A Closed-Form Location Algorithm Without Auxiliary Variables for Moving Target in Noncoherent Multiple-Input and Multiple-Output Radar System

Abstract

This paper focuses on the moving target localization and velocity measurement in incoherent centralized multiple-input and multiple-output (MIMO) radar systems with widely separated antennas in 3D space. In this paper, we assume that parameters such as time-of-arrival (TOA), frequency-of-arrival (FOA), azimuth angles and elevation angles have already been measured. With these measurements, a final closedform solution of the target position and velocity can be obtained via the weighted least squares (WLS) method. When the target is located in the far field, due to poor system observability, a first-order Taylor expansion based on the WLS solution is necessary to obtain a more accurate and unbiased solution. Unlike the preceding papers which were based on the two-stage weighted least squares (2SWLS) method [1]-[3], in this paper, the angle information is introduced into the time delay equations and the Doppler frequency equations, so that the intermediate variables in the estimator can be eliminated [4], [5]. Meanwhile, the time delay equations and the Doppler equations are transformed into linear equations only related to the position and the speed of the target. This method, unlike 2SWLS-based methods [1]-[3], does not introduce auxiliary variables, so it does not require the decorrelation procedure. Simulation results show that the root meansquare error (RMSE) of position and velocity can reach Cramer-Rao lower bound (CRLB) when the noise is at a moderate level before the thresholding effect occurs.

Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis

“…) are the true and estimated target positions, velocities, and rotation rate at the N runs of the Monte Carlo iterative [54,55].…”

A robust lp‐norm localization of moving targets in distributed multiple‐input multiple‐output radar with measurement outliers

“…The intersection of the curves/surfaces is considered to estimate the target location parameters. Existing studies primarily focus on target localisation based on only one or two types of measurements [4–24]. TD‐based localization in refs.…”

“…Recently, there has been growing literature on the AOA–TD–DS‐based target localisation problem [21, 25–27], which indicates the importance of this issue. Falcone et al.…”

“…Therefore, it has been exploited to describe a variety of noise distributions with impulsive nature over recent years, such as clutters in radar applications, burst noises in indoor localisation applications, and underwater acoustic noises in sonar applications [29–32]. On the other hand, when the AOA–TD–DS measurements are contaminated by impulse noise following the α‐stable distribution, existing algorithms [21, 25–27] based on the LS criterion fail to provide reliable localisation performance since it is susceptible to outliers. The $${l}_{p}$$‐norm minimisation (with 1 < p < 2) is an attractive alternative to the LS (i.e., $${l}_{2}$$‐norm minimisation) and can be used to comprehensively estimate parameters under impulse noise.…”

Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations

“…This algorithm is very popular for applications in medicine [ 19 ] but also has application in unmanned aerial vehicle navigation [ 20 ]. Close in meaning to ToF is the algorithm ToA (Time of Arrival), which is based on distance calculation by measuring the signal transit time from the mobile terminal to the base station as the difference between the time the signal was sent and received [ 21 , 22 , 23 , 24 ]. The algorithm ToA is often used with TDoA (Time Difference of Arrival) [ 25 ] and FDoA (Frequency difference of arrival) [ 26 ], which are also often used together [ 27 , 28 , 29 , 30 ].…”