Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications -- Part III: Multi-Target Tracking

TL;DR

A low-complexity procedure based on Barzilai-Borwein gradient algorithm is proposed to obtain the design parameters and mixed-integer linear program for distributed target localization and localize and track multiple targets of the distributed FD ISAC system.

Abstract

As a next-generation wireless technology, the in-band full-duplex (IBFD) transmission enables simultaneous transmission and reception of signals over the same frequency, thereby doubling spectral efficiency. Further, a continuous up-scaling of wireless network carrier frequencies arising from ever-increasing data traffic is driving research on integrated sensing and communications (ISAC) systems. In this context, we study the co-design of common waveforms, precoders, and filters for an IBFD multi-user (MU) multiple-input multiple-output (MIMO) communications with a distributed MIMO radar. This paper, along with companion papers (Part I and II), proposes a comprehensive MRMC framework that addresses all these challenges. In the companion papers, we developed signal processing and joint design algorithms for this distributed system. In this paper, we tackle multi-target detection, localization, and tracking. This co-design problem that includes practical MU-MIMO constraints on power and quality-of-service is highly non-convex. We propose a low-complexity procedure based on Barzilai-Borwein gradient algorithm to obtain the design parameters and mixed-integer linear program for distributed target localization. Numerical experiments demonstrate the feasibility and accuracy of multi-target sensing of the distributed FD ISAC system. Finally, we localize and track multiple targets by adapting the joint probabilistic data association and extended Kalman filter for this system.

Figures (8)

• Figure 1: Illustration of the considered D-ISAC system, where the ${m_\mathrm{r}}^{\underline{\text{th}}}$ (${n_\mathrm{r}}^{\underline{\text{th}}}$) radar Tx (Rx), ${m}^{\underline{\text{th}}}$ FD RRH, ${j}^{\underline{\text{th}}}$ (${i}^{\underline{\text{th}}}$) DL (UL) UE are located at $\left({x^{\mathrm{rt}}_{m_\mathrm{r}}, y^{\mathrm{rt}}_{m_\mathrm{r}}}\right)$ ($\left({x^{\mathrm{rr}}_{n_\mathrm{r}}, y^{\mathrm{rr}}_{n_\mathrm{r}}}\right)$), $\left({x^{\mathrm{RRH}}_{m},y^{\mathrm{RRH}}_{m}}\right)$, $\left({x^\mathrm{d}_{j},y_{\mathrm{d},j}}\right)$ ($\left({x_{\mathrm{u},i},y^\mathrm{u}_{i}}\right)$), respectively; the ${n_\mathrm{t}}^{\underline{\text{th}}}$ target is located at $\left({x_{\mathrm{t},n_\mathrm{t}},y_{\mathrm{t},n_\mathrm{t}}}\right)$.
• Figure 2: Convergence curves of Algorithm \ref{['convexalgorithm']} given the step-size rule determined by the BB algorithm and Polyak's rule.
• Figure 3: Proposed D-ISAC design approach compared with the conventional communications precoding and radar coding techniques given CSI errors.
• Figure 4: Proposed D-ISAC design approach compared with the conventional communications precoding and radar coding techniques given CSI errors.
• Figure 5: Probability of correct association for different antenna geometries and number of targets.