POSE: Design of Hardware-Friendly Particle-Based Observation Selection PHD Filter

Abstract

Abstract-Particle probability hypothesis density (PHD) filtering is a promising technology for the multitarget-tracking problem. Traditional particle PHD filter solutions usually have high computational complexity, and the lack of dedicated hardware has seriously limited their usages in real-time industrial applications. The hardware implementation difficulty of the particle PHD filtering in field-programmable gate array (FPGA) platforms lies in that the number of observations for filtering is time varying while the number of parallel processing units in circuit is fixed. To overcome this challenge, we propose a novel particle-based observation selection (POSE) PHD filter algorithm and its hardware implementation in this paper. Specifically, we opportunistically select a fixed number of observations out of a varying number of observations for filtering, where the approximation error is proved to be negligible by adapting the circuit budget to the environment accordingly. To implement the proposed POSE PHD filter, the hardware design issues are addressed in depth. Extensive simulations demonstrate that the POSE PHD filter has a comparable performance with the traditional one while its hardware implementation challenge is overcome. The hardware experiment results of the POSE PHD filter on a Xilinx Virtex-II Pro FPGA platform match the simulation ones well. Furthermore, the execution time of the implemented hardware circuit is evaluated, and the results show that it can achieve a processing rate of 6.892 kHz with a 50-MHz system clock.Index Terms-Field-programmable gate array (FPGA) platform, hardware design, multitarget tracking (MTT), particle probability hypothesis density (PHD) filter, real-time performance.

Energy-Efficient Target Tracking by Mobile Sensors With Limited Sensing Range

“…From (21), EL(m) is a standard ellipse whose long half shaft and short half shaft satisfy (17). Besides, according to the property that translation transformation and rotation transformation are both shape preserving [28], EL(m) has the same long half shaft and short half shaft with EL(m), which completes the proof.…”

“…Target tracking is one of the most important applications of wireless sensor networks, such as data gathering [9], indoor localization [10], industrial systems [11], [12], smart grid [13], [14], and has attracted increasing research interests [15]- [17].…”

Algorithm design for parallel implementation of the SMC-PHD filter

“…The situation gets worse in the SMC-PHD filter where the computational requirements also grow with the number of targets/ observations [17]. Thence, fast computing techniques such as gating [18,19] and parallel processing [20][21][22][23] appear as promising approaches to ensure real-time performance, which however are often based on significant approximations. With the fast development of multi-core and multi-threading compatible hardware and software, the parallelization becomes increasingly attractive and even necessary.…”

“…The parallelization technique for resampling achieved in these particle filters e.g. [24][25] may be applied in the MTT-based particle PHD filter as they have been done in [20][21][22] at the price of inevitable considerable communication overhead. However, there are more challenging operations that consist of joint processing of particles in the SMC-PHD filter in addition to the resampling.…”

“…First, the weight updating of each particle in the PHD updater requires the weight information of all the other particles thereby preventing the parallelization that distributes particles among PEs. Secondly, what used in [20][21][22][23] for extracting multiple estimates in the SMC-PHD filter is still the (k-means) clustering method which is computationally intensive and is less suitable for parallelization. It is unclear how the k-means clustering can be parallelized without introducing significant communication overhead.…”