Aliakbar A. Gorji

Multiple Unresolved Target Localization and Tracking using Colocated MIMO Radars

In this paper localization and tracking of multiple unresolved targets using a colocated multiple input multiple output (MIMO) radar is addressed. The commonly-used model for colocated MIMO radars is modified in order to guarantee the observability in received measurements. Then, a maximum likelihood estimator is derived for localizing multiple targets falling within a certain resolution cell. The Cramer-Rao lower bound (CRLB) for localization with the new model is also derived. For the tracking part, a multiple-hypothesis-based approach is used to deal with the uncertainty in target state estimation. In addition, an unscented Kalman filter (UFK) based estimator is used to tackle the nonlinearity in the measurement model. Finally, the posterior CRLB (PCRLB) is derived to evaluate the consistency of tracking results. Simulation results confirm the superiority of the proposed approach in resolving multiple targets over using the standard localization results for tracking.

Widely Separated MIMO versus Multistatic Radars for Target Localization and Tracking

The detection, localization, and tracking performance of multiple input-multiple output (MIMO) radars with widely separated antennas is investigated and compared with that of multistatic radar systems. A multiple-hypothesis (MH)-based algorithm is proposed for multitarget localization for the case where extended targets with multiple spatial reflections become unobservable in certain transmitter-receiver pairs. A particle filter (PF)-based algorithm is then proposed to handle dynamic multitarget tracking. Finally, simulation results are provided to demonstrate the relative capability of MIMO radars in localizing and tracking extended targets under various signal-to-noise ratio (SNR) conditions compared with multistatic radars.

Emitter localization using received-strength-signal data

This paper considers a scenario in which signals from an emitter at an unknown location are received at a number of different collinear locations. The receiver can determine the received signal strength, but no other parameters of the signal. Postulating a log-normal transmission model with a constant but unknown path loss exponent and, also, an unknown transmit power and known noise variance, the paper shows how the localization problem can be solved, along with estimating the parameters appearing in the log-normal transmission model, given enough measurements at different points. The log-normal transmission model parameters can be determined first. An algorithm based on construction of a Gram matrix is proposed to estimate the path loss exponent and transmit power parameters from the received noisy power measurements. Since the estimated parameters are biased due to the nonlinearity of the model and constraints, a pattern-matching algorithm is also proposed to remove the bias in the estimates. The distances corresponding to the different received signal strength measurements can then be bounded, and finally the location estimation is formulated as a convex optimization problem where the estimated distances are used as the new measurements. Simulation results are finally provided to assess the efficacy of the proposed methods in the parameter and location estimation. Highlights? Emitter localization using Received-Strength-Signal data is discussed in this paper. ? A Gram-matrix approach is chosen to estimate unknown parameters of the signal model. ? A new pattern-matching technique is proposed to remove the bias in the estimates. ? A Semidefinite Programming algorithm is formulated for the emitter localization.

Optimal Antenna Allocation in MIMO Radars with Collocated Antennas

The work presented here is concerned with the sensor management problem in collocated multiple-input multiple-output (MIMO) radars. After deriving the Cramer-Rao lower bound (CRLB) as a performance measure, the antenna allocation problem is formulated as a standard semi-definite programming (SDP) for the single-target case. In addition, for multiple unresolved target scenarios, a sampling-based algorithm is proposed to deal with the nonconvexity of the cost function. Simulations confirm the superiority of the localization results under the optimal structure.

MIMO vs. multistatic radars for target localization

Multiple-Input Multiple-Output (MIMO) radars are a new generation of radar systems that bring with them many benefits compared to the traditional phased-array radars. This paper discuses localization techniques for multiple targets when a MIMO radar is used as a measurement tool. A multiple hypotheses-based approach is used to estimate parameters of targets from raw measurements. Received amplitudes and associated range bins are taken as raw measurements. The multiple hypothesis-based method is implemented in two steps. First, hypotheses are initialized using the fist q pairs of transmitters and receivers. Then, a sequential method is applied to initial hypotheses to find final estimates of targets. A comparison is also made between multistatic and MIMO radars for target detection and localization via simulations. The effect of putting threshold on raw data is taken into consideration in both detecting and localizing targets for multistatic radars. Finally, simulation results confirm the superiority of MIMO radars for multiple target localization.

An Assignment Based Algorithm for Multiple Target Localization Problems Using Widely-separated MIMO Radars

Multiple-Input Multiple-Output (MIMO) radars with widely-separated antennas have attracted much attention in recent literature. The highly efficient performance of widely-separated MIMO radars in target detection compared to multistatic radars have been widely studied by researchers. However, multiple target localization by the enlightened structure has not been sufficiently explored. While Multiple Hypothesis Tracking (MHT) based methods have been previously applied for target localization, in this paper, the well-known 2-D assignment method is used instead in order to handle the computational cost of MHT. The assignment based algorithm works in a signal-level mode. That is, signals in receivers are first matched to different transmitters and, then, outputs of matched filters are used to find the cost of each combination in the 2-D assignment method. The main benefit of 2-D assignment is to easily incorporate new targets that are suitable for targets with multiple scatters where a target may be otherwise unobservable in some pairs. Simulation results justify the capability of 2-D assignment method in tackling multiple target localization problems, even in relatively low SNRs.

A new co-located MIMO radar system for multi-target tracking and localization

Multiple Input Multiple Output (MIMO) radars are a new generation of radar systems that bring with them many benefits compared to traditional phased-array and multistatic radars. Target localization using MIMO radars with co-located antennas has been recently discussed in the literature. It has been shown that the maximum number of targets that can be uniquely localized in one cell is bounded. This paper presents a new application of MIMO radars in Multi-Target Tracking (MTT) problems. Firstly, the previous model for co-located MIMO radars is modified in order to guarantee the observability in received measurements. Afterwards, it is shown that using prior information about the motion of targets relaxes the limitation on the number of uniquely localized targets. For filtering part, an Un-scented Kalman Filter (UKF) algorithm is used to update states of targets. Simulation results confirm the superiority of proposed approach in estimating states of multi-targets falling in the same resolution cell.

Widely-separated MIMO vs. multistatic radar for target localization

This paper considers the localization performance of MIMO radars with widely-separated antennas. A Multiple-Hypothesis (MH) based algorithm is proposed for multiple target localization problems where targets become unobservable in certain pairs of transmitters and receivers. In addition, the performance of MIMO radars in localizing multiple-scatterer targets is compared to that of multistatic radar systems. Finally, simulation results are provided in order to demonstrate the capability of MIMO radars in localizing multiple-scatterer targets.