# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Monostatic Multisensor Data Fusion and Target Recognition  
An example of soft fusion in cooperative sensing systems, comprised of BSs acting as monostatic multibeam MIMO transceivers, is illustrated in Figure 4. In this example, targetsare modeled as extended, and multipath effects are considered. The BSs provides range-angle maps shared with the FC. The considered system is a multibeam JSC scheme, with one beam directed toward the UE and another sequentially pointing in different directions to sense the environment. For each sensing direction \(j\), starting from the frequency domain OFDM grid of received symbols, a periodogram can be computed to estimate the range and speed of the target by considering each grid element \(g_{k}^{(m)}\), obtained at the \(k\)th subcarrier and \(m\)th time slot after reciprocal filtering, as follows [22]:  
\[\mathcal{P}_{j}(q,p)=\left|\sum_{k=0}^{K_{\mathrm{p}}-1}\left(\sum_{m=0}^{M_{ \mathrm{p}}-1}g_{k}^{(m)}e^{-2\pi\frac{np}{M_{\mathrm{p}}}}\right)e^{j2\pi\frac {\mathrm{i}q}{K_{\mathrm{p}}}}\right|^{2}, \tag{1}\]  
where \(q=0,\ldots,K_{\mathrm{p}}-1\), and \(p=0,\ldots,M_{\mathrm{p}}-1\). Here, \(K_{\mathrm{p}}\) and \(M_{\mathrm{p}}\) denote the next powers of two of \(K\) and \(M\), respectively, which are the number of active subcarriers and OFDM symbols (or time slots). The term \(g_{k}^{(m)}\) comprises two complex sinusoids per target, with frequencies related to \(f_{\mathrm{D}}\) (Doppler frequency) and \(\tau\) (round-trip time). The periodogram defined in (1) generates a range-Doppler map, which serves as the basis for constructing range-angle maps. By assuming a receiving beamforming vector with a relatively narrow beamwidth, it is reasonable to consider that only one target is present in a given sensing direction. Consequently, the range profile \(\mathbf{r}\) for the \(j\)th direction is extracted by isolating the column of the periodogram containing the peak value. Specifically, \(\mathbf{r}=[P_{j}(1),\ldots,P_{j}(K_{\mathrm{p}})]^{T}\), where \(P_{j}(q)=\mathcal{P}_{j}(q,p)|_{p=\hat{p}}\), and \(\hat{p}\) represents the index of the column where the periodogram's peak occurs. Repeating this process for all \(N_{\mathrm{dir}}\) sensing directions produces a collection of range profiles, which are then organized into a matrix \(\mathbf{R}=[\mathbf{r}^{(1)},\,\mathbf{r}^{(2)},\ldots,\mathbf{r}^{(N_{\mathrm{ dir}})}]\). These maps, obtained at each BS, are then resampled to create a consistent grid. The resampled maps are then fused using an element-wise summation, followed by division by the total number of maps, to produce a unified map for target position inference. Here, soft fusion was employed instead of hard fusion to preserve information about the entire scene and avoid losing details about weaker targets, such as pedestrians. Notably, the fusion of range-angle maps reveals a dynamic interplay in target strengths. A signal that might be weak for one BS due to distance or low reflection amplitude can emerge as a strong detection for another. Conversely, seemingly strong targets, such as vehicles, may appear weak when located far from a receiver. The resulting composite image captures the intricate relationship among target strengths across the network, offering valuable insights into the diversity of the detections.