Multitarget-multisensor tracking in an urban environment: a closed-loop approach

Abstract

When compared to tracking airborne targets, tracking ground targets on urban terrains brings a new set of challenges. Target mobility is constrained by road networks, and the quality of measurements is affected by dense clutter, multipath, and limited line-of-sight. We investigate the integration of detection, signal processing, tracking, and scheduling by exploiting distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted radar waveform according to the scene conditions; and (3) motion model diversity by using a bank of parallel filters, each one matched to a different maneuvering model. Specifically, at each scan, the waveform that yields the minimum one-step-ahead error covariance matrix determinant is transmitted; the received signal is then matched-filtered, and quadratic curve fitting is applied to extract range and azimuth measurements that are input to the LMIPDA-VSIMM algorithm for data association and filtering. Monte Carlo simulations are used to demonstrate the effectiveness of the proposed system on a realistic urban scenario. A more traditional open-loop system, in which waveforms are scheduled on a round-robin fashion and with no other modes of diversity available, is used as a baseline for comparison. Simulation results show that our closed-loop system significantly outperforms the baseline system, presenting both a reduction on the number of lost tracks, and a reduction on the volume of the estimation uncertainty ellipse. The interdisciplinary nature of this work highlights the challenges involved in designing a closed-loop active sensing platform for next-generation urban tracking systems.

Detection–Localization Algorithms in the Around-the-Corner Radar Problem

“…Target localization based on multipath exploitation was also investigated in tracking contexts [4] [14].…”

“…Carlo simulation and the other with the theoretical formulas (9) and (14). The detection probability of the classic matched filter (MF) for one single path is also computed by Monte Carlo simulation, as well as the detection performance of the clairvoyant matched filter.…”

Adaptive sensing for target tracking in covert operations

“…5 The problem of airborne tracking of ground targets was recently revisited by Miller et al, 6 where a POMDP framework was used in the coordinated guidance of autonomous unmanned aerial vehicles (UAVs) for multitarget tracking. We expand upon our previous work on a closedloop multitarget-multisensor tracking system for urban terrain, 7 and consider an active sensing platform where multiple waveform-agile radars scan a hostile surveillance area for targets. A central controller adaptively selects which transmitters should be activated and which waveforms should be transmitted.…”

“…Myopic methods do not explicitly consider long-term performance, hence are less computationally expensive than their non-myopic counterparts, and have been extensively investigated in the literature. [7][8][9][10][11][12] Non-myopic strategies, on the other hand, are capable of trading off short-term performance for long-term performance, thus taking into account the future benefit of current actions via lookahead. [13][14][15][16][17] However, optimal non-myopic solutions present prohibitive computational costs for most applications, and therefore approximate solution techniques or heuristics are often adopted.…”

Radar surveillance in urban environments

“…In an urban environment there are multiple challenges for radar surveillance, such as a lack of direct observations, obscuration from buildings, and the clutter returns from buildings and other objects [1,2,3]. In order for the radar to perform well in the complicated urban environment and achieve high performance accuracies, the surveillance system must be capable of incorporating urban environment features and adapt dynamically to its changes.…”