO) radar transmitting multiple subcarriers signals. How the number of subcarriers influences the estimation accuracy is illustrated by considering the joint Cramer-Rao bound and the mean square error of the maximum likelihood estimate. The noncoherent MIMO radar ambiguity function with multiple subcarriers is developed and investigated by changing the number of subcarriers, the pulse width and the frequency spacing between adjacent subcarriers. The numerical results show that more subcarriers mean more accurate estimates, higher localization resolution, and larger pulse width results in a worse performance of target location estimation, while the frequency spacing affects target location estimation little.

Subspace Optimization in Centralized Noncoherent MIMO Radar

We consider the problem of subspace optimization for centralized noncoherent multiple input-multiple output (MIMO) radar based on various measures such as capacity, diversity, and probability of detection. In subspace centralized noncoherent MIMO radar (SC-MIMO), a subset of stations is selected based on channel knowledge or channel statistics to reduce system complexity while simultaneously attempting to optimize the performance of the reduced-dimension centralized MIMO radar system. The radar transmitters are assumed to be sufficiently separated (e.g., at different locations) to yield spatially white channel transfer gains and are assumed to operate on a noninterference basis through time-division or frequency-division multiplexing. Detection optimization for the SC-MIMO system in a Neyman Pearson (NP) sense is found to be equivalent to selecting the subspace that maximizes the Frobenius norm of the corresponding channel matrix. Information-theoretic measures for capacity and diversity are also applied to the problem of subspace selection. Channels with temporal coherence times that are long relative to the radar system's latencies and channels with coherence times that are short relative to the radar system's latencies are considered. In the former case, metrics are based upon instantaneous channel estimates, whereas in the latter case, average channel estimates are used. Numerical analyses are conducted to illustrate the use of the metrics for optimizing system performance.

Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance

This paper presents an analysis of the joint estimation of target location and velocity using a multiple-input multiple-output (MIMO) radar employing noncoherent processing for a complex Gaussian extended target. A MIMO radar with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> transmit and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> receive antennas is considered. To provide insight, we focus on a simplified case first, assuming orthogonal waveforms, temporally and spatially white noise-plus-clutter, and independent reflection coefficients. Under these simplifying assumptions, the maximum-likelihood (ML) estimate is analyzed, and a theorem demonstrating the asymptotic consistency, large <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MN</i> , of the ML estimate is provided. Numerical investigations, given later, indicate similar behavior for some reasonable cases violating the simplifying assumptions. In these initial investigations, we study unconstrained systems, in terms of complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Following this, we also look at constrained systems, where the total system energy and complexity are fixed. To approximate systems of fixed complexity in an abstract way, we restrict the total number of antennas employed to be fixed. Here, we show numerical examples which indicate a preference for receive antennas, similar to MIMO communications, but where systems with multiple transmit antennas yield the smallest possible mean-square error (MSE). The joint Cramér-Rao bound (CRB) is calculated and the MSE of the ML estimate is analyzed. It is shown for some specific numerical examples that the signal-to-clutter-plus-noise ratio (SCNR) threshold, indicating the SCNRs above which the MSE of the ML estimate is reasonably close to the CRB, can be lowered by increasing <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MN</i> . The noncoherent MIMO radar ambiguity function (AF) is developed in two different ways and illustrated by examples. It is shown for some specific examples that the size of the product <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MN</i> controls the levels of the sidelobes of the AF.