xi(t) = di(t)pi(t) is defined as the product of the spreading code or pseudo random noise (PRN) code, pi(t) and the navigation data bits, di(t). w(t) is input additive white Gaussian noise. Here the noise w(t) is assumed independent of the signal and has a flat power spectrum over the pre-correlation bandwidth. In the indoor case, the channel gain series can be further broken down as follows [16]:hi(t)=KiKi+1ej(��Di,maxcos��0cos��0t+?0��)+1Ki+11Mi��mi=1MiAmi(t)ej��Di,Maxcos��micos��mit+j?��mi=hLOS,i(t)+hNLOS,i(t)(2)From Equation (2), The NLOS channel gain has Mi multipath components, Ami, ��mi, ��mi are the weighting factor, azimuth and elevation angles for mith multipath component. Ki is the Ricean factor for ith satellites, which is the ratio between LOS and NLOS signal powers.
The subscript 0 represents LOS component, while subscript m represents one NLOS path. It is noted that the channel gain series is decomposed into two components; one for the LOS signals and one for all NLOS signals. The first term on the right hand side in the above equation is the LOS channel gain series. The term involving the summation is the channel gain due to NLOS signals. For convenience, the total channel gain series is defined to have unity power.Having presented the basic signal model with dense multipath, attention is now given to how this signal is handled within a GNSS receiver, and how it affects the conventional HSGNSS Doppler estimation. The conventional block processing technique for Doppler measurement is discussed in [14] and is based on the Doppler frequency MLE.
With the notation introduced above, Cilengitide the correlator output for ith satellite with code delay and Doppler frequency (��I,j, fD,I,k) can be expressed as:yi[n](��i,j,fD,i,k)=r[n]xi(nTs?��i,j)e?j2��fD,i,knTs(3)where xi(nTs?��i,j)e?j2��fD,i,knTs represents the local code multiplied with the local carrier re
Two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals has received much attention in many applications, such as radar, wireless communication and sonar in the multipath environment [1�C5]. There are several high resolution techniques proposed to solve the rank deficiency of spatial covariance matrix caused by the presence of coherent signals. The conventional solution to this problem is the spatial smoothing method [6,7], which partitions the original array into a series of overlapping subarrays.
Although it is efficient to decorrelate the incoming signals, peak searching of the spectrum in a 2-D space is required, which costs a large amount of computations. In order to reduce the computational complexity, an efficient method is performed by Hua [8]. This method, called the matrix enhancement and matrix pencil (MEMP) algorithm, exploits the structure inherent in an enhanced matrix from the original data.