[[??].sub.CSJ] (t) denotes the covariance of the zero-mean colored Jerk noise, and w(t) denotes zero-mean Gaussian
Since TOA measurements have errors, which consist of the true TOAs corrupted by additive independent zero-mean Gaussian
noise, we actually model distance estimates associated with receiver m as
where [a.sub.0] = 0.05, [a.sub.1] = 0.1, and [a.sub.2] = 0.15 and [b.sub.0] = 0.04, [b.sub.1] = 0.08, and [b.sub.2] = 0.12; [[epsilon].sub.k] is uncorrelated zero-mean Gaussian
white noises with unity covariances.
where the target state [x.sub.k] = [[[x.sub.k] [[??].sub.k] [y.sub.k] [[??].sub.k]].sup.T]; [x.sub.k] and [y.sub.k] denote the positions and [[??].sub.k] and [[??].sub.k] denote the velocities in x and y directions, respectively; [x.sub.s] and [y.sub.s] denote the positions of radar installing and three radars are used as tracking sensors; [OMEGA] is a known and constant turn rate; [DELTA]t is the time interval between two consecutive measurements; the process noise [w.sub.k] and measurement noise [v.sub.m,k] are cross-correlated zero-mean Gaussian
white noise with covariance [Q.sub.k] and [R.sub.m,k], and [Q.sub.k] satisfies
In Section 3, we propose the GLRT for cyclostationary multi-antenna spectrum sensing, and then demonstrate how to construct the proposed GLRT under the assumption that the additive noise is low-pass zero-mean Gaussian
noise with uncertain power.
We use the following parameters: time interval is 1 second, tracking period is 20 seconds, the value of s is fixed at 2 m/s, the value of [bar.[theta]] is initially 90 degrees but changes over time according to the edge proximity of targets, communication radius is 20 m, standard deviation of the zero-mean Gaussian
noise for the distance observation is 1m, and the number of particles for messages is 500.
Given that the system output x(p) is a zero-mean Gaussian
random process, then the (i,j) th entry of the FIM for P snapshots equals
where [mu] is a drift term and [[epsilon].sub.t] is a sequence of independent and identically zero-mean Gaussian
In this case, Rayleigh fading is exhibited by the assumption that the real and imaginary parts of the response are modelled by independent and identically distributed zero-mean Gaussian
processes so that the amplitude of the response is the sum of two such processes.
where each noise value n is drawn from a zero-mean Gaussian
where [[alpha].sub.0] is a numeric constant; [epsilon] is a zero-mean Gaussian
random variable with standard deviation [[sigma].sub.[alpha]] and [gamma] = [??] - 2, [??] is a gamma distributed random variable given in  using fitting parameters u and v.
with the measurement matrix H and the zero-mean Gaussian
distributed measurement noise [n.sub.k] ~ N(0, [[sigma].sub.n]) with standard deviation [[sigma].sub.n].