[[??].sub.CSJ] (t) denotes the covariance of the zero-mean colored Jerk noise, and w(t) denotes

zero-mean Gaussian white noise.

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 random variables.

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 distribution.

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 [14] 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].